Renal Transplantation: Sense and Sensitization

RENAL TRANSPLANTATION: SENSE A N D SENSITIZATION D E V E L O P M E N T S IN N E P H R O L O G Y Renal Transplantatio...

Author: S.M. Gore | B.A. Bradley

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RENAL TRANSPLANTATION: SENSE A N D SENSITIZATION

D E V E L O P M E N T S IN N E P H R O L O G Y

Renal Transplantation: Sense and Sensitization by

SHEILA M: G O R E M R C Biostatistics Unit, Cambridge, U.K. and

BENJAMIN A. B R A D L E Y U.K. Transplant Service, Bristol U.K.

Council of Europe

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STRASBOURG

Kluwer Academic Publishers D O R D R E C H T / BOSTON / LONDON

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Library of Congress Cataloging-in-Publication Data Gore, Sheila M. Renal transplantation : sense and sensitization / Sheila M. Gore, Benjamin A. Bradley. p. cm.--(Developments in nephrology) ISBN 0-89838-370-6 (U.S.) 1. Kidneys--Transplantation--Immunological aspects. I. Bradley, Benjamin A., 1942II. Title. III. Series. [DNLM: 1. Graft Survival. 2. HLA Antigens. 3. Kidney-transplantation. 4. Transplantation Immunology. W1 DE998EB / WJ 368 G666r] RD575.G67 1988 617.4'610592--dc19 DNLM/DLC 88-1408 for Library of Congress CIP ISBN 0-89838-370-6 Kluwer Academic Publishers incorporates the publishing programmes of Dr W. Junk Publishers, MTP Press, Martinus Nijhoff Publishers, and D. Reidel Publishing Company. Distributors for the United States and Canada: Kluwer Academic Publishers, I01 Philip Drive, Norwell, MA 02061, USA for all other countries: Kluwer Academic Publishers Group, P.O. Box 322, 3300 AH Dordrecht, The Netherlands

Copyright © 1988 by Kluwer Academic Publishers, Dordrecht and Council of Europe, Strasbourg. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, mechanical, photocopying, recording, or otherwise, without the prior written permission from the copyright owners. PRINTED IN THE NETHERLANDS

DIRECTOR OF STUDIES Professor B A Bradley UK Transplant Service and National Tissue Typing Reference Laboratory Southmead Road, BRISTOL BS10 5ND UK BIOSTATISTICIAN TO THE STUDY Dr S M Gore MRC Biostatistics Unit 5 Shaftesbury Road CAMBRIDGE CB2 2BW UK MEMBERS OF THE STUDY GROUP Prof Dr med E Albert National Reference Laboratory on Histocompatibility Universitat Munchen Pettenkoferstrasse 8a D-8000 MUNCHEN 2 Germany Dr F H J Claas Department of Immunhaematology and Bloodbank University Hospital Rijnsburgerweg 10 2333 AA LEIDEN The Netherlands Dr R Fauchet and Prof B Genetet Laboratoire d'Histocompatibilite Centre Regional de Transfusion Rue Pierre Jean Gineste 3500 RENNES France

Dr M Jeannet H6pital Cantonal Division d'Immunologie 24 Rue Micheli-du-Crest 1211 GENEVE 4 Switzerland Dr M Madsen and Dr L Lamm Tissue Typing Laboratory Aarhus Kommunehospital DK-8000 AARHUS C Denmark Dr G Persijn Eurotransplant Foundation c/o Bloodbank University Hospital Rijnsburgerweg 10 2333 AA LEIDEN The Netherlands Dr M Scalamogna and Prof G Sirchia Centre de Transfusion et d'Immunohaematologie via Francesco Sforza 35 MILAN Italy

Secretariat: Mrs Vera Boltho-Massarelli, Council of Europe ORGAN SHARING ORGANISATIONS INVOLVED IN THE STUDY: Eurotransplant France-Transplant Hispano Transplant Luso Transplant North Italy Transplant Scandia Transplant Swiss Transplant UK Transplant Service

vi The Study Group acknowledge the invaluable assistance of the following centres who have provided data to this study.

AUSTRIA I Medizinische Universit~itsklinik GRAZ I Universit~itsklinik ffir Chirurgie INNSBRUCK Allgemeines Krankenhaus LINZ Krankenhaus der Elisabethinen LINZ Allgemeines Krankenhaus WlEN Institut fiJr Blutgruppenserologie WlEN Kinderdialyse Allgemeines Krankenhaus WIEN

BELGIUM Academisch Ziekenhuis der Vrije Universiteit BRUSSEL/JETTE Cliniques Universitaires St Luc BRUXELLES Hopital Erasme BRUXELLES Universit6 Catholique de Louvain Tissue Typing Laboratory BRUXELLES Academisch Ziekenhuis Antwerpen EDEGEM Bloedtransfusie Centrum Antwerpen Belgische Rode Kruis EDEGEM Academisch Ziekenhuis GENT

Bloedtransfusiecentrum Belgische Rode Kruis LEUVEN Kinderdialyse U Z St Rafael Gasthuisberg LEUVEN Univ Ziekenhuizen St Rafael Gasthuisberg LEUVEN Laboratoire des Groupes Sanguins Universit~ de Liege LIEGE Universit+ de Liege LIEGE

DENMARK Department of Clinical Immunology The Tissue Typing Laboratory Blood Bank and Blood Grouping Laboratory Aarhus Kommunehospital AARHUS Urological Department Aarhus Kommunehospital AARHUS Department of Medicine Rigshospitalet COPENHAGEN The Tissue Typing Laboratory Rigshospitalet COPENHAGEN Renal Unit K A S Herlev HERLEV Department of Nephrology Odense Sygehus ODENSE

vii FINLAND Tissue Typing Laboratory Finnish Red Cross Blood Transfusion Service HELSINKI University Central Hospital Unioninkatu HELSINKI

FRANCE Transplant Unit BORDEAUX Centre de Transfusion Sanguine LYON Transplant Unit NANTES Laboratoire d'Histocompatibilit6 Centre Hayem H6pital Saint-Louis PARIS Transplant Unit RENNES Transplant Unit TOULOUSE

GERMANY Neues Klinikum der RWTH Abt Med Mikrobiologie Gewebetypisierung AACHEN Rheinisch Westfalische Technische Hochschule AACHEN

Klinikum Steglitz Haematol Zentrallabor Labor ffir Gewebetypisierung BERLIN Medizinische Einrichtungen der Univ Kliniken BONN Bluttransfusionsdienst Klinikum Freien Hansestadt BREMEN Institut ffir Blutgerinnung und Transfusionsmedizin DUSSELDORF Medizinische Einrichtungen der Universit/it DUSSELDORF Institut ffir Klinische Immunologie ERLANGEN Medizinische Einrichtungen der Universit~it ESSEN Zentrum fiir med Okologie fiir Immunogentick ESSEN Immunhaematologische Abteilung der Universit/~t Blutspendedienst Hessen FRANKFURT Johan Wolfgang Goethe Universit/it FRANKFURT/MAIN

DRK Blutspendedienst HLA-Labor BAD KREUZNACH

Albert Ludwigs Universit/it FREIBURG

Klinikum Steglitz der Freie Universit/it BERLIN

Blutspendedienst Tissue Typing Labor FREIBURG/BREISGAU

viii Inst fiir Klin Immunologie und Transfusionsmedin Tissue Typing Labor GIESSEN Universit~itsklinik HLA-Labor GOTTINGEN Medizinische Einrichtungen der Universit/it GOTTINGEN

Universit/its Kinderklinik KOLN Medizinische Hochschule LUBECK Medizinische Hochschule Institut fiir Immunologie und Transfusionsmedizin LUBECK

Universit~itskrankenhaus Eppendorf HAMBURG

Klinikum der Phillipsuniversit~it Zentrum f'tir Innere Medizin HLA-Labor MARBURG

Nephrologische Zentrum Niedersachsen HANN MUNDEN

Kinderpoliklinik der Universit~it HLA-Labor MUNCHEN

Medizinische Hochschule HANNOVER

Klinikum Rechts der Isar MUNCHEN

Institut ffir Immunologie und HLA-Labor HEIDELBERG

Ludwig Maximilians Universit~it MUNCHEN

Ruprecht Karls Universit~it HEIDELBERG Medizinische Universit~it Klinik HOMBURG/SAAR Institut fiir Rechtsmedizin HLA-Labor KAISERSLAUTERN St~idtisches Krankenhaus KAISERSLAUTERN

Westfalische Wilhelms Universit~it MUNSTER SRidtische Krankenanstalten NURNBERG Abteilung fiir Transfusionswesen HLA-Labor TUBINGEN Eberhard Karls Universit/it TUBINGEN

Institut fiir Rechtsmedizin KASSEL

D R K Blutspendedienst Abt fiir Transplantationsimmunologie HLA-Labor ULM

Abteilung Immunologie HLA-Labor KIEL

Universitfit Ulm ULM

Christian Albrechts Universit~it KIEL

Julius Maximilians Universit~it WURZBURG

Institut fCir Transfusionmedizin HLA-Labor KOLN/MERHEIM Medizinische Universit~it KOLN

IRELAND Beaumont Hospital (Formerly Jervis Street Hospital) DUBLIN

ix ITALY

NETHERLANDS

Ospedale Regionale "Spedali Civili" Divisione Nefrologia e Dialisi BRESCIA

Academisch Medisch Centrum AMSTERDAM

Ospedale Regionale "S Martino" Anatomia Chirurgica dell 'Universita' GENOVA Ospedale Maggiore Policlinico di Milano Divisione Nefrologia e Dialisi MILAN Ospedale Nigurada Divisione Nefrologia e Dialisi MILAN Ospedale Maggiore Policlinico di Milano Instituto di Science Mediche dell 'Universita' MILAN Istituto Scientifico "S Raffaele" Divisione Medicina I SEGRATE Ospedale Regionale "S Maria dei Battuti" Divisione Nefrologia ed Dialisi TREVISO Ospedale Regional "Maggiore di S G Battista e della Citt~ di Torino-Molinette" Divisione Nefrologia e Dialisi TORINO Ospedale Regionale "Civile Maggiore" Catterdra di Nefrologia Chirurgica VERONA

Centraal Laboratorium Bloedtransfusiedienst Nederlandse Rode Kruis AMSTERDAM Academisch Ziekenhuis GRONINGEN •

Academisch Ziekenhuis Bloedgroepenlaboratorium GRONINGEN Academisch Ziekenhuis LEIDEN Academisch Ziekenhuis MAASTRICHT St Radboud Ziekenhuis Leucocytenlaboratorium NIJMEGEN St Radboud Ziekenhuis NIJMEGEN Academisch Ziekenhuis Dijkzigt ROTTERDAM Sophia Kinderziekenhuis ROTTERDAM Academisch Ziekenhuis UTRECHT Wilhelmina Kinderziekenhuis UTRECHT

NORWAY Rikshospitalet OSLO

LUXEMBOURG PORTUGAL Centre Hospitalier de Luxembourg Tissue Typing Laboratory LUXEMBOURG

Centro de Histocompatibilidade do Sol LISBOA

SPAIN

SWITZERLAND

Clinica Puerta de Hierro Servicio de Immunologia MADRID

Abteilung fiir Nephrologie Kantonsspital BASEL

Hopital 1° de Octubre Carretara Andalucia MADRID

Transplantations-Labor Institut fiir Klin Immunologie Inselspital BERN

SWEDEN Department of Transplantation Surgery and Institute for Transplantations Immunology The Blood Central Sahlegrenska Sjukhuset GOTHENBURG Department of Transplantation Surgery Sahlgrenska Sjukhuset GOTHENBURG Department of Clinical Immunology Huddinge Hospital HUDDINGE

Blutspendezentrum Kantonsspital St GALLEN Division de N6phrologie H6pital Cantonal Universitaire GENEVE Division d'Immunologie & Allergie Department de M6decine LAUSANNE Abteilung fiir Chirurgie Universifiitsspital ZURICH

UNITED KINGDOM Departments of Transplantation Surgery and Clinical Immunology Royal Infirmary* Huddinge Sjukhus ABERDEEN HUDDINGE Belfast City Hospital Laboratory of Transplantation BELFAST The Blood Centre Queen Elizabeth Hospital Lasarettet BIRMINGHAM LUND Southmead Hospital Department of Surgery BRISTOL Malmoe Allmaenna Sjukhus MALMOE Department of Clinical Immunology Akademiska Sjukhuset UPPSALA

Addenbrookes Hospital CAMBRIDGE Cardiff Royal Infirmary CARDIFF

Department of Transplantation Surgery Akademiska Sjukhuset UPPSALA

Western General Hospital EDINBURGH

* Only waiting list data from these centres were included in the study.

St James Hospital LEEDS

Western Infirmary GLASGOW

xi

General Hospital

The London Hospital

LEICESTER

LONDON

Royal Liverpool Hospital LIVERPOOL

Manchester Royal Infirmary MANCHESTER

Charing Cross Hospital

Nephrologische Klinik

LONDON

MANNHEIM

Guys Hospital

Royal Victoria Infirmary

LONDON

NEWCASTLE-UPON-TYNE

Hammersmith Hospital

City Hospital

LONDON

NOTTINGHAM

Royal Free Hospital

John Radcliffe Hospital

LONDON

OXFORD

St Bartholomews Hospital*

Derriford Hospital

LONDON

PLYMOUTH

St Mary's Hospital*

St Mary's Hospital

LONDON

PORTSMOUTH

St Paul's Hospital

Royal Hallamshire Hospital

LONDON

SHEFFIELD

St Thomas's Hospital*

North Staffs Royal Infirmary*

LONDON

STOKE-ON-TRENT

Contents

PREFACE

xxi

C H A P T E R 1: S E N S I T I Z A T I O N TRANSPLANTATION

AND SURVIVAL IN RENAL

1. O r i g i n s o f a l l o i m m u n i t y 1.I Natural phenomena I. 1.1 T cells, HLA-vigilantes 1.1.2 Individual variation in responsiveness to alloantigens 1.1.3 Neonatal immunity to maternal alloantigens 1.1.4 Maternal immunity to neonatal alloantigens 1.1.5 Multiparity and paradoxical allograft protection 1.2 Blood transfusion 1.2.1 Immune responses to transfused blood 1.2.2 Unwanted effects of transfusion in renal transplantation 1.2.3 Donor specific blood transfusions and renal allograft protection 1.2.4 Third party blood transfusion and renal allograft protection 1.3 Transplants 2. A s s e s s i n g s e n s i t i z a t i o n 2.1 Sequential studies of alloantibody production 2.2 The prognostic value of the pre-transplant cross-match test

10 10 12

3. F a c t o r s affecting i m m e d i a t e graft f u n c t i o n 3.1 Cold IgM antibodies 3.2 Immediate non-function as a prognostic indicator of graft outcome 3.3 Recipient and donor hydration

14 14 15 15

4. H i s t o c o m p a t i b i l i t y 4.1 ABO 4.2 HLA 4.3 The potential of organ sharing

15 15 16 17

CHAPTER

2: C O U N C I L

OF EUROPE

STUDY PLAN

19

1. B a c k g r o u n d a n d t e r m s o f reference

19

2. P r a g m a t i c d e f i n i t i o n o f h i g h l y sensitized: m o r e t h a n 8 0 % p e a k reaction frequency

19

xiv . Questions posed 3.1 Questions relating to serological pattern 3.2 Questions relating to highly sensitized patients on renal transplant waiting lists 3.2.1 Sources of sensitization 3.2.2 Responder phenotypes 3.2.3 Transplantation rates 3.3 Questions relating to transplanted highly sensitized patients 3.3.1 Transplant survival 3.3.2 Daily clinical course: from transplant to hospital discharge 3.4 Questions relating to special schemes for transplanting highly sensitized patients

4. Sources of information: networked through the European Transplant Organizations . Strategy for data collection: pragmatically designed and activated studies 5.1 5.2

Data collection for questions relating to serological pattern Data collection for questions relating to highly sensitized patients on renal transplant waiting lists 5.2.1 Sources of sensitization and responder phenotype 5.2.2 Transplantation rates and other transactions 5.3 Data collection for questions relating to transplanted highly sensitized patients 5.3.1 Transplant survival 5.3.2 Daily clinical course 5.4 Data collection on special schemes for transplanting highly sensitized patients

6. Design faults

20 20 20 20 20 20 21 21 21 21

22

22 23 23 23 26 26 26 36 36 42

7. Strategy 8. General notes on statistical thinking and methods 8.1 8.2 8.3 8.4 8.5 8.6 8.7

Regression models Regression coefficients and risk score summation Covariate structure Standard error, z-score and confidence interval for regression coefficient Comparison of regression coefficients; also of percentages Statistical reasoning: goodness of fit ~(2 and regression ~2 8.6.1 Goodness of fit Z2 8.6.2 Regression ~(2 For reference

C H A P T E R 3: CAUSES

45 45 46 46 47 48 49 50 51 52

53

1. Introduction

53

2. Plan of study

53

3. Characterization of the data

54

4. Variations in laboratory practices

54

5. Modal patterns of response

56

6. Concordance of B and T patterns with time

58

xv

7. Responsiveness to blood transfusions

58

8. Profile with former failed transplants

59

9. Discussion

59

10. Conclusions

59

C H A P T E R 4: S E N S I T I Z A T I O N 1986: P R E V A L E N C E A N D

SOURCES ACROSS EUROPEAN WAITING LISTS

74

1. Introduction

74

2. Waiting lists: standardizing differently reported sensitization levels

74

3. Council of Europe Study intended sensitization levels: prevalence 1986

78

4. Reported sensitization levels: by source (sex, graft number, blood group, registry) 4.1 4.2 4.3

Study method (Regression) model sequence Results: main effects (see COMPOSITE diagrams) and their registry-specific variation (first order interactions with registry) 4.3.1 Sensitization 4.3.2 Sex 4.3.3 Graft number 4.3.4 Blood group 4.3.5 Registry 4.4 Results: first- and higher-order interactions with sensitization origins of sensitization 4.4.1 12 Sensitization with sex 4.4.2 13 Sensitization with graft number 4.4.3 14 Sensitization with blood group 4.4.4 123 Sensitization with sex and graft number 4.5 Results: first-order and other interactions not involving sensitization 4.5.1 23 Sex with graft number 4.5.2 34 Graft number with blood group

5. Discussion

78 78 80 86 86 86 86 88 88 88 88 93 93 95 97 97 97 98

C H A P T E R 5: R E S P O N D E R P H E N O T Y P E S

1. Introduction

104

2. Study outline

104

3. Motivation for linear-logistic regression

106 106 106 109 109 109

3.1 3.2 3.3 3.4 3.5

Odds on being unsensitized Naive Bayes rule Ln odds on being unsensitized Linear-logistic regression Linear-logistic regression: goodness of fit

xvi 4. E x p l o r a t o r y analysis o f c o v a r i a t e s a n d sensitization

III

5. M o d e l building

114

6. F i n a l regression m o d e l

120

. R o b u s t n e s s o f r e l a t i o n s h i p o f H L A p h e n o t y p e with p a n r e a c t i v i t y 7.1 A or B homozygotes; A and B heterozygotes 7.2 Graft number and sex 7.3 Registry specific variation in HLA-associations with panreactivity 7.4 Transplanted database: highly sensitized and control grafts, 198~85

120 120 120 124 127

. H a r d y - W e i n b e r g e s t i m a t i o n o f gene frequencies in waiting list, transplanted and donor databases 8.1 Waiting list patients 8.1.1 Waiting list patients: HLA-DR 8.1.2 Waiting list patients: HLA-A and B 8.2 Transplanted patients 8.2.1 Transplanted patients: HLA-DR 8.2.2 Transplanted patients: HLA-A and B 8.3 Reference donor gene frequencies: Eurotransplant and UK Transplant 8.3.1 Eurotransplant and UK Transplant: HLA-DR 8.3.2 Eurotransplant and UK Transplant: HLA-A

129 130 130 130 132 132 132 134 134 139

9. Discussion

143

Appendix

145

C H A P T E R 6: T R A N S P L A N T A T I O N

RATES

1. I n t r o d u c t i o n

149

2. S t u d y m e t h o d

149

3. T r a n s p l a n t a t i o n rates by registry a n d sensitization level 3.1 Postulate1: systematic underestimation of peak reaction frequency for patients registered as having 1-50% peak reaction frequency 3.2 Postulate2: Eurotransplant medians inferred for different sensitization levels 3.3 Transplantation rates for first versus regrafts: Eurotransplant

153 156 158 158

4. Difficulties in assessing the rate o f a c c u m u l a t i o n o f highly sensitized patients: r e a c t i o n frequency changes for new a n d registered p a t i e n t s

159

5. N e w registrants a n d re-registrations

t61

6. D e a t h s on the w a i t i n g list a c c o r d i n g to sensitization level

161

7. D e - r e g i s t r a t i o n s o t h e r t h a n t r a n s p l a n t o r d e a t h a c c o r d i n g to sensitization level

162

8. Discussion

162

xvii C H A P T E R 7: T R A N S P L A N T SURVIVAL: F O L L O W - U P OF H I G H L Y SENSITIZED A N D C O N T R O L R E N A L G R A F T S T R A N S P L A N T E D 1982 TO 1985

163

1. Study design

163

2.

163

Realization

3. Risk factor report: first grafts and regrafts 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 .

Transplant year Recipient sex and pregnancy history Recipient age Mismatching of HLA antigens Antigen sharing Recipient homozygosity Beneficial/DR matching subdivided by donor DR homozygosity Selected HLA-DR antigens: DR1, DR2 and DR7 Cold ischaemia time Positive crossmatches Cyclosporin A on day 1 and day 1 graft function Blood transfusion Duration of previous graft and wait from previous graft failure to regraft Subdivision of highly sensitized recipients by latest reaction frequency

Statistical method: stratified piece-wise proportional hazards ( = relative risks) 4.1 Stratification 4.2 Piece-wise versus constant proportional hazards (=constant relative risks) 4.3 Covariate structure 4.4 Testing interactions or differences between corresponding regression coefficients

. Transplant survival: results 5.1 Time dependent penalty associated with high sensitization 5.2 Model building: additions to background covariates 5.2.1 Tissue matching: HLA mismatches or antigen sharing (overall and in two distinct epochs) 5.2.2 Model building: high-low sensitization 5.2.3 Model building: pregnancy 5.2.4 Model building: ischaemia time 5.2.5 Model building: transplant year 5.2.6 Model building: INTERACTION of high sensitization and non-beneficial matching 5.2.7 Model building: recipient homozygosity 5.2.8 Model building: recipient HLA-DR phenotype 5.2.9 Recipients as well as donors fully typed: HLA-DR homozygosity (donor or recipient) and recipient HLA-DR phenotype revisited 5.2.10 Eurotransplant excluded: positive crossmatch 5.2.11 Registry: to stratify or not? 5.2.12 Model building: duration of previous graft and waiting time from previous graft failure to regraft 5.3 Preferred coding for HLA-mismatches: model preference amalgamating other covariates

164 164 164 165 165 165 166 166 166 166 167 167 167 168 168

168 170 170 171 173 174 177 177 182 190 191 191 192 193 195 196

199 200 204 204 207

..o X V l n

5.4 Final regression models including day 1 graft function 5.4.1 Day I graft function 5.4.2 Final regression models 5.4.3 Transplant survival: summary

219 219 221 229

6. Mortality: results

229

7. Discussion

230

Appendix I

Realization of study design

234

Appendix II

Risk factor report by first versus regraft

235

Appendix III

Risk factor report by registry

248

Appendix IV

Covariate names, structure and description

250

C H A P T E R 8: D I S T I N C T P O S T - T R A N S P L A N T C O U R S E F O R H I G H L Y SENSITIZED RECIPIENTS? ( K A L M A N FILTER MONITORING)

253

1. Introduction

253

2. Study method and Kalman filter

253

3. Pilot study results 3.1 Designand data quality 3.2 Distinct post-transplant course for SOS patients: "grumbling start" 3.3 Kalman filter: analysis 1 on nine SOS/control pairs 3.4 Retuned Kalman filter: analysis 2

254 254 256 258 262

,

Validation exercise

262

5. Discussion

267

C H A P T E R 9: SPECIAL SCHEMES F O R T R A N S P L A N T I N G HIGHLY SENSITIZED PATIENTS

268

1. Introduction to 11 special schemes

268

2. Special schemes: logistics

273

3. Crossmatches and other features of special schemes

274

4. Maximum acceptable donor HLA-A, B or D R mismatches for highly sensitized patients and priority in recipient selection

275

5. Organ exchange hierarchy

277

6. Discussion

277

C H A P T E R 10: M A K I N G SENSE OF S E N S I T I Z A T I O N

282

1. Introduction

282

2. Definition of high sensitization

282

xix 3. Promoting inter-registry collaboration

283

4. Antecedents of high sensitization

284

5. Exploding the myth of sensitization being confined to HLA-A and B antigens

284

6. Quality of tissue-typing

286

7. Responder phenotypes

286

8. International variation in gene frequency

287

9. Transplantation rates and special schemes

287

10. Transplant survival

287

11. Clinical implications and future studies

289

References

290

Index

299

Preface

In 1986, the Committee of Experts on Blood Transfusion and Immunohaematology of the Council of Europe chose for their Programme of Co-ordinated Research "An investigation of the procurement and sharing of transplantable organs for potential recipients who are highly sensitized to HLA-antigens". This topic was of common concern to all centres practising renal transplantation. The terms of reference of the study were: 1. To estimate the number of patients who are virtually "untransplantable" because of high sensitization in each European country. 2. To study the nature of immunization in terms of the type and specificity of antibodies present in the blood and techniques used for their detection. 3. To investigate possible practical solutions - both current and future, involving cross-matching procedures, the circulation of reference material from patients, and the willingness of the national organizations to share resources. 4. To explore other methods of resolving this problem. Although the study did not offer the prospect of a brilliant new insight into the problem of high sensitization, it was unique in several ways: for the first time we saw all European organizations collaborating in a common project to provide information on their activities, their problems and the methods to resolve them; it introduced, for this subject, relatively novel statistical methods to investigate susceptibility to sensitization and factors affecting transplant outcome; it enabled a large database of transplanted highly sensitized patients and matched controls to be assembled, that would have been unavailable as a research resource at any single centre. In the final analysis, both transient and persistent risk factors influencing graft function were identified. Our understanding of the central role of HLA matching in the survival of all grafts was confirmed and expanded, and we slew a few myths, particularly one relating to the innocence of a positive B cell cross-match. Enthusiasm for the project developed spontaneous!y and was carried forward to the directors of the national transplant services.Without the willing

xxii support of the Study Group and others, notably Dr. Bernard Cohen (EUROTRANSPLANT), Professor Joachim Machado Caetano (LUSO TRANSPLANT), Professor Jacques Hors (FRANCE TRANSPLANT) and Dr. Antonio Arnaiz Villena (HISPANO TRANSPLANT), it would not have been possible to collect clinical data on over 16,000 patients awaiting transplantation throughout Europe and over one thousand transplanted highly sensitized recipients. To preserve confidentiality we cannot individually thank clinicians and scientists who provided data, but we would, nevertheless, like to offer our sincere thanks in centre acknowledgment to all those who contributed. The time constraint on the Study Group was to complete its work between February and December 1986. This was achieved through very generous provision of resources by the MRC Biostatistics Unit, which provided most of the facilities for data management and statistical analysis; the UK Transplant Service which provided spirited liaison; and the Council of Europe's Health Division which provided a venue and funds to convene the study group meetings. Dr. Kerry Gordon and Professor Adrian F. M. Smith (Department of Mathematics, University of Nottingham) co-author Chapter 8 on Kalman filter monitoring of post-transplant course and provided resources thereunto. Special thanks are due to Miss Margaret Fowler, then Statistical Assistant in the MRC Biostatistics Unit in Cambridge, who resourcefully solved computing problems and martialled the data management with excellent good humour. Her assistance was invaluable and was supported by kind permission of Dr. Nicholas Day, Director of the Unit. Illustrations were commissioned from Clifton Studios in Bristol, their rapid throughput and constructive suggestions on over a hundred figures are worthy of our acknowledgement. No research group functions well without superb secretarial support, and we were privileged to have access to the diligent and dedicated offices of both the MRC Biostatistics Unit and the UK Transplant Service. To Mrs. Margaret Cowling especially, and Mrs. Iris Castleton (MRC Biostatistics Unit, Cambridge) and to Mrs. Janet Stinchcomb and Mrs. Ruth Couzens (U K Transplant Service, Bristol), we give our sincere thanks. The Council of Europe sponsored the study; funding was provided by the Council of Europe, MRC Biostatistics Unit and the United Kingdom Transplant Service. The text is the sole responsibility of the authors. B. A. Bradley Director of Studies August 1987

1. Sensitization and Survival in Renal Transplantation

1. Origins of ailoimmunity 1.1. Natural phenomena 1.1.1. T cells, HLA-vigilantes. During foetal development lymphocytes (T cells) learn to recognize self HLA gene products (Jerne 1981). Many react vigorously to foreign HLA antigens carried by other individuals, even without prior contact with these antigens. How this apparent commitment to foreignness develops has no complete explanation, but the most plausible theory, not necessarily correct in detail, is that alloimmunity is a by-product of T cell differentiation. Normally pre-T cells enter thymic cortical tissue, differentiate into cells that bind loosely to self HLA and emerge into the post-thymic pool there to function as HLA-vigilantes, surveying the body for HLA associated unwanted or foreign antigens. This vital role is dramatically illustrated by individuals born with defective expression of HLA; they develop fatal immunodeficiency disease (Touraine et al 1978, Schuurman et al 1979). Thymic tissue acts as a sieve allowing only those cells to pass that exhibit a low avidity for self-HLA; from thence they pass into peripheral lymphoid tissues where they mature and perform two major functions. They regulate development of immune responses and they kill target cells bearing foreign antigens (Benacerraf 1980). Both functions require T cell receptors to interact with HLA on the target cell membrane (Marrack et al 1983). Normally self HLA, interacting with T cell receptors, fails to elicit a response, but the slightest abnormality in the target cell brought about by infective or neoplastic change triggers the T cell into an activated state. Thereafter altered cells are targeted and destroyed by T cell aggressors (Zinkernagel and Doherty 1979). Most allotypic variants of HLA are excellent inducers of T cell responses. This has been seen as the inevitable consequence of selection of low avidity clones in the developing thymus, a process that favours cells binding to epitopic variants of

HLA with high avidity. Thus alloimmunity is a by-product of T cell recognition of altered self. Virgin B lymphocytes are devoid of precocious inclinations to engage HLA alloantigens spontaneously and secrete alloantibody. Germ free animals fail to generate alloantibodies even though their T cells display spontaneous alloimmunity in mixed lymphocyte cultures (Wilson and Fox 1971). Later, during immunological development B cells do produce alloantibodies to major blood group antigens absent from self. However, this is attributable to cross reacting oligosaccharide epitopes on gut bacteria (Springer and Horton 1969). It is a cross reactive response. Similar cross reactions have been described between streptococcal antigens and human transplantation antigens (Rappaport and Chase 1964, Hirata and Terasaki 1970). After grafting with foreign tissues anti-HLA antibody is the predominant component of humoral immune responses. This is a consequence of the amplification by T cells that carry the capacity to react to foreign HLA; a process which directly engages MHC molecules (Kindred et al 1972). Since T cell regulation of B cell differentiation is antigen specific, anti-HLA is the preferred B cell product. 1.1.2. Individual variation in responsiveness to alloantigens. Blind spots in the range of specificities recognised by killer T cells correspond to self-HLA thereby avoiding autodestruction. These develop during ontogeny (Klein 1980). Allelic products of HLA genes contain multiple antigenic sites or epitopes some of which are unique and some are shared with other allelic products. Epitopes that are shared between self and foreign HLA might extend the limits of non-responsiveness by inadvertently creating blind spots to part of another individual's HLA type particularly those belonging to the same crossreactive group. In experimental animals subtler variations in the ability to respond have been identified (Butcher and Howard 1982). In experiments with rat recipients of grafts from the coisogenic RT1A strain donors, alloimmunity to RT1A is controlled by a gene termed Ir-RT1A", thought to map in the Class II region of the rat Major Histocompatibility Complex (MHC). The c allele is associated with low alloantibody production, low levels of killer T cells and prolonged organ graft survival. The u allele is associated with high levels in these modalities. These variations in responsiveness are mediated through T helper cells. Responsiveness to non-MHC antigens is also under genetic control but has different allelic associations; for example, responsiveness to the male linked histocompatibility antigen, H-Y, varies between the RT1A c haplotype, which is associated with slow rejection via, and the a allele which is associated with fast rejection of syngeneic male grafts (G/inther et al 1985). In a similar way

the response to the H-Y antigen in mouse varies depending on the H-2 context in which the antigen is seen. H-2 b haplotypes give less vigorous skin graft rejection than H-2 k haplotypes (Bailey and Hoste 1971, Gasser and Silvers 1971, Stimpfling and Reichert 1971). The best documented example of phenotypic variation in human allo-reactivity is in renal transplantation where low responder status is attributed to HLA-DR1 (Katz et al 1985, Cook et al 1987). HLA-DR1 individuals are less likely to produce alloantibody and to reject renal transplants irrespective of the mismatch. An opposite effect has been described in the Eurotransplant data for HLA-DRw6 recipients (Hendriks et al 1982); but this effect may have been compromised by multiple blood transfusions, rendering confirmation of this finding difficult in subsequent series (Lagaaij et al 1987). 1.1.3. Neonatal immunity to maternal alloantigens. Healthy non-transfused males are occasionally found to produce alloantibody to foreign HLA antigens carried by their mothers (Chardonnens and Jeanett 1980, Yamagachi et al 1983, Werneberg et al 1984, Miyagawa 1984). In healthy offspring there is no evidence that maternal cells have entered or that they persist in foetal circulation but immunologically compromised neonates, born with combined immuno-deficiency disease sometimes carry B cells of maternal origin (Kadowaki et al 1965, Geha and Reinherz 1983). These cells exist in a state of stable chimerism with their neonatal host. Under other circumstances it was reported that maternal cells attacked foetal tissue and caused abortion at the twelfth week of gestation (Taylor and Polani 1965). 1.1.4. Maternal immunity to neonatal alloantigens. Pregnancy induced leukoagglutinins were first recognised in 1958 (Payne and Rolfs 1958) and have since been a major source of HLA typing reagents (Van Rood et al 1959). Rodent experiments suggest that the induction of antibodies during pregnancy is under genetic control (Smith et al 1982a,b). In mice a quantitative difference in the relative distribution of immunoglobulin isotypes of alloantibodies occurs during pregnancy compared with graft rejection (Bell and Billington 1980). The differences depend on mouse strain combinations but in those studied noncomplement fixing anti-MHC antibodies are predominant during pregnancy whereas complement fixing antibodies are predominant after graft rejection. The relationship between past pregnancies and renal allograft survival is such that pregnancy appears to be associated with a significantly lower graft survival in recipients of first renal transplants (Sanfillipo et al 1982). This may be related to heightened sensitivity to blood transfusions in parous women. High sensitization is more common after transfusions in parous women than in nulliparous women (Opelz et al 1981). However the relationship between level of sensitization, transfusion and parity is complex (Sanfillipo et al 1982).

Parity is associated with decreased graft survival in untransfused recipients, but in transfused recipients it is associated with a significant benefit even though patients are moderately sensitized (0-60% panel reaction frequency). 1.1.5. Multiparity and paradoxical allograft protection. Thirty years ago a report of an investigation into sources of skin donors for burned patients claimed that skin grafts taken from offspring and transplanted onto mothers survived longer than from offspring to fathers (Peer 1957). These studies were sparsely recorded, difficult to repeat and have been largely forgotten; but there followed some very elegant rodent experiments in multiparous mice (Prehn 1960, Breyere and Barrett 1960a,b, Breyere and Barhoe 1963). Tumours and skin allografts survived significantly longer in multiparous than in nulliparous recipients, when either major (H-2) or minor (H-Y) histocompatibility barriers were breached. The effect in the mother was specific for paternal alloantigens carried by her offspring. An infusion of paternal bone marrow cells several days postpartum strengthened the suppression and rendered it more specific. A quantitative relationship existed between the number of pregnancies and the extent of protection. Protection persisted for the lifetime of the mother (Breyere 1967). Heart allografts in in-bred rats between paternal strain donors and their multiparous mothers survived longer than in corresponding transplants from paternal strain donors into nulliparous controls (Heron 1971, 1972a,b). Similarly in rabbits, neonatal hearts taken from babies and transplanted back into the offspring's mother enjoyed longer survival than similar transplants into the father. In parous women renal allograft survival was compared between offspringto-mother and offspring-to-father transplants; 222 offspring-to-father grafts had a survival of 70% (se = 3.1%) but 117 offspring-to-mother grafts had a survival of 80% (se = 3.6%). The paradox between these findings and those showing an increasing risk with multiparity may be explained by differences in the histocompatibility relationship between offspring and mothers. Child to mother transplants may reactivate specific suppressor mechanisms.

1.2. Blood transfusion 1.2.1. Immune responses to transfused blood. Until 1956 leukoagglutinins and thromboagglutinins were widely studied as potential aetiological agents for leukoneutropenia and thrombocytopoenia. Thus in multiply transfused patients they were of interest for their autoreactive rather than their alloreactive properties (Moeschlin and Wagner 1952, Dausset and Neuna 1952, Dausset 1954). Leukoagglutinins were identified as causal agents in non-haemolytic transfusion reactions in 1956 (Van Loghem et al 1956, Brittingham and Chaplin 1957). By this time it became apparent that transfusions induced 'complete'

antibodies, which agglutinated leukocytes directly and 'incomplete' antibodies which required anti-globulin reagents for leukoagglutination (Van Rood et al 1959). It is difficult to anticipate immune responses after transfusions. Many factors are involved including: the patient's disease (uraemics are more immuno-depressed than healthy individuals); prior experience of alloantigen through pregnancy or failed transplants; the volume of the blood given; the interval between transfusions; the frequency of test samples; the constituents of the transfusate; and the HLA mismatches between transfusion donor and recipient. One study documented an immunization rate of 90% following four, small transfusions of 160 mls of blood spaced at six monthly intervals (Soulillou et al 1980). In 63% of patients anti-B lymphocyte antibodies emerged and tended to appear earlier after transfusion. In 49% of patients anti-T lymphocyte antibodies appeared but developed later after transfusion. Washed blood was administered, hence the induction of alloantibodies was surprisingly high considering the poor leukocyte content. In other studies, alloantibody production was dependent on the number of viable leukocytes in the transfusate (Martin et al 1985). A correlation existed between transfusions and panreactivity as reflected in alloantibody production against the population. Panreactivity of greater than 60% occurred more frequently with more transfusions, and after six to ten transfusions the overall rate was 9% but in parous females it had risen to 20% (Sirchia et al 1981). In another study only 2% of untransplanted male patients who were given ten blood transfusions developed lymphocytotoxic antibodies to more than 90% of the population, but 14% of females and 30% of multiparous females with three or more pregnancies, reacted with 90% of the population. The relationship between numbers of transfusions and proportion of the population against which antibody is produced was non-linear; after 5 transfusions the fraction developing alloantibodies levelled off and only a small relative increment occurred between 5 and 15 transfusions (Opelz et al 1981). Thus transfusions appeared to amplify immunity, induced by prior pregnancies. In parous women who had rejected a graft the majority developed persistent panreactive antibody following transfusion. Persistent responders to transfusions had all exhibited alloantibody binding to lymphocytes detectable by flow cytometry. Transient responders to blood transfusions had no such antibody (Scornik et al 1984). Other types of antibodies that develop after transfusion are directed to targets other than HLA. These include "anti-idiotype" antibodies, so-called because of their capacity to inhibit anti-HLA antibody reactions of serum samples taken from the same individual at earlier times in the immunization history. However, formal demonstration of binding of these antibodies to the V r~ regions of HLA antibodies is a requirement for true idiotypy, and this has

never been demonstrated. Nevertheless these reactions are of interest; they appear six to eight months after the disappearance of transfusion induced alloantibodies and in a few instances their presence is associated with good graft function (Reed et al 1983a, b, 1985, Sucia-Foca et al 1985). Enhanced cellular immunity to alloantigens was demonstrated after multiple blood transfusions. Patients with aplastic anaemia who had received many transfusions of platelets from both HLA identical siblings and random donors at a rate of 4 units, twice weekly for over a year developed high levels of killer cells (Wunderlich et al 1972). These findings are consistent with later studies in which T killer cells produced under similar circumstances reacted against the combined target of self HLA and the male linked histocompatibility antigen H-Y (Goulmy et al 1981). The induction of T killer cells after more modest levels of transfusion may occur and is consistent with the observed increase in the number of activiated T cells of the OKT8 series after transfusion (Lenhard et al 1982). Paradoxically the same authors found that cellular immunity in cellular proliferative assays was depressed. In vivo depressed cellular immunity was evidenced by significant reduction, post-transfusion, in the DNCB skin reactivity (Watson et al 1979). Decreased cellular immunity may result from autoantibody directed against T cell receptors (Ludwin et al 1986); however chemical confirmation of the target molecule involved in these reactions is unavailable. An alternative explanation, suggested in several studies, is that T suppressor cells, known to increase after transfusion, are responsible for suppressed immunity (Smith et al 1982). In summary, blood transfusion may lead either to panreactivity, or suppressed activity in complement dependent lymphocytotoxicity assays. It is followed by enhanced specific T killer cell activity and non-specific suppression of cellular immunity. Six months after the transfusion some form of auto anti idiotypic or alloantibody develops. 1.2.2. Unwanted effects of transfusion in renal transplantation. In previous sections we have raised the spectre of transfused blood providing a trigger for sensitization in two ways: firstly as an immunogen in its own right and secondly as an activator of B cells already primed to produce allo-antibodies. In 1966 Kissmeyer-Nielsen described two cases of hyperacute rejection of cadaveric kidney grafts in multiparous women attributable to high levels of alloantibody induced by multiple transfusions (Kissmeyer-Nielsen 1966). Thereafter the practice of cross-matching was introduced to avoid this catastrophe. The culpability of blood transfusions was seriously questioned by Opelz and colleagues in 1973 when they demonstrated that patients with no detectable alloantibody had better graft survival if they had received more than ten blood

transfusions (Opelz et al 1973). The non-transfused recipients were expected to have the best survival, however they turned out to have the worst. The question of whether or not patients who have produced alloantibody benefit from transfusions in terms of improved graft survival has been addressed in some studies. Opelz et al in 1973 showed no detectable benefit from tranfusirns in patients who were producing lymphocytotoxic antibodies. Dausset in 1974 even showed a correlation between the numbers of transfusions and poorer graft survival in patients producing lymphocytotoxic antibody (Opelz et al 1973, Dausset et al 1974). However, by 1977 cross-match testing was more widely practised and the likelihood of hyperacute rejection reduced. This allowed the beneficial effects of transfusion in sensitized recipients transplanted with cross match negative kidneys to be evaluated. Thus Sanfillipo and colleagues studied a series transplanted between 1977-1981 and showed that the transfusion benefit was greatest in the unsensitized but was also present in the moderately sensitized. In patients with higher levels of sensitization (over 60% panel reactivity) no detectable benefit was discernible (Sanfillipo et al 1982). Thus the beneficial effect of transfusions occurred irrespective of graft number and parity, and was apparently separate from its alloantibody triggering properties. The notoriety attributed to transfused blood for its sensitizing properties is ill founded in individuals who have not been previously immunized. 1.2.3. Donor specific blood transfusions and renal allograft protection. In 1964 blood from a kidney donor given subcutaneously to dogs prior to transplantation was shown to be associated with prolonged renal allograft survival in related donor-recipient pairs mismatched for a single DLA haplotype (Halasz et al 1964). Research in rodents has subsequently indicated that similar phenomena can be achieved by infusions of nucleated B cells, platelets or erythrocytes and that both heat treated and ultra violet irradiated blood are effective (Batchelor et al 1977, Lauchart et al 1980, Wood et al 1985, Wakerley et al 1985, Martinelli et al 1987, Malik et al 1985). In humans alloantibody against prospective kidney donors is generated in 30% of recipients after multiple transfusions from a single donor but such transfusions also induced significant suppression of graft rejection (Salvatierra et al 1980, 1981). The mechanism of the suppression appears resistant to treatment with Azathioprine and possibly Cyclosporin-A when given concurrently with transfusions (Glass et al 1983, Anderson et al 1984, Salvatierra 1985). But the generation of lymphocytotoxic antibody appears to be sensitive to these drugs. Thus specific suppression may be retained whilst sensitization is ablated. Further speculation on mechanisms involved in this form of graft protection has invoked a sequence of events involving T suppressor cells and their capacity to down regulate T helper cell function (Hutchinson 1984).

1.2.4. Third party blood transfusion and renal allograft protection. In 1973 patients who had received no transfusions and who had developed no lymphocytotoxic antibodies were widely assumed to enjoy a superior renal allograft survival compared to those who had been transfused; but the opposite was true (Opelz et al 1973). Patients given ten or more transfusions, who had developed no detectable lymphocytotoxins when compared to non-tranfused patients, enjoyed a significantly improved survival. These results were repeatedly confirmed in human and animal studies (Tiwari 1985). An incremental benefit was demonstrated between numbers of transfusions given and graft survival; the more transfusions, the better the survival; but one study clearly showed that a single transfusion was sufficient to induce graft protection (Persijn et al 1977). A single transfusion had the theoretical advantage of reducing the risk of sensitization. The component parts of transfused blood survive in the recipient's circulation or tissues for varying times. The most potent fraction of blood responsible for graft protection appears to be lymphocytes. Animal experiments suggest that B lymphocytes may be more effective than non-B lymphocytes in terms of graft protection (Lauchart et al 1980). However other components are also effective; purified platelets given in sufficient quantity induce graft protection and have the added advantage of being poor inducers of primary immunization to HLA antigens (Borleffs et al 1983). However washed human red cells are ineffective (Persijn et al 1981). In recent studies the beneficial effect of blood transfusions has apparently weakened (Opelz 1987, Groth 1987). This observation, made in several series, coincided with improvements of graft survival attributable to other therapeutic procedures. It is difficult to concede that a beneficial effect of this magnitude could suddenly disappear and the suspicion is that results may have been inadvertently biased by a centre effect. The effect remains to be confirmed in a prospective controlled clinical trial. Transfusion of the donor prior to transplantation also protects grafts from rejection (Jeekel et al 1983). 1.3. Transplants In 1944 Medawar's theory of actively acquired immune responsiveness explained skin allograft failure in a novel way (Medawar 1944). Central to his theory was the observation that second grafts from one donor, placed after failure of the first, were broken down in accelerated time; the accceleration being unrelated to the graft interval. This, he termed the "second set phenomenon". Ten years later a parallel phenomenon was described by Simonsen in second kidney grafts in dogs (Simonsen 1953a,b). Dempster then demonstrated that

actively acquired immunity to graft antigens was a systemic process (Dempster 1953). Having first sensitized dogs with skin grafts from prospective kidney donors, he observed that they immediately rejected kidney transplants. This established that immunity to alloantigens expressed on one tissue heightened reactivity to subsequently transplanted tissue, even although the tissues originated from different organs. Chief culprit for this heightened alloimmunity in dogs was thought to be alloantibody and supporting evidence came from serum transfer experiments (Altman 1963). Skin graft sensitized dogs donated immune serum to non-sensitized recipients of kidney grafts from the same donor animal. Within twenty minutes kidneys were rejected, implicating alloantibody. A distinction is often made between hyperacute, acute and chronic kidney graft rejection. In each case elements of antibody mediated and T killer cell mediated tissue destruction have been described. Cellular immunity plays an important part in second set rejection as illustrated many years ago in male to female skin grafts within in-bred mouse strains. Recipients who had already rejected one skin graft from a male donor were sensitized exclusively to the mismatched H-Y antigen (Eichwald et al 1957); no antibody has been convincingly demonstrated in mice to this antigen and rejection is attributed to killer T cells. Cellular immunity as a cause of hyperacute rejection was implicated in a kidney graft model in mini-pigs (Kirkman et al 1979). Pairs of unrelated animals were cross-sensitized with multiple skin grafts and grafted with kidneys from the same donors. Rejection occurred minutes later but no antibody was found either before or after transplantation. Enhanced anti donor cellular responses were demonstrable. In man rapid rejection of kidney grafts was first documented in 1968 with several descriptions of hyperacute rejection following second, third and fourth transplants (Williams et al 1968). Histological hallmarks were widespread infiltration with polymorphonuclear leukocytes and capillary thrombosis accompanied alloantibody production to HLA (Morris et al 1968). In chronic graft failure there is a significant association between the appearance of alloantibody to donor Class II mismatches (Soullilou et al 1978); but there is also a significant correlation between the emergence of donor specific T killer cells and graft failure (Goulmy et al 1981). When rejectingtransplants cease to function and immunosuppressive therapy is discontinued, panreactive anti-HLA antibody tends to be produced in some cases (Hardy et al 1979). This occurs especially when grafts are left in situ; if removed, antibody tends to be less panreactive (Norman and Barry 1985). Furthermore if immunosuppression (with Azathioprine) is continued after graft nephrectomy anti-HLA activity is suppressed to negligible levels for the duration of the immunosuppression.

10 2. Assessing sensitization

2.1. Sequential studies of alloantibody production Studies of the development and persistence of alloantibody following immunization are usually insufficient to provide a complete sequence of events. Various aspects have been documented with greater or lesser detail during pregnancy, transplantation and after one or more blood transfusions. Such studies are largely confined to lymphocytotoxic antibodies that fix complement; occasional distinction is made between IgG and IgM. But little attention has been paid to non-complement fixing opsonic antibodies that may be responsible for antibody mediated unresponsiveness or graft enhancement (Lems et al 1981a, b, Hutchinson and Zola 1977). Furthermore, IgG isotypes capable of arming host killer cells (K cells) are unjustifiably ignored when assessing sensitization. During pregnancy lymphocytotoxic antibody (hereafter alloantibody) production is influenced by parity and phase of gestation. Vives et al showed that alloantibody appeared progressively earlier in gestation with increasing parity; thus it was detectable after the fourth, second and first months of gestation during first, second, and third or subsequent pregnancies (Vives et al 1976). 19% of prima-gravidae and 30% of multi-gravidae (5 or more) develop alloantibodies during pregnancy but a significant decline occurs in the proportion of antibody positive women between the sixth and eighth month. The specificity of sera also narrowed.(Tongio and Mayer 1977). The proportion of women producing lymphocytotoxic antibodies was related to parity; in this study after four or more pregnancies 47% of women developed antibodies. Several other studies confirmed an association between parity and proportion of women producing alloantibodies. Doughty and Gelsthorpe revealed an association between antibody titre and parity at all stages of gestation. Furthermore a post-natal decline in the proportion of positive women from 17% at delivery to 12% six months later Occurred irrespective of parity (Tongio et al 1973, Doughty and Gelsthorpe 1976). In theory the vast majority of mothers should produce alloantibody to foreign HLA, but many are reputed to carry small numbers of foetal lymphocytes in their circulation, some up to 12 months post-natally (Herzenberg et al 1979). Schr/Sder et al observed that 37% primiparous mothers carrying male offspring had detectable levels of foetal lymphocytes in their peripheral blood and these were apparently of B cell type (Schrrder and de la Chapelle 1972, Schrrder et al 1974, 1975). Their disappearance at various times postnatally was inversely correlated with the appearance of alloantibody. Thus mothers who are negative at delivery become positive, microchimerism has waned.

11 Further discussion of pregnancy associated humoral immunity awaits more detailed, sequential studies aimed at clarifying immunoglobulin class, sub-class, specificity and biologic function (Head and Billingham 1983). The alloantibody response to kidney grafts is difficult to evaluate. Most patients at some stage prior to transplantation have been primed to alloantigens and as a consequence the Post-graft response is modified in several ways; by the absorptive capacity of the kidney or its antigeneic products; by non-specific polyclonal B cell reactivation; and by therapeutic immuno-suppression. Graft verus host antibody reactions are also documented; mismatches perceived in the host by donor cells can elicit antibody production by donor lymphocytes transferred in the graft. These sequest into host tissues and generate antibodies to blood group mismatches (eg Rhesus D) in the host and may persist for many months after graft nephrectomy (Ahmed et al 1987, Ramsey et al 1986). Blood transfusion of recipients before or during the transplant operation might have been expected to sensitize patients to alloantigen after grafting. However, Ting and Morris observed that although most non-transfused patients developed donor-specific alloantibodies after-transplantation this occured in significantly fewer of the patients who had been transfused (Ting and Morris 1979). None of the patients studied had detectable pre-transplant alloantibodies and all were receiving their first transplant. More than most other studies this addressed the question of the relevance of graft induced antibodies during the post-engraftment period. In another study Roy et al showed that, in the majority of patients, alloantibody produced post-engraftment was of the cold reacting (4°C), IgM type and was irrelevant as a predictor of graft failure. IgG alloantibodies were associated with poorer graft outcome (Roy et al 1981). This observation took no account of the anti-donor specificity of the IgG alloantibody or graft number (the study included both primary and regrafts). Further dissection of post-engraftment antibody responses with special attention to specificity, class, and biological function is required. Only then a final judgement can be made as to its clinical relevance. After graft rejection the evolution of alloantibodies depends on events such as blood transfusions, graft nephrectomy and the discontinuation of immunosuppression. The majority of alloantibody occurring after graft failure is directed towards HLA, as revealed by segregation studies using a panel of cells derived from pairs of HLA identical siblings (Soulillou et al 1981). The proportion of recipients who develop humoral immunity to donor antigens after graft failure is difficult to estimate. Scornik used a sensitive flow cytometry technique to detect IgG alloantibodies that bound at 37°C; he found that virtually all rejectors produced detectable anti-donor antibody to a much higher titre than would be detected by lymphocytotoxicity. This persisted after

12 rejection for at least five months (Scornik et al 1984). In many of these cases no alloantibody was detectable by conventional cytotoxicity tests but, if during the post rejection period blood transfusions were administered, kidney donor specific lymphocytotoxic antibody production was triggered. It is generally assumed that the sensitized state following graft rejection is little different from that following pregnancy or blood transfusion, however evidence from absorption experiments shows that the lymphocytotoxicity following graft rejection is accompanied by antibodies directed towards kidney specific polymorphisms (Mohanakumor et al 1981). In summary alloantibody development after engraftment and after graft failure is poorly documented, not only with regard to its specificity within the HLA system, but also with regard to its immunoglobulin class, its sub-class and biological function. 2.2. The prognostic value of the pre-transplant cross-match test In 1966 Kissmeyer-Nielsen observed an association between the presence of leukoagglutinating antibodies directed towards donor cells and subsequent hyperacute rejection in two multiparous women who had been multiply transfused. The role of alloantibody in hyperacute graft rejection was then poorly understood (Kissmeyer-Nielsen et al 1966). This was followed in 1969 by the first convincing evidence of the prognostic value of the cross-match test as we now use it (Patel and Terasaki 1969). A micro-lymphocytotoxicity test was used to show that 24 out of 30 transplants performed across a positive cross-match failed to function. The remaining transplants were later reviewed (Belleil et al 1972); of these two had excellent function at 3 years post transplant but the rest had failed between 3 and 24 months. Another isolated case of a positive crossmatch transplant that functioned after 12 days of oliguria was reported by Heale and Morris (1969). Another series published in 1974 confirmed the association between cross-match positivity and graft failure; of 21 patients with anti-donor antibody prior to transplantation, only seven were still functioning 18 months later (Myburgh et al 1974). Thus, in its unsophisticated form, the cross-match test is an important but not an absolute prognosticator of hyperacute rejection. In recent years evidence has emerged to suggest that clinically relevant antidonor alloantibody is of IgG class and directed towards HLA mismatches on the donor cells (Chapman et al 1987). Antibody directed towards auto-antigens and IgM alloantibody appear innocuous in terms of hyperacute rejection (Fabre and Morris 1972, Reekers et al 1977, Park et al 1977). However, cold agglutinating antibodies are an exception (see below). Similarly alloantibody directed towards non-HLA targets, especially those confined to B cells, is irrelevant (Reed et al 1983).

13 In theory, a positive cross-match test against B lymphocytes may be attributed to IgG anti-HLA-DR antibody. However if anti~HLA-A and B antibodies have not been removed from sera by platelet absorption prior to testing, positive tests attributable to anti HLA-A and B antibodies will occur despite negative results against T cells (Jeannet et al 1981). 50% of all positive B cell tests performed with unabsorbed sera are attributable to anti-HLA-A and B antibodies. The temperature dependency of serum reactivity is prognostically relevant in two ways; firstly, cold reactive anti-donor antibodies, including IgM allo and auto antibodies, are associated with graft non-function during the early post transplant period when cold kidneys are transplanted (see below). Secondly, cold reactive anti-donor antibodies, particularly IgM auto anti B cell antibodies, are associated with improved graft survival (Klouda et al 1976, Iwaki et al 1978, Jeannet et al 1980, Ayoub et al 1980, Ettenger et al 1983). Until 1982 it was widely assumed that once a patient was sensitized he remained sensitized for all time. Sound immunological reasoning would dictate that an anamnestic response followed by hyperacute rejection would occur if a transplant was performed across a positive cross-match test using any of the sera obtained from the patient throughout his dialysis history. Cardella, having confirmed an interesting phenomenon that multiple transfusions given to sensitized patients were in many cases followed by declining sensitization (Cardella et al 1982a), developed an immunosuppressive protocol that allowed the recipient to overcome past sensitization (Cardella et al 1982b). Fifteen patients including nine regrafts were transplanted. In all cases the non-current (historic sera) gave positive cross-match results with the donor but the current sera gave a negative cross-match test. The reaction frequency of the non-current sera ranged from 20-100% with peaks of over 50% in all cases. Current sera ranged from 0-90% reaction frequency. In all but one patient the reaction frequency was falling slowly between peak and the current sera and in nine cases the patient received transfusions during the interval between the two serum samples (range 2-36 months). Prior to transplantation patients were treated with rabbit anti-thymocyte serum for 24 hours and this was continued for 21 days post transplant. In addition patients received Prednisolone and Azathioprine. The results in terms of graft survival were not significantly different from the 79 conventionally treated controls. Though not conclusive in itself several other studies confirmed these initial observations (Norman et al 1985) but the impression gained was that positive cross-matches in any serum in patients receiving a re-graft should be respected (Kerman et al 1985). The lack of sensitivity of the conventional cross-match test has been shown in several ways. Fuller developed a sensitive cross-match test that featured a second stage of development in which an antiglobulin antiserum cocktail was added to the test prior to the addition of complement (Fuller et al 1978). He

14 identified 17 patients whose conventional cross-match result was negative but whose enhanced test result was positive. 16 of these cases rejected within two months post-transplant. Kerman showed a significant asssociation between positivity in a crossmatch test based on 51Chromium release and graft failure at one year post transplant (Kerman et al 1985). This was significantly better than the conventional cross-match test. Garavoy using fluorescence activated cell sorter (FACS), not only detected higher titres of anti-donor lymphocyte antibody, but also found an association between the "FACS positive-conventional test negative" transplants and graft failure during the first 3 months post transplant (Garavoy et al 1983). All these studies suggest that whereas conventional cross-matching may be sufficient to predict hyperacute rejection, a more sensitive cross-match assay may help to predict graft failure during the first year.

3. Factors affecting immediate graft function 3.1. Cold IgM antibodies In 1971 Belzer et al described a patient carrying cold reactive auto anti-N antibody who received a cadaveric transplant (Belzer et al 1971). The kidney failed to function and on histological examination the vessels were full of erythrocyte aggregates and infarcts. The second kidney from the same donor, now forty hours old, was transplanted into the same patient but was irrigated with warm perfusion fluid prior to opening up the blood supply. This kidney functioned immediately and continued well beyond five months. They suggested that patients with cold anti-erythrocyte antibodies should not be transplanted with cold kidneys. These observations were later extended in a larger series (Schweizer et al 1982). In 1976 and again in 1980 Kjellstrand and Brophy and colleagues reported an incidence of initial non-function in transplants from living related donors of 10-11% and in cadaveric transplants of 32% (Kjellstrand et al 1976, Brophy et al 1980). An independent study documented a similar rate of early non-function in cadaveric transplants (Lobo 1980a,b). Initial non-function was linked to the presence of cold reactive IgM anti-donor antibodies. Biopsies taken one hour after transplantation revealed in 22 out of 23 non-functioning kidneys segmental glomerular capillary aggregates and fibrin deposits. This was significantly associated with cold IgM lymphocytotoxic antibodies in the recipient prior to transplantation. These lesions were distinct from hyperacute rejection and acute tubular necrosis traditionally associated with ischaemic damage beyond 72 hours. No significant difference in the

15 cold-ischaemia time existed between kidneys with glomerular lesions (27 + 6 hours) and kidneys without glomerular lesions (26 + 7 hours). Warming the kidney prior to establishing the blood supply reduced the incidence of nonfunction (Lobo et al 1984). 3.2. ffnmediate non-function as a prognostic indicator of graft outcome Multifactorial analyses of risk factors affecting the outcome of kidney transplants have shown that delayed graft function is one of the strongest factors affecting the outcome of transplants up to six months (Sanfillipo et al 1985). The relative risk is 1.46 (Z = 5.37 P < 10-5). This risk factor is separate from the centre effect, panel reactive antibody, anti-rejection therapy, source of donor (local or shared), transfusion history, HLA-A and B matching and ischaemia time. Of interest, since delayed graft function is often assumed to be attributable to acute tubular necrosis induced by prolonged ischaemia, was its effect over and above the method of organ preservation and the preservation (ischaemia) time. Belitski and colleagues showed that the duration of non-function correlated both with transplant survival and with post-operative serum creatinine levels in the recipient (Belitski et al 1987). 3.3. Recipient and donor hydration Of equal importance to all immunological factors affecting immediate function is the degree of hydration of both recipient at the time of transplantation and the donor prior to nephrectomy. Under-hydration leads to early anuria (Woods et al 1972, Anderson and Etheredge 1977, Luciani et al 1979, Carliev et al 1982).

4. Histocompatibility Three major aims of tissue matching are to avoid targets for hyperacute rejection, to reduce the need for immunosuppressive therapy and to achieve prolonged survival of the graft, if possible for the patient's lifetime. 4.1. ABO In 1956 Merrill and colleagues reported a successful kidney transplant in uniovular twins (Merrill et al 1956). At that time virtually nothing was known of the HLA system. The ABO system became a prime suspect in 1964 when Starzl published a small series implicating AB blood group mismatches in early graft loss (Starzl et al 1964). This was confirmed in a more extensive series in 1967 but it was evident that one-third of the AB mismatched transplants were

16 still functioning several months after transplantation (Gleeson and Murray 1967). Thus AB mismatching is not an insurmountable barrier to kidney graft survival, a fact borne out in recent studies (Slapak 1981, Brynger 1984, Alexandre et al 1985). 4.2. H L A In 1965 the association between HLA mismatching and survival was seen simply as a matter of matching a few alleles at a single locus (Simonsen 1965). The role of HLA matching was confirmed in several studies of transplants performed between siblings within families (Van Rood et al 1967, i968, Dausset et al 1968, 1969, Singal et al 1969). Relatively crude tissue typing reagents directed towards a few high frequency antigens (eg HLA-Bw4) were available at the time and the complexity of the HLA system that was subsequently to emerge had in no way been appreciated. Based on an anticipated demand for kidneys from cadaveric donors, national and international organ sharing programmes were established with the aim of matching donors and recipients and avoiding wastage of transplantable organs. From then until the early 1980's there followed a period of confusion during which the role of HLA matching was strengthened by increasing evidence from matched living related transplants; but was weakened by paradoxical demonstrations of little or no effect in unrelated transplants. This was by no means a universal finding. Nevertheless there was sufficient doubt to question the validity of matching programmes. The main reason for this confusion was the unforeseen multiplicity of loci at the HLA system which have since been more fully unravelled by DNA hybridization studies; also, the complexity of the serological reagents used for tissue typing and the accuracy of the typing results obtained was and in some respects still is suboptimal. Now with the introduction of DNA hybridization techniques for tissue typing the genetic map of the HLA region has been almost completely drawn, and with the introduction of monoclonal antibodies to tissue typing the multitude of epitopes that characterize a single HLA specificity is more fully appreciated. The variation in quality of typing results is now carefully monitored at both national and international levels. Currently the products of the HLA-A, B and DR loci are matched but the quality of tissue typing is poorest with HLA-DR. The true potential of HLA matching, equivalent to that seen in HLA identical sibling transplants, can best be appreciated when the dependence between loci is taken into consideration in the analysis; mismatching at one locus crucially depends on mismatching at adjacent loci. In siblings this dependence is almost absolute. Thus in a statistical model developed to evaluate the full effects of HLA matching Gilks et al (1987) examined the independent effect of 27 varieties of mismatch given by 0, 1 and 2 mismatches at HLA-A, B and DR loci

17 Table 1.1. % 1-year graft survival (first transplants)

HLA-(A,B,DR) mismatches

United Kingdom and Irelanda

North Americab

Europec

000 100 010

93% (73) 86% (108) 81% (97)

83% (98) 68% (97) 62% (117)

92% (39) 90% (87)

67% 73% 70% 71% 65%

64% 62% 62% 57% 55%

67% 68% 65% 61%

001 2 in 3 in 4 in 5 in

total total total total

(96) (603) (720) (399) (154)

(107) (1229) (1406) (820) (194)

(74) (299) (238) (90)

a Estimated multifactorially controlling for transplant centre and year of transplant; based on UKTS data. b Based on results of Mickey (1985). c Based on results of Persijn (1985).

(3 x 3 x 3 = 27). The greatest matching benefit occurred when there were no mismatches at the HLA-A,B or DR loci (abbreviated to 000 for mismatches at HLA-A, B and DR), and next best when there was at most one mismatch for HLA-A or B (100 or 010). The matching increment beyond these levels was small but significant. The relationship can be better appreciated by comparing three large independent databases (Table 1.1). Although the long-term mismatching risk cannot be fully appreciated before the HLA-DR typing era (circa 1980), studies in family transplants performed at least 10 years ago, where one-haplotype mismatch donors were compared with HLA identical sibling donors show that the hazard of graft loss associated with mismatching persists for at least 10 years. Patients receiving one haplotype mismatch transplant from family members have a significantly shorter "half life" than HLA identical siblings (Takiff et al 1986). This implies that the risk of HLA mismatching is persistent throughout all post operative epochs up to ten years and as such is one of the most potent factors affecting long term outcome of kidney transplants. 4.3. The potential of organ sharing It has been suggested that in the "Cyclosporin era" kidneys can be safely transplanted into patients irrespective of mismatch. This would deny recipients the full benefits of matching and substitute the hazards of immunosuppression. However, matching is limited by the extensive polymorphism of the HLA-system; and its benefit can only be realized fully through multicentre organ sharing programmes. Simulation studies have been performed to investigate how many patients could be beneficially matched. The results are summarized in Table 1.2.

18 Table 1.2, HLA-A,B and DR mismatch category

Simulations* (recipient pool size) 100 500 1000 3000 5000

000

I00

010

Other

Donor kidneys

3% 7% 10% 17% 28%

4% 11% 14% 22% 24%

5% 12% 17% 22% 21%

88% 70% 59% 39% 26%

10000 10000 10000 10000 10000

* Simulations assumed that: all donors and recipients are typed for HLA,A,B and DR; all kidneys offered to the pool initially; ABO blood group identity is required (to avoid accumulation of group O patients in the pool); and the pool excludes highly sensitized patients. This shows the relationship between p r o p o r t i o n beneficially matched and pool size. With a pool size o f 100 only 12% beneficially matched (000 + 100 + 010) transplants would be achieved; such a pool would equate to an average size transplant centre. With a pool size o f 5,000, which is roughly equivalent to the entire Eurotransplant waiting list, up to 73% o f transplants could be beneficially m a t c h e d . Hence organ sharing is justifiable in terms o f the matching benefit gained. There are other cogent reasons, that together constitute a rational basis for organ sharing (Bradley 1987).

2. Council of Europe Study Plan

1. Background and terms of reference

In brief, the Council of Europe Study was to collect basic data to compare the incidence and prevalence of high sensitization across European registries; to assess origins and associations; to quantify the transplant penalties associated with high sensitization compared to other risk factors; and to detail special schemes for transplanting highly sensitized patients.

2. Pragmatic definition of highly sensitized: more than 80% peak reaction frequency

In Chapter 1 we explored the diverse immunological origins, time course and assessment of high sensitization. Faced with such heterogeneity, what is meant by "highly sensitized"; what do the transplant organizations mean by "highly sensitized"; and how do we in the Council of Europe Study define "highly sensitized"? European registries have faced the common consequence of high sensitization: that patients carrying broadly reactive antibodies which kill cells of the majority of potential donors remain untransplanted. The registries needed a measure of sensitization status by which to recognise such patients, and by common consent adopted peak reaction frequency. Peak reaction frequency is specious both immunologically (see Chapter 1) and statistically - because duration and intensity of monitoring differ between patients; and because cells tested and test environment, as well as panel size and composition, vary between laboratories. Yet, it affords the only working registry definition of highly sensitized. Accordingly, the Council of Europe Study definition of highly sensitized is likewise pragmatic: an endstage renal failure patient awaiting transplantation whose serum is registered as having reacted with more than 80% of the

20 population or, if transplanted, whose serum prior to that transplant reacted with more than 80% of the population, as reported by local test procedure.

3. Questions posed The Council of Europe Study Group posed questions of, and collected data from, four sources: tissue-typing laboratories; registry waiting lists; transplant follow-up; and about the special schemes for finding crossmatch negative kidneys for highly sensitized recipients. The questions were as follows.

3.1. Questions relating to serological pattern Chapter 3 is a retrospective survey of the practical monitoring of alloimmunity. How frequently are tests performed; are T and B cell antibodies measured? How many response patterns are discerned when % reaction frequency in consecutive serum samples is charted for individual patients? How do these patterns relate to immunising events such as blood transfusion, pregnancy and failed transplants? What proportion of patients exhibit the various patterns? Is clinical management altered by responder status? 3.2. Questions relating to highly sensitized patients on renal transplant waiting lists 3.2.1. Sources of sensitization. Chapter 4 asks the question: what proportion of patients awaiting renal transplantation in Europe in 1986 are highly sensitized? How does the proportion differ between registries; with the recipient's sex; whether awaiting a first or regraft; by blood group (0 versus non-0); and by combinations of these? 3.2.2. Responder phenotypes. Chapter 5 investigates responder phenotypes: what is the relationship between HLA-type and high sensitization amongst patients awaiting renal transplantation? Is homozygosity for class I or class II antigens implicated in antibody production? Are specific antigens more common in the unsensitized than in highly sensitized patients awaiting transplantation, and vice versa? Is the relationship between HLA phenotype and antibody production the same for patients who have been transplanted, as for patients who await transplantation? Whether responder phenotype influences transplant survival is investigated later in Chapter 7. 3.2.3. Transplantation rates. Chapter 6 asks at what rate highly sensitized patients are being transplanted compared to unsensitized and moderately

21 sensitized cases; what is the morbidity and death-rate on the waiting list of highly sensitized patients; at what rate are highly sensitized patients being added to waiting lists? 3.3. Questions relating to transplanted highly sensitized patients 3.3.1. Transplant survival. Chapter 7 answers such questions as: what is the overall survival rate of kidneys transplanted between 1982 and 1985 into highly sensitized recipients? Are highly sensitized patients worth transplanting - what is the relative risk of transplant failure for highly sensitized compared to other recipients? Is the increased relative risk of transplant failure persistent or shortterm? How does its time dependence compare to other prognostic factors? Is the relative risk of transplant failure mitigated if the highly sensitized patient's latest reaction frequency prior to transplant is nil? What transplant penalty is associated with a B cell positive crossmatch test? What is the relationship of HLA-A, B, DR matching to transplant survival in highly sensitized patients? Taking high sensitization into account, do specific recipient DR antigens and A or B or DR locus homozygosity influence transplant survival? Does the survival of highly sensitized transplants (first or regrafts) vary between registries? Is day 1 graft function determined to a greater or lesser extent by the recipient being highly sensitized? 3.3.2. Daily clinical course: from transplant to hospital discharge. In Chapter 8 we ask: is the detailed post-operative course of transplanted highly sensitized patients different from that of appropriate controls? Comparison is made of reciprocal creatinine profiles, how and when rejection episodes are treated, and dialysis support in the immediate post-operative period.

3.4. Questions relating to special schemes for transplanting highly sensitized patients Chapter 9 details each scheme. Questions are asked as to the date of initiation of the scheme, how many centres are served by it, how many transplants to date and what proportion of donor kidneys are so used; what is the manner and frequency of distribution of sera, what quality control is imposed on % peak reaction frequency and how highly sensitized must recipients be before acceptance into the scheme. What about HLA matching requirements; treatment of autoantibodies; whether crossmatches with donor tissue are performed on all historical sera; and what priority is accorded to the scheme in the organ exchange hierarchy?

22

4. Sources of information: networked through the European Transplant Organizations The transplant organizations- Eurotransplant, France Transplant, Hispano Transplant, Luso Transplant, North Italy Transplant, Scandia Transplant, Swiss Transplant and UK Transplant - were the natural liaison centres for the Council of Europe Study because much relevant information was held already on their confidential databases of waiting-listed and transplanted patients. Anonymous data could be transferred centrally on floppy disk or magnetic tape to minimise transcription errors. The Study Group accessed follow-up information through the registry directors, who held the key to coded patient identifiers. Confidentiality was essential and so data acquisition and error checking outwith the registry database was also through the intermediary of registry directors (or their nominee) who undertook correspondence with individual doctors to resolve data management queries.

5. Strategy for data collection: pragmatically designed and activated studies The Study Group met in Strasbourg on 13 and 14 February 1986 to determine a strategy for data collection to answer the questions posed about serological pattern; highly sensitized patients on renal transplant waiting lists; transplanted highly sensitized patients; and special schemes for finding crossmatch negative kidneys for highly sensitized patients. The time constraint on the Study Group, to complete its work by December 1986, dictated that the Council of Europe Study should be pragmatically designed. A study manual was drafted which scheduled the manoeuvres to be enacted within the various European registries to initiate designated studies in the target areas. Speed was of the essence for several reasons: first, for all studies to be contemporaneous; second, for initiative and enthusiasm to be sustained; third, to permit data checking and correction prior to analysis; and fourthly, for analyses uniquely to reflect the flow of ideas across parallel fields of study. The Study Group met in June 1986 to review its progress on data collection and to comment on preliminary analyses of serological pattern and transplantation rates. Interim analyses of all studies were discussed in September 1986. The latest date for receiving answers to data management queries was 14 November 1986. The Council of Europe Study Group met for the last time in mid-December 1986 to review a draft report on final analyses of all studies. Studies were designed and activated as follows:

23

5.1. Data collection for questions relating to serological pattern Transplant centres which were represented on the Council of Europe Study Group (Bristol, Geneve, Leiden, Milan, Miinchen, Rennes) collaborated in charting serological data. Each centre was enjoined to send to the Study Director by 7 April 1986 a complete list of its renal patients who were awaiting transplantation and who, at some time, had had a reaction frequency of more than 50%. If the submitted list exceeded 30 cases, as for Leiden, the serological study was limited to 30 patients, selected by simple randomization. Mfinchen volunteered to chart all of its eligible patients; other centres listed fewer than 30 cases. Table 2.1 shows the questionnaire and antibody fluctuation chart which were completed for each patient. The chart allows for documentation of serological history and immunizing events between January 1980 and June 1986; requires panel size and cell type to be noted as well as % reaction frequency; and requests that serial events within the same month be written in chronological order. 5.2. Data collection for questions relating to highly sensitized patients on renal transplant waiting lists 5.2.1. Sources of sensitization and responder phenotype. The Council of Europe Study entailed computerised central transfer at the beginning and end of a 49 day period (7 April to 26 May 1986) of waiting lists from the following organ exchange organizations: Eurotransplant, France Transplant, Hispano Transplant, Luso Transplant, North Italy Transplant, Scandia Transplant, Swiss Transplant and UK Transplant. Individual patients were identified only by code numbers, the cipher to which was retained by the registry. Provision was also made for trial transfers prior to 7 April 1986 in case there should be difficulties in reading the floppy disks or magnetic tapes; such difficulties as there were being resolved in the MRC Biostatistics Unit. On 7 April 1986 a complete listing and tape of the waiting patients, including suspended patients, was generated to include: patient's code centre code ABO blood group H L A - A, B, DR type % peak reaction frequency transplant number (awaiting first or regraft) sex H L A - A, B, DR type of previous donors and dates of previous transplants.

24 Table 2,1. COUNCIL

Antibody

fluctuation

more t h a n

over

time:

50X p e a k r e a c t i v i t y

OF

E~ROPE

multi-centre

who a r e

STUDY

collaboration,

currently

awaiting

based

renal

on

patients

~ith

transplantation

~.~,-<,.-¢.,w~:~-,.>-,.-...,-............ ~::.~...:,~.~:~:.>:..¢.:..¢~:~v~.>:.>•:.~. .>:•.,. :.•~:.,.. .:.•:.,. :.•:,. .:.7" :¢"+~ .:.:"+', .:.:.•:"~ .:~>:.."~+:+'~ .:".:~.:~..:.+:•~."~ :~.•.+"~." .:..~:.~'~ .~:.+~.~~'~ .~+.~" .:•+~" ..~•.•:•.•:."~;'~;:; ~.:.:.:";:;; +~.~~:. ..:..•.:.:. ...:.+:. ...•...:.; .~..~.~.,:. >...-> ...•• ~Number of p r e v i o u s renal g r a f t s (0 = untransplanted)i.:i ~:::;:~:~¥:~:::::i:.~i:i:i:~:~;~:i:~ ~:;:~:~$~::::::::~:::::.~:::::~::>Y¢~::::::~.:::.~::::.~:~:::~::.~:::::~::::::::::::::::::::~:~:::::::::::~.:::::::::::::::::::~::::::::::i:!:~

Sex of p a t i e n t

(| = male,

2 = female)

IF the

is female,

please

patient

has

IFtA - t y p e

of

she e v e r

recipient

been

F~ i.~

answer: pregnant

and p r e v i o u s

RECIPIENT

~ii

( ! = no,

2 = yes,

9 = not k n o w n )

D

donors

|st D O N O R

2rid D O N O R

3rd or most recent DONOR

A , B loci . . . . . . . . DR

locus . . . . . . . .

Transfusion

Panel

Date

history

.... t i v l t y

of most

Lowest

of p e a k

reactive

percentage

| = never transfused 2 = transfused 9 = transfusion history

.....

(peak)

of a n t i b o d y

(%) . . . . . d i n g

serum

since

(day,

not k n o w n

to T .... p l a n t

month,

] January

~--~ ~

Registry

I

i

I

1

year)

|980

Ill[

25

(continued)

Table 2.1.

COURCIL OF EUROPE STUDY Antibody fluctuatlon o v e r ti~e: m u l g i - c e n t r e c o l l a b o r a t i o n b a s e d on p a t i e n t s with more than 50~ peak reactivity who are currently awaiting renal transplantation

Month

Year

Ol

80

Month Ol

Events* . . . . . . . . . . . . . .

Year 83

Events* . . . . . . . . . . . . . .

83

. . . . . . . . . . . . . . . . . . . . . . . . . . .

02

80

. . . . . . . . . . . . . .

02

u3

80

. . . . . . . . . . . . . .

03

83

04

80

04

83

05

80

05

83

06

80

06

83

07

80

07

83

O~

80

08

83

0[,

80

I( ~

. . . . . . . . . . . . . .

09

83

80

10

83

I~

80

11

83

12

80

. . . . . . . . . . . . . . .

12

83

ol

8)

. . . . . . . . . . . . . . .

01

84

02

81

. . . . . . . . . . . . . . .

02

84

03

8~

. . . . . . . . . . . . . . .

03

84

O~

81

. . . . . . . . . . . . . . .

04

84

O?

81

. . . . . . . . . . . . . . .

05

84

06

81

...............

06

84

07

81

. . . . . . . . . . . . . . .

07

84

08

8~

. . . . . . . . . . . . . . .

08

84

09

8]

. . . . . . . . . . . . . .

09

84

Io

81

. . . . . . . . . . . . . .

10

84

I1

81

. . . . . . . . . . . . . .

II

84

~

81

. . . . . . . . . . . . . .

12

84

0~

82

. . . . . . . . . . . . . .

Ol

85

02

82

. . . . . . . . . . . . . .

02

85

03

82

. . . . . . . . . . . . . .

03

85

04

82

. . . . . . . . . . . . . .

04

85

O~

82

. . . . . . . . . . . . . .

05

85

06

82

. . . . . . . . . . . . . .

06

85

07

82

. . . . . . . . . . . . . .

07

85

08

82

. . . . . . . . . . . . . .

08

85

no

82

. . . . . . . . . . . . . .

09

85

~o

82

. . . . . . . . . . . . . .

lO

85

11

82

. . . . . . . . . . . . . .

11

85

12

85

O| 02

86 86

12 *

. . . . . . . . . . . . . .

82

In chronological

R

waiting

deregistered reason other

. . . . . . . . . . .

. . . . . . . . . . . . .

order

Event codes: please use the following codes document a n t i b o d y f l u c t u a t i o n s from J a n u a r y to the present and to record r e l e v a n t intervening events,

X

. . . . . . . . . . . .

to ~980

list r e g i s t r a t i o n

from w a i t i n g l i s t f o r than d e a t h or t r a n s p l a n t

03

86

04

86

05

86

06

86

T

transplanted

p

pregnancy

Y

graft

WB

whole

blood

D

patient

B

blood

transfusion

A

aucoantibodies

?%

L8

lymphocyte-free

reaction frequency of serum tested against a panel of lymphocytes/panel size/cell type e.g. 70%/30/U (T,B, or U for T cells, B cells Or u n s e p a r a t e d l~phocytes).

failure death detected blood

transfusion

(nos.)

transfusion

(nns.)

(nos.)

26 Listing, floppy disk or magnetic tape, and format details were sent to the Study Director and forwarded to the MRC Biostatistics Unit after preliminary tabulation (see Table 2.2) and for direct analysis of the relationship between registered HLA phenotype and sensitization status. 5.2.2. Transplantation rates and other transactions. Table 2.3 shows an example of the tally charts on which registry transactions between 7 April 1986 and 25 May 1986 were accumulated daily by sensitization status. Sensitization status was read from a reference hard copy of the 7 April 1986 waiting list of active plus suspended patients on which were listed only patients' code and % peak reaction frequency. To back up the tally charts and monitor successive transactions on individual patients, the hard-copy was annotated, using four coloured pens, with event dates for individual patients. Colours were assigned as follows: green records date of transplantation red records date of death blue records date of temporary suspension from the waiting list yellow records date and code for other transactions such as U % N F R X

unacceptable antigen change % reaction frequency change new registration re-registration after failed transplant other re-registration other transaction such as name change

The annotated hard-copy plus tally charts completed through 25 May 1986 summarised waiting list ebb and flow over a seven-week period by sensitization level (not recorded; unsensitized; 1-50% peak reaction frequency; 5180% peak reaction frequency; more than 80% peak reaction frequency at 7th ~ April 1986). 5.3. Data collection for questions relating to transplanted highly sensitized patients 5.3.1. Transplant survival. The Council of Europe database of highly sensitized and control patients transplanted between 1 January 1982 and 31 December 1985 was established through liaison with directors of the transplant registries (or their nominee). No patient or centre identifiable information was required by the Council of Europe Study Group; instead the registry directors forwarded a simple printout which identified highly sensitized and control transplants by

27 Table 2.2. COUNCIL OF EUROPE STUI)Y Summary of renal transplant registry current waiting list by peak sensitization

Registry

Waiting list analysis date

I_~__~ day

Please complete summary tables separatelyfor Blood Group 0 and Blood Group non-O $ waiting-llst patients BLOOD ~ O U P :

0

Total petients on aetlve file

S U B G R 0 U P female male female male no previous no previous previously previously + ~ t~l[--~ I i ~ ~d t~ ~ d i ~

|IIII

C~rent waltinE time !das_~__ mean

N ~ r of ~tients by peak sensitizationeate~ryl__~__T~ unsensitized (0%) IIIII I-I0%

--~

--~--

--~--~[

l lll~l ~

~°°~ 0~o~ ~_~o~ ~_~o~ ~+~

.T~T T T~T, T~T T~T ~ ~ . , ~ ~III ~ ~ ~ ~ ~ ~

~T~ ~ ,~ IIIII ~ ,~,

not recorded

~

~ ~

~ ~

,,~,,~

~

IIIII~

,,,,,,

"

I

month year

28 Table 2.2.

(continued) CO~I{CIL OF E~ROPE STUDY

Reaction frequency of most reactive serum prior to index transplant

-'--~1%

Date tested

Reaction frequency of latest serum prior to index transplant

~

Date tested

%

!

: ....... :k ~:~~.::.::!i:.::ii:!!!:! .~:~.~L.:~'L.:'::.~...~..,,:~,,.!~.~.~!_~.~.).~Y..~.~.~ .~:~.:%C~:~.~:~..' ~ ~:~: ~:~~:~~:~ ~..~%~:.~:~:. ~::~k..~:~.~.~'~x.~':~~.~.~~.;'~:.'~.~.~.~.~.:'~:

~Please record cross match results at index transplantation ~ ~ ~ ~:~:~:~:~:~:~:~:~:~:~:~~::~~::~~::~~:~:~:~:~:~~::~~::~:~~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~$~:~:~

::

~

:: ~B c e l l s

~ ~

~1 ~

1 = negative 2 = pos£t£ve

~~ ; ~ ~u

~unseparated 1 ~phocyt es ~ ~:~::~:~{~:{~{~:~:~%~{~{~ Please record total ischaemia time in HOURS (999 = not known) for the index transplant

~

HRS ~

~

~

Was Cyclosporin included in INITIAL (DAY I) immunosuppression I = no for the index transplant 2 = yes 9 = not known

--

Was there immediate kidney function of the index transplant?

--

_

I = no 2 = yes 9 = not known

_

Recipient status at last known follow-up Please record recipient's last known status I = alive, index 2 = alive, index 3 = dead, index 4 = dead, index

graft graft graft graft

functioning failed functioning at time of death failed

Date of last known follow-up (for status I, 2 recipients) Date of return to chronic dialysis after index transplant (for status 2, 4 recipients) Date of death (for status 3, 4 recipients):

Name of recipient

day, month, year

...............................................

Registry recipient code

I

I

I

1

1

1

1

1

1

1

1

1

29

c~o

~l

®!

|

X ~

~

°~

~

°~

~

~

~

~

30 code numbers, the key to which remained with the registry. The list gave for each transplant: recipient's code centre code recipient date of birth recipient date of transplant transplant number (first or subsequent graft) recipient sex whether diabetic % peak reaction frequency pairmate's code. Instructions on how to sample matched controls for highly sensitized patients had been sent to the registries. Because transplant survival varies by graft number, year and centre of grafting, control transplants were centre, sex, graft number (first versus regraft) and year of transplant matched to highly sensitized recipients, sex matching being invoked because the prevalence of high sensitization differs between the sexes. First transplant controls were determined to be 10% or less sensitized (peak reaction frequency). A pilot search at UK Transplant for regraft controls showed that a more liberal upper % peak reaction frequency was necessary to find sufficient centre, sex and year of transplant matched control regrafts. Accordingly, regraft controls were determined to be 30% or less sensitized - see sampling instructions in Table 2.4. The decision to compare highly sensitized to lowly sensitized control recipients was taken to ensure that the Council of Europe Study gave a 'worst case' answer to the question "are highly sensitized patients worth transplanting?" by comparison of the practical extremes of sensitization. The importance of listing all highly sensitized transplants irrespective of whether a complete follow-up record was then available to the registry was stressed, for the Council of Europe database would be completed from individual patient questionnaires (see Table 2.5) sent out through the intermediary of registry directors who, of course, held the key to patient identification. Individual questionnaires were generated and checked from the lists received via registries; Council of Europe Study numbers were assigned to identify matched highly sensitized and control grafts, for example UAH301 and UAC301 designate the highly sensitized (H) and control (C) members of graft pair 301 for UK Transplant (U). UK pair 301 was transplanted in centre A.Questionnaires also carried the patient's code as given by each registry, so that the registry director could identify the patient for correspondence with individual doctors. The partially completed Council of Europe questionnaires were returned with a covering letter from the Study Director and the Council of Europe

31 COUNCIL

OF E U R O P E

SECRETARIAT

GENERAL

P|¢ale quote :

Strasbourg, 16 April 1986 VMB/kf

SUBJECT:

Procurement and sharing of transplantable organs for highly sensltlsed patients

Dear Professor/Doctor, As you may "know, the Council of Europe has been very active in the field of blood transfusion and hlstocompatibllty for many years, through its Committee of Experts (SP-}LM~). One of this Committee's most important activities is a biennial programme of co-ordinated research, which for 1986 is concerned with "an investigation of the procurement and sharing of transplantable organs for potential recipients who are highly immunised to HLA-antlgens". This subject will assume particular importance as we prepare for the Conference of European Health ~linisters in France next year, which will be devoted to the whole question of organ transplantation. The Conference will naturally focus attention on the various problems and developments in this field and the results of recent research. The Study Group set up to supervise the SP-~LM research programme agreed at its first meeting that baseline information could most easily be collected through the various European organisations responsible for organ sharing. Confidentiality of the data would, of course, be a paramount consideration. You will be receiving, together with this letter, a personal invitation from the Study Director to participate in the study, I do hope you will be able to accept this most important invitation.

~,~ Vfira N/~SSARELLI-BOLTItO Health Division

LETTER ADDRESSED TO F~DICAL PERSONNEL COOPERATING IN THE ABOVE COUNCIL OF EUROPE STUDY

Figure 2. l.

32 U.K. TRANSPLANT SERVICE Benjamin A. Bradley

Medical Director

Derek R. Moras Neville H. Selwood Peter 7[ Klouda

Administrator Data Processing Immunogenetics

Oornet

c/o Regional Transfusion Centre Southmead Bristol BS 10 5ND. Telephone: (0272) 507777 Telex : 449384

$4.35/mjc

Date: 6 May 1986

Dr Lars Lamm Tissue Typing Laboratory Aarhus Kommunehospitalet DK-8000 AARHUS C Denmark Dear Lars

Council of Em-ope Study Please find enclosed two packages. The first contains letters of introduction to the doctors in charge plus a letter and codicil to be returned giving their consent to participate in the study. The second consists of a letter to the doctors in which their transplanted highly sensitized patients and controls should be listed. This should be sent together with the appropriate number of labelled forms (white for first transplants and yellow for regrafts). Please could you: I,.

Read the instructions on "how to sample controls for transplants etc."

2.

Make lists for each centre.

3.

Fill in the Doctor's name and the estimated number of patients on whom you expect to ask for information (last p a r a ~ a p h ) and dispatch the introductory letters.

4.

Prepare a sufficient number of forms for each centre and prefill forms with as much data as you already have on file.

5.

Send prefilled forms together with the covering letter (complete the name of the doctor, the list of patients and the registry name) to the Doctor in Charge for a) checking and b) by completion Of data and c) returning to you by 26 May 1986. You should send a eoverlng letter to this effect.

6.

Collate the completed forms and dispatch them to me.

Sorry to burden you. Thank you for your help ....

Ben Bradley Ene:

"Package [" 1.

Instructions on selection of controls.

2.

Copies of introductory letter from Vera Massarelli-Boltho on behalf Of the Council of Europe and letter of introduction (number of patients to be inserted) and a ~ e e m e n t to participate, return to Dr Ben Bradley.

"Package If" 3.

Copies of white and yellow forms. Letters of instruction t o d o c t o r s .

Figure 2.2a.

Please read carefully.

33 U.K. TRANSPLANT SERVICE Benjamin A. Bradley

Medical Director

Derek R. Moras Neville H. Selwood Peter 7: Klouda

Administrator Data Processing Immunogenetics

ou~ ~ :

Dr Dr

c/o Regional Transfusion Centre Southrnead Bristol BS 10 5ND. Telephone: (0272) 507777 Telex : 449384 Oa,e:

6 May 1986

..........

Study of the procurement and sharing o f transplantable ors~ns for hlgl~ly sensitized patients You are invited to participate in this study whose objective is to answer questions relating to the outcome of transplantation in highly sensitized patients. Too few patients exist at any one centre and it is necessary to assemble a database consisting of donor-reclpient pairs from several European centres. These will be studied on a strictly confidential basis both in terms of preserving the anonymity of the donor and recipient as well as the name of the transplant centre. None of these pieces of information is relevant to the study and it need not be revealed to the study team. Anonymously coded data will be collected on a computer database and analysed centrally. Needless to say you will receive a copy of the final repot and your centre will be acknowledged therein should you wish to participate. Our deadline is short and we hope to have the final report assembled in July 1987. We anticipate that the work involved would not extend to more than ..... patients. Please indicate your wishes below and return the reply sllp to me immediately. Thank you. Yours sincerely

B A Bradley Director of Study

Name of Centre:

...............................................................

Address:

...............................................................

I accept/refuse the invitation to partlcpate in the Council of Europe Study. Signature:

.........................

Position:

.........................

Encl:

Figure Z2b.

Section 5.4 of Study Manual Section 5.5.3 of Study Manual

34 Table 2.4. How to sample controls for first transplant~ hi~hl~ sensltlsed (HS) 1982-85 recipients and for re-transplants I.

EXCLUDE LIVING RELATED DONOR GRAFTS

2.

FOR EACH CENTRE SEPARATELY: construct 8 centre flats Of "more than 80% sensltised Ist transplanted recipients" as follows : ist TRANSPLANT

Ist TRANSPLANT CENTRE-LIST

Hi~hlz sensltlsed (more than 80%) recipients

CONTROL (10% or less) recipients

I

RS males transplanted in 1982

C 10% males transplanted in 1982

2

HS males transplanted in 1983

~ I0% males transplanted in 1983

3

HS males transplanted in 1984

( 10% males transplanted in 1984

4

RS males transplanted in 1985

( 10% males transplanted in 1985 4 10% females transplanted in 1982

5

HS females transplanted in ]982

6

HS females transplanted in 1983

~ 10% females transplanted in 1983

7

HS females transplanted in 1984

4 10% females transplanted in 1984

8

HS females transplanted in 1985

~ I0% females transplanted in 1985

3.

FOR EACH CENTRE: generate g corresponding centre-lists of "10% or less sensitised ist transplanted receipients" (see above table)

4.

FOR EACH CENTRE SEPARATELY: match controls from list 1 to highly sensitised recipients on list 1; controls from list 2 are used as matches for highly sensitlsed recipients on llst 2 and so on. If llsts are in order of pool number~ take controls from the top of the list; otherwise, sample randomly. If the control llst is shorter than corresponding HS llst, then select additional controls (of the same sex) from the top of the list for the following year. Do not select the same control twice. If the control llst is longer than the corresponding HS list~ ignore additional controls.

Example:

UKTS

CENTRE-LIST A

5.

1

Ist TRANSPLANT

Ist TRANSPLANT

High[ ~ sensitlsed recipients

CONTROL

pool number 0090

.... MATCH ....

pool number 0103

recipients

pool number 0105

.... MATCH ....

pool number 0554

pool number 5006

.... MATCH ....

pool number 1143

IGNORE ....

pool number 2}01

In the example above, Council of Europe study numbers might he assigned as : UAHO01 (UKTS)(CentreA)(Highly sensitised)

UACO01 (UKTS)(CentreA)(Control match for H001)

UANOO2

UACOO2

UAROO3

UACOO3

For re-transplants: As a~ove, but controls " 30Z or less semsltlsed" and NOT MATCHED for graft number ( s e c o n d , t h i r d , . . . g r a f t ) . 6.

PRECODED revised Council of Europe forms to be sent to the transplant centres with request for data checking and entry of new details. ~{:~:~:~:{:~:~{${:~{:{~{:~{:~:{:~:~:~:~:~:~$~:~:~9~:~{:{~:{:~:{:~{:~:~:~:~e~Z~:~:::::::::~:::~::~:~:~:~:~:~:~:{:~:{:~:~:~:~{:~:{:{:9{:~:{:~e{:{~:~e~:~:::~::~::~:::~::;~{ !ii~Transplant centres should be asked tO check whether a 'so-called' highly sensitised patlent~i!~ Ii was more than 80% sensltised at the time of Index transplant. ~ If YES, please complete highly sensitlsed and control forms. If NO, please return the palr (H,C) of forms marked: "inellglble because HS-reclpient was not highly sensltlsed (more than 80%) at tlme of index transplant"

7.

Revised Council of Europe forms will be circulated to registries by 5th May 1986.

35 Tab& Z ~ COUNCIL OF EUROPE STUDY

Follow-up of 1982-85 highly sensitized (higher than 80~ peak reactivity) and control (IO~ or lower peak reaotlvity) eadaverie renal FIRST TRANSPLANT recipient pairs. Council of Europe Follow-up:

recipient number

Recipient date of birth (day, month, year)

l

Date of index transplant on which follow-up is requested (day, month, year) Graft number of index transplant

(I = first, 2 = second...)

13

IF the index transplant is the recipient's second or subsequent transplant, please record: date of immediately preceding transplant (day, month, year)

I

I

I

type of donor kidney for immediately (I= cadaver, preceding2 = live transplant donor) date of failure of immediately preceding transplant (day, month, year)

I

3 I

I I

I I

I I

I I

I I

Sex of recipient (I = male, 2 = female)

~

Is the recipient diabetic?

~--~

(I = no, 2 = yes, 9 = not known)

IF the recipient is female, please answer: has she ever been pregnant prior to index transplant? (I = no, 2 = yes, 9 = not known) ~:~{~!~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i~i{i~i~i{i~i~i~i~i{i~i~i~....i~i~i~ iiiHLA - type and mismatohes at index transplant ~ii ~iiiii~i~i~i~!~!~!~!~!~!i!~!i!i~!i~i!i!i!i!i!i!i!i!i!i!i!i!i!~....i~i~i~i~i~i!i~i~i~i~i!i~i~i~i~i!i!i!~!~!i!~!~!~!~!~!~!~!~!~!~!~!~!~!~!~!~!~!~!!~i~!~!~!~!~!~)!~!~.~i~!~!~!! RECIPIENT A

locus

........

B

locus

........

DR locus

........

Transfusion history prior to index transplant

DONOR

I = never transfused 2 = transfused 9 = transfusion history not known

2 MISMATCHES

36 Health Division Secretariat (see Figures 2.1 and 2.2) to the organ exchange organizations, where additional data were transcribed from the registry database before the director (or his nominee) approached individual doctors to ask for their consent to the Council of Europe Study and participation - by checking the prefilled data, supplying missing information and carefully completing the section on recipient status at last known follow-up. Questionnaires were returned from the organ exchange organizations to the MRC Biostatistics Unit where a data checking program was implemented; queries were resolved by reference back to registry directors and so to individual doctors. In Eurotransplant most data required for the Council of Europe Follow-up Study were held already on the Eurotransplant database, so that a magnetic tape of the relevant data for all highly sensitized transplants from 1982 to 1985 could be prepared. A second tape was sent which reported on all control transplants; matching, as prescribed, was implemented at the MRC Biostatistics Unit. Only the dates of peak reaction frequency for highly sensitized grafts were not transferred on magnetic tape and so paperwork was involved in their acquisition. 5.3.2. Daily clinical course. The detailed post operative course of matched controis and recipients transplanted under UK special scheme for highly sensitized patients (SOS scheme) was monitored in selected centres, in continuation of a pilot study initiated by the UK Transplant Service Management Committee.Table 2.6 shows the record of serum creatinine and immunosuppression (and whether dialysed or treated for rejection) which was completed retrospectively from the day of transplant until hospital discharge. 5.4. Data collection on special schemes for transplanting highly sensitized patients Table 2.7 illustrates an open questionnaire designed as a prompt for more or less detailed answers to questions about special schemes for transplanting highly sensitized patients. The questionnaire was addressed to the director of each scheme who was invited additionally to record any other special features not covered by the questionnaire. Details of the following schemes are discussed in Chapter 9: Eurotransplant Scheme I (European highly sensitized patient) Eurotransplant Scheme II (European immunized file) Eurotransplant Scheme III (acceptable HLA-A and B mismatches) France Scheme I (serum exchange between six centres) Heidelberg Highly Immunized Trial (HIT Scheme) North Italy Transplant Scheme I (more than 30% peak reaction, frequency)

37

o

~ ~ll ~ D ~

o u

~v

U

38 Tab~ 2. 6b. HIGHLY SE~SITISED STUDY : SUMMARY SHEET OF REJECTION EPISODES IN FIRST YEAR A~TER (INDEX) TRANSPLANTATION

Patient's

name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

HSS serial number O _ _ N I 3 centre

Date of transplant O~e

o~ o - - ~ o~ ~ o ~

~ooo~oo

Date Of discharge

0333N UKTSno.

0----~ day

OD month

0_~

$3

day I~

month

day

month

I~

__N code : I = HS patient 2 = control patient O~ year

~ra~t number 0 3

~_~ year

I~ year

Date of onset of each rejection episode for vhich steroid or other therapy is given ~o~oo

~ o ~

: oo.~

I_~ ~_~ ~_~

re_~ection episode 2 : onaet

~ day

month

year

day rejection episode 4 : onse~ J ~

month I~

year I~

rejection episode 5 : onset ~

mon~

~__

re~ecc~on episode 6 : onset ~ d ~

~on~

~__

~e~=~oo

r~

JeccZon

~ o ~ ,

: oo.~

L~N

O~ ~

O~

.~.o~ 7 ~ o_~ day

month

year

39 Tab~ 2.6c. HIGHLY SENSITISED STUDY :

Patlent~s

.~"

n~me . . . . . . . . . . . . . . . . . . . . ~ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

.~.~ ooo~,~ Cl_-I_-EI

:l_-I_-iZl_-I

centre

~S

code : I = HS patient

no.

2 = control patient FOLLOW UP RECORD ~

Patient status at last known follow-up I 2 3 4

= = =

alive, alive, dead, dead,

[I

l ~

functioning graft failed graft (PLEASE SEND CRAFT FAILURE RECORD) functioning graft at time of death failed graft (PLEASE SEND GRAFT FAILURE RECORD)

Date of death (for status 3,4 patients)OR d a t e of l a s t

follow-up (status

~

I ~

~

1,2 p a t i e n t s ) day

month

year

CRAFT FAILURE RECORD D a t e of g r a f t

failure

day

month

da y

month

I_~

D a t e o f r~moval of g r a f t (999999 i f g r a f t has n o t been removed)

D...

o~

..,~o

~o

..,~..

,,.,y.,.

u_~

_~a

day

Reason for graft failure

(please specify

I = rejection 2 = non-i~unolo~lcal

...................................

year

month

[~ ~ )

year

~-~ year

40 U.K. TRANSPLANT S£RVICE Benjamin A. Bradley

Medical Director

Peter M. Brooman Neville H. Selwood Peter T, Klouda

Administrator Data Processing Immunogenetics

OU¢Ref:

South Western Regional Transfusion Centre Southmead Bristol BS I 0 5ND. Telephone: (0272) 5 0 7 7 7 7 Telex : 449384

BAB/RC

Date:

PROFESSOR G D TRANSPLANT UNIT GENERAL HOSPITAL Dear Geoffrey, Re: Verification of ~our data for Council of Europe Study on Highly Sensitized Patients. Further to my letter of 5th June 1986 please find enclosed semi-filled forms containing UKTS held information on your transplanted-highly sensitized and non-sensitized control patients who have been matched for sex, year and transplant number. The white forms contain information on first transplants and the yellow forms contain information on retransplants. The patients name and UKTS number are in the bottom right hand corner and the index transplant under study is identified by a date and a number on the left hand-side, line 6. The Council of Europe reference number contains a U for United Kingdom, a centre code (your centre is A), a sensitization code (H for highly sentized, C for control) and a patient number (001 etc.). From our records we have identified 9 highly sensitized first transplants and 6 highly sensitized retransplants together with an appropriate number of controls. However since our records may not be fully comprehensive I am enclosing a set of blank forms for use at your discretion. Please could you do the following: I.

Carefully check the date and number of the index transplant.

2.

Check the prefilled information against your record for accuracy. ( Corrections in colour are preferred).

3.

Fill in the missing information

4.

D i s p a t c h the completed forms in the envelope provided to Dr. S.M. Gore, MRC Biostatistics Unit, 5 Shaftesbury Road, CAMBRIDGE CB2 2 B W - to arrive by the DEADLINE of IST. AUGUST 1986.

(indicated by a ?)

I am sorry for the tight schedule but we intend to have the study analyzed and the report written by the end of November 1986. Thank you for your help. Yours sincerely,

Encl: first transplants (9) retransplants (6) 3. Matched controls - first transplant (8) 4. Matched controls - retransplants (5)

Ben Bradley Director of Study

Figure 2.3.

1. 2.

HSP HSP

5.

Blank

-

forms

6. Addressed envelope

41 Table 2. 7.

Open questionnaire for details of special schemes to find acceptable kidneys for highly sensitized patients. Name of Scheme

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Date Scheme initiated

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Material distributed (Lists,serum sets, duplicate serum sets etc ............................................................................................... Frequency of distribution of material to centres .......................................................................................... Number of centres served ............................................................................................ % RF cut off for eligible patients .......................................................................................................... Treatment of autoantibodies

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Maximum acceptable H L A - A & B mismatches .......................................................... Minimum acceptable H L A - A & B shared antigens ..................................................... Maximum acceptable H L A - D R

mismatches ...............................................................

Minimum acceptable H L A - D R

shared antigens .........................................................

Are positive pre-operative cross-matches ignored?. .......................................................................................

B cell

Are crossmatches w i t h donor tissue performed w i t h A L L pre-operative (historic) serum samples ~ ............................................................................................

i!i!i~i~i:i:!;i;!;!;i;i:i;!;!;i;i:i:i~i~i:i;!;!;!~:~;~;!;;:!~;:;:;~;~;:;~::::;:;:;:;~;:;;;;;;;;~:::;:;;;;;~;;~:~:::~:~;~;~::::~;:;:;;;~:i

;ii Total number of transplants il !:ililcarried out under this schemei~i!..................................................................................... What % of all kidneys handled by your service are transplanted into HSP under this scheme ? ....................................................................................... ~=~i~i~i~i~i~i~i~i~i!~i!~!=!i~i!~i!i~i~i~¢i!i!i$i~i$i~:$i;!~i~i~i~i~$~$!~!i!=!~!$~i!:~i~=~$~!~!i!~!;!i!~!$~:~i~';~i

i;ii Wh'~t are the priorities for iili iiiii offering kidneys in your routine iiill iili organ exchange programme ? .....!~!................................................................................. ::::::::::::::::::::::::::::::~:~:~:;:;:;:~:;:~::::::::::::::::::::::::~.:;:i:!:i~::i:i:i~i:;:i:i:i:i:!:!:!~!:!:!:!:!:!:!:!:!:i:i:~:i~i:;i:::i:i~:~i~

Other special features of the scheme ..............................................................................................................

42 North Italy Transplant Scheme II (more than 85% peak reaction frequency) Scandia Transplant Scheme I (more than 90% peak reaction frequency) Swiss Transplant Scheme I (more than 50% peak reaction frequency) Swiss Transplant Scheme II (more than 80% latest reaction frequency) UK Transplant SOS (Save our Sensitized) Scheme.

6. Design faults Pragmatism characterised the study plan, as it did the definition of high sensitization. Some deficiencies of questionnaire or study design were the price of that expediency. Pilot studies were limited to pre-testing at UK Transplant of the antibody fluctuation chart (Table 2.1), the tally chart for registry transactions (Table 2.3) and the scheme for sampling control grafts (Table 2.4) in the follow-up study on transplant survival. The daily record of serum creatinine and immunosuppression (Table 2.6) had been piloted previously. Ironically the only serious deficiency- in the transactions s t u d y - escaped detection, despite piloting at UK Transplant. It emerged that registries differed in how they record the sensitization status of patients entering the waiting list. Design faults are highlighted on the study documents and discussed here: Table 2.1. "Number of previous transplants" should have read "Number of

transplants prior to January 1980", all subsequent transplants being evident from the serological chart. Some respondents quoted total transplants; others reasoned that the number of transplants prior to January 1980 was the missing datum and answered accordingly. The ambiguity could have been avoided by asking for the dates of all transplants, as was eventually done to resolve this design fault. Table 2.3. Two design faults foiled our estimation of the rate at which highly

sensitized patients are added to waiting lists. Firstly, the practice in several registries (see Chapter 6) is initially to register all new patients on the waiting list as "sensitization status not known", so that these registries tallied no new registrations of sensitized patients. Future transaction studies in such registries should plan to follow-up new registrants to discover their peak reaction frequency as first designated after compliance with registry protocol. The second problem was that "OTHER TRANSACTIONS: antibody frequency change" should have read "OTHER TRANSACTIONS: peak antibody frequency change" and made clear that only changes between sensitization categories (~) were to be tallied:

43 + + + + +

not recorded; 0% peak reaction frequency; 1-50% peak reaction frequency; 51-80% peak reaction frequency; more than 80% peak reaction frequency.

Table 2.4. Instructions on how to sample matched controls for highly sensitized patients were received in Scandia Transplant when selection of controls had already been completed. The local procedure failed to match adequately for year of transplant (see Chapter 7). The sampling instructions and also the Study Director's letter of instruction to doctors (see Figure 2.3) asked for verification that so-called highly sensitized patients had had, prior to the transplant of interest, serum which reacted with more than 80% of the population. The check was instituted because peak reaction frequency could be overwritten at UK Transplant. For example, 5% peak reaction frequency before first graft could be overwritten by 85%, if the recipient's serum prior to regraft reacted with 85% of test panel cells - such a patient is genuinely highly sensitized at regraft, but not at first graft. Thus, sampling was provisional until highly sensitized (and control) status had been verified in the transplant centre. The burden of verification fell most heavily on UK centres, with consequent mismatching in totals of and year of transplant for highly sensitized and control grafts (see Chapter 7). Table 2.5. Recipient blood group and the HLA type of previous donors were

overlooked in the follow-up questionnaire. The questions on positive crossmatch results were ill-conceived because, whereas some laboratories test all historical sera, others test only peak and latest serum. The crossmatch results on peak and latest serum prior to index transplant should have been elicited separately; any other positive crossmatch tests should have been qualified by the dates of the sera which reacted against the donor's cells. In addition, the form did not ask for the target tissue, eg spleen versus peripheral lymphocytes. Follow-up questionnaires would have been returned more quickly if clinical and laboratory data (HLA-type, reaction frequencies, crossmatch results) had been requested on separate forms, avoiding sequential completion. Table 2. 7. The total number of transplants carried out under a special scheme needs to be related to the scheme's duration. A supplementary letter was sent to ask for the number of transplants up to a recent convenient, specified date, date of initiation of the scheme being known already. The letter also gave an example of offer priority for special scheme (SOS) patients in the UK and invited corresponding flowcharts from other registries to explain what priority is accorded to their special scheme patients in the organ exchange hierarchy.

44

7. S~a~gy Time constraints emphasised the need to anticipate data management, analysis and presentation of results at the very design and planning of the Council of Europe Study. Accordingly, in advance of receiving the data: (1) checking routines were written to complement the designed data collection forms; (2) analysis plans were formulated in sufficient detail that analysis programs could be written, including appropriate data management subroutines for dates to be transformed to graft survival times, HLA-splits transformed to broads when determining number of mismatches, and for indicator variables to be created to test interactions, as between high sensitization and beneficial matching (ie zero DR mismatches and at most one A ÷ B mismatch); and (3) graphical representation, the art of statistics, was envisaged for results of complex analyses, and the necessary graphics programs were devised. Because research is an iterative cycle of design, experimental data, analysis, discussion followed by redesign, more experimental data, re-analysis, more discussion, the Study Group scheduled meetings strategically for June, September and December 1986. In June (pour encourager les autres) the Study Group reviewed data acquisition and preliminary analyses of transplantation rates and serological pattern, which were new fields of study. Discussion resulted in (1) re-experiment - Eurotransplant could provide data separately on transplantation rates for patients awaiting first versus regraft - and (2) re-analysis by improvement of the graphical output on serological pattern. In September (ved skillevejin) analyses, as formulated, of all studies were discussed, albeit data were then incomplete. Discussion resulted in (l) re-experiment - UK Transplant and Eurotransplant were to provide HLA-type of first donors for highly sensitized/control second graft pairs to test whether mismatching at first graft was associated with later sensitization; acquisition of France Transplant waiting list to validate sources of sensitization elucidated by other registries' analysis; acquisition from Eurotransplant of latest reaction frequency (and date) prior to index transplant to evaluate more definitively peak positive, currently unsensitized transplants. Discussion led to (2) reanalysis- to investigate homozygosity and recipient phenotype and antigen sharing, as well as mismatching, in relation to transplant survival. In December (wha dare meddle wi' me) the final analysis report, incorporating suggestions to date, was presented. Still, discussion resulted in (1) re-experiment- UK Transplant and Eurotransplant, and other registries if possible, were to provide current Hardy-Weinberg analysis of donor phenotype as a backcloth for analyses of recipient phenotype and high sensitization; and (2) re-analysis-to investigate day 1 graft function in relation to transplant survival. Moreover responder phenotype was to be assessed in transplan.ted as well

45 as waiting recipients and complemented by Hardy-Weinberg analyses of waiting list and transplanted databases, separately for highly sensitized and control patients. Responder phenotype was a speculative field of study for which only exploratory analysis had been planned - its interest revealed, appropriate multifactorial methods were finally invoked which resolved the Study G r o u p ' s unease at trying to assimilate a series of antigen-specific analyses. The iterative research cycle thus continued beyond December 1986 to deal with issues raised at the Study G r o u p ' s final meeting. Advance preparation of data management and analysis routines was crucial if studies were to be not only designed but analysed contemporaneously, facilitating uniquely the flow of ideas between parallel studies, and for the final report to reflect that intercourse.

8. General notes on statistical thinking and methods

"Nature will best respond to a logically and carefully thought out questionnaire; indeed if we ask her a single question, she will often refuse to answer until some other topic has been discussed". Sir R A Fisher. Nature's refusal to answer a single question can be seen as a warning against simplistic analysis. Analysing factors one by one can mislead: (1) by overlooking important influences discernable only when some co-factor is also present in the equation; (2) by ascribing significance when there is none except by confounding with another covariate; and (3) by overclaiming significance for companion covariates, any one of which could stand as proxy for the others and which therefore do not give separate insights. Multifaceted problems require multifactorial methods for their solution; the multifactorial methods which we apply in the Council of Europe Study involve regression models of one sort or another. 8.1. Regressio n m o d e l s

Briefly we consider the range of regression problems resolved in three chapters of the Council of Europe Study. Chapter 4

How does the number of patients awaiting renal transplantation relate to sensitization status, recipient sex, graft number (first versus regraft), blood group, registry and interactions amongst these? Chapter 5 In addition to the foregoing factors, which aspects of recipient phenotype weight in favour of high sensitization and which point to the recipient being unsensitized, and by how much is the risk score tipped in either direction? Chapter 7 How do the many covariates, such as HLA-mismatching, high sensitization, year of transplant, Cyclosporin A, ischaemia time, jointly influence transplant survival and how can their several influences be quantified? Technically, counts - that is numbers of waiting patients belonging to the same crossclassification when described by sensitization status, sex, graft number, blood group and

46 registry - are the response variables in the first problem. One such count is the number of highly sensitized, blood group 0 females awaiting a first transplant in the UK. In the second problem, the binary outcome for which a risk score should be evolved is whether the patient is highly sensitized or unsensitized. And in the third problem, the risk score should relate to transplant survival time, so that the response variable is a

survival outcome. The three outcome variables require different links between risk score and response variable. The first is a log-linear regression model in which the In counts (natural logarithm to the base e of counts) are regressed on the risk score; the second is a linear-logistic regression because the logistic or In(odds) transform of the probability of being unsensitized is regressed on the explanatory variables; the third is a relative risks or proportional hazards regression model in which the logarithm of the relative risk of transplant failure is regressed on the covariates (alias explanatory variables alias prognostic factors). 8.2. Regression coefficients and risk score summation Although the link is different in the three problems, and so the statistical estimation is technically different, the goal of that estimation is the same in all three settings: namely to estimate the regression coefficients which quantify the influence of covariates in the risk score. Thus central to any regression model in the Council of Europe Study is a risk score, which is defined as a weighted sum of covariates, the weights being the (regression) coefficients which are estimated from the data. We illustrate a (hypothetical) risk score for distinguishing between highly sensitized and unsensitized patients awaiting transplantation in the UK. All covariates in this example are coded 0, 1. Because 1 "indicates" presence of the covariate and 0 its absence, such covariates are often called "indicator variables". HYPOTHETICAL U K RISK SCORE FOR BEING UNSENSITIZED risk score = 2.2 - 1.3 x (female) - 2.5 × (regraft) + 1.1 x (female regraft) - 0 . 4 x (A homoz.) - 0 . 3 x (B homoz.) +0.1 x (DR homoz.) To work out the risk score for a regraft female who is A homozygote but B and DR heterozygote we sum:

= =

2.2-1.3 × 1 -2.5 x 1 +1.1 × 1 (female) (regraft) (female regraft) -0.4x 1 -0.3×0 +0.1 × 0 (A homoz.) (not B homoz.) (not DR homoz.) 2.2-1.3-2.5+1.1 -0.4 -0.9

The BASELINE 2.2 is the risk score for a baseline individual who is male, heterozygote on A, B and DR loci, and awaiting a first graft. Figure 5.1 in Chapter 5 shows how to convert a linear-logistic risk score of -0.9 to the probability (28% chance) of being unsensitized; the BASELINE male has a 90% chance of being unsensitized. 8.3. Covariate structure How covariates are coded is important - model comparison may involve comparing different covariate coding schemes. Compared to 1982 (BASELINE), three indicator

47 variables could identify separately the.transplant years 1983, 1984 and 1985 or a linear trend could be imposed by coding transplant year as 1 if 1982, 2 if 1983, 3 if 1984 and 4 if 1985. Coding for trend thus forces the risk score contribution for "transplanted in 1985" to be four times that for "transplanted in 1982". In Chapter 7, we explore a variety of coding schemes for HLA-mismatching in search of a scheme for intelligent HLA-mismatching which still safeguards transplant survival. 8.4. Standard error, z-score and confidence interval for regression coefficient Estimating regression coefficients is not the end of business. We need a measure of precision (so-called standard error) to judge that the estimated regression coefficient EITHER deviates significantly from zero so that the covariate is deemed influential, OR is a quite plausible realisation even had the covariate truly no influence on the outcome of interest. Approximately, a 95% confidence interval for the true regression coefficient runs from two standard errors below the estimated coefficient to two standard errors above the estimated coefficient. If the 95% confidence interval includes zero then the observed regression coefficient is consistent (at the 5% significance level) with the covariate having no true influence on the outcome of interest. If the 95% confidence interval excludes zero then the estimated regression coefficient deviates significantly from zero (at the 5% significance level) and the covariate is deemed influential. Throughout we assume that regression coefficients and their standard errors are estimated multifactorially, that is jointly with the coefficients for other explanatory variables also in the risk score. The confidence interval tells us more than mere significance testing because it substitutes interval estimation for point estimation - the limits of the 95% confidence interval reveal the lowest and highest true influence of the covariate in the light of which the observed regression coefficient is plausible - that is, would not be rejected as extreme by a significance test at the 5% level which compared the observed regression coefficient to any value in the confidence interval. Likewise there is an affinity between 99% confidence intervals and significance tests at the 1%0 level - a 99%0 confidence interval consists of all possible true covariate influences with which the observed regression coefficient is consistent at the 1% level. Whereas the width of a 95% confidence interval is 4 standard errors, the width of a 99% confidence interval is 5.2 standard errors. Several regression coefficients from the above hypothetical U K risk score are tabulated in Table 2.8 along with standard error (se), z-score, (see below), lzl (z-score without its sign) and 95% confidence interval. The 95 % confidence interval for the regression coefficient for A homozygosity (from - 0 . 5 6 to -0.24) lies entirely to one side of zero; the 95% confidence interval for the influence of DR homozygosity (from - 0 . 0 5 to 0.27) straddles zero and so DR homozygosity is not a significant risk factor at the 5% significance level. Table 2.8 shows also for each covariate its z-score, that is by how many standard errors the regression coefficient deviates from zero. Thus: z-score

-

regression coefficient standard error

Given regression coefficient and z-score: standard error = regression coefficient z-score

48

Table 2.8. Covariate

Coefficient

se

95% confidence interval

z-score

lzl

female regraft A homoz. DR homoz.

1.33 -2.52 -0.40 0.11

0.13 0.16 0.08 0.08

1.07 to 1.59 -2.84 to -2.20 -0.56 to -0.24 -0.05 to 0.27

10.23 - 15.75 -5.00 1.38

10.2 15.8 5.0 1.4

F o r brevity, z-score is often reported as l z l , the direction of the deviation from zero being evident from the sign of the regression coefficient. Standard error is always positive; given regression coefficient and l z l : standard error = ]regression coefficient] lzl Mostly, tables in subsequent chapters will give regression coefficient and l z l from which standard error can be deduced. The z-score or l z l is useful as a quick guide to statistical significance - significance at the 5% level corresponds to l z l being 2 or more. This quick guide lacks subtlety when covariates form a structured set, such as indicator variables for increasing ischaemia time. Then interest lies not in individual coefficients or their deviation from zero, but in whether there is an orderly trend through the regression coefficients denoting stronger influence for longer ischaemia times. Alternative coding of the covariates allows such trends to be assessed formally. And the regression Z 2 statistic (see below) can be used to assess the influence of an unordered, but related, set of indicator variables such as those identifying different registries. 8.5. Comparison of regression coefficients; also of percentages Comparison of corresponding, independently estimated regression coefficients, as when the same regression model has been estimated separately for patients awaiting a first or regraft, can be approximated as follows. We borrow from Chapter 5 the (linear-logistic) regression coefficients for A10 in relation to unsensitized versus highly sensitized waiting patients. For patients awaiting a first graft the A10 coefficient is 0.09 (se = 0.13) and for regrafts is - 0 . 3 7 (se = 0.22). To check whether the two estimates are consistent with each other, we test whether the difference between them deviates significantly from zero, and may do so by formulating a z-score, which expresses the deviation from zero in standardized units. W h a t is the standard error for the difference between the regression coefficients? The answer derives from the variance of the difference being the sum of the two variances, when samples are independent. Thus: difference between regression coefficients for A10 = - 0 . 3 7 - 0 . 0 9 = - 0 . 4 6 variance of the difference ( = sum of variances) = 0.132 + 0.22 z = 0.0653 standard error of the difference ( = x/variance) x/0.0653 = 0.256 z-score = difference = - 0.46 its se 0.256 =

-

1.80

49 The z-score is not extreme (ie does not exceed 2 in modulus) and so there is no compelling evidence to reject the contention that the influence of A10 on sensitization status is the same for patients awaiting first as for regrafts, given the other covariates also in the regression model. Alternatively, a 95% confidence interval for the difference in regression coefficients runs from two standard errors below to two standard errors above the estimated difference: - 0 . 4 6 - 2 × 0.256 to - 0 . 4 6 + 2 × 0.256 ie from - 0 . 9 7 to 0.05 The 95% confidence interval straddles zero, but only just; the interval is wide and so the difference between first and regraft coefficient for A I 0 is not tightly estimated. The Scottish verdict of 'not proven' is apposite. Comparison of two percentages, such as the percentage of the 10690 patients awaiting first grafts who are blood group 0 (55%) and the percentage (44%) of the 2909 patients awaiting regrafts who are blood group 0 likewise begins with working out the standard error for the difference between the two percentages. As above, the variance of the difference, this time between percentages, is the sum of the variances provided the samples are independent. What is the variance of a percentage? The answer is in the mnemonic "success" rate × "failure" rate number in the sample where blood group 0 counts as "success", blood group non-0 as "failure". Knowing the difference between the two percentages and the standard error of that difference, we can calculate either a z-score or a 95% confidence interval for the difference in percentages. Thus: difference between blood group 0 percentages

= 55% - 44% 55 × 45

= 11%

standard error of the difference in percentages

= x/1.0785

= 1.04%

z-score ,

= difference its se

= 11 1.04 = 10.6

variance of blood group 0 percentage: 1st grafts variance of blood group 0 percentage: regrafts sum of variances

= 0.2315 10690 44x56 = = 0.8470 2909 = 0.2315 + 0.8470 = 1.0785

Alternatively, a 95% confidence interval for difference in percentages runs from: 11 - 2 x 1.04 to 11 + 2 x 1.04 ie from 9% to 13% The narrow width of the 95% confidence interval confirms that we have estimated rather precisely the difference in blood group 0 prevalence on first versus regraft waiting lists. 8.6.

Statistical reasoning: goodness of fit )~2 and regression )~

Simple or parsimonious answers, as distinct from naive ones, are recognised by a trail of reasoning which leads E I T H E R from a maze of initial complexity to the goal of end

50 simplicity OR starts off on low ground and climbs higher until a sufficiently clear view emerges. Statistical thinking guides each step, EITHER shedding a layer of complexity whenever that can be done without loss of fit to the data (see goodness of fit Z2) OR stepping up the regression ladder until there is no further significant prognostic information to be wrung from the data (see regression ;(2). 8.6.1. Goodness offit Z 2. Statistical method can be boiled down to comparison between (regression) models for the intrinsic structure underlying data. One possibility is that each model is summarized by a goodness of fit statistic, typically chi-squared (Z 2) with degrees of freedom which reflect how much structure has been imposed on the data there are fewer remaining degrees of freedom when more structure, usually in the form of regression coefficients, has been estimated. Comparing two statistical models we ask: (a) whether the simpler model fits the data and (b) whether significant explanatory value has been lost by shedding the extra structure which distinguishes the more complex from the simpler model. In answering (a) and (b) we make use of three easily memorized properties of the family of ;~2 distributions. i. the mean, or expected value, o f a ~(2 distribution with n degrees of freedom equals n, its degrees of freedom. ii. the variance ( = standard deviation squared) of a ;(2 distribution with n degrees of freedom equals 2n, twice its degrees of freedom. iii. the difference between two independent ;(2 statistics is itself a ~(2 statistic, with degrees of freedom equal to the difference in degrees of freedom between the two comparison Z2 statistics. To illustrate model comparison using goodness of fit ;(2 we borrow an example from Chapter 4, in which the difference between models D and E for waiting list composition is that model D allows the association of blood group with graft number to vary between registries whereas model E simplifies to a common association of blood group (0 versus non-0) and graft number, irrespective of registry. The goodness of fit ;t 2 for model D is 93.26 on 79 degrees of fredom, and for model E (simpler structure and so more remaining degrees of freedom) is 101.99 on 83 degrees of freedom. Question (a) asks: does model E fit the data? The answer is yes, provided that its associated goodness of fit ;(823 statistic 101.99 deviates only randomly from expectation. Answer (a) : consult ;(~3 tables and discover that the upper 10% critical value for the ;t823 distribution is 99.88, the upper 5% critical value being 105.27, so that goodness of fit of model E to the data is suspect at the 10% significance level but passable at the 5% level since 101.99 does not exceed the 5% critical value. Before answering question (b) we digress to introduce a calculation which substitutes when statistical tables are not at hand and works well enough if the ~ 2 degrees of freedom are at least five. To test whether an observed ;(2 value deviates "significantly" from its expectation, calculate how many standard deviations the observed ;(2 value is from its expectation (calculate so-called z-score): observed ;(2_ expected ~2 101.99-- 83 18.99 -

-

-

-

1 . 4 7

x/variance x/166 12.88 If the z-score exceeds 2 in magnitude, goodness of fit is disputed at the 5 % significance level at least - strictly the p-value approaches 0.025 when the degrees of freedom are sufficient for the approximation to work well. Reverting to question (b)' has significant explanatory value been lost by shedding the extra structure in model D, that is by postulating a common blood group with graft number association across registries?

51 Answer (b) : if the additional structure in model D is unnecessary then the difference in the goodness of fit ;t 2 statistics, 101.99 - 93.26 = 8.73, deviates only randomly from expectation, that is from 4, the difference between corresponding degrees of freedom, 83-79 (Property iii above). Does 8.73 significantly exceed its expectation? Consult ;~ tables to discover that 10% of the ;(~ distribution exceeds 7.78 and 5% of it lies above 9.49. The extra regression ;(2 on 4 degrees of freedom (see later: regression ~2) is not significant at the 5% level and so the simpler model E suffices. Summarizing, model E represents adequate fit (answer a) and sufficient structure (answer b). 8.6.2. Regression ;(2. Chapter 5 starts off from the ground gained in Chapter 4 and climbs higher by successively adding locus-specific homozygosity and antigen-specific terms to the regression model which distinguishes between highly sensitized and unsensitized patients awaiting renal transplantation. Successive models are compared in respect of their regression ;(2, which measures how much of the variation has been explained by the regression coefficients, the number of coefficients determining the degrees of freedom for the regression ;(2. Comparing two nested regression models - nested meaning that the explanatory variables (alias covariates alias prognostic factors alias regression variables) in the simpler model are a subset of the covariates in the extended model - we ask: (c) whether significant explanatory value has been added by including the extra covariates? The sequence of regression models in Chapter 5 for distinguishing between unsensitized and highly sensitized recipients begins with a model which features indicator variables for being female; awaiting a regraft; Eurotransplant; UK Transplant; Scandia Transplant; interactions between sex and the three registry covariates; and interactions between graft number and the three registry covariates. The above model comprises 12 indicator variables in all, for which the regression ;(2 on 12 degrees of freedom is 1824.0, very highly significant. Model 2 adds to model 1 three indicator variables (alias covariates) for A locus homozygosity; B locus homozygosity; DR locus homozygosity. The regression ;(2 for model 2 on 15 degrees of freedom is 1865.2. Question (c) asks: has significant extra explanatory value been contributed by the three locus-specific homozygosity covariates. Answer (c) : if the extra covariates are not discriminatory between highly sensitized and unsensitized recipients, then the difference in the two compared regression ~2 should deviate only randomly from its expectation, that is from 3. A random ;(~ value exceeds 16.27 with probability 1 in 1000; the observed difference, 41.2 ( = 1865.2-1824.0), between the two regression ~2 statistics far exceeds 16.27 and so is even less likely as pertaining to the g~ distribution. We conclude therefore that the extra set of three covariates contributes very highly significant explanatory value. Having established that the set of locus-specific homozygosity indicators is highly significant, we may ask whether all three indicators,for class II as well as class I, make a siginificant contribution to the regression ~2. Two solutions are approximately equivalent. EITHER investigate DR-locus homozygosity by dropping its covariate from model 2; the difference between the two regression ~2 would be Z 2 on 1 degree of freedom. OR inspect the z-scores ( = regression coefficients divided by standard error) for A, B and DR locus homozygosity in model 2; reckon that a covariate whose z-score deviates from zero by at least 2 (that is lzl exceeds 2) makes a regression contribution which is significant at the 5% level. Since lzl for DR homozygosity is only 0.04 (see Chapter 5: model 2), class II homozygosity does not influence sensitization status

52 significantly. This second approach is not advised when covariates form an ordered set such as indicator variables for increasing ischaemia t i m e - t h e n , the regression coefficients should likewise display some order, making it not sensible to draw inferences from individual z-scores. Recoding such covariates to evaluate any trend is more appropriate than crude significance testing - by either of the methods discussed in this paragraph. 8.7. For reference In Chapters 4, 5 and 7 we apply the statistical thinking and methods which have been outlined here for general reference. Individual chapters give additional detail on, or motivation for, how we tackle particular regression p r o b l e m s - counts in multi-way tables, In (odds) on being unsensitized or In relative risk of transplant failure. Goodness of fit ~(2 or regression X2 statistics or z-scores guide model choice throughout, and so examples of how to interpret these statistics have been worked through in detail in the above reference section. Confidence interval estimation is preferred to significance testing because the width of a confidence interval informs us about precision (or lack of it) in estimation; the location of the confidence interval (straddling zero or not) reveals statistical significance.

3. Causes

I. Introduction The percentage reaction frequency ( % RF) of a patient's serum is an estimate of the proportion of kidney donors who would react positively in a crossmatch test. The accuracy of this estimate depends on the composition of the test panel and on a number of technical variables. Sequentially tested sera reveal fluctuations in the °/ORF. These result from changes in the antigenic repertoire of alloantibodies. Expansion of the antigenic repertoire to include larger numbers of antigens gives higher % R F and a narrowing of the range of antigen specificities gives a lower °/ORF. In this study the practice of monitoring sensitization was compared between six laboratories throughout Europe. In order to achieve comparability complex clinical charts were reduced to simplified sets of symbols. Thereafter sensitization profiles were related to immunizing events.

2. Plan of study The method of collection of serological profiles from six laboratories is described in 2.5.1. Records spanned a 78 month period from January 1980 to June 1986. The size and type of cell panel (U, for unseparated T or B for separated lymphocytes) was recorded as was the sequence of immunizing events. In the analysis, centre codes were used in order to facilitate objective discussion. Profiles were analysed for features that corresponded to spikes, plateaux and niveaux as defined in Table 3.1. An immunizing event was recorded as occurring before a feature if it took place within the six month interval prior to the first point defining that feature, ie in the case of a spike or a plateau, the first point showing a 30% rise. All blood transfusions, single or multiple, occurring in six months were considered as a single immunizing event. An event was recorded as occurring during the feature if it occurred between the first and last points defining that feature. Where the serological record contained less than

54 Table 3.1. Definition of spike, plateau and niveau Feature*

Test panel size

Increment in % R F

Decrement in % R F

Duration (M)

Point estimates of % R F

Spike

< 50 >~50 < 50 >~50 < 50 ~>50

~>30 ~>20 ~>30 ~>20 < 30 <20

~>20 ~>15 < 20 <15 < 20 <15

~< 12 ~<12 ~< 12 ~<12 ~< 12 ~<12

~>3 ~>3 ~>4 ~>4 ~>3 ~>3

Plateau Niveau

* Each feature was enclosed in a twelve m o n t h window which ended on the last point defining that feature.

three points during a twelve month period no feature was recorded. The features can be best appreciated by overlaying a perspex window onto the chart and shifting it from left to right across the chart. The twelve month span enclosing a feature was defined in retrospect from its last plotted point.

3. Characterization of the data

Of the 232 charts submitted 62 were considered eligible for study. The reasons for ineligibility were; fewer than four points during a 12 month span; a charted peak of less than 50%; incomplete blood transfusion history; incomplete transplant failure history; and incomplete history of reaction frequency. Of the 62 eligible charts 43 were from highly sensitized patients (Table 3.2). Further characterization is contained in Table 3.3. Only one profile was obtained from a patient who had never been transfused (D11); 18 patients had neither been grafted before the study period nor received a transplant during the study period; 29 had received a transplant before the study period; 12 received a transplant during the study period; 3 received two transplants, one before and one during the study period. All 50 acceptable B cell charts were obtained after testing with platelet absorbed sera.

4. Variations in laboratory practices

These are summarized in Table 3.4. In 19% of profiles the panel size for estimating % R F changed radically (eg 15-20 cells or more). Intervals between point estimates were sometimes too long to give a continuous profile: for example in 45% of cases there were at least six consecutive months with no point charted.

55 Table 3,2. Charts received: eligible charts and rejected patients

Centre (typical panel size)

No. of patients received

Total

A

13

5

B

4

3

C D E

31 35 30

F G

30 89

TOTAL 1 2 3 4

28 26 selected ~ charts

Charted peak > 8 0 % RF

Acceptable B cell chart

Grafted prior to 1980

3 2 20 18

0 0 24 26

1 1 13 17

43

50

32

04

selected2,3 charts

232

Incomplete Incomplete Incomplete Incomplete

ELIGIBLE CHARTS

62

blood transfusion history graft history transplant failure history R F history

Table 3.3. Eligible patients: sex, parity, blood transfusions and graft history

Centre Male

Sex and parity Female

Nullips

A B C D TOTAL

3 2 14 13

Parous

Blood charted

?*

Yes

No

1 1 4

5 2 27 14

1 1 11

15

6

48

2 6 3 32

* Parity unknown.

7 6 9

Never trans.

Graft history: Pre/post 1980 PRE 1980: Yes Yes No No POST 1980: Yes No Yes No

1 1

3 1

1 1

1

12 15

7 1

8 8

13

3

29

1218

56 Table 3.4.

Variation in laboratory procedure

1. Change in panel size (within patient) 2. At least 6 consecutive months with no RF charted 3. At least 2 consecutive months after blood transfusion with no RF charted 4. Consecutivereaction frequencies separated by transplant failure, with testing interval at least 6 clear months

12/62 patients

19%

28/62 patients

45%

10/48 patients

21%

6/25* patients

24%

* Approximate number of distinct transplants and failures.

This occurred in 24% of patients between the date of a transplant and the date of its failure. At least two months elapsed after a blood transfusion with no point charted in 21% of patients.

5. Modal patterns of response

Selected individual profiles are charted in Figs. 3.1 to 3.6 and appropriate summaries are given in code below each chart. A collective summary of patterns obtained with all of the charts is illustrated in Fig. 3.7 and is summarised more simplistically in Table 3.5a for T or U cell profiles, and in Table 3.5b for B cell profiles. The selected profiles included only patients whose peak % R F exceeded 50% with the exception of C28 (Fig. 3.6) whose B cell profile alone exceeded 50%. All selected profiles were classified as highly sensitized by virtue of having % R F above 80%, with the exception of C28 (highly sensitized against B cells but not T cells) and C13 (Fig. 3.2). Fig. 3.1 portrays two multi-transfused male patients who had not been previously grafted.Note the sporadic spikes associated with blood transfusions. In Fig. 3.2 a previously ungrafted nulliparous female patient is shown (C13) responding sporadically against both T and B cell panels with a spiking pattern; note the independence of the T from the B cell pattern. In the lower half of this figure a nulliparous female (CO2) who had previously rejected a transplant is shown responding to multiple transfusions with a declining response pattern. Fig. 3.3 shows an example (D1 l) of gradually ascending % R F following failure of a six year old transplant ( F occurred at one month). No blood transfusions were reported to have been given to this patient. In the lower panel

57 Table 3.5. Patterns: spike, plateau, niveau (a) unseparated lymphocytes and T cells Pattern* ALL patients

Grafted before 1980

Not grafted before 1980

S.. SP. S.N SPN

9 1 29 7

3 1 12 4

6 0 17 3

... .P. ..N .PN

0 1 7 8

0 0 6 6

0 1 1 2

Totals

62

32

30

(b) B cells Pattern

ALL patients

Grafted before 1980

Not grafted before 1980

S.. SP. S.N SPN

6 1 12 5

3 1 5 5

3 0 7 0

0 1

0 1

0 0

... .P. •. N

.PN Totals

22

11

I1

3

2

1

50

28

22

* S = Spike, P = Plateau, N = Niveau,. = Absence of spike, plateau or niveau.

is a patient (C010) who responded to multiple transfusions with successively lower %RF only to be followed by a series of multiple spikes. This patient had rejected his first transplant in 1978. Figs 3.4, 3.5 and 3.6 show different patterns of responses associated with transplants. In Fig 3.4 two first transplants (C05 and C23) were followed after rejection by a high plateau. In both cases these were accompanied by multiple blood transfusions.In C23 it was remarkable that the response to B cells remained so low (40-50%RF). In Fig 3.5 a multiparous female patient (C17) is shown rejecting a first transplant that had been placed as long ago as 1974. Rejection was heralded by fluctuating anti-B cell activity and a high spike of T cell activity. Note that no blood transfusions preceded this spike. The patient in the lower panel D24 was a multiparous female regraft; she received her transplant whilst the %RF was

58 well above 50%. The level showed little change until after graft rejection at month 52, thereafter it rose steadily to 100%. B cell responses after graft failure showed a series of declining spikes followed by ascending spikes. Fig 3.6 shows a multiparous female (C08)rejecting a first transplant. She had only low levels of %RF after graft failure despite multiple transfusions. In the lower panel C28 is offered as an example of a multiparous female regrafted patient who, after rejection, became only modestly sensitized to T cells, but highly sensitized to B cells. These examples illustrate the protean nature of the responses to alloantigens accompanying immunizing events. It will be noted that spike followed by niveau was the commonest pattern seen with unseparated cells and a simple niveau, usually below 30%RF, typified many B cell patterns (Tables 3.5a and 3.5b).

6. Concordance of B and T patterns with time

The question here was whether or not responsiveness to B cells could be assumed from a knowledge of the anti-T cell reactivity. We wished to know whether B and T cell patterns were concordant. The summary in Fig. 3.7 shows, almost without exception, complete temporal discordance between the T cell profiles and B cell profiles.

7. Responsiveness to blood transfusions

Blood elicited individual response patterns in different patients. Some individuals gave repeated spikes to single transfusions whereas others gave neither spike nor plateau with multiple transfusions and yet others gave sporadic responses. Based on a majority response against three or more consecutive transfusions, four patients could be said to be responders (A01, C10, C11, C18); and by the same criteria five were non-responders (C2, C12, C13, C14, C25); of the remainder 12 were equivocal and the rest were unclassifiable. Overall there was insufficient data available to identify a pattern typically associated with transfusion. Although the most common pattern was no response at all (low niveau). No evidence was obtained to support the view that a spike pattern typified a blood transfusion since in some cases the transfusion was followed by a high niveau, and in other cases transfusion progressed to frank and rapidly rising levels above 80%. In short responsiveness to blood was sporadic and almost completely unpredictable.

59 8. Profile with former failed transplants

Patients who had lost a transplant prior to the study period (before January 1980) were significantly more likely to exhibit a niveaux profile against unseparated or separated T lymphocytes than patients who had not had a failed transplant prior to that period (Table 3.5). 9. Discussion

Each profile consists of an accumulation of alloantibody responses directed to a variety of HLA and non-HLA targets. Each HLA target constitutes several public, private, split or interlocus epitopes. For example the phenotype defined according to private specificities HLA-A2; B 12 and 17; might include the splits, B44 and B57; the public Bw4, and the interlocus antigen, A2-B17. The %RF is determined by the frequencies of these targets within the test population. It is also determined by the constellation of targets represented within the serum. Synergistic effects might occur between weak antibodies directed to separate specificities. The effect of blood transfusion on this profile might be severalfold: it may elicit an alloantibody response de novo; it may reactivate former alloimmunity, as an anamnestic response; it may function as a non-specific polyclonal B cell activator stimulating all cells primed for alloantibody production; it may add antibodies passively in the donated plasma; alternatively, antibody producing B cells may be transfused, that continue to secrete alloantibody into the patient's serum until they are finally eliminated. By contrast, blood components may absorb low titre antibodies causing a transient fall in the %RF. Repeated transfusions are sometimes associated with an inexplicable and gradually declining %RF over a long period of time. Typical examples of such slow decay are given in Fig 3.2 (C02) and 3.3 (C10). From this survey we expected to find typical profiles that followed certain immunizing events; none were found. An exception perhaps was that failed transplants were more often followed by plateau or high niveau than nontransplanted individuals (Table 3.5). I0. Conclusions

Sensitization was poorly monitored in the majority of laboratories. Information relating to crucial immunizing events such as failure of transplants or times of transfusions was lacking. There were long gaps in the monitoring sequence during which important events may have passed unrecognised (e.g., transient spikes).

60 Highly sensitized patients constitute a heterogeneous collection. The natural history of serological profiles is but one aspect of this heterogeneity. In the absence of discernible modal patterns relating to different immunizing events no distinction can be made between the sequence of these events. We have no alternative but to continue with our initial definition of high sensitization based on purely pragmatic and arbitrarily chosen level of reaction frequency of over 80%.

61

100-

80-

8

'~ 4 0 -

20-

0 BLOOD ] TRANSFUSIONTII

...... I 2O I IIII II

"]" 4O

¸..L I

Months

I

I

Cll

112

(3,0 %

SUMMARY: B~

100-i

80 ¸-

~

60-

'~ 4 0 -

I 20-

0

~__ I~_l.~.~_~ 1

I

"

"

"

I

Mo~ths

60

BI-OOD l

TRANSFUSIONT/I ....

I

III I

~-~

C12

SUMMARY: B~

I

I

l] r~ ....~,30% "1j ~,."'~,.~ B ~

112

~x~N~

Figure 3.1. T w o multi-transfused male patients, not previously grafted: sporadic spikes associated with blood transfusion - -

T cells, - - -

B cells.

62

100--

80--

I I I I I I I I I I

~ 60-

'~ 40-~, m..

I

I 1 I

20-

/~ II/I

~l.I

~ 0 BLOOD I TRANSFUSIONT

I - , - J ~ • " "

I

I

I

II I

II I

|1 I

,, -

II

~l

Months

60

4O

C13

~30%

SUMMARY:B/

100

I I I I I I I I I I

~30%

211

E ~ - - ~ . - - - . . ~ ZC ~

-

80f

I

f , ~I~

-I. ~11 ] ~

I

8

'~ 4 0 -

,

i"ltn

~?t,

I ~ ~

L3 ~

20-

0

I

i

20 i

C02 SUMMARY:~I

i

40 II

I

60 II

I

Mo~rths

I

I I III

)3o~ B ~ E-~-~ : E~-~ __~-~

B

IIIIIII

:

222

Figure 3.2. T w o multiparous female patients, C13 previously ungrafted and C02 previous grafted failure. - -

T cells, - - -

B cells.

100-

'~., - g

~m

~.r', .Jid, I Ik,,lll~.'h2.

63

~

!!

I;11 I [ I II!111 Illl!J~llll!!! "] fl I~

80-

II II II II II

II

I~

I~ ~ iJll,

II II

FI

II II II

~

~!~ ~ IIiii i

II

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U

I I

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20-

0 NO TRANSFUSION BLOOD I Dll

~

r

•

20

I

60

Months

>50%

~ ~ / - - - ~

T

I

40

>50%

~_~- ~ , ~ .

,~. . . .

122

SUMMARY: B

~

(F)

100--

~

~

80--

IIII

~=~

II ~

60--

IL--

i III 11

~

1

- , d ; I~m~i

~._j

nl

g

.~ ll~J •

g '~ 40--

f,

~J ~ III I

I@ ~

-

20-

o I

I

BroOD I I I I I

TNANSFUSIONT []

C10 su~:

~~

~ ~

~8 o %

~ I

I

40 I I I II

~

II

I

IIII

~~

~

Mo~ths

60

II

II

II

I I

~

122

~ ~ ~ ~ ~ B

B

>50%

>30%

Figure 3.3. Two male patients with failed graft: respectively ascending and descending spiked %RF. T cells, - - - B cells.

64

100 -

80-

'60-

'~ 40-

20-

(::05

0 BLOOD I TRANSFUSIONT{I

I

II

II

"-1"

1

40

60

II

I B

B~

Months

I

I

>50%

==,_~_~~ . = , , , , , ~ SUMMARY:

I

112

,'- _~-- ~ (F)

(T)

~ : ::;::.'::::;

100"-

80.-

'~

~-~ 40,-

: ~1 ~

20-

.~ I1~

"II

~II1_

II

~-wm

1

O.

I

TRANSFUSION it

C23

I

i

I

--

I I I I II I I IIIII II >80% F ~ ~ "-e-~, B/ i I

SUMMARY: (T)

(v)

212

~-~%~

Figure 3.4. Two first transplants: high plateau a n d multiple blood transfusions after rejection. --

T ceils, - - -

B ceils.

100

65

-

80-

g '~

40-

,'--'~

III

~ I

~ I I

ql III I

~

I

~11

I

III

20-

.__,__. 0

~.__~___~___~I ~.-J 20 w

BLOOD TRANSFUSIONT/I

.

.

I "

I .

I

~.__.,__,,ll~.~.

l

40

.....

60

I

Momhs 422

I

C17

SUM~:

B~

.

,

~

i

430%

(~)

100 -

80-

I

g

i

~ 40-

I I III

I I III ~ III

20-

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III

I I I I

0

BLOOD TRANS~SlON

• i

I

I

20

40

I ~

~50%

D24

T

~

'

(T)

M~hs >80%

'

SUMMARY: B,

$'-" ~

~

B

"~

~2~'x~ L

~

422

(F)~

Figure 3.5. Rejection: heralded by anti-B cell activity (C17) or followed by ascending % R F and B cell responses (C24) in multiparous females. ~

T ~lls, - - -

B cells.

66 100

-

80-

~

60-

'~

40--

I 20-

ii

II

I I~.

BLoo ~-~

0

C08

= ~J 20

I II

TRANSFUSION

~l~.~

~-~ p

I

40

I I

,~30%

F_~Z]

SUMMARY:

........

lh~ I

60

Months

I I II II IIII <~30% ~ , ~ ~

412

~ o %BT ~ . ~

~\~B~\~

100 -

(F)

m

~'1

80-

I

~I I '~

40-

20-

e-~ I&l I I I I ill~~' ~1f'-~ !1f'-~ !_

;,

0

BLOOD I

TRANSFUSION

{

T~ Not summarised B~I in fig. 3.7 C28

SUMMARY:

l

20

I I I I

III ~ ,>30% ,~

T

Ill

~

40

N

I

~30% ~ ~_ ~ ~ __ __-.a ~

Months

421

T ~

(~l

(F)

Figure 3.6. Multiparous females with low %RF after graft failure despite multiple transfusions (C08) or high sensitization to B cells (C28).

T cells, - - -

B cells.

67 KEY TO COVARIATES e.g.411 SEX A N D P R I O R I T Y

G R A F T E D PRIOR T O 1 9 8 0 7

CHARTED PEAKRF>80%

4 3 2 1

2 = yes 1 = no

2 = yes 1 = no

= Parous Female = .7 Female = Nulliparous Female = Male

[

~

1

~

Unseparated Lymphocytes or T cells B cells

~ /f-~_- - I I------3 L _ _ - J

Transplanted in situ

p Pregnancy

Spike

B

B l o o d ( i n c . multiple)

Plateau

F

Failed graft

Niveau Niveau b e l o w 10%

m Transplanted

PATIENT I.D.

10

20

30

I

I

I

40

50

60

I

I

I

~V

I

cov 411

A01 r

- ~ ' - --~

111

A04 ~<30% BTF

A05

i

I

412 >30% t' ~ i - . ~ ~ -.~

A06

112

AIO

122 B T F_,~=

112

B01 8

31

B02 r

B04 Figure 3. 7.

>50%

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68 10

PATIENT

20

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I

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I

430%

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122

* >80% I I

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I I

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C010 Figure 3. 7a.

>50%

8~ - ~ . - ~ _ _ ~ ~

>30%

~

122

69 PATIENT

10 "z

I.o.

20 z

30 ~

40 ~.

50 z

60 z

70 l

COV

C 11 see fig

112

i__

C12

m

m

,

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see f i g

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-~ 3

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~--~ J

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see f i g

~

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li~~~=~k. C19

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C20 Figure 3. 7b.

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411

?0 PATIENT

10

20

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40

50

60

1

I

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r

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see fig

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Figure 3. 7c.

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71 PATIENT

10

20

30

40

50

60

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1.0.

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cov

* B B . , ~ . ~

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72 10

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.

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73 PATIENT

10

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30

40

50

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>50% i,- - - ~ ~ = l L . . . . . .I

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Figure 3.7f

,~,,~.~.,~.~~,~.~.~,,,=,~

321

4. Sensitization 1986: Prevalence and Sources Across European Waiting Lists

I. Introduction The crossmatch test, though imperfect, is in effect the only safeguard against hyperacute rejection of a kidney graft. When positive, the crossmatch test vetoes transplantation. This veto means that highly sensitized patients, because they react positively with more than 80% of donors, accumulate on waiting lists and are increasingly prevalent amongst renal failure patients awaiting transplantation, especially so in countries where transplantation has been longer established. Unchecked, this process could lead to dialysis places being blocked by virtually untransplantable patients. Schemes aimed at giving priority in organ allocation to highly sensitized patients have been introduced, and are reviewed in Chapter 9; these have been partially successful (see Chapter 6). This chapter takes a 1986 snapshot of sensitization. In it, European registries are compared with regard to the proportion of patients at different levels of sensitization who are awaiting kidney transplantation (PREVALENCE 1986). The associations of sensitization status with sex, graft number (first versus regraft) and blood group (0 versus non-0) are also explored (SOURCES).

2. Waiting lists: standardizing differently reported sensitization levels Prior to August 1986, six registries provided tapes of their waiting list, on which patients were identified by sensitization status (see Table 4.1); sex (male or female); graft number (awaiting first or regraft); and blood group (group 0 or non-0). The waiting list for France Transplant became available only in December 1986 - too late for inclusion in the formal analysis and so-called COMPOSITE, that is combined registries', diagrams - but happily served to validate independently sources of sensitization as indicated in the six registries' analysis; the results for France Transplant are shown separately from those for the six other registries.

75 Reported sensitization levels (see Table 4.1) differed from those intended for the Council of Europe Study because all registries except Eurotransplant and U K Transplant precode peak reaction frequency either by rounding to the nearest 5% (for example, North Italy Transplant) or by using some broader classification still. Failure of the reported sensitization levels for Eurotransplant and U K Transplant to conform to the intended definitions was occasioned by a program slip at U K Transplant; so that in practice there was no uniformity in reported sensitization levels. Figure 4.1 shows sensitization prevalence in 1986 as reported from the registries and in Figure 4.2 after conversion to the intended Council of Europe definition of sensitization levels as (1) unsensitized (2) 1-50% peak reaction frequency (3) 51-80% peak reaction frequency and (4) more than 80% peak reaction frequency. Converted percentages are exact, following corrected programming, for Eurotransplant and U K Transplant. For other registries, conversion from reported to intended sensitization level 4 depended upon how many of the 1184 patients registered as highly sensitized with Eurotransplant or U K Transplant had peak reaction frequency 81-85% ( 127 patients: see N Italy conversion); 81-90% (342 patients: see Scandia conversion); and upon how many Eurotransplant or U K Transplant registered patients had peak reaction frequency 76-80% (163 patients: see Luso, Swiss and France conversion). For Luso, Swiss and France Transplant, reported level 4 prevalences were multiplied down by 1184/( 1184 + 163); for North Italy and Scandia Transplant reported level 4 prevalences were multiplied up by 1184/(1184-127) and by 1184/(1184-342) respectively. Table 4.1.

Reported sensitization levels by registry

Registry

Eurotransplant UK Transplant Scandia Transplant N Italy Transplant Luso Transplant Swiss Transplant France Transplant

Waiting list

April July March May May April December

Reported sensitization levels

1986 1986 1986 1986 1986 1986 1986

level 2

level 3

1-49 1-49 1-50 " " . . "

50-79 50-79 51-90 51-85 51-75 . . "

level 4 80+ 80+ 91 + 76+ 76+ "

Council of Europe Study

INTENDED sensitization levels2-4

Waiting list totals

1-50

51-80

Eurotransplant and UK Transplant

2177 903

508 313

651 533

combined:

3080

821

1184

81 +

7(; EURO (6369)

UKTS (3058)

FRANCE (2867) N.ITALY (1891) SCANDIA (1!48)

LUSO (822) COMPOSITE (13599)

:i:;,:i::: Level4

~ i~i~!~:.'.,,hly~ns,t,z~' ~: ~ Level2

~

Level3

Level 1 Unsen$itized

Figure 4.1. Reported sensitization for all: 1985,

~ ExcludingFranceTransplant

77 EURO (6369)

UKTS(3058)

N.ITALY (1891)

FRANCE(2867) SCANDIA(1148) ..::~.",'~:'i~

.~ii~iiii~::~, ~ LUSO(822) SWISS (311)

COMPOSITE (13599)

N ~

Level481%+peak ~ 'HighlySensitized' Level351-80%peak

Level2 1-50%peak Level1Unsensitized

Figure 4.2. Conversion to intended sensitization levels: 1986.

~ ExcludingFranceTransplant

78

3. Council of Europe Study intended sensitization levels: prevalence 1986 The six registries' diagram (COMPOSITE) in Figure 4.2 shows that 47% of registered patients are unsensitized, one third have peak reaction frequency 1-50%, and 12% are highly sensitized (defined as more than 80% peak reaction frequency). From the registry-specific diagrams, drawn with area proportional to numbers awaiting transplantation, we observe that highly sensitized patients are most prevalent on the Swiss, French and UK waiting lists (23%, 19% and 17%). North Italy and Scandia Transplant have a relative excess of unsensitized patients (59%). The conversion to uniform sensitization levels implied that no patients in Scandia Transplant had peak reaction frequency of 51-80%, which is so surprising that we question whether the conversion, based on Eurotransplant and U K Transplant registered peak reaction frequencies, approximates adequately to procedures in Scandia Transplant. For that reason, and also because conversion factors dependent on sex, graft number and blood group could be entailed, subsequent investigation of the associates of sensitization status reverts to reported sensitization levels.

4. Reported sensitization levels: by source (sex, graft number, blood group, registry) 4.1. Study method Z 2 tests applied to two-way tables of patient numbers, to compare sensitization levels between males and females for example, are quite familiar. Multi-way tables are an extension of the same methods of analysis to take account of more than two factors of interest. The factors which concern us in investigating the origins of sensitization are sex, graft number, blood group and, because there may be international differences in origin, as well as extent, of sensitization, registry. International differences would be expressed as interactions involving registry. The patients on each registry waiting list have been cross classified by sensitization status (4 levels), sex (2 levels), graft number (2 levels), and blood group (2 levels) giving 4 x 2 x 2 x 2 = 32 cross-classifications for each registry. The number of patients in each cross-classification is shown per registry in Table 4.2. The first two rows of Table 4.2 relate to UK Transplant, the next two rows to Eurotransplant, the third pair relates to Swiss Transplant, the fourth pair to North Italy Transplant, the fifth pair to Scandia Transplant, the sixth pair to Luso Transplant and the last two rows to France Transplant. The first row of each pair accounts for blood group 0 patients, the second row gives subtotals of non-0 patients. Columns 1-4 correspond to registry- reported sensitization

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80 levels 1-4 (see Table 4.1) for male patients awaiting their first graft; columns 5-8 are again in order of sensitization but this time give the numbers of females awaiting first graft; columns 9-12 give subtotals by sensitization level for male patients awaiting regrafts and columns 13-16 give the corresponding counts for female patients awaiting regrafts. The analysis is a multifactorial version of the goodness of fit Z2 for 2-way tables. We begin by recognizing that the observed number of patients in any cross-classification is a random realization of the true (or expected) count, the nature of deviation from expectation being as for a Poisson random variable. The analysis therefore concentrates upon building up a (regression) model for the expected count in each cross-classification. Provided the constructed model is an adequate description of the data structure- that is, of the origins of sensitization- observed patient numbers should deviate only randomly from expected counts. Any proposed model is assessed by a goodness of fit statistic which follows a chi-square distribution and which measures deviations of observed from the expected counts which emerge from the proposed model structure. We seek as simple a regression model as is sufficient to approximate to the observed counts in all cross-classifications with the proviso that if a higher order interaction is included in the model so necessarily are antecedent lower order terms. The fit of the model for individual cross-classifications is checked by computing the Freeman-Tukey deviates: for cross-classification ~ the Freeman-Tukey deviate is: ~

+ v/(count~ + 1) - x/(4 modelled count~ + 1)

Freeman-Tukey deviates are distributed approximately normally with mean 0 and variance 1 when the count for cross-classification ~t follows a Poisson distribution with mean equal to the modelled count,. The modelled or expected counts for each cross-classification and the corresponding deviates are shown in Tables 4.3 and 4.4 respectively for Model C (see later). A deviate of __+2 for any cross-classification signifies notable lack of fit to its observed count. 4.2. (Regression) model sequence Waiting list composition was analysed first for the five registries UK Transplant, Eurotransplant, Swiss Transplant, North Italy Transplant and Scandia Transplant for which early summaries of the relevant tapes were available. The sequence of models by which the waiting list composition of the five registries was investigated is reported in Table 4.5. The sequence begins with the most complex structure (Model A) which invokes not only the main effects of sensitization, sex, graft number, blood group, and registry and all 10 first order interactions, for example 1 sensitization with 2 sex (12), 1 sensitization with 3 graft number (13), 1 sensitization with 4 blood group (14) etc, but also

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83 accommodates all second order interactions: such as allowing the distribution by 1 sensitization to differ not only between 2 males and females but, in addition, according to 3 whether the patient is awaiting a first or a regraftthe described second order interaction between these factors is abbreviated as 123. There are 10 such second order interactions (listed under Model A in Table 4.5). Resultant Model A fits the data well, the goodness fit chi-square statistic being 47.62 on 55 degrees of freedom. We next seek relaxations in structure without compromising goodness of fit. Model B resulted from inspecting the estimated regression coefficients (not shown) for second order interactions from Model A and eliminating those which did not contribute significantly to the fit of Model A. Thus Model B dropped second order interactions I24 (that is between 1 sensitization, 2 sex and 4 blood group); 134 between sensitization, graft number and blood group; 234 between sex, graft number and blood group; and 254 between sex, registry and blood group. In addition, the second order interaction 24 between sex and blood group was dropped. Notice that the deleted interactions all involve 4 blood group but two second order interactions relating to blood group are retained namely: 145the interaction of sensitization, blood group and registry and 345 the interaction of graft number, blood group and registry. The dropped interactions have released 12 degrees of freedom and the Z2 goodness of fit for Model B on 67 degrees of freedom is 66.16 indicating that Model B gives a good representation of the data for all five registries. Further simplification is attempted in Models C and D, each of which explores resolution of second order interactions involving blood group. Model C drops second order interaction 345 between graft number, blood group and registry without seriously compromising goodness of fit (~(2= 76.07 on 71 degrees of freedom). Model D drops instead second order interaction 145 between sensitization, blood group and registry and results in a chi-square statistic on 79 degrees of freedom of 93.26 which also represents an adequate fit to the data, albeit the regression ZI~2of 27.1 is significant at the 1% level. Consequently, we proceed to Model E which drops both second order interactions involving blood group resulting in Z2 goodness of fit on 83 degrees of freedom of 101.99; still an adequate representation of the data, overriding the minor concern that the interactions 145 and 345 were associated with a regression Zl~6 of 35.83, which is significant at the 1% level. Thus, Model E is our bottom line and represents a fairly simple structure for the data, namely that we fit all main effects, all first order interactions, excepting 24 (sex with blood group), and four second order interactions. The second order interactions featured in Model E are 123 between sensitization, sex and graft number, relating directly to the origins of sensitization, together with pairs of these three factors in association with registry (5) namely: 125, 135and 235. Thus, for example, the second order interaction 125 means that not only does

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86 sensitization status differ between males and females but the male/female sensitization patterns show registry specific variation. Models F and G explore respectively the deletion of first order interactions 45 between blood group and registry, which fails (Z8~7= 127.12, p < 0.01), implying l~lood group 0 prevalence differs amongst registries; likewise the attempt to omit the first order interaction 14 between sensitization and blood group fails (Z8~6= 135.31, p ~ 0.001) and so we accept that sensitization profile differs between patients who are blood group 0 and those who are non-0. Inclusion later of the counts for Lusotransplant upset the above model sequence as shown in Table 4.6. Model A, including all second order interactions, is just acceptable at the 10% level but Models C and E fail to account sufficiently for the observed counts, the goodness of fit Z 2 statistics being significant at the 1% a n d . 1% levels respectively. 4.3. Results: main effects (see C O M P O S I T E diagrams) and their registryspecific variation (first order interactions with registry) 4.3.1. Sensitization. Whether we look at the 1986 prevalence by reported sensitization levels (see Fig. 4.1) or by intended sensitization levels (see Fig. 4.2) the broad picture across six registries is the same with 47% of patients awaiting transplantation being unsensitized, one third have peak reaction frequency of 1-50% and 12 to 13% of recipients are highly sensitized. Differences in reported sensitization levels (see Table 4.1), of course, contribute to the registry-specific variation in sensitization profile, as illustrated in Fig. 4.1 and analysed in Model E, but are by no means the main contributor, as the companion Fig. 4.2 shows, wherein conversion to uniformly defined sensitization levels has been made, and variation by registry persists. Swiss, France and UK Transplant have the greatest prevalences of highly sensitized patients. Luso Transplant is remarkable as having two-thirds of its patients with peak reaction frequency of 1-50%. In both North Italy Transplant and Scandia Transplant there is an excess, compared to other registries, of unsensitized patients (59%). 4.3.2. Sex. Sixty percent of patients on the COMPOSITE waiting list (that is: Eurotransplant, U K Transplant, N Italy Transplant, Scandia Transplant, Luso Transplant and Swiss Transplant) are male. Figure 4.3 shows the sex distribution also for each of the registries (first order interaction 25 between sex and registry). The male excess is most pronounced for North Italy (63%) and Luso Transplant (66%) and least evident in Scandia and Swiss Transplant whose waiting lists comprise 54% and 53% males respectively. 4.3.3. Graft number. The majority, 79% of patients, are awaiting first grafts. Besides the COMPOSITE picture, Fig. 4.3 shows the proportions awaiting

87 (a) Sex : 60% male patients on COMPOSITE* waiting list

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88 first and regraft by registry (first order interaction 35 between graft number and registry); notice that there are relatively more patients awaiting regrafts on the UK and Scandia waiting lists (30% and 34% respectively) as compared, for example, to Eurotransplant (16%) or Luso Transplant (13%). 4.3.4. Blood group. Fifty-three percent of patients on the COMPOSITE waiting list are blood group 0. The registry-variation diagram (see Fig. 4.3) shows the first order interaction 45 between blood group and registry, there being a greater excess of blood group 0 waiting patients for UK Transplant (56%) than for Luso Transplant (48%), for example. 4.3.5. Registry. The main effect of registry, to describe the relative size of waiting lists, is illustrated throughout, because each display of registry-specific variation, be it by sensitization (see Fig. 4.2), sex, graft number or blood group (see Fig. 4.3), shows the registries' diagrams drawn with area proportional to the numbers of patients awaiting transplantation. The main effect of registry is that, in order of size, the registries rank: Eurotransplant, UK and France Transplant (each about half the size of Eurotransplant), North Italy Transplant with waiting list one third of Eurotransplant's, Scandia and Luso Transplant with lists about one sixth of Eurotransplant, while Swiss Transplant, the smallest registry, is about a twentieth of the size of Eurotransplant.

4.4. Results: first- and higher-order interactions with sensitization - origins o f sensitization

4.4.1. 12 Sensitization with sex. Whereas 52% of males awaiting renal transplantation are unsensitized only 40% of female patients are so classified (see COMPOSITE diagrams in Fig. 4.4). For both sexes, just over 30% of patients are registered with peak reaction frequency 1-50% but the proportion of females who are highly sensitized (18%) is twice the male prevalence of high sensitization (9%). The salient feature of registry-specific variation in the association of sensitization with sex (second-order interaction 125) is that whereas the overall prevalence of high sensitization is twice as great for females as for males, in UK and North Italy Transplant the prevalence ratio (female : male) is around 1.5; and likewise in France Transplant (see Fig. 4.4). For example, 23% of females and 16% of males on the UK waiting list are highly sensitized. By contrast, in Eurotransplant the prevalence of high sensitization is 2.4 times greater amongst females than males but there is a compensating credit in favour of modestly sensitized (1-50% peak reaction frequency) males.

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93 4.4.2. 13 Sensitization with graft number. Unsensitized patients constitute 54% of those awaiting a first transplant but represent only 20% of patients awaiting regrafts. Higher sensitization is more predominant amongst patients awaiting regrafts of whom 28% are highly sensitized compared with only 8% of patients registered for a first graft (see COMPOSITE diagrams in Fig. 4.5). In Fig. 4.5 the registry-specific diagrams for first and regrafts have areas standardized on the respective Eurotransplant totals of patients awaiting first and regrafts, so that the previously noted relative excess of patients awaiting regrafts on the U K and Scandia waiting lists shows up as increased U K and Scandia Transplant piechart areas for regrafts compared to first grafts. Registry-specific variation in the sensitization by graft number profile (second order interaction 135) highlights the 77% prevalence of being unsensitized amongst patients awaiting a first graft in Scandia Transplant. Moreover, in North Italy and Luso Transplant, the prevalence of being unsensitized at first graft outstrips the corresponding regraft prevalence only 2 : 1 or 1 : 1 compared to more than 3 : 1 in other registries (see Fig. 4.5). Eurotransplant meanwhile is debited in respect of highly sensitized patients awaiting first grafts- prevalence 7% compared to 29°/0 amongst regrafts whereas in U K Transplant the prevalence ratio of high sensitization for patients awaiting a first versus regraft is greater at 12% : 33%. 4.4.3. 14 Sensitization with blood group. Blood group 0 is more prevalent in lowly sensitized patients- 53% of those whose peak reaction frequency is 50% or less are blood group 0 compared to 51% of patients whose peak reaction France Transp~nt

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frequency exceeds 50% (see COMPOSITE Fig. 4.6). The France Transplant data, shown separately, corroborate this picture. Although statistically significant at the 0.1% level, the association of sensitization status with blood group is much less striking than with sex or graft number, as the COMPOSITE summaries in Fig. 4.6 reveal. Whereas male prevalence is 66% versus 43%, and first graft prevalence is 91% versus 51% amongst waiting patients who are respectively unsensitized or highly sensitized, for blood group 0 the prevalence comparison shrinks to 52% versus 50%.

4.4.4. 1 2 3 S e n s i t i z a t i o n with s e x a n d g r a f t n u m b e r . This crucial second order interaction shows that the tendency for females, rather than males, to be highly sensitized is much more pronounced at first graft than at regraft (see COMPOSITE Fig. 4.7). Whereas 14% of females and 5% of males awaiting first grafts are highly sensitized, the prevalences for high sensitization diverge less in females (31%) and males (26%) awaiting regrafts. Again, the data for France Transplant corroborate the COMPOSITE pattern.

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97 4.5. Results: first-order and other interactions not involving sensitization 4.5.1. 23 S e x with graft number. Superimposed on the basic picture of there being more males than females on waiting lists, the first-order interaction between sex and graft number (see Fig. 4.8) points to a relatively greater representation still of males amongst patients awaiting regrafts than amongst those waiting for a first graft. Males constitute 62% of those awaiting regraft compared to 59% of patients registered for a first renal transplant. Idiosyncratic registry-specific variation in the association of sex with graft number is also shown in Fig. 4.8. Mostly the registries conform. Thus for North Italy Transplant, wherein 63% of patients are male and 82% await first grafts, in the absence of any association between sex and graft number, we should expect 51.7% first graft males (63% × 82% = 51.7% ), 30.3% first graft females, 11.3% regraft males and 6.7% regraft females. The observed percentages 51%, 31%, 12%, 6%, by comparison, exaggerate slightly the male/female regraft differential in broad conformity with the COMPOSITE pattern. 4.5.2. 34 Graft number with blood group. Whereas 55% of patients awaiting a first graft are blood group 0, its prevalence drops to 44% amongst patients who are waiting for regrafts (see COMPOSITE Fig. 4.9), a feature which is present also in the data from France Transplant.

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~~ ; , 58% ~42 I / , , ~/,~," ~/.' ~2.~./, i ~ . ~,"~,2~./, . ? ~~,1,2~.' ; ~' ~,g~,2~/~,2~"~,/J

98 5. Discussion

With the exception of Eurotransplant and UK Transplant, registries record peak reaction frequency to the nearest 5%, or more crudely still. This prior grouping of reaction frequency sacrifices detail and limits international comparisons by lack of agreement between registries on sensitization cut-offs. Therefore, with a view to future studies, it is recommended that essentially continuous variables- peak reaction frequency, age, serum creatinine- be not subject to precoding. Across Europe, more than 2100 patients (including France Transplant), constituting 13% of those awaiting renal transplantation in 1986, were highly sensitized, as defined by over 80% peak reaction frequency. These patients require extraordinary efforts to find crossmatch negative kidney donors; A 1986 sensitization snapshot does not, of course, reveal whether the prevalence of high sensitization is increasing with calendar year, this being dependent both on the rate of generation of new highly sensitized patients and upon the transplantation rate for highly sensitized patients (see Chapter 6). Registries vary in the prevalence of high sensitization. It was greatest in Swiss (estimated 23%), France (estimated 19%) and UK Transplant (17%). There are many possible explanations for differential prevalence by registry including: (1) that high sensitization would be more prevalent the longer transplantation (and hence the registry) had been established; (2) duration and effectiveness of schemes giving organ exchange priority to highly sensitized patients (see Chapter 9); (3) variation in blood transfusions, a procedure established to enhance graft survival, yet risking sensitization; (4) recipient selection policy and whether there is preferment for patients awaiting regrafts; (5) variation in how sensitization is monitored, including the frequency, environment and accuracy of testing; (6) the accuracy also of registry databases, and procedures for updating peak reaction frequency; and (7) variation in crossmatch practices, such as whether a B cell positive crossmatch vetoes transplantation, whether IgM antibodies are heeded, whether transplantation goes ahead when the crossmatch test is "past positive but current negative", and whether all historical sera are subject to crossmatch testing. We subscribe to no definite ranking of the above series of possible explanations for inter registry differences. Fourteen percent of females awaiting first grafts are highly sensitized compared to only 5% of males. Amongst patients awaiting regrafts, the prevalence of high sensitization is also greater in females, 31%, than in males, 26%. Hyper-responsiveness of females could, of course, be X linked; but there is no convincing evidence of higher graft failure rate attributable to gender per se. Males and females differ in the essential respect of pregnancy; but we cannot rule out the possibility of differential transfusion rates between the sexes, since

99

transfusion history is poorly documented. Nonetheless, we speculate that pregnancy is a more dominant source of sensitization at first graft than is blood transfusion, to which both sexes are exposed (see also Patel and Terasaki, 1969). The importance of pregnancy in sensitizing females before first graft is confirmed in the Council of Europe follow-up of highly sensitized and sex-matched control grafts, from which Table 4.7 is derived. Sixty-one percent of highly sensitized females, first transplanted in 1982-5, were known to have been pregnant, (see Table 4.7) compared to only 46% of first graft female controls (10% or less peak reaction frequency). This difference is not an artefact o f the older age at transplant in highly sensitized patients, because 73% of the 341 highly sensitized women aged over 30 years at first graft had been pregnant, compared to 62% of the 293 female controls in the same age range at first graft (p < 0.02). Amongst female regrafts, however, pregnancy history is similar (see Table 4.7) for highly sensitized patients and their controls (30% or less peak reaction frequency). A diminished role for pregnancy as source of sensitization prior to regraft accords with greater similarity between the sexes in prevalence of high sensitization before regraft, 31% for females and 26% for males, compared to 14% and 5% respectively before first graft. The greatly increased prevalence of high sensitization amongst patients awaiting regrafts (28% compared to 8% before first graft) shows graft failure as an overriding source of sensitization, supporting what is known already about failed grafts and antibody status. The alternative to graft failure as direct cause of high sensitization is that patients whose grafts fail are enriched for responder phenotypes, of which graft failure is mere proxy. Table

4. 7. Pregnancyhistory for highly sensitized and control female transplants (see Chapter 7)

FIRST GRAFTS

HS patients

Control patients

Total

125 (29%) 268 (61%) 49 (11%) 442

174 (40%) 202 (46%) 62 (14%) 438

299 470 111 880

HS patients

Control patients

Total

96 (44%) 80 (36%) 44 (20%) 220

71 (39%) 78 (43%) 33 (18%) 182

167 158 77 402

Ever pregnant ?

no yes not known Z ~2 =

_

_

-

-

-

-

18.80

REGRAFTS Ever pregnant ?

no yes not known ~2 = 1.76

-

-

-

-

100 Table ,$.8.

Duration of previous graft (see Chapter 7)

REGRAFTS

HS patients

Control patients (peak RF ~<30%)

Total

81 (15%) 42 ( 8 % ) 61 (12%) 95 (18%) 94 (18%) 154 (29%)

53 34 58 56 49 189

134 76 119 151 143 343

Duration of previous graft ~<7 days 8-15 days 16-40 days 41-100 days 101-365 days 366 + days

(12%) (8%) (13%) (13%) (11%) (43%)

~(~= 26.78, p = 0.0001

Which graft failures risk high sensitization? Typically they are those that are rapidly rejected and those with more HLA-A and B mismatches. Table 4.8 summarizes the duration of previous graft for patients who were highly sensitized prior to regrafting (some of whom may also have been highly sensitized before previous graft) and for control regrafts. Duration of previous graft is significantly shorter for patients who are highly sensitized at next graft, compared to controls whose peak reaction frequency remains at or below 30°,/o. Up to 40 days, transplant failure rates are similar but, whereas 43 % of previous grafts in controls lasted for more than a year, the percentage for patients who are highly sensitized at regraft was only 29% (Z~ = 26.78, p = 0.0001). Thus, earlier failure of previous graft is implicated, but with the reservation that regraft highly sensitized patients may already have been highly sensitized prior to a previous graft - their peak reaction frequency then was not elicited in the Council of Europe follow-up questionnaire. Likewise, data concerning mismatches at previous graft were not requested. As an additional exercise, therefore, pairs of control and highly sensitized second grafts, with date of high sensitization inter-transplant, were identified from Eurotransplant and UK Transplant contributions to the Council of Europe follow-up study. Details of mismatching at first graft were abstracted from registry data for 142 Eurotransplant and 64 U K Transplant second graft pairs and are summarised in Table 4.9 for each locus separately. For HLA-A, B and DR in turn, Table 4.9 identifies the number of second graft pairs whose members were discordant for number of first graft mismatches at that locus. If mismatching a t first graft were unrelated to sensitization at regraft, then in half of the discordant pairs at any locus we should expect poorer first graft matching in the highly sensitized patient. Of the 123 pairs discordant for A locus matching at first graft, the highly sensitized member was the worse matched in 73 pairs (pooled Z~l .... = 5.16, p = 0.023; with allowance for multiple testing p = 0.07); for the B locus, the highly sensitized member had the inferior match in 73 out of 127 discordant pairs (pooled ~(B 2 l.... = 2.84,

101 Table 4.9. Mismatching at first graft for second graft pairs: Eurotransplant and U K Transplant (ref Chapter 7) Locus

Registry

Sex

Concordant pairs

Discordant pairs

# discordant pairs with HS patient worse matched at 1st graft than control member

A locus

Euro

m f m f

31 30 15 7

40 41 32 10 123

24 23 20 6 73 (59%)

47 40 29 11 127

25 20 20 8 73 (57%)

UKTS

_

B locus

Euro UKTS

D R locus

Euro UKTS

m f m f

m f m f

24 31 18 6

41 35 30 8

_

30 36 17 9 92

_

_

_

_

_

15 15 7 2 39 (42%) _

p=0.10); and on the DR locus, the highly sensitized patient was worse matched at first graft in only 39 out of 92 DR-discordant pairs (pooled ~ ) R 1. . . . = 2.13; reverse association). Our additional investigation based on about 100 discordant pairs per locus had a 50:50 chance of identifying as significant at the 5% level a genuine shift of 10%, as from 50% to 60%, in the proportion of discordant pairs for whom the highly sensitized patient was the poorer matched at first graft. Since our foray had limited power it does no more than implicate tentatively mismatching for class I antigens in subsequent sensitization. Our finding is corroborated by Sanfilippo et al (1987), however, who reported a significantly greater increase in panel reactive antibody following graft failure for patients who rejected a first transplant which was matched for at most one HLA-A or B antigen. Although the prevalence of high sensitization is greatest amongst regraft females and least for males awaiting a first graft, in absolute numbers it is first graft females, followed by regraft males, who dominate the waiting list of highly sensitized patients; thereafter, nearly equal numerically, come first graft males and regraft females. Blood group 0 is less prevalent amongst highly sensitized (50%) than amongst unsensitized (52%) patients awaiting renal transplantation. Possible explanations include: blood group 0 recipients being low responders (see Joysey et al 1973; shown also in the Cambridge/King's College liver transplant series, see

102 Gore et al 1987); or lower transplantation rates for highly sensitized patients (see Chapter 6) diluting the impact of diversion of 0 kidneys to A or B recipients; or priority accorded to highly sensitized patients in organ exchange schemes protecting them against the diversion of 0 kidneys. The possibility of low responder status ~f blood group 0 recipients is discussed again in relation to first versus regraft blood group 0 prevalences. Sixty percent of patients awaiting renal transplantation are male, in keeping with the higher male (age-specific) acceptance rates for renal replacement therapy reported by the European Dialysis and Transplant Association (see Challah et al 1984). Male preferment in registration for transplantation or geographical variation in primary renal disease (not all renal diseases having greater incidence in males than females) could also be implicated. Luso and North Italy Transplant have the highest male prevalence. Regraft males (62%) outnumber regraft females more than first graft males (59%) compared to first graft females. This could have various explanations: physical or social tolerance of returning to dialysis after graft failure may be sex-dependent; a protective effect of parity, identifying a low responder subgroup of parous females at minimal risk of sensitization; or first graft survival advantage for females, for which there is little support in the general literature (see Chapter 1). In Chapter 7, however, we find weak evidence of an advantage in first graft survival for nulliparous female recipients compared to other females and males. Seventy-nine percent of patients were awaiting first grafts. The proportion varied across registries with Eurotransplant and Luso Transplant having relatively fewer regraft patients ( 16% and 13% respectively) while UK and Scandia Transplant ranked highest with 30% and 34% respectively of patients awaiting regrafts. As the success rate of first transplants has increased, so the proportion returning for regrafts would be expected to go down each year; but a fall in the number of patients re-registered after a failed graft depends on how many first transplants there are, as well as their success rate, and on whether the re-registration rate after a failed graft changes with the years. Explanations of inter-registry difference in the proportion of patients who are awaiting a regraft may lie in: differential availability of dialysis places; differential recipient selection policies favouring regrafts; or in the stringency of HLA-matching. Interestingly, Eurotransplant, which has only 16% regraft prevalence, achieved the second highest proportion of grafts with zero DR mismatches, 57% (see Chapter 7). Fifty-three percent of patients awaiting renal transplantation are blood group 0, higher than the phenotypic frequency of blood group 0 in European populations. Variation in the prevalence of blood group 0 across European registries could be a reflection of: population differences (Cope6 et al 1976); or different policies concerning the diversion of 0 kidneys to A or B recipients; or waiting list dynamics - the impact of the diversion of 0 kidneys on blood group

103 0 prevalence depends on the ratio of new registrants to transplants. The higher the ratio the closer the blood group 0 waiting list prevalence will approximate to the (renal) population prevalence. Because blood group 0 patients accumulate on waiting lists, they are, of course, under-represented amongst first transplantees and consequently amongst patients who are re-registered after a failed graft. From the UK Transplant Service Annual Report for 1983, 48% of transplanted kidneys and only 39% of transplantees were blood group 0. From 1980 to 1983, blood 0 prevalence on the UK waiting list averaged 57%, and was 56% on the 1986 waiting list- made up of 59% first graft prevalence and 48% prevalence on the regraft waiting list. Notice that the ratio of first graft prevalence to donor frequency of blood group O (59% divided by 48%) is 1.23, and is the same as the ratio of blood group 0 prevalence on the regraft waiting list to prevalence in its source population, namely transplantees (48% divided by 39%)! Waiting list dynamics thus largely determine the important first order interaction whereby blood group 0 patients represent 55% of those awaiting first grafts but only 44% of patients awaiting regrafts. Low responder status for blood group 0 recipients may contribute a little, as inferred from its lower 50% prevalence in highly sensitized waiting patients compared to 52% in the unsensitized. Waiting lists vary in size, not least because the populations they serve vary in size, but inequalities in dialysis provision or transplantation rates (see Chapter 6) may be contributory. No checks have been made to trap multiple registrations across registry waiting lists; these are known to occur and could be identified on routinely pooled waiting lists if sorted by HLA-phenotype, sex and date of birth to locate duplicate entries. In summary, against a backcloth of registry-specific variation, sources of sensitization are ordered as failed graft and whether female or male, with blood group 0 making a minor contribution.

5. Responder Phenotypes

1. Introduction We have established that the 1986 prevalence of high sensitization depended upon whether the recipient was awaiting a first or regraft and, particularly for patients awaiting first grafts, that the risk of high sensitization was considerably greater for females than for males. The relative frequency of blood group 0 was slightly less on the waiting list of highly sensitized compared to unsensitized patients. An aspect of high sensitization not so far investigated in depth is the relationship between HLA phenotype and the extent of panreactivity expressed in the patient's serum. Individual antigens at the HLA-A, B and DR loci may differ in frequency between highly sensitized and unsensitized patients awaiting transplantation. Individuals who are homozygous on a particular locus see more of the world as foreign at that locus and so may be particularly vulnerable to high sensitization. Linkage disequilibrium means that while it is sufficient for an exploratory analysis to investigate individual antigens singly, comprehensive analysis calls for a multifactorial approach (linear-logistic regression) which takes account of the intercorrelations between individual antigens, both within and across loci.

2. Study outline Tapes of registry waiting lists (see Chapter 4) gave details of recipient phenoo type as well as sensitization status, sex, graft number and blood group. The analysis of responder phenotypes is restricted to patients at the extremes of sensitization, that is, unsensitized or highly sensitized, primarily so that responder phenotypes are recognised most efficiently but also because the follow-up study of highly sensitized and control transplants concentrated upon these, and parallel analyses of responder phenotype were envisaged for

105 waiting and transplanted recipients. Moreover, there is evidence in Chapter 6 that registered peak reaction frequency between 1 and 50% could underestimate sensitization level, and so obscure looked-for trends with level of sensitization. Patients who were untyped for any of the three loci A, B or DR were ineligible for analysis. This criterion excluded 2%0, 13%, 17% and 11% respectively of otherwise admissable patients on Eurotransplant, UK Transplant, France Transplant and Scandia Transplant waiting lists. The waiting lists for three other registries were not used in the analysis of recipient phenotype for reasons as follows: North Italy Transplant, because 77% of its 1303 unsensitized or highly sensitized patients were not DR-typed, raising concern over selectivity in those 299 patients whose DR type was ascertained; Swiss Transplant, because its waiting list included too few, 168, unsensitized or highly sensitized individuals; and Luso Transplant, because of frequent changes in registered antibody status (see Chapter 6). We are aware that selection biases may dictate (a) which patients are registered and (b) which patients accumulate on renal waiting lists so that the relationship between registered HLA phenotype and the extent of panreactivity may be a distortion of the actual relationship between HLA phenotype and sensitization status, by reason of selective non-registration or accumulation of specific phenotypes on the waiting list. One of the reasons for analysing the extremes of sensitization was that the follow-up study of highly sensitized and control transplants (see Chapter 7) concentrated upon these. Thus we could apply the same analytic methods to the database in Chapter 7 to assess the relationship of sensitization to HLA phenotype in transplanted as well as waiting patients. Unfortunately, recipient blood group was not elicited in the transplant follow-up questionnaire (see Chapter 2: design faults). For the sake of comparable analyses between waiting and transplanted patients and with the assurance that the very modest influence of blood group was recognised anyway in our previous analysis of waiting lists, blood group was passed over as a covariate in analyses of recipient phenotype. Since inter-registry differences in the frequency of HLA phenotypes (or their association with high sensitization) could depend upon the quality and extent of typing for individual HLA antigens, Hardy-Weinberg analyses were applied both to the unsensitized and the highly sensitized, to patients waiting and patients transplanted. The quality of typing was thus monitored in the combined registries' data and separately in Eurotransplant and UK Transplant, wherein recent donor gene frequencies were available for cross-reference. Against this backcloth, we depict the relationship between HLA phenotype and panreactivity, but first we brush up a little on the relevant art of statistics.

106 3.

Motivation

for

linear-logistic

regression

3.1. Odds on being unsensitized

On the combined waiting lists of Eurotransplant, U K Transplant, France Transplant and Scandia Transplant (see Table 5.1) there were 5630 unsensitized and 1753 highly sensitized HLA-A, B and DR-typed patients. The odds on being unsensitized (versus highly sensitized) are thus 5630 : 1753 or 3.2 : 1. Writing Pr(unsensitized) for the probability of being unsensitized, we have odds on being unsensitized: 5630 Pr (unsensitized) 7383 5630 - - 3.2 Pr(highly sensitized) 1753 1753 7383 -

-

Instead of talking about probabilities of being unsensitized, we shall discuss the odds on being unsensitized. Why gamble on odds? Intuition is the first answer: intuition tells us that to shift the probability of being unsensitized from, say, 95% (odds 95 : 5 or 19 : 1) to 97.5% (odds 39 : 1) - thus doubling the prior odds on being unsensitized, the ratio of 39 to 19 being approximately two - is the equivalent of modifying the probability of being unsensitized from 50% (odds 1 : 1), not to 52.5% (a minor change in uncertainty), but to nearer 67% (odds 2:1). In both cases, we have doubled the prior odds on being unsensitized. The second answer is statistical: there is a naive rule for taking account of additional patient information, such as being female or an A locus homozygote, when calculating odds. We demonstrate this naive Bayes rule with reference to Table 5.1 and show how it leads on to linear-logistic regression. 3.2. Naive Bayes rule From Table 5.1, the prior odds on being unsensitized (versus highly sensitized) are 5630:1753. Table 5.1. Combined waiting lists of Eurotransplant, UK, France and Scandia Transplant

unsensitized highly sensitized

unsensitized highly sensitized

TOTAL

male

FEMALE

5360 1753 7383

3727 786

1903 967

TOTAL

A heterozygote

A HOMOZYGOTE

5630 1753 7383

4387 1242

1243 511

107

QUESTION: How

does being female change the odds on being unsensitized? From Table 5.1 we see 5630 Pr(unsensitized) 7383 1753 Pr(highly sensitized) 7383 -

prior ie odds on being unsensitized

-

D

5630 1753

Whereas, also from Table 5.1: FEMALE odds in being unsensitized

=

1903 967

rewriting...

1903 5630 x - 5630 967 1753 × - 1753

rewriting.,.

1903 5360 5630 175~x-967 1753

interpreting .

-

.

.

.

proportion o f t h e unsensitized who are F E M A L E prior x proportion of the highly odds sensitized who are F E M A L E %Frequency F E M A L E amongst the unsensitized %Frequency F E M A L E amongst the highly sensitized

rewriting...

= prior odds

interpreting...

= prior x F E M A L E likelihood ratio odds

×

The posterior (so-called) odds on being unsensitized given that the patient is F E M A L E is simply the prior (overall) odds multiplied by the F E M A L E likelihood ratio, that is by the ratio of % frequency F E M A L E in the unsensitized versus the highly sensitized. The likelihood ratio is also known as the Bayes factor - what we have worked through is an application of Bayes Theorem. Suppose we are now told that the patient is not only female but is

108 homozygous on the A locus. H o w do we update the F E M A L E odds on being unsensitized to take into account that the patient is A homozygous? Again we apply Bayes Theorem. prior female odds on being unsensitized

1903 =

967

posterior 1903 A HOMOZ. female odds on being unsensitized = 967

proportion of unsensitized females who are A locus HOMOZ. ×

proportion of highly sensitized females who are A locus HOMOZ.

From Table 5.1 we know the proportions of the unsensitized and highly sensitized who are A locus homozygotes; we do not know whether the proportions change in association with being female. The "naive, independence or idiot's Bayes" solution is ** to assume no change and approximate the proportion

of unsensitized females who are A locus homozygotes by the proportion of the unsensitized who are A locus homozygotes. **Thus: Naive Bayes posterior odds on A locus H O M O Z . female being unsensitized

-

--

proportion of the unsensitized who × are A locus H O M O Z . proportion of the highly sensitized who are A locus H O M O Z . 1243

1903 967

1903 967

×

**

5630 511 1753

5630 1753 = prior odds = prior odds

1243

1903 5630 × 967 1753 × F E M A L E likelihood ratio

5630 x 511 1753 × A locus H O M O Z . likelihood ratio

%Frequency F E M A L E × amongst the unsensitized %Frequency F E M A L E amongst the highly sensitized

%Frequency A locus H O M O Z . × amongst the unsensitized % Frequency A locus H O M O Z . amongst the highly sensitized

109 Generalizing the naive Bayes rule to cope with more covariates is simple each additional covariate is represented by its likelihood ratio multiplier. 3.3. Ln odds on being unsensitized The scale of natural logarithms is preferred because on it addition replaces multiplication, so that the naive Bayes rule becomes a simple sum: A HOMOZ. FEMALE In (posterior odds) = In(prior odds) + In(% Frequency ratio) + In (% Frequency ratio) for FEMALE for A HOMOZ. 3.4. Linear-logistic regression Why does naive Bayes not suffice for all applications? The difference between the naive or independence Bayes In posterior odds and linear-logistic regression of the In odds for being unsensitized (on such covariates as female, A locus homozygosity etc) is that the conditional independence assumption ** entailed in the above naive Bayes formulation is dispensed with and intercorrelations are taken fully into account in linear-logistic regression. (Linear-logistic regression is so called because In odds is otherwise known as the logistic transform and the regression risk score is linear.) Figure 5.1 plots the conversion from In odds on being unsensitized to the probability of being unsensitized. Thus a In odds risk score of 1.1 corresponds to a 75% probability of being unsensitized; In odds of - 2 . 9 corresponds to a 5% chance of being unsensitized. Linear-logistic regression formulates the In odds of being unsensitized as a weighted sum of the covariates of interest. In our application the covariates are all indicator variables (coded 0, 1) identifying specific antigens and homozygosity on the A, B or D R locus together with the main sources of sensitization inferred from Chapter 4: sex; graft number; and registry together with the interactions amongst these. If the covariates were essentially independent of each other in their association with sensitization, then the linear-logistic risk score for an individual would differ little from the naive Bayes approach of summing the relevant In(% Frequency ratio) for covariates which describe an individual to derive a nett adjustment for him/her which is added to the In prior odds on being unsensitized. 3.5. Linear-logistic regression: goodness of fit How do we assess the goodness of fit of a linear-logistic regression model to the initial data-set?

110

100 - -

90"-

80--

70--

~

60--

~,

50--

.~ ~

,,~

40-

I~

30-

20-

10-

I

I

I

I

I

I

I

-3

-2

-1

0

1

2

3

,

Ln odds on being unsensitized

Figure 5.1. Ln odds transform. Having estimated the regression coefficients (see Chapter 2) from the database, we then use these coefficients and the covariate values (see above) for individual patients to compute the predicted In odds risk score for each patient (see Chapter 2) in the database. Patients are grouped in order of their predicted In odds (for example, predicted risk score less than - 1; between - 1 and - 0 . 5 ;

111 from -0.5 to 0; 0 to 0.5 and so on OR lowest 5% of predicted In odds; next 5% of patients in order of predicted In odds and so on). For each group of patients, the observed In odds are given by fobserved number unsensitized in the group + ½"~ l n ~ ~ number highly sensitized in the group + ½J" Addition of ½to numerator and denominator ensures unbiassed estimation of the true odds. Goodness of fit of a linear-logistic model to the training data-set can be assessed informally by plotting the observed In odds for each patient group against their predicted In odds (eg median predicted In odds for the group). Good calibration occurs when a straight-line relationship obtains between the observed and predicted In odds but the performance of the predictive index can only be tested fully by applying it to a new set of patients and comparing predicted with actual sensitization status. Typically regression coefficients estimated from the training set need to be shrunk towards zero for better calibrated prediction. Remember that we are describing the correlates of established sensitization status - interventions designed to avert high sensitization, such as limiting blood transfusions in new patients whose In odds risk score predicts high sensitization, need to be tested in randomized clinical trials in which graft outcome is monitored as well as the incidence of high sensitization.

4. Exploratory analysis of covariates and sensitization

The naive Bayes rule identified the In likelihood ratio for each covariate: ln{ [~o covariate frequency in the unsensitized "[ covariate frequency in highly sensitizedJ as the relevant statistic by which to update the In prior odds on being unsensitized to take account of covariate information. Table 5.2 gives the basic counts for covariates defining recipient phenotype and alongside each covariate computes its In likelihood ratio as, for example: 1903+½ 5630 + 1t In(% F ratio) for females = Inf - 967 + ½ 1753 + 1 Addition of ~1 to covariate counts and 1 to total counts ensures unbiassed

112 estimation. The standard error of In(% F ratio) is computed not optimally, but memorably, as

{

1

square root of ~nsensitized covariate count + ½

1

}

+ highly sensitized covariate count + ½ Scanning the In likelihood ratios for individual covariates in Table 5.2 suggests the major aspects of how recipient phenotype relates to sensitization status. We note that homozyosity on the A or B locus is more common in the highly sensitized (see Table 5.2a); that In( % F ratio) for antigen-specific homozygosity is negative for most A and B antigens (see Table 5.2b) and so the association Table 5.2a. Orientation: covariate counts for fully typed waiting listed patients

Unsensitized (5630)

HS (1753)*

In(% F ratio)

(s.e.)

Count

% Frequency

Count

% Frequency

male ~male

3727 1903

66 34

786 967

45 55

0.39 -0.49

(0.04) (0.04)

waits 1st graft waits re-graft

5153 477

92 8

926 827

53 47

0.55 - 1.72

(0.04) (0.06)

A homo. B homo. DR homo.

1243 831 1660

22 15 29

511 328 541

29 19 31

-0.28 -0.24 -0.05

(0.05) (0.07) (0.05)

Euro. UK Scandia France

2992 1115 603 920

53 20 11 16

636 486 106 525

36 28 6 30

-0.61

(0.05)

A19 B16 B27

1340 463 490

24 8 9

457 166 167

26 9 10

-0.09 --0.14 --0.09

(0.05) (0.09) (0.09)

DR1 DR2 DR3 DR4 DR6 DR7

1040 1321 1425 1634 1254 1109

18 23 25 29 22 20

249 548 386 513 408 313

14 31 22 29 23 18

0.26 -0.29 0.14 -0.01 - 0.04 0.10

(0.07) (0.05) (0.06) (0.05) (0.06) (0.06)

* U K and Eurotransplant assume Council of Europe definition: 81 + % peak reaction frequency. Scandia Transplant classification of high sensitization: 91 + % peak reaction frequency. France Transplant classification of high sensitization: 76 + % peak reaction frequency.

113 Table 5.2b. Orientation: covariate counts for fully typed waiting listed patients Unsensitized (5630)

HS (1753)

Count

% Frequency

Count

% Frequency

A 1 homoz. A 2 homoz. A 3 homoz. A 9 homoz. A10 homoz. A l l homoz. A19 homoz. A28 homoz.

193 550 157 111 41 34 130 27

3 i0 3 2 1 I 2 0.5

63 178 68 67 21 21 72 21

4 10 4 4 1 1 4 1

B 5 B 7 B 8 B12 B14 B16 B22

57 94 118 138 6 26 7

1 2 2 2 0.1 0.5 0.1

18 60 38 67 6 11 2

172 238 266 338 154 215

3 4 5 6 3 4

DR1 DR2 DR3 DR4 DR6 DR7

-

homoz. homoz. homoz. homoz. homoz. homoz. homoz. homoz. homoz. homoz. homoz. homoz. homoz.

38 120 85 106 60 53

ln(% F ratio)

(s.e.)

-0.05 -0.04 -0.33 -0.66 -0.51 -0.69 -0.58 -0.92

(0.14) (0.09) (0.14) (0.15) (0.27) (0.27) (0.15) (0.29)

1 3 2 4 0.4 0.7 0.1

-0.03 -0.72 -0.04 -0.45 - 1.17 --0.33 - 0.07

(0.27) (0.16) (0.19) (0.15) (0.55) (0.35) (0.73)

2 7 5 6 3 3

0.33 -0.48 -0.03 -0.01 -0.23 0.23

(0.18) (0.11) (0.12) (0.11) (0.15) (0.15)

of homozygosity with high sensitization is probably locus, not antigen, specific; that DR1, DR3 and DR7 are associated with being unsensitized, whereas the reverse association holds for DR2 since the % frequency of DR2 is 31% in the highly sensitized, compared to only 23% in the unsensitized (see Table 5.2a). In view of homozygosity at the A or B locus being more frequent in the highly sensitized, inspection of individual A or B antigen associations with sensitization has been limited in Table 5.2c to recipients who are A and B heterozygotes otherwise greater antigen frequency in the unsensitized could be a counting artefact from their having more antigens via the aforementioned negative association with homozygosity. From Table 5.2c we note greater frequency in the unsensitized of A1 and A2 but reduced frequency compared to the highly sensitized of A10, A19, B14 and B27. For comparison, the analyses orAl9, B16 and B27 in Table 5.2a are based on all registered patients - as we anticipated, inclusion of homozygotes has reduced the % frequency of A19 more in the highly sensitized (from 32% in A and B heterozygotes to 26% in all) than in the unsensitized (from 27% in A and

114 Table 5.2c. Orientation: covariate counts for A and B locus heterozygotes

Unsensitized (3801)

HS (1035)

ln(% F ratio)

(s.e.)

Count

% Frequency

Count

% Frequency

A 1 A 2 A 3 A 9 A10 All A19 A28

1200 1830 1065 980 546 517 1033 426

32 48 28 26 14 14 27 11

296 424 302 269 178 146 327 128

29 41 29 26 17 14 32 12

0.10 0.16 -0.04 -0.01 -0.18 -0.04 -0.15 -0.I0

(0.06) (0.05) (0.07) (0.07) (0.09) (0.09) (0.06) (0.10)

B 5 B 7 B 8 B12 B14 B16 B22 B27 B35

534 793 847 874 197 357 218 378 741

14 21 22 23 5 9 6 10 20

147 219 209 223 72 126 67 120 186

14 21 20 22 7 12 7 12 18

-0.01 -0.02 0.10 0.06 -0.30 -0.26 -0.13 -0.16 0.08

B13 B15 B17 B18 B21 B27 B40

217 563 351 435 266 127 524

6 15 9 11 7 3 14

46 135 112 129 70 31 133

4 13 11 13 7 3 13

0.24 0.13 --0.16 --0.09 0.03 0.10 0.10

(0.08) (0.14) (0.10) (0.14) (0.10)

(0.16) (0.10) (0.11) (0.I0) (0.20) (0.I0)

B heterozygotes to 24% in all recipients) with corresponding reduction in significance for A19 as single covariate. This initial survey of covariates gives us a naive orientation, directing the steps we take next to arrive at a linear-logistic regression model for the In odds on being unsensitized. The linear-logistic model sequence is described in the next section.

5. Model building The combined waiting list of 7383 unsensitized or highly sensitized patients was constituted as follows: 3628 from Eurotransplant, of whom 636 (18%) were highly sensitized: 1601 from UK Transplant of whom 486 (30%) were highly sensitized: 1445 from France Transplant of whom 525 (36%) were highly sensitized and 709 from Scandia Transplant of whom 106 (15%) were highly sensitized (see also Table 5.2a). Of all 7383 patients on the combined waiting list,

115 4836 were heterozygous for both A and B loci (66%) and 2547 (34%) were homozygous for A or B; 3556 patients (48%) were heterozygous for all three loci (HLA-A, B and DR) and 52%0 were homozygous on at least one of them. The sequence of linear logistic models (for the In odds on being unsensitized) which was fitted to the data for the combined waiting list is given in Table 5.3 with brief signposting which is amplified here. The simplest model (1) imputes only the structure inferred from Chapter 4 of sex, graft number, registry and their interactions as being associated with high sensitization and gives rise to a regression ~2 of 1824.0 on mere 12 degress of freedom, emphasising again the fantastic significance of these descriptors. Because the influence of homozygosity seemed to be locus-specific, not antigen-specific (see Table 5.2b), the next model in sequence, model (2), adds homozygosity on each of the three loci: A, B, DR. The addition of these three covariates is very highly significant (~3~ = 41.2); details of model (2) are shown in Table 5.4, from which we note highly significant [zl-scores associated with A or B locus homozygosity (respectively 4.9 and 3.9 greatly exceeding two and implying extreme significance) but for DR homozygosity a regression coefficient which is close to zero, consistent with there being little impact of DR homozygosity on sensitization status. Class I homozygosity is negative for being unsensitized, that is to say Class I homozygosity is more frequent in the highly sensitized. The BASELINE individual in model 2 is defined as "not female, not awaiting regraft, not homozygous on A or B or DR locus and not registered with Eurotransplant, UK Transplant or Scandia Transplant". The BASELINE individual is thus a male, heterozygous on A and B and DR loci, registered with France Transplant and awaiting his first graft. From Figure 5.1 we translate his In odds on being unsensitized, ie model 2 BASELINE 1.79, to the probability that he is unsensitized, namely 86%0. Models 3 to 7 introduce additional antigen-specific covariates. The baseline individuals for models 3 to 7 carry antigens other than those specified in the regression model. The model 3 BASELINE individual- besides being male, heterozygous on A and B and DR loci, registered with France Transplant and awaiting his first graft - carries the antigens A3 and A9. To avoid a nonsensical baseline individual at least two antigens per locus must remain unspecified by the covariates - these being the antigens from which our BASELINE heterozygous individual derives his phenotype. It does not matter which two, or more, antigens are omitted because the regression Z2, for example for HLA-A antigens, measures how heterogeneous HLA-A antigens are in respect of their association with sensitization. Coefficients for specific antigens, and corresponding z-scores, measure that antigen's relationship with sensitization relative to the baseline. For the baseline it is convenient to chose two, or more, neutral antigens so that deviations are measured essentially from

116

0

0

~

0

o

~8 ~

o

~

#

8

~

~ _~ ~

~_~

~

~

.,~~

0

~,~ ~.- ~

~ ' ~ .~

0

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~ ~

~ g.~ ~

N_

~

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0-~ ~

117 zero and z-scores of around two have their conventional interpretation of significance at about the 5% level. Thus A3 and A9 were chosen as the A locus baseline (see Table 5.2c); B18, B21, Bw41, Bw47 and Bw53 as the initial B locus baseline; and DR4, DR5, DRw8, DRw9, D R w l 0 as the D R locus baseline. In model 3 there is evidence of highly significant association between panreactivity and specific HLA-A locus antigens, as indicated by a regression Z~ of 40.3 on 6 degrees of freedom. Our impression from Table 5.4 is that A2 and A1 are more frequent in the unsensitized, while A28 is more common in association with high sensitization. In model 4 the regression Z 2 of 44.9 on 14 degrees of freedom denotes heterogeneity amongst B antigens in their association with sensitization, though less strongly than for the HLA-A locus. From Table 5.4 we note a plethora of modest z-scores: B27 is negatively and B8 positively associated with being unsensitized. Model 5 specifies a reduced set of B antigens; those not fitted are transferred to the BASELINE - B13 and B37 because rare, together with B15, B17 and B40 which have similar model 4 regression coefficients averaging about 0.18. The model 5 BASELINE is shifted up about 0.2 towards being unsensitized - this shift is compensated for by coefficients for individual B antigens, and for B locus homozygosity, being reduced by about 0.1. Notice that whereas B antigen regression coefficients (and corresponding z-scores) have altered from model 4 to model 5, individual risk scores have not. Thus, comparing model 4 and 5 risk scores for a male, heterozygous on A, B and D R loci with antigens B5 and B27, registered with France Transplant and waiting a first graft, risk scores are equal, allowing for rounded coefficients: model 4 risk score = 1.66 (model 4 BASELINE) model 5 risk score = 1.87 (model 5 BASELINE)

+ 0.20 (B5) + 0.09 (BS)

- 0.29 (B27) -0.41 (B27)

= 1.57 = 1.55

Moreover, parsimonious deletion of the extra five B antigens has not obscured significant heterogeneity amongst B antigens in their association with sensitization, the pertinent regression Z2 being 37.8 on 9 degrees of freedom. Model 6 examines specific D R antigens: DR1, DR2, DR3, DR6, DR7. There is highly significant evidence that D R antigens are heterogeneous in their association with sensitization, the additional ~2 being 55.6 on 5 degrees of freedom. From Table 5.4 we are alerted to DR1, DR7 and DR3 being more likely and DR2 less likely in the unsensitized.

118

.~8 N

0

0

0

0

.-z8

119

120

6. Final regression model The final regression model (7) accommodates broad structure, locus-specific homozygosity and antigen-specific covariates at each of the three loci: A, B and DR. Its salient HLA-phenotypic features are illustrated in Figure 5.2 and summarized here. Regression coefficients are reported in Table 5.5; in combination, coefficients for Class I homozygosity and specific antigens can be as influential as the patient's sex. Homozygosity on the A or B locus is associated with high sensitization, not so D R homozygosity. Antigens DR2, B27 and (marginally) B14 and A28 are more common in patients registered as highly sensitized; antigens more common in unsensitized patients are A2, DR1, DR7 and, of borderline significance, A1 and DR3. Figure 5.2 shows that if the baseline B antigens are positive, not neutral, in their association with being unsensitized, then B5, BI2 and B35 are three B antigens most likely associated with being unsensitized. Figure 5.3 shows that the risk score is generally well-calibrated, although extreme probabilities are predicted more than realized. Figure 5.4 compares naive Bayes and linear-logistic risk scores: the dog-leg is because the naive Bayes rule does not account for the highly influential female x regraft interaction, nor for other interactions: of sex or regraft with registry. The naive Bayes risk score underestimates high probabilities of being unsensitized.

7. Robustness of relationship of HLA phenotype with panreactivity 7.1. A or B homozygotes; A and B heterozygotes

Table 5.6 shows that the pattern of association between HLA-antigens and panreactivity is sustained separately in two subsets of the waiting list: (1) amongst those 2547 patients who are homozygous on either the A or B locus and (2) for patients who are heterozygous on both A and B loci. That A or B locus homozygosity is associated with high sensitization is, of course, further illustrated by the fact that 28% of patients who are homozygous on one or other of these loci are highly sensitized compared to only 21% of patients who are heterozygous on both A and B loci. 7.2. Graft number and sex Table 5.7 shows risk scores for the In odds on being unsensitized for patients awaiting a first versus regraft. There are no compelling disparities: not even for A10 is the difference in estimated regression coefficients statistically significant,

121

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122 Table 5.5. Linear logistic models: In odds on being unsensitised

Model 2 BASELINE Covariate

Model 7

1.79 coeff

1z 1

- 1.33 - 2.52 1.14 -0.35 -0.33 0.003

10.4 15.8 7.7 4.9 3.9 0.04

1.29 0.43 2.26

9.6 3.0 7.5

1.29 0.44 1.24

9.4 3.0 7.4

female, Euro female, UK female, Scandia

-0.27 0.03 -0.67

1.7 0.2 2.2

--0.26 0.05 -0.66

1.6 0.3 2.2

regraft, Euro regraft, UK regraft, Scandia

-0.75 -0.14 -0.76

3.9 0.7 2.5

-0.68 -0.05 0.69

3.5 0.2 2.3

female regraft female regraft A homoz. B homoz. DR homoz. A 1 A 2 AI0 All A19 A28 B 5 B 7 B 8 BI2 BI4 B16 B22 B27 B35 DR1 DR2 DR3 DR6 DR7 Euro UK Scandia

0.09

(s.e=0.13)

for

patients

awaiting

a first g r a f t

1.59 coeff

1z 1

- 1.37 - 2.63 1.12 --0.37 -0.37 0.09 0.19 0.30 -0.04 -0.10 -0.11 --0.21 0.17 -0.02 0.01 0.07 -0.32 -0.24 -0.25 -0.41 0.09 0.44 -0.23 0.20 0.05 0.38

10.5 16.2 7.4 4.3 4.0 1.1 2.0 3.9 0.3 0.9 1.3 1.8 1.6 0.2 0.05 0.8 2.2 2.0 1.7 3.5 1.0 4.4 2.7 1.9 0.5 4.1

compared

to

-0.37

(s.e = 0.22) f o r p a t i e n t s a w a i t i n g r e g r a f t [ c o m p a r i s o n z - s c o r e = 1.81]. T a b l e 5.7 f u r t h e r s u b d i v i d e s p a t i e n t s a w a i t i n g t h e i r first g r a f t a c c o r d i n g t o sex; t h e r e a r e t o o f e w r e g r a f t s t o m a k e s u b d i v i s i o n a u s e f u l exercise. S i m p l e c o m p a r i s o n o f t h e s e t w o i n d i c e s , a n t i g e n b y a n t i g e n ( n o t u s i n g full c o r r e l a t i o n

123

OBSERVED Ln odds = Ln{ n o .

unsensitized

+ ½

no. highly sensitized

~.

+ ½

!

~

4-

/ 98%

/,

3-

•

-95%

188% ~' i

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I

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i

0

I

1

I

2

I

3

I

4

5

44 175 203 231 408 770 879 707 872 814 363

116 38

Predicted In odds on being unsensitized no.unsensitized

OBSERVED

0

10

no. highly sensitized

OBSERVED 3 42 196 318 263 193 201 203 159 75 60 29 8 2 1 Figure 5.3. Final model for In odds on being unsensitizcd (waiting list patients). Calibration plot of O B S E R V E D versus predicted In odds.

matrices), identifies only one of the 20 antigens, A l l , as having a different association with sensitization between the sexes, - 0.51 (se = 0.20) for first graft males and 0.10 (se =0.17) for females awaiting first graft [comparison zscore = 2.34]. Since there is no prior rationale for an interaction between sex and A11 in respect of sensitization status, the observation is put down as a

124 Number of female regrafts per group of 200 patients (first group has only 183 patients) -

5

10~7 183

919068 474329

17 1 4 2 3 0 2 0

:

4-

X

• Xe •

3-

•

ee

X

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/x I

I

I

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-1 0 1 2 3 Mean linear-logistic risk score : per 200 patients in rank order

I

4

Figure 5.4. Naive Bayesversus linear-logisticrisk score: Ln odds on being unsensitized.

chance phenomenon. The regression coefficients for common antigens A1, A2, B12 (but not B7, B8), DR3 are more positive for females being unsensitized at first graft than for males, but individually they are not significantly elevated. 7.3. Registry specific variation in HLA-associations with panreactivity In Eurotransplant, as distinct from other registries, presence of the antigen DR6 is associated with high sensitization (see later Hardy-Weinberg analysis). UK Transplant is the only registry whose waiting list significantly associates DR homozygosity with unsensitized patients (see later Hardy-Weinberg analysis).

125

Table 5.6. Linear logistic models: In odds on being unsensitized

BASELINE Covariate female regraft female regraft

A 1 A 2 A10 A 11 AI9 A28 B 5 B 7 B 8 B12 B14 B16 B22 B27 B35 DRI D R2 DR3 D R6 DR7 Euro UK Scandia

A/B homozygote: 718 HS out of 2547 (28%)

A/B heterozygote: 1035 HS out of 4836 (21%)

1.24 coeff

lzl

1.49 coeff

- 1.46 -- 2.39 0.96

7.5 9.4 3.5

- 1.28 - 2.69 I. 16

7.3 12.8 6.2

0.23 0.32 0.21 - 0.20 0.04 -0.24 0.21 0.15 0.10 -0.05 -0.42 -0.005 --0.37 - 0.24 0.19 0.30 - 0.24 0.16 0.18 0.54

1.4 2.6 1.0 0.9 0.3 1.1 1.2 0.9 0.5 0.4 1.7 0.02 1.3 1.1 1.2 1.9 1.8 1.0 1.3 3.6

0.16 0.33 -0.10 - 0.05 -0.15 - 0.20 0.19 -0.05 --0.01 0.18 -0.20 -0.28 -0.18 --0.46 0.06 0.46 - 0.25 0.17 -0.08 0.23

1.4 3.4 0.8 0.4 1.4 1.5 1.5 0.4 0.1 1.7 1.2 2.1 1.1 3.3 0.5 3.8 2.5 1.4 0.7 2.1

lzl

1.15 0.21 2.38

5.3 0.9 4.6

1.44 0.64 2.23

8.2 3.4 5.9

female, Euro female, U K female, Scandia

-0.38 0.32 -0.43

1.4 1.1 0.8

-0.21 --0.12 -0.78

1.0 0.5 2.0

regraft, Euro regraft, U K regraft, Scandia

- 1.04 -0.51 - 1.13

3.2 1.5 2.2

- 0.59 0.09 - 0.49

2.4 0.4 1.3

126

~

~8

.~8

~8

~

~

d

~

~

~

~

~

127 7.4.

Transplanted database: highly sensitized and control grafts, 1982-85

In this subsection we use the database of highly sensitized and control grafts, d e s c r i b e d i n C h a p t e r 7, t o c o m p a r e H L A p h e n o t y p e b e t w e e n h i g h l y s e n s i t i z e d t r a n s p l a n t e e s a n d t h e i r c o n t r o l s . B y d e f i n i t i o n , first t r a n s p l a n t

controls had

1 0 % o r less p e a k r e a c t i o n f r e q u e n c y ; r e g r a f t c o n t r o l s h a d 3 0 % o r less p e a k reaction

frequency.

Controls

were matched

f o r sex w i t h h i g h l y s e n s i t i z e d

recipients.

Table 5.8. Orientation: covariate counts for fully typed transplanted recipients Unsensitized (1014)

HS (1063)

ln(% F ratio)

(s.e.)

Count

°/o Frequency

Count

°/o Frequency

male female

425 589

42 58

449 614

42 58

--0.01 0.01

(0.07) (0.06)

waits 1st graft waits re-graft

611 403

60 40

588 475

55 45

0.09 --0.12

(0.06) (0.07)

A homoz. B homoz. DR homoz.

159 112 265

16 11 26

254 177 270

24 17 25

- 0.42 -0.41 0.03

(0.10) (0.12) (0.09)

A 1 A 2 A 3 A 9 AI0 All AI9 A28

286 532 253 218 112 140 232 96

28 52 25 22 11 14 23 10

303 506 287 242 89 105 247 93

29 48 27 23 8 10 23 9

-0.01 0.10 -0.08 -0.06 0.28 0.33 -0.02 0.08

(0.08) (0.06) (0.09) (0.09) (0.14) (0.13) (0.09) (0.15)

B 5 B 7 B 8 B12 BI4 B16 B22 B27 B35

151 236 195 278 46 78 43 92 194

15 23 19 27 5 8 4 9 19

140 293 219 252 68 77 43 92 177

13 28 21 24 6 7 4 9 17

0.12 -0.17 -0.07 0.15 -0.34 0.06 0.05 0.05 0.14

(0.12) (0.09) (0.10) (0.09) (0.19) (0.16) (0.21) (0.15) (0.10)

DR1 DR2 DR3 DR4 DR6 DR7

186 284 248 279 208 227

18 28 24 28 21 22

156 358 257 305 222 195

15 34 24 29 21 18

0.22 -0.18 0.01 -0.04 -0.02 0.20

(0.11) (0.08) (0.09) (0.08) (0.10) (0.10)

128 In the introduction we remarked that various selection biasses dictate which patients (a) are registered and (b) accumulated on renal waiting lists, transplantation being itself selective. The same analytic methods as above are used to scrutinize the relationship of sensitization status to HLA phenotype in transplanted patients with a view to identifying consistent associations for waiting and transplanted patients. Table 5.8 gives covariate counts for the transplant database. A similar sequence of models to those reported in Table 5.3 for registry patients was fitted to the fully typed transplant database. Specifically, the regression g2 for sets of A, B or DR antigens fitted over and above background and homozygosity covariates were: 17.3 on 6 degrees of freedom for A antigens; 14.2 on 9 degrees of freedom for the reduced set of B antigens (p ~ 0.11); and 16.6 on 5 degrees of freedom for DR antigens. In respect of both A and DR loci there was highly significant heterogeneity in antigen-specific associations with sensitization. The full model, in which homozygosity indicators were fitted together with antigen-specific covariates on all three loci, is reported in Table 5.9 (g2 of 84.7 on 26 degrees of freedom). The negative regression coefficients for regrafts ( - 0 . 3 3 , lzl = 2.3) warns of design failure (see Chapter 2) to find as many regraft controls as there were highly sensitized regrafts - pairing should have ensured null coefficients for the basic structure covariates: female, regraft and female by regraft interaction. In transplanted patients, as for those on registry waiting lists, homozygosity on the A or B locus is associated with high sensitization; DR homozygosity in both contexts (see Figure 5.2) inclines towards the patient being unsensitized. The A antigen profiles for registered and transplanted patients have some divergent features - unlike the B and DR profiles, which are essentially concordant (see Figure 5.2), albeit DR2 is not significantly more frequent in highly sensitized than in control transplants. Antigens A1 and A2 are associated with being unsensitized in both registered and transplanted patients; however, antigens A10 and A11, which have neutral or negative coefficients in the registry model, are positively associated with being unsensitized in the transplanted database. Regression coefficients differ significantly between the two contexts at the 7% and 0.7% level respectively. Again, we caution that unless prior rationale supports a distinctive function for A10 and A11, such discrepancies should be ascribed to chance. Although tantalizing that A 11 featured before as the sole antigen possibly having a sex-specific association with sensitization in patients awaiting first grafts, the association was reversed (!) for patients awaiting regrafts and in the transplanted database (A 11 coefficients: males awaiting regraft 0.46 (se = 0.27); females awaiting regraft 0.87 (se = 0.40); transplanted males 0.79 (se = 0.24); transplanted females 0.23 (se = 0.21)). The only other discrepancy of any note between waiting list and transplanted patients relates to B27, at the 8% level.

129 Table 5.9. Linear logistic model: In odds on being unsensitized amongst HS/control transplants HS/control transplants BASELINE Covariate

-- 0.13 coeff

lzl

female regraft female regraft A homoz. B homoz. D R homoz. A 1 A 2 AI0 A 11 AI9 A28 B 5 B 7 B 8 B12 B 14 B16 B22 B27 B35 DR1 DR2 DR3 DR6 DR7

-- 0.14 -0.33 0.09 -0.38 - 0.46 0.18 0.11 0.33 0.35 0.40 0.002 0.09 0.06 --0.12 --0.20 0.14 - 0.42 -0.06 -0.12 -0.05 0.10 0.33 -0.10 0.24 0.01 0.28

I. 1 2.3 0.5 2.9 3.2 1.5 0.8 3.0 2.1 2.7 0.02 0.5 0.4 1.0 1.2 1.2 2.0 0.3 0.5 0.3 0.7 2.5 0.9 1.6 1.0 2.2

8. Hardy-Weinberg estimation of gene frequencies in waiting list, transplanted and donor databases

In this section we apply the Hardy-Weinberg equations to estimate gene frequencies in the registry and transplanted databases, separately for highly sensitized and unsensitized, or control, patients. We consider these against the backdrop of donor gene frequencies reported by UK Transplant and Eurotransplant. Provided that random assortment applies, Hardy-Weinberg estimation of gene frequencies solves the counting problem posed by individuals having one or two antigens identified. When random mating applies: known gene relative frequency = 1 - x / / 1 - k n o w n antigen relative frequency

130 The relative frequency of the blank or unidentified gene(s) is computed as one minus the sum of the relative frequencies of known genes. Klouda et al (1985) analysed the HLA-A, B and DR phenotype of 2041 kidney donors reported to the UK Transplant Service - the HLA-A, B and DR locus phenotypes were in Hardy-Weinberg equilibrium and the unidentified gene frequencies were zero, 0.7% and 3.9% at the A, B and DR loci respectively. Our exclusion from the waiting list and transplanted databases of untyped individuals is correct only for cases where DR typing was not attempted; otherwise we have excluded inadvertently patients for whom no antigen was identifiable. The )~2 test for goodness of fit of observed phenotypes to HardyWeinberg equilibrium is valid only insofar as the "no DR type attempted" presumption is correct.

8.1. Waiting list patients Figure 5.5 shows estimated gene frequencies at the A, B and DR loci for patients in the combined Eurotransplant, UK Transplant, France Transplant and Scandia Transplant waiting list. Phenotypes at all three loci A, B and DR contravene Hardy-Weinberg equilibrium, with the exception of A locus phenotypes for unsensitized patients (goodness of fit )~z7 = 32.6). Appendix Table A5.1 lists the major deviations between observed and Hardy-Weinberg expected phenotype counts; of these only the observed deficit of B12, 18; and observed excesses of B17,21 and B 17,53 are common to both unsensitized and highly sensitized waiting patients.

8.1.1. Waiting list patients: HLA-DR. Missed DR gene frequency is 9% in the unsensitized and is similar, 9.5%, in the highly sensitized. Estimated gene frequencies for DR1 are 9.5% in unsensitized and 7.5% for highly sensitized waiting patients; but for DR2 are 12.5% and 17% in unsensitized and highly sensitized patients respectively. DR phenotypes of neither unsensitized nor highly sensitized patients on the combined renal waiting list conform to HardyWeinberg equilibrium. More unsensitized patients are DR7 homozygotes and fewer are DR6 homozygotes than predicted by Hardy-Weinberg equilibrium and unsensitized phenotypes DR3,4 and DR5,6 exceed their Hardy-Weinberg expectation (see Appendix Table A5.1a). 8.1.2. Waiting list patients: H L A - A and B. For the A and B loci an intriguing result emerges whereby estimated missing gene frequency is one and a half to two times greater in highly sensitized than in unsensitized patients on registry waiting lists - missing gene frequency at the A locus is 8% versus 3.5% in highly sensitized compared to unsensitized patients and for the B locus is 6.5% versus 4%. Estimated missing class I gene frequencies for all patients on renal waiting

131

A locus I

0"3-

registry waiting lists

Goodnessof fit )~227 5630 [ ] umnsitized 32.6 1753 [ ] highlysendlJzed50.5

3

19

0"2-

0'1-

unidentified A

1

antigens

0.3-

2

B locus

9

10

11

registrywaiting lists

28

Goodness of fit X.2170 D unsensitized 248.6 ~ ] highlysensitized266.4

0.2-

0.1.

unidentified

5

7

8

12' 13' 14' 15' 16' 17' 18' 21' 22' 27' 35 37' 40'

antigens

B41, B47, B53 not shown DR locus

0"3-

registry waiting lists

Goodnessof fit X.244 D uasensitized 146.1 []highly sensitized73.4

0"2-

uaiaant~i=l DR

antigens

1

2

3

4

5

6

7

8

9

10

Figure 5.5, Hardy Weinberg gene frequencies: registry waiting list (7383 typed patients)

132 lists were markedly higher than corresponding rates in UK donors, a reflection of the acknowledged difficulty in tissue typing uraemic patients; but the differential rates between highly sensitized and unsensitized patients at the A or B (but not the DR) locus are consistent with class I, non antigen specific, homozygosity being associated with high sensitization, as elucidated previously. Since missing class I gene frequencies differ between unsensitized and highly sensitized patients, comparison of specific gene frequencies should be made after normalizing them to sum to 100%. For unsensitized patients on the combined waiting list known A gene frequencies sum to 96.5% and gene fre14.5 quency for A1 is 14.5%; after normalization, the A1 gene frequency is ~ or 12.5 15% for unsensitized patients and is ~ or 13.6% in the highly sensitized.

8.2. Transplanted patients Figure 5.6 shows estimated gene frequencies at the HLA-A and DR loci for fully typed transplanted patients. HLA-B known gene frequencies are not illustrated, being poorly estimated in 1000 patients; but missed B gene frequency is reported. 8.2.1. Transplanted patients: HLA-DR. Figure 5.6 shows estimated DR gene frequencies for 1063 fully typed highly sensitized and 1014 control transplants. Missing DR gene frequency is comparable in highly sensitized (6.5%) and control transplants (7.1%), but is lower than for waiting list patients. Estimated gene frequencies for DR1 are 9.5% in control and 7.5% in highly sensitized transplants; and for DR2 are 15% and 18.6% in control and highly sensitized transplants respectively. In the transplanted database the most striking deviation from Hardy-Weinberg HLA-DR equilibrium occurs for highly sensitized recipients (goodness of fit ~(33 71.7, DR9 and 10 summed throughout) amongst whom are fewer DR6 homozygotes than predicted by Hardy-Weinberg equilibrium but an excess of DR6 in combination with DR9/10 and of the phenotype DR2,8 (see Appendix Table A5.2). =

8.2.2. Transplanted patients: HLA-A and B. Estimated HLA-A gene frequencies for fully typed highly sensitized and control transplants are also shown in Figure 5.6. The Bernstein estimates of known A gene frequencies for control grafts sum to more than one, so that the gene frequency for unidentified antigens is negative! This nonsense can be avoided by maximum likelihood estimation - for Eurotransplant donors Table 5.10 compares estimates of A gene

133

A locus

HS/control typed transplants

Goodness of fit XZZ7

03-

1014 ~]¢ontrols38.7

1063[]HS

56.7

0.2-

o.1-

unidentified antigens

1

2

3

B locus

9

10

11

19

28

HS/control typed transplants

Unidentified Antigens

Frequency

E~]controls1.6%

1

[]HS

4.5%

unidentified antigens

DR locus

03-

HS/control typed transplants

Goodness of fit X 2 33 ~ controls49.6

[]HS

71.7

0.2-

•

O.1

unidnntified DR antigens

1

2

3

4

5

6

~

7

8

9

10

Figure 5.6. Hardy Weinberg frequencies: transplanted database (2077 recipients).

frequencies computed by the two methods (Joe D'Amaro: personal communication) and demonstrates the similarity of the normalized gene frequencies. In our experience, failure of the Bernstein estimator is a predominantly A-locus phenomenon, an observation which implies as yet unidentified A locus polymorphism. Unidentified A gene frequency is 4% in highly sensitized transplants, lower than the 8% registry estimate, but much higher than the essentially zero fre-

134

Table 5.10. Comparison of maximum likelihoodand Bernsteinestimates for A locus: Eurotransplant donors 1981-86 (n = 4569; personal communicationJoe d'Amaro) Eurotransplant donors (normalized to 100%)

unidentified A 1 A2 A3 A9 A10 A11 A19 A28

Maximum likelihood %

Bernstein estimates %

.1 15.2 28.3 15.3 12.0 5.9 5.4 12.8 4.9

-

1.5

15.6 (15.4) 28.8 (28.4) 15.5 (15.2) 12.4 (12.2) 6.0 (5.9) 5.4 (5.4) 12.9 (12.7) 4.9 (4.8)

quency for control grafts. Whereas the control transplants conform to A locus Hardy-Weinberg equilibrium, amongst the highly sensitized grafts we observe, as in highly sensitized patients awaiting transplantation, an excess of A2 homozygotes and also of the phenotype A1,3 (see Table A5.2). Diverse other salient deviations from A locus Hardy-Weinberg equilibrium exist in registry and transplanted patients and are listed in Appendix Tables A5. l b and A5.2. Many B phenotypes occur rarely in 1000 transplantees. B gene frequencies are not plotted; but we note that the unidentified B gene frequency was estimated as 1.6% in control transplants and 4.5% in highly sensitized grafts. 8.3. Reference donor gene frequencies: Eurotransplant and UK Transplant The reference comparison in the foregoing section has been to U K donors; the performance of U K tissue typing laboratories may not be typical of other laboratories throughout Europe, not least because of the use of different reagents. In this subsection we restrict the registry database to U K Transplant and Eurotransplant, for which recent reference data on donor phenotypes are available, and consider only the A locus (representative of class I) and the D R locus. A second comparison group is the transplanted database derived from U K Transplant and Eurotransplant. 8.3.1. Eurotransplant and UK Transplant: HLA-DR. Missing D R gene frequencies (see Table 5.11) are generally higher in the U K patients - whether waiting (10.6% unsensitized; 7.0% highly sensitized), transplanted (3.4% controls; 7.1% highly sensitized) or U K donors (3.9%) - than reported in Eurotransplant for waiting (5.0% unsensitized; 6.1% highly sensitized) and

135 transplanted patients (2.9% unsensitized; 2.3% highly sensitized) or for Eurotransplant donors (2.3%, but inflated because DR9 and DR10 are labelled unidentified). Approximately, the standard error qualifying gene frequency g% is given by

{

/

100 x square root of 4 x number of patientsJ

For example, the standard error of missing DR gene frequency (7%) for 486 highly sensitized patients on the UK waiting list is

100 × square root of

4 x 486

j

100 x 0.0083 = 0.83%

and for 2041 UK donors is

.1 ( 3.9 2 -

100 x square root of

4 x ~0~

J

100 x 0.0031 = 0.31%

Missing DR gene frequency is significantly less for highly sensitized patients than for unsensitized patients on the UK waiting list (z-score for comparison of UK waiting list missing DR gene frequencies = 4.3), but reversed in the transplanted UK database. We recall that UK Transplant was the only waiting list which showed DR homozygosity to be significantly positively associated with being unsensitized. Other features of note in Table 5.1 la and b are firstly the higher normalized DR6 gene frequency in Eurotransplant (DR6:15% unsensitized; 18% highly sensitized; 14% donors) compared to UK Transplant (DR6: 13% unsensitized and highly sensitized patients on UK waiting list; 12% donors) and indeed compared to the transplanted patients from both registries (see Table 5.1 lb). Moreover, in Eurotransplant's waiting list, DR6 is significantly associated with high sensitization; and the failure of the observed phenotypes in unsensitized patients registered with Eurotransplant to agree with Hardy-Weinberg equilibrium (;t~6 = 73.9) is accounted for principally by a deficit of DR6 homozygotes, and an excess both of DR1 homozygotes and DR3,4 heterozygotes (see Table AS.3a and b). The normalized DR3 gene frequency in Eurotransplant (DR3: 13% unsensitized; 11% highly sensitized; 11% donors) is distinctly lower than in UK Transplant (DR3: 18% unsensitized; 15% highly sensitized; 15% donors). We refer now to Figure 5.7 which plots the normalized (to sum to 100%) known DR gene frequencies for the highly sensitized versus the unsensitized waiting listed (open circles) and transplanted patients (solid squares) -

136 s e p a r a t e l y for U K T r a n s p l a n t a n d E u r o t r a n s p l a n t . T h e d i a g o n a l line r e p r e s e n t s e q u a l gene frequencies i n h i g h l y sensitized a n d u n s e n s i t i z e d p a t i e n t s ; p l o t t e d a b o v e the d i a g o n a l are genes w h o s e f r e q u e n c y is greater in the h i g h l y sensitized, a n d b e l o w the d i a g o n a l are f o u n d genes w h o s e e s t i m a t e d f r e q u e n c y is less in the h i g h l y sensitized t h a n in the u n s e n s i t i z e d . T h e s q u a r e o u t l i n e s are d e r i v e d f r o m e s t i m a t e d gene f r e q u e n c i e s in U K d o n o r s a n d E u r o t r a n s p l a n t d o n o r s respectively. T h e p l o t t i n g s y m b o l s , o p e n circles o r solid squares, lie close to their d o n o r reference f r a m e if d o n o r a n d r e n a l disease gene f r e q u e n c i e s are Table 5.11a. DR locus: Hardy-Weinberg gene frequencies for Eurotransplant and UK Transplant

waiting list Eurotransplant (normalized to 100%) Donors: 4419 % unidentified DR DR DR DR DR DR DR DR DR

I 2 3 4 5 6 7 8 9/10

2.3 includes DR9/10 10.5 16.1 l l.0 14.0 15.0 14.5 13.0 3.6 included in DR null ~227 =

20.9

Comment

Unsensitized: 2992 % 5.0

Highly sensitized: 636 % 6. l

10.5 ( l l . l ) 12.7 (13.4) 12.2 (12.8) 14.4 (15.2) 16.0 (16.8) 14.1 (14.8) 9.9 (10.4) 2.5 (2.6) 2.7 (2.8)

7.6 (8.1) 12.6 (16.6) 10.0 (10.6) 14.4 (15.3) 15.6 (16.6) 17.3 (18.4) 8.4 (8.9) 2.7 (2.9) 2.2 (2.3)

~3~5= 76.88

Z325= 42.18

highly significant UK Transplant (normalized to 100%)

unidentified DR 1 DR 2 DR 3 DR 4 DR 5 DR 6 DR 7 DR 8 DR 9/10

Comment

Donors: 2041 %

Unsensitized: 1115 %

Highly sensitized: 486 %

3.9 8.6 (8.9) 16.7 (17.4) 14.8 (15.4) 20.4 (21.2) 7.2 (7.5) 11.8 (12.3) 13.8 (14.4) 1.6 (1.7) 1.1 (1.1)

10.6 7.2 (8.1) 13.3 (14.9) 15.9 (17.8) 17.6 (19.7) 8.7 (9.7) 11.5 (12.9) 12.7 (14.2) 1.3 (1.5) 1.2(1.3)

7.0 6.9 (7.4) 18.7 (20.1) 14.3 (15.4) 18.2 (19.6) 10.2 (11.0) 12.0 (12.9) 9.9 (10.6) 1.8 (1.9) 1.0(1.1)

~3~5= 24.9

;~5 = 62.35

Z3~5= 27.59

p < 0.01

137 Table 5.1lb. DR locus: Hardy-Weinberg gene frequencies for transplanted HS/control recipients

Eurotransplant (normalized to 100%)

unidentified DR1 DR2 DR3 DR4 DR 5 DR6 DR 7 DR 8 DR 9/10

Typed, transplanted controls: 522 %

Typed, transplanted HS: 534 %

2.9 10.3 15.2 11.9 14.3 18.1 12.7 11.1 2.4 1.1

2.3 7.9 19.5 12.2 14.8 17.2 12.8 9.4 2.4 1.6

(10.6) (15.7) (12.3) (14.8) (18.7) (13.1) (11.4) (2.5) (1.1)

30.23

51.56

Comment

(8.1) (20.0) (12.4) (15.1) (17.6) (13.1) (9.7) (2.4) (1.6) p < 0.05

Observed excess of DR1,3: 19 Obs. v. 10.3 Exp; Z1~ = 7.3 UK Transplant (normalizod to 100%) Typed, transplanted controls: 184 %

Typed, transplanted HS: 217 %

unidentified DR1 DR 2 DR3 DR4 DR 5 DR6 DR7 DR8 DR9/10

3.4 7.0 15.0 16.6 18.9 7.8 13.7 15.0 1.6 0.8

7.1 5.9 19.1 14.7 20.0 8.9 11.5 11.2 0.4 1.1

~227=

34.21

(7.3) (15.5) (17.2) (19.6) (8.2) (14.2) (15.5) (1.7) (0.8)

(6.4) (10.6) (15.8) (21.5) (9.6) (12.4) (12.1) (0.5) (1.2)

25.63

equivalent. The left-hand figure relates to Eurotransplant patients; the righthand figure to UK Transplant. Firstly, comparing donor reference frames, we note that DR gene frequencies differ markedly between UK Transplant and Eurotransplant donors (see, for example: DR1, DR3, DR4, DR5, DR6, DR8). The following genes: DR1, DR7, DR3 are plotted below the diagonal, whether for Eurotransplant or UK Transplant, registered or transplanted patients; DR3 transplantees in Eurotransplant are an exception. DR2 appears always above the diagonal; DR2 we have shown multifactorially is associated

138

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m

r0 I

m

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pozp,!suos AIq§!H

~i~

"~!~i .

~i

•

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L~

-~ ~'~ ~

CN m

j~ ]~

"! . . ~ I ~

I I ~mo ~

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~z!}!su~ Alqi!H (%) $ 3 1 0 N 3 ~ 0 3 ~ 3N39 HO

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139

-

with high sensitization. Figure 5.7 illustrates well the above discussion of DR6 whereas the plotting position of DR6 for UK registered patients and transplantees is close both to the diagonal and to the frequency in UK donors (top right-hand corner of DR6 outline), the Eurotransplant open circle, the plotting symbol for waiting patients, appears well above both diagonal and Eurotransplant donor square indicating positive association with high sensitization for patients on the Eurotransplant waiting list and increased DR6 frequency in the highly sensitized compared to Eurotransplant donors. The DR6 plotting position for transplanted patients in Eurotransplant does not echo the waiting list story, but corresponds more closely to the UK position. Gene frequencies for DR5 and DR4 differ between UK Transplant and Eurotransplant. Although there is UK excess of DR4 (DR4: 20% in unsensitized and highly sensitized waiting patients; 21% donors) compared to Eurotransplant (DR4: 15% for unsensitized and highly sensitized patients on the waiting list; 14% donors), the plotting positions for Eurotransplant and UK Transplant waiting li~ted and transplanted patients (see Figure 5.7) approach the top right-hand corner of the respective DR4 donor square outlines. Thus DR4 has similar population-specific gene frequency in donors, registry and grafted patients. The story is different for DR5, which is more frequent in uraemic patients than in donors. From Figure 5.7, first notice the UK deficit of DR5 (DR5: 10% in unsensitized and 11% highly sensitized waiting patients; 8 % donors) compared to Eurotransplant (DR5: 17% in unsensitized and highly sensitized waiting patients; 15% donors); see also Table 5.11 for comparison of frequencies in transplantees. And now observe that the plotting positions for DR5 are well outside Eurotransplant and UK donor outlines for DR5, so that besides the UK deficit of DR5 compared to Eurotransplant, we infer from comparison of donors and registry on transplanted renal patients that DR5 is more frequent in the registered patients and transplanted patients than in the donor populations. 8.3.2. Eurotransplant and UK Transplant: HLA-A. We now turn to Table 5.12 and Figure 5.8 to scrutinise and plot A locus normalized gene frequencies, as above for DR genes. The first observation is the increase in missing A gene frequency for highly sensitized compared to unsensitized patients which is evident from Hardy-Weinberg analysis of both the UK Transplant and Eurotransplant registry and transplanted databases. Donor HLA-A gene frequencies differ between UK Transplant and Eurotransplant in respect of A1, A9, A10, A28 and A11. The plotting symbols for AI and A2 lie below the diagonal, their gene frequency being less in the highly sensitized, except for Eurotransplant A1 transplantees. Moreover, the A2 plotting position for patients on the waiting list (open circle) is lower than for transplantees (solid square), most evidently

140 in U K T r a n s p l a n t where A 2 is u n d e r r e p r e s e n t e d on the waiting list ( p l o t t e d well within the U K d o n o r reference frame) a n d s o m e w h a t enriched a m o n g s t t r a n s p l a n t e e s ( p l o t t e d a b o v e d o n o r reference frame). I n E u r o t r a n s p l a n t , A19 is m o r e frequent in highly sensitized t h a n unsensitized patients a w a i t i n g transp l a n t a t i o n , whereas in U K T r a n s p l a n t A10 is enriched in highly sensitized waiting list patients. T h e ' r e g i s t r y p l o t t i n g p o s i t i o n s ( o p e n circles) for A19 ( E u r o t r a n s p l a n t ) ; a n d for A9, A10 a n d A28 ( U K T r a n s p l a n t ) lie well outside their respective d o n o r square outlines. H o w e v e r , these p o s i t i o n s are (1) n o t c o n f i r m e d in trar~splantees Table 5.12a. A locus: Hardy-Weinberg gene frequencies for Eurotransplant and UK Transplant

Eurotransplant (normalized to 100%) Donors: 4 5 6 9 %

Unsensitized: 2992 %

Highly sensitized: 636 %

unidentified A I A 2 A 3 A 9 A10 All A19 A28

.1 15.2 28.3 15.3 12.0 5.9 5.4 12.8 4.9

2.7 15.1 27.6 13.9 11.9 6.4 5.8 12.3 4.2

8.6 11.3 23.5 11.6 12.3 6.7 6.2 14.9 4.9

.~227=

46.63 p<0.01

Comment

personal communication: Joe d'Amaro

(15.2) (28.3) (15.2) (12.1) (5.9) (5.4) (12.9) (4.9)

(15.5) (28.4) (14.3) (12.2) (6.6) (6.0) (12.6) (4.3)

44.67 p<0.02

(12.4) (25.7) (12.7) (13.5) (7.3) (6.8) (16.3) (5.4)

45.95 p<0.02

UK Transport (normalized to 100%) Donors: 2 0 4 1 %

Unsensitized: 1115 %

Highly sensitized: 486 %

unidentified A I A 2 A 3 A 9 A10 All A19 A28

0.0 18.2 28.9 15.1 9.4 4.3 6.3 14.1 3.7

2.6 16.1 25.3 12.6 11.1 4.8 6.4 15.5 5.6

6.4 14.8 21.0 13.3 11.0 6.9 6.0 14.3 6.2

~(227 =

not cited

50.00 p<0.01

Comment

personal communication: Peter Klouda

(16.5) (26.0) (12.9) (11.4) (4.9) (6.6) (15.9) (5.7)

25.16

(15.8) (22.4) (14.2) (11.8) (7.4) (6.4) (15.3) (6.6)

141 Table 5.12b. A locus: Hardy Weinberg gene frequencies for transplanted HS/control recipients

Eurotransplant (normalized to 100%)

unidentified A 1 A 2 A 3 A 9 A10 All A19 A28

Typed, transplanted controls: 522* %

Typed, transplanted HS: 534 %

- 2.0 15.1 31.2 14.2 11.2 6.5 8.6 10.0 5.1

(14.8) (30.6) (14.0) (10.9) (6.4) (8.4) (9.8) (5.0)

3.2 16.0 27.5 15.4 12.6 5.1 4.7 11.2 4.3

*

43.89

~27=

Comment: Observed excess of A28 homozygotes: 2 Obs v. 0.3 Exp; ~2 = 9.0 and of A9,10:13 Obs v. 7.6 Exp; ~ = 3.8

(16.5) (28.4) (15.9) (13.0) (5.3) (4.9) (ll.6) (4.5) p <0.05

Observed excess A1,3:41 Obs v. 26.3 Exp; Z~2= 8.2 and of A2,28:21 Obs v. 12.6 Exp; z, ~ =

5.5

UK Transplant (normalized to 100%)

unidentified A 1 A 2 A 3 A 9 A10 All A19 A28 Z~7= Comment:

Typed, transplanted controls: 184" %

Typed, transplanted HS: 217 %

- 2.1 22.3 32.3 12.2 10.6 2.8 6.5 11.8 3.9

(21.9) (31.4) (11.9) (10.4) (2.7) (6.3) (11.6) (3.8)

3. I 16.6 30.1 15.9 7.2 3.3 5.9 13.6 4.5

*

37.98 p
(17.1) (31.1) (16.3) (7.4) (3.4) (6.1) (14.0) (4.6)

Observed excess of Al,10; 7 0 b s v. 2 Exp; Z~2= 9.1

* Estimate for missing gene frequency is negative; excess of heterozygotes.

142

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o

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•

•

E 8 i

i

.

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0

~ ~

pezp.!sues AIqB!H \

0

O~

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143 and (2) not consistent between registries; they are therefore unlikely to be specific for renal disease. Differential quality of typing could be implicated, however, via re-typing of transplanted patients.

9. Discussion

Homozygosity on the HLA-A or B, but not DR, locus is more likely in highly sensitized than unsensitized patients, whether they are on the waiting list or have been grafted. Sensitization status is conventionally assessed only against Class I antigens, no account being taken of anti DR antibodies. DR homozygosity would only be expected to be associated with sensitization assessed in a B cell assay. DR antigens act as a target for antibody and we have evidence that DR antigens are heterogeneous in their association with sensitization status and so may have differential resilience against antibody attack. The Class I homozygosity effect appears antigen non-specific. Were it antigen specific, comparison of observed and Hardy-Weinberg predicted phenotype frequencies would have shown enrichment of specific HLA-A or B homozygotes in the highly sensitized. Instead, we observed an increase in missing HLA-A and B gene frequencies in highly sensitized versus unsensitized recipients. Specific antigens in positive association with high sensitization amongst patients awaiting transplantation are DR2, B27, B14 and A28. Only the DR2 and B14 associations are confirmed in transplanted patients. Specific antigens which are more frequent in unsensitized than in highly sensitized patients, both on the waiting list and transplanted, are DR1, DR7, DR3, A2 and to a lesser extent A1. Although A2 is the most frequent HLAantigen, and could thereby have a greater potential for being matched, DR1, DR7 and DR3 have only moderate gene frequencies of the order of 10% to 15%. Matching potential is not just a function of gene frequency, however, and further analysis could usefully include as an extra covariate the patient's potential for receiving a beneficially matched graft (see Gilks et al 1987). Neither homozygosity nor antigen specific associations with sensitization were influenced by whether the patient was awaiting a first or regraft; this implies that associations with sensitization are not attributable to a function of immunogenicity since effects would have been exaggerated with regrafts - however, they are largely unchanged. Does this imply that specific antigens have regulatory effects? Concordant features between the analysis of patients on the waiting list and in the transplanted database are Class I homozygosity being associated with high sensitization; antigens DR1, DR7, DR3 and A2 being more frequent in unsensitized patients; and of those antigens in positive association with high

144 sensitization concordance for DR2 and B14. A feature which emerged from analysis of the transplanted database was DR homozygosity being more frequent in control transplantees, a similar association having been evident in the analysis of the UK Transplant waiting list. Only two divergent features emerge when we compare analyses of waiting list and transplanted databases. The first is a reduced affinity of B27 with highly sensitized patients in the transplanted database compared to patients on the waiting list, but even that comparison does not reach significance at the 5% level; moreover, it is only one of 20 comparisons which could have been made. The second is that A 11 is associated with being unsensitized amongst transplantees but features neutrally in the analysis of waiting list phenotypes, except for males awaiting first grafts where A 11 is more frequent in highly sensitized males awaiting first grafts! These paradoxical interactions are regarded as chance phenomena. Plots of Hardy-Weinberg estimated gene frequencies for UK Transplant and Eurotransplant served three purposes. First, they visualized separately for the two largest registries the features which were identified multifactorially in analysis of the combined waiting list and transplanted databases; secondly, they revealed disease-specific deviations from donor gene frequencies; and thirdly they showed international differences in gene frequency. Perturbations from the donor gene frequency, as for DR5, which are consistent for all patients (unsensitized or highly sensitized, waiting or transplanted) are probably disease related. International differences, as for DR4 which is more frequent in UK patients than in the Eurotransplant populations, are discerned by comparison of donor gene frequencies. Unlike DR5, the gene frequency for DR4 is similar in donors and uraemic patients. Sensitization phenomena are apparent when gene frequencies differ significantly between unsensitized and highly sensitized patients. For DR 1, which has an increased gene frequency in unsensitized compared to highly sensitized patients, it is of interest to note that both for Eurotransplant and UK Transplant the gene frequency of DR1 is similar in donors and unsensitized patients. This pattern is suggestive of DR 1 individuals being less likely to become highly sensitized. The pattern is less sure for DR7 because in Eurotransplant the gene frequency of DR7 in patients is reduced compared to donors but, nevertheless, it is reduced further in the highly sensitized than in the unsensitized. In UK Transplant, on the other hand, DR7 gene frequency is comparable for donors and unsensitized patients, and, like DR1, is reduced in the highly sensitized. The case of DR2 is different again. In UK Transplant and in Eurotransplant, donor gene frequency lies between the DR2 gene frequency for unsensitized and highly sensitized patients.

145 O n E u r o t r a n s p l a n t ' s waiting list we n o t e d that D R 6 was increased in the highly sensitized c o m p a r e d to b o t h E u r o t r a n s p l a n t d o n o r s a n d unsensitized waiting patients, b u t t r a n s p l a n t e d patients show a r e d u c t i o n in D R 6 gene frequency relative to donors. I n s u m m a r y , Class I homozygosity is more frequent in highly sensitized t h a n in unsensitized patients; specific antigens D R 2 a n d B27, a l o n g with B14 a n d A28, are associated with high sensitization in waiting or t r a n s p l a n t e d patients. A n t i g e n s D R 1 , D R 7 , D R 3 , A2 a n d to a lesser extent A1 are m o r e c o m m o n in unsensitized t h a n highly sensitized waiting a n d t r a n s p l a n t e d recipients. I n t e r n a tional differences in D R a n d A locus gene frequencies were m a n y . Diseaserelated p e r t u r b a t i o n s in gene frequency, for example for D R 5 , have been illustrated.

Appendix Table A5. la. Specificdeviations from Hardy-Weinberg expectation: for combined waiting list

DR locus

All 4 registries unsensitized: 5630 Z~4= 146.14

HS: 1753 ~4 = 73.38

Deviations

Obs

Exp

~(z

Obs

DR4 homoz. DR6 homoz. DR7 homoz. DR3, 4 DR4, 7 DR5, 6 DR5, I0 DR3 homoz. DR3, 6 DR5, 9 DR6, 10

340 155 217 298 153 214 20

303.2 203.0 168.3 242.6 184.6 178.6 12.5

4.5 11.3 14.1 12.7 5.4 7.0 4.4 87 37 8 5

Exp

64.9 51.2 3.7 2.2

Z2

7.5 4.0 4.8 3.5

A locus

All 4 registries unsensitized: 5630 Z~7= 32.57

HS: 1753 Z927= 50.51

Deviations

Obs

Obs

Exp

;t2

A10,28 A null,null A 2 homoz. A1,3 A2,3 A10,19

49 0

0 178 77 80 46

11.7 154.4 56.7 102.2 33.7

11.7 3.6 7.3 4.8 4.5

Exp 32.9 6.5

~2 7.8 6.5

146 Table A 5.lb.

B locus

All 4 registries unsensitized: 5630 Z~7o = 248.64 Highly significant

HS:1753 ~7o = 259.44 p ~ 0.02

Deviations

Obs

Exp

~(2

Obs

Exp

B35 homoz. B 5,12 B 7,13 B 7,41 B 8,15 B 8,35 B12,18 B13,15 B16,35 B16,40 B17,21 B17,53 B18,37 B21,27 B21,53 B22,27 B27,40 B35,40 B37,53 B40,53 B null,null B 7 homoz. B12 homoz. B22 homoz. B41 homoz. B5,7 B 5,35 B7,8 B 7,12 B 7,14 B 7,27 B 8,37 B12,13 B12,47 B13,40 B15,18 B16,18 B21,47 B22,40 B37,47

120 73 20 4 109 88 54 20 62 16 23 5 16 7 4 5 52 58 3 0 0

97.8 91.3 31.3 11.9 87.9 114.5 71.9 12.6 45.7 33.9 15.4 2.0 8.9 16.3 1.5 12.1 35.9 78.0 0.7 3.4 9.7

5.1 3.7 3.9 5.3 5.0 6.1 4.5 4.3 5.8 9.4 3.7 4.6 5.7 5.3 4.0 4.1 7.2 5.1 7.9 3.4 9.7

12

22.0

4.6

10 5

5.2 0.8

4.5 10.6

60 67 2 5 11 29 45 28 27 27 8 19 4 I1 18 15 2 13

44.3 50.9 7.6 2.2 24.1 19.0 34.3 44.9 13.2 18.5 4.2 9.7 1.2 5.2 11.7 9.0 0.4 6.3 0.1

5.6 5.1 4.1 3.7 7.1 5.3 3.4 6.3 14.5 3.9 3.5 8.9 6.7 6.4 3.4 4.0 7.7 7.3 5.7

1

X2

147

Table A5.2. Specific deviations from Hardy-Weinberg expectation: transplanted database

D R locus

Transplanted controls: 1014 Z3z3= 49.6

HS: 1063 Z~3 = 71.7 p ~ 0.02

Deviations

Obs

Obs

D R null,null D R 6 homoz. DR2,8 DR4,6 DR6,9/10

0

Exp

;t 2

5.1

5.1 13 19 49 8

Exp

28.3 9.0 36.6 2.7

~(2

8.3 11.1 4.2 10.7

A locus

Transplanted controls: 1014 ;~27 = 38.67

HS: 1063 Z227 = 54.99 p ~ 0.02

Deviations

Obs

Exp

Z2

Obs

Exp

~2

A10 homoz. A 9,11 A 2 homoz. A28 homoz. A 1, 2 A 1, 3 A 1,I0 A 2,28

5 9

2.2 16.6

3.7 3.4 129 1 72 65 26 38

104.9 6.0 90.7 47.8 14.0 26.3

5.5 4.2 3.8 6.2 10.2 5.2

Table A5.3a. Specific deviations from Hardy-Weinberg D R expectation: Eurotransplant and U K Transplant waiting lists

Eurotransplant

Registered

D R locus ~5 =

Unsensitized: 2992 76.88

Deviations

Obs

Exp

~2

DR DR DR DR DR DR DR DR

85 144 69 78 140 68

64.5 124.2 101.4 58.5 105.2 85.0

6.5 3.1 10.3 6.5 11.5 3.4

1 homoz. 5 homoz. 6 homoz. 7 homoz. 3,4 4,7 2 homoz. 6,7

HS: 636 42.18 Obs

Exp

X2

37 27

27.6 18.6

3.2 3.8

148 Table A.3a. (continued)

UK Transplant

Registered

DR locus ~(~

Unsensitized: 1115 62.35

Deviations

Obs

Exp

~2

DR DR DR DR DR DR

67 26 19 52 48

51.3 18.4 34.2 40.9 34.0

4.8 3.1 6.7 3.0 5.7

2 homoz. 1,6 2,6 3,6 4,5 5,9/10

HS: 486 27.59

Obs

Exp

~2

3

1.0

3.8

Table A5.3b. Specific deviations from Hardy-Weinberg A expectation: Eurotransplant and UK Transplant waiting lists

Eurotransplant

Registered

A locus ;t~27=

Donors: 4569 46.63

Deviations

Obs

Exp

;t ~

A 1 homoz. A 9 homoz. A 2,28 A10,28 A 1,28 AI0 homoz. A11 homoz. A null,null

72 41

107 68

11.4 10.7

UK Transplant

Registered

A locus Z~7 =

Donors: 2041 Not cited

Deviations

Obs

A A 1,2 1 homoz, A19 homoz. A11 homoz.

Exp

t not cited

Unsensitized: 2992 44.67

~2

Highly sensistized: 636 45.95

Obs

Exp

.~2

Obs

Exp

~2

49 29

69.9 16.1

6.3 10.3

22

14.7

3.6

2 4 4 0

7.1 10.1 9.2 4.7

3.7 3.7 2.9 4.7

Unsensitized: 1115 50.00

Highly sensitized: 486 25.16

Obs

Exp

Obs

74 57 21 15

91.1 38.4 35.8 8.2

g2 3.2 9.1 6.1 5.6

11

Exp

~2

5.6

5.2

6. Transplantation Rates

1. Introduction Sensitized patients have less chance of receiving a transplant because they are more likely to be positive in the cross-match test. Therefore sensitized patients accumulate on waiting lists and non-sensitized patients are transplanted preferentially. The longer a patient is waiting for a transplant, the higher the chance of complications, perhaps even death. Our aim in this study was to Calculate for different registries the transplantation rates associated with each of four levels of sensitization (unsensitized, 1-50% , 51-80% and more than 80% peak reaction frequency) and to offset these transplantation rates against the rate at which highly sensitized patients are generated. Crude estimation of transplantation rates - by counting the number of transplants performed during a fixed period and expressing them as a daily rate of transplantation per 1,000 patients waiting at the start - can be improved upon by taking into account changes in the composition of the waiting list occasioned by new registrations, deaths from the waiting list, deregistration for reasons other than death or transplantation, and any transitions between sensitization levels which occur during the observation period.

2. Study method Six registries (Eurotransplant, UK Transplant, North Italy Transplant, Scandia Transplant, Luso Transplant and Swiss Transplant) together with collaborating centres in France and Spain took part in the Council of Europe study of transplantation rates. All transactions on waiting lists at these European registries were documented by sensitization level during a 49 day observation period (50 days for Eurotransplant), starting on 7 April 1986 and terminating on 25 May 1986. Tally charts, illustrated in Figure 6.1, were used to record the

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151 day to day transactions in each sensitization category and were forwarded by the registries to the MRC Biostatistics Unit at the end of the seven week period. In addition, transactions were colour-annotated on a printout of the registry's 7 April 1986 waiting list; this printout and magnetic tapes of the registry's waiting list for 7 April and 26 May 1986 were forwarded also. Because of the variety of transactions which may occur for an individual patient during a seven week observation period - new registration, change of sensitization status, temporary deregistration, reregistration, transplantation - comparison of initial and final waiting lists would have been inadequate as a cross-check on correct completion of the tally sheets. Moreover, in the case of UK Transplant, France and Spain, no initial tape was available anyway. Eurotransplant did not document transactions daily but supplied transplant totals for a 50 day period ending with 26 May 1986 and documented also deregistrations and deaths on the waiting list. Transplant totals were supplied separately by Eurotransplant for patients awaiting a first or regraft, thus extending usefully the original study design. Table 6.1 summarises the registry waiting lists for 7 April 1986 according to sensitization status. Registries vary in the upper limit for peak reaction frequency which qualifies as sensitization level 3. With the exception of Eurotransplant and UK Transplant, registries variously precode the peak reaction frequency of patients on their renal waiting lists. Precoding prevented standardization across registries. In North Italy Transplant, for example, sensitization level 3 represents patients whose peak reaction frequency is 51-85%, whereas in Swiss Transplant level 3 sensitization describes patients whose peak reaction frequency is 51-75%. Also variable between registries is the designation: sensitization status not recorded. In UK Transplant, 116 patients of whom 62 were transplanted in the 7 week observation period had unknown sensitization status; the high transplantation rate in this subgroup of patients suggests that they were genuinely unsensitized and/or that registration coincided with transplantation. Accordingly, in UK Transplant, and also in Luso Transplant, patients whose sensitization status was not recorded have been analysed as unsensitized. In North Italy Transplant, by contrast, all re- or new registrations on the waiting list are designated: sensitization status not recorded; presumably until there is an opportunity to check antibody status. Accordingly, for North Italy and other registries which adopt a similar practice, patients whose sensitization status was not recorded were ignored in the analysis of transplantation rates. A third set of registries, which includes Eurotransplant and Scandia Transplant, has a low proportion of patients in whom sensitization status is not known, but who are fully registered and may be offered transplants. The analysis policy has been that such patients and their corresponding transplants are excluded from all calculation.

152 Table 6.1.

Sensitization status

Unsensitized

1-50%

Level 3 (upper limit)

Level 4

Remarks

Eurotransplant

3033

2177

508(80%)

651

339 not recorded, 74 'positive'; ignored at analysis, totalled 10 transplants

UKTransplant

1339 1455

966

312(80%)

456

116 not recorded; included at analysis as unsensitized, totalled 62 transplants

N Italy Transplant

1034

365

137(85%)

165

163 not recorded, all re/new registrations; ignored at analysis, totalled zero transplants

Scandia Transplant

679

302

43(90%)

124

I not recorded; ignored at analysis, totalled zero transplants

France (6 centres)

802

533

256(80%)

337

32 not recorded; ignored at analysis, totalled zero transplants

Spain ( 1 centre)

381

364

61(80%)

71

61 not recorded, new registrations enter this category only; ignored at analysis, totalled zero transplants

Luso Transplant

138 195

534

57(75%)

38

57 not recorded, included in analysis as unsensitized, totalled 3 transplants

Swiss Transplant

92

109

37(75%)

85

2 not recorded; ignored at analysis, totalled zero transplants

153 3. Transplantation rates by registry and sensitization level Table 6.2 records for each registry the observed number of transplants (O) at each sensitization level during the 7 week study period. Also shown for each registry are the expected numbers of transplants (E), taking account of daily changes in waiting list composition, and assuming that transplants are equally distributed over sensitization categories. The ratio O/E is the actual or observed number of transplants divided by number expected if transplantation rates were identical between sensitization categories. Thus, for UK Transplant 153 unsensitized patients were transplanted during the 49 day observation period when only 113.8 transplants would have been expected, assuming equal transplantation rates (O/E = 153/113.8 = 1.34) so that the ratio of observed to expected transplants for unsensitized recipients exceeds 1. Logrank comparison of transplant rates results in a ;t 2 statistic on three degrees of freedom, for which the 5% and 1% critical values are respectively 7.9 and 11.4. Except for Spain and Luso Transplant, in which low expected numbers of transplants ( < 2.5) make the test statistic unreliable anyway, there are significant differences in transplant rates between sensitization levels - with fewer than expected transplants in the highly sensitized, level 4 patients. Except for Eurotransplant, in all registries whose transplantation rates vary with sensitization level, the major variation is explained by a linear trend: decreasing transplantation rate with increased sensitization level. Figure 6.2 illustrates by registry the daily transplant rates per 1,000 patients waiting at each level of sensitization. Eurotransplant has the most favourable transplantation rate for highly sensitized patients but ranks centrally in respect of the unsensitized. Figure 6.2 shows also the COMPOSITE sensitization-specific rates for all registries combined. Whereas there are 1.50 transplants per day per 1,000 unsensitized patients on the COMPOSITE waiting list, the daily transplant rates are 0.86 and 0.90 per 1,000 patients waiting in sensitization levels 2 (1-50% peak reaction frequency) and 3 respectively, with only 0.56 transplants per day per 1,000 highly sensitized cases. The COMPOSITE analysis of all transplants (see Table 6.3), while confirming a decreased transplant rate with increasing level of sensitization, highlights significant non-linearity, insofar as the difference between the ;t 2 statistic for trend (76.50) and the overall Z3~ (93.35) is itself highly significant, (Z~2 = 16.85, p = 0.001). The important non-linearity in transplant rates is accounted for by the relatively favourable transplant rate for patients waiting in sensitization level 3, their rate being at least as good as for level 2 patients, whose peak reaction frequency is 1-50%. Figure 6.2 shows that the overall pattern is evident in several individual registries, notably: Eurotransplant, Scandia Transplant, North Italy Transplant and Swiss Transplant.

154 Table 6.2. Transplants performed during a 49-day observation period (Eurotransplant: 50 days)

Sensitization status

Waiting

Transplanted

Comparison of Tx rates

n

Obs

Exp

(O/E)

EUROTRANSPLANT unsensitized 3033 1 50% 2177 51-80% 508 81+ % 651

174 94 36 31

159.5 114.5 26.7 34.2

1.09 0.82 1.35 0.91

8.51

0.44

1.15 0.86 1.42 0.95

UK TRANSPLANT unsensitized 1455 1 5O% 966 51-80% 312 81 + % 456

153 71 10 11

113.8 73.2 23.5 34.4

1.34 0.97 0.42 0.32

37.32

36.33

2.07 1.50 0.66 0.50

Z3~

Daily transplant rate per 1000 waiting

Z~2trend

N I T A L Y T R A N S P L A N T (includes 14 transplants performed outwith N. Italy) unsensitized 1034 43 33.3 1.29 9.02 4.76 0.87 1 50% 365 5 11.9 0.42 0.28 51-85% 137 5 4.5 1.12 0.76 86+ % 165 2 5.4 0.37 0.25 SCANDIA TRANSPLANT unsensitized 686 70 1-50% 306 12 51 90% 45 3 91+ % 127 3

52.4 22.7 3.5 9.4

1.33 0.53 0.87 0.32

15.33

11.38

2.05 0.81 1.34 0.49

F R A N C E (6 centres) unsensitized 802 1-50% 533 51 80% 256 81 + % 337

88 25 3 3

49.6 32.8 15.8 20.8

1.77 0.76 0.19 0.14

57.11

50.82

2.24 0.96 0.24 0.18

SPAIN (1 centre) unsensitized 381 1-50% 364 51-90% 61 81 + % 71

13 7 1 1

9.5 9.2 1.6 1.8

1.34 0.76 0.64 0.56

2.36

1.83

0.71 0.40 0.33 0.29

LUSO T R A N S P L A N T unsensitized 195 1-50% 534 51 75% 57 76+ % 38

4 6 1 0

2.9 6.9 0.7 0.5

1.40 0.87 1.35. nil

1.15

0.63

0.34 0.23 0.36 nil

SWISS T R A N S P L A N T unsensitized 92 1-50% 109 51-75% 37 76+ % 85

20 6 3 2

8.5 10.6 3.6 8.4

2.36 0.57 0.84 0.24

22.65

15.11

4.90 1.17 1.73 0.49

155

5.0

SensitizationLevel UNSENSITIZED 1-50%

level levet 3 4

4"0

COMPOSITE Waiting List ~ arnendti=~l

',~ 3.0 ._

| - E~'~

~1~ ~t~

~ 2"0

TRANSACTIONS PER DAY : summary 1.0

EURO

16.

Per 16,000 ~ ! waitingpatients I

I

12. ;.';o°s.' UKTS SWISS SCANDIA N.ItALY SPAIN FRANCE

8' 4-

LIISO DEATHS OTHER TRANSPLANTS OERE61STRATION

Figure 6.2. Daily transplant rate per 1000waiting

A major issue raised by the similarity of transplant rates for sensitization levels 2 and 3 is: does the regularity with which patients' sera are screened for reactivity depend on previous sensitization levels? If level 2 patients are screened less regularly than are level 3 patients, there is a greater chance that registered peak reaction frequency underestimates the maximum that the level 2 patient truly has attained; correspondingly, with more frequent screening, registered peak reaction frequency outstrips the average serum reactivity of the individual more and more. Table 6.4 explores two comparisons of observed and postulated transplants taking account of eligibility based on a negative crossmatch test.

156 Table 6.3. R e g i s t r i e s ' p o o l e d w a i t i n g lists: all t r a n s p l a n t s Sensitization status

Waiting

Transplanted

n

Osum

Esum

S U M M E D OVER ALL REGISTRIES unsensitized 7678 565 424.8 1-50% 5354 226 296.2 level 3 1413 62 78.2 level 4 1930 53 106.8 16375 906 906.0 _

_

Comparison of Tx rates (O/E)

~32

~(~2trend

1.33 0.76 0.79 0.50

93,35

76.50

Daily transplant rate per 1000 waiting

1.50 0.86 0.90 0.56

3.1 Postulatel: systematic underestimation of peak reaction frequency for patients registered as having 1-50% peak reaction frequency In the first set of postulates P1, patients whose sensitization level is 2 are assumed all to be maximally sensitized within that level, that is against 50% of donors - corresponding to systematic underestimation of their peak reaction frequency by registered sensitization level. Moreover, level 3 patients are assumed to be minimally sensitized, that is against 51% of donors -because more frequent monitoring of reaction frequency can only increase the registered peak without there necessarily being a change in the patient's average reactivity. Likewise, level 4 patients are assumed all to be minimally sensitized within that category, that is against 81% of donors. The final Pi postulate is that all cross-match negative patients have the same chance of being transplanted which is to ignore matching criteria. For the combined registries, the P~ postulates would mean that an estimated 2,677 cross-match negative sensitization level 2 patients are awaiting transplantation, and only 367 out of the 1,930 level 4 registered patients are expected to be cross-match negative for any particular donor. Thus the cross-match negative waiting list totals 11,414 under postulates P~ of whom 906 were transplanted. Assuming that equal transplant rates apply to all cross-match negative patients, we would expect 609.5 transplants to have occurred to unsensitized recipients and 29.1 postulated transplants in sensitization level 4. Overall comparison of the observed and Pi postulated transplants results in a Z2 statistic on three degrees of freedom of 24.65, most of which is accounted for by a linear trend towards increasing rate of transplantation with greater levels of sensitization, so that far from being disadvantaged, amongst those who are cross-match negative the highly sensitized patients have a transplant advantage. Such an advantage could accrue because several registry schemes for highly sensitized patients (see Chapter 9) give them priority in the organ exchange hierarchy and ignore tissue matching except insofar as better class I matching is achieved in highly sensitized patients anyway in virtue of the cross-match test (see Chapter 7).

157

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158 3.2. Postulate2: Eurotransplant medians inferred for different sensitization

levels A different set of P2 postulates displayed in Table 6.4 ascribes to each sensitizatiosa level the median peak reaction frequency which was registered at that level in Eurotransplant. For example, the median peak reaction frequency for Eurotransplant level 2 patients was 13%. Assuming that all sera are tested against all donors, our P2 postulate is that 87% of the patients waiting in sensitization level 2 would be cross-match negative for a given donor. Similarly, the median peak reaction frequency was 65% for Eurotransplant level 3 patients and 95% for sensitization level 4 Eurotransplant patients. These median peak reaction frequencies are postulated for the COMPOSITE waiting list and result, for example, in an estimated 97 cross-match negative sensitization level 4 patients awaiting transplant. Comparison of observed and P2 postulated transplant numbers points to a significant shortfall of transplants for patients who are registered with peak reaction frequency 1-50% and favouring of patients whose registered peak reaction frequency exceeds 50%, particularly if they are highly sensitized, as in sensitization level 4, for whom only 6.8 transplants would have been expected under postulates Pz, whereas 53 were in fact performed. Both sets of postulates leading to theoretical transplant rates based on cross-match eligibility point to the success of measures designed to favour transplants in highly sensitized patients. Only the more radical scenario PI which postulates that sensitization status is underestimated for patients whose peak reaction frequency is registered between 1 and 50% and that patients who are registered as sensitization level 3 or 4 have lower typical reaction frequencies - goes some way to explaining the similar transplant rates in sensitization levels 2 and 3. The P1 scenario is all the more credible in view of the haphazard monitoring of serum reactivity by even well established laboratories, as described in Chapter 3, and because a common practice is to perform the crossmatch test on peak and current sera only.

3.3. Transplantation rates forfirst versus regrafts: Eurotransplant Eurotransplant enumerated first and retransplants separately during their 50 day observation period and so Table 6.5 compares the daily transplantation rate per 1,000 Eurotransplant patients awaiting first graft with the corresponding rates per 1,000 awaiting retransplant. Comparison of first versus retransplant rate for each level of sensitization confirms their similarity; formal testing was achieved by a Mantel-Haenszel ~2 statistic (pooled across sensitization levels) on one degree of freedom.

159 Table 6.5. EUROTRANSPLANT: Ist transplants and retransplants performed during a 50-day observation period

Sensitization status

Waiting

Transplanted

n

Obs Exp

FIRST TRANSPLANTS unsensitized 2854 166 1-50% 1767 76 51 80% 342 22 81+ % 359 15 RE-TRANSPLANTS unsensitized 179 1-50% 410 51-80% 166 81+ % 292

8 18 14 16

Comparison of Tx rates

Daily transplant rate per 1000 waiting

(O/E)

~

~ trend

149.6 92.6 17.9 18.8

1.I1 0.82 1.23 0.80

6.48

1.89

1.16 0.86 1.29 0.84

9.6 21.9 8.9 15.6

0.84 0.82 1.58 1.03

3.92

0.86

0.89 0.88 1.69 1.10

Mantel-Haenszel X~ 2 = 0.15: comparison of 1st versus retransplant rates, stratified by sensitization

status.

4. Difficulties in assessing the rate of accumulation of highly sensitized patients: reaction frequency changes for new and registered patients The study design assumed that the rate of accumulation of highly sensitized patients could be calculated from knowing the number of new registrants entered into each sensitization category ~on the waiting list and the number of transitions between sensitization levels. Unfortuanately, our objective was frustrated by unforeseen differences in registry practices in respect of new registrations (see Table 6.1) and recorded changes in sensitization status (see Table 6.6 and Chapter 2: design faults). Table 6.1 notes that some registries enter all new patients into a category: "sensitization status not recorded" until they have tested serological reactivity whereas other registries enter new patients directly into the appropriate sensitization category. In designing the tally charts for this study, we had assumed that changes in sensitization status (peak reaction frequency) were always upwards to a higher reaction frequency but registries also modify reaction frequencies downwards, when notified, for example, that peak reaction frequency had been wrongly reported. Moreover, in North Italy Transplant, all changes in peak reaction frequency are made at specified intervals of 2 months; and this event was not covered by the study period! For five other registries, Table 6.6 shows registry activity in relation to changes in sensitization status. In each of these five registries, the rates of change in reaction frequency vary with sensitization level

160 Table 6.6. Changes in sensitization status ( % R F ) during a 49 day observation period Sensitization status

Waiting

Changes in sensitization status

n

Obs Exp

Comparison of changes

(O/E)

UK TRANSPLANT unsensitized 1455 1-50% 966 51-80% 312 81+ % 456

71 41 13 9

65.3 1.18 41.7 0.98 13.4 0.97 19.6 0.46

SPAIN (1 centre) unsensitized 381 1-50% 364 51-80% 61 81+ % 71

14 74 28 28

61.7 60.3 10.3 11.8

;(23

Daily rate of change in R F per 1000 waiting

~l2 trend

7.88

7.14

1.04 0.87 0.86 0.41

0.23 1.23 2.73 2.38

93.10

84.39

0.77 4.18 9.34 8.12

30 123 20 12

46.3 0.65 117.8 1.04 12.6 1.59 8.3 1.44

11.90

10.59

2.81 4.70 7.14 6.51

8 12 17 4

17.2 0.47 11.3 1.07 5.4 3.13 7.1 0.56

30.93

4.09

0.20 0.46 1.36 0.24

SCANDIA TRANSPLANT unsensitized 686 4 1-50% 306 14 51-90% 45 4 9l+ % 127 0

13.2 0.30 5.7 2.47 0.9 4.64 2.3 nil

32.33

LUSO T R A N S P L A N T unsensitized 195 1 50% 534 51-75% 57 76+ % 38 F R A N C E (6 centres) unsensitized 802 1-50% 533 51-80% 256 81+ % 377

2.85

0.12 0.94 1.79 nil

but the variation is different from registry to registry so that a single summary across registries is inappropriate. In UK Transplant, for example, fewer changes in peak reaction frequency are made the more highly sensitized the patient becomes. UK Transplant monitors peak reaction frequency and, of course, the higher the previous sensitization level, the less scope for upward change there is, while at the other end of the spectrum, frequent updates are expected for newly registered, lowly sensitized patients. Spain and Luso Transplant share a different, common pattern which is: the more frequent changes in reaction frequency, the more highly sensitized the recipient becomes. This typifies registries which seek to transplant peak positive, currently lowly sensitized recipients. France and Scandia Transplant

161 exhibit a third pattern with more frequent changes in reaction frequency in patients who are registered in sensitization levels 2 and 3 than for patients who are either unsensitized or are highly sensitized. Thus, diverse registry practices in respect of new registrations and the handling of changes in reaction frequency mean that the rate of generation of highly sensitized patients cannot be directly estimated: a revised study design would concentrate on the transitions illustrated in Chapter 2 (design faults). A final commentary on registry activity in relation to changes in sensitization status is that the updating of reaction frequency is as frequent or more frequent an occurrence in some registries than is transplantation. Unless this activity is focused, as in specially designed studies to establish transplant windows, it may be more efficient to update peak reaction frequency at regular intervals and so encourage the more regular monitoring of serum reactivity in all recipients.

5. New registrants and re-registrations Inspection of the tally charts suggested that not all registries distinguished correctly between new registrations, re-registrations after failed graft and other re-registrations. This disquiet was confirmed when yet another registry practice was discovered whereby new registrations, as well as changes in antibody status, are scheduled, increasing registry efficiency. For these registries the study period may not have included the appropriate number of scheduled registration times. Thus, no C O M P O S I T E data on registrations are reported.

6. Deaths on the waiting list according to sensitization level Seventy-six deaths were observed during the study period over all registries combined (see Table 6.7). There was no significant heterogeneity in death rate according to sensitization status, the daily death rate being estimated at 0.09 per 1,000 registered patients. Table

6. 7. Registries' pooled waiting lists: deaths on the waiting fists

Sensitization status

Waiting

Deathson the waiting list

n

Osum

Esum

SUMMED OVER ALL REGISTRIES unsensitized 7678 44 36.7 1-50% 5354 21 24.5 level 3 1413 3 6.1 level 4 1930 8 8.7 16375 76 76.0 _

_

Comparison of death-rates (O/E)

~

1.20 0.86 0.49 0.91

3.58

Dailydeath-rate per 1000 waiting

0.12 0.08 0.04 0.08 0.09

162 Table 6.8. Registries' pooled waiting lists: deregistrations other than transplant or death

Sensitization status

Waiting

Other deregistrations

Comparison of registration rates

n

Osum

(O/E)

ZJ

1.07 0.86 0.89 1.13

2.02

Esum

SUMMED OVER ALL REGISTRIES unsensitized 7678 88 81.9 1-50% 5354 44 51.3 level 3 1413 12 13.5 level 4 1930 23 20.3 16375 167 167.0

Daily deregistration per 1000 waiting

0.23 0.17 0.17 0.24 0.21

7. De-registrations other than transplant or death according to sensitization level

Deregistrations from the waiting list for reasons other than transplant or death, for example, illness or vacation, were also accounted for by level of sensitization. Over all registries, 167 other deregistrations were notified at rates which did not differ by level of sensitization and are best summarised as a daily deregistration rate per 1,000 patients of 0.21 (see Table 6.8).

8. Discussion

The study successfully measured the relative rate of transplantation according to level of sensitization and showed that highly sensitized patients are transplanted much less frequently than their unsensitized counterparts in all European registries. Amongst cross-match negative patients, recipients who are highly sensitized have a transplant advantage in virtue of the various schemes which have been established throughout Europe to find transplants for these patients. The similarity of transplant rates for patients who are registered as having peak reaction frequency 1-50% or in sensitization level 3 points either to the mildly sensitized being underprivileged in respect of transplantation or, more likely, to the unreliability of peak reaction frequency as a measure of the typical sensitization status of the patient - unless all patients are systematically monitored to an extent which does not depend upon prior sensitization level. Although the actual rates of transplantation were measured, the rate of generation of highly sensitized patients could not be assessed owing to unforeseen differences in registry practices, in respect of the registration of new patients and amendments to peak sensitization status. The study should therefore be repeated in a modified form with special attention to the latter problem and it seems appropriate that the rate of generation of highly sensitized patients should be monitored in future on an annual basis.

7. Transplant Survival: Follow-up of Highly Sensitized and Control Renal Grafts Transplanted 1982 to 1985

1. Study design Eight European registries - Eurotransplant, France Transplant, Hispano and Luso Transplant, North Italy Transplant, Scandia Transplant, Swiss Transplant, UK Transplant - collaborated to establish a database of highly sensitized and control renal grafts. Each registry identified recipients who were transplanted between 1982 and 1985 and whose peak reaction frequency prior to the index graft was more than 80%. For each such graft, a control transplant was selected, matched for recipient sex, year of transplant, first versus regraft, and transplant centre. Controls for highly sensitized first grafts required to have peak reaction frequency of at most 10%. Controls for highly sensitized regrafts were eligible if peak reaction frequency prior to the index graft was at most 30%.

2. Realization Appendix I summarizes for each registry separately how far the design criteria were met. The percentage of unpaired grafts is acceptably small for all registries except UK Transplant. Until recently, peak sensitization status was successively overwritten on the UK database so that peak sensitization prior to an index transplant had to be ascertained from records held by individual renal units. In the UK, wrongly assigned highly sensitized grafts, those, for example, whose date of peak reaction frequency was after the date of the index transplant were excluded unless the renal unit or its tissue-typing laboratory confirmed that the patient was already highly sensitized prior to the index graft and supplied the appropriately dated peak reaction frequency. Some control grafts were also discovered to have been highly sensitized prior to the transplant of interest and were excluded. Rejected highly sensitized patients are a selected sub-group in virtue of our knowledge that subsequently they became highly sensitized and so were not routinely accepted as substitute controls.

164 Discordance within pairs - in respect of centre, year of transplant, graft number or recipient sex - ranged from minimal in Eurotransplant to the majority (72%) in Scandia Transplant. Failure to match for year of transplant accounted for most disagreements within pairs. All registries satisfied keenly the definition of high sensitization; but the definition of control grafts was contravened more often - in 16%, 14% and 10% of Scandia Transplant, Swiss Transplant and UK Transplant pairs, respectively. Ischaemia time was unknown for the majority of patients from France Transplant but was well documented in Eurotransplant and North Italy Transplant, the other registries being intermediate. The percentage of grafts in which donor or recipient DR type was unknown varied from a low of 2% (Eurotransplant and Hispano Transplant) to highs of 23% for UK Transplant and 69% in North Italy Transplant. The percentage of first grafts varied across registries, UK Transplant and Scandia Transplant each having 41% of grafts as first grafts whereas in North Italy and Spain, the majority (81% and 100% respectively) were first grafts. The database which comprised the lowest percentage of females was that of UK Transplant at 47% female grafts.

3. Risk factor report: first grafts and regrafts The tables in Appendix II characterize separately the Council of Europe database of first grafts and regrafts in respect of explanatory variables for graft survival and risk factors for high sensitization. Appendix III documents by registry selected explanatory variables for all grafts combined. 3.1. Transplant year We were alerted in Appendix I to discordance for year of transplant within highly sensitized/control pairs. This failure to match for year of transplant is evident in the UK Transplant patients and also in patients notified by Scandia Transplant (see Appendix III: Table A7.22) and has resulted in an excess of 1985 transplants amongst highly sensitized regrafts compared to control regrafts (see Table A7.1). 3.2. Recipient sex and pregnancy history The pairing by sex of highly sensitized and control patients took no account of pregnancy status for female recipients. In Table A7.2 we observe that for first grafts, but not regrafts, there is a highly significant excess of female recipients

165 with known pregnancy amongst highly sensitized first grafts compared to control first grafts (see also Chapter 4: Discussion). Pregnancy is not a significant risk factor for high sensitization at regraft, however. 3.3. Recipient age Age-distribution (see Table A7.3) is similar for highly sensitized and control recipients, more than half of them being aged 31-50 years. Whereas 19% of patients who had been transplanted for the first time were older than 50 years, only 13% of regrafted patients were over 50 years of age. Children, less than 16 years, made up 4% of the transplanted database. 3.4. Mismatching of HLA antigens Tables A7.4 to A7.6 compare HLA-A, B and DR mismatches between highly sensitized and control first and regrafts. For both, there is a highly significant trend towards better matching on A and B loci for highly sensitized recipients, whereas the distribution of DR mismatches does not depend on sensitization status. Appendix III Table A7.23 confirms registry by registry - with the exception of Scandia Transplant (;t2~ = 6.74, p < 0.05) - that DR mismatching is the same for highly sensitized and control recipients. From Table A7.23 we note that Eurotransplant and Spain achieve the highest proportion of recipients with zero DR mismatches (57% and 61% respectively). Table A7.7 documents beneficial/DR matching and Table A7.8 gives the numbers of B + DR mismatches. Beneficially matched grafts (so-called) have zero DR mismatches and at most one A + B mismatch. Beneficial matching was achieved in 24% of highly sensitized and 20% of control first grafts; the corresponding percentages for regrafts, 30% and 23%, emphasise the differential rate of beneficial matching between highly sensitized and control patients. The remaining non-beneficially matched grafts are shown subdivided by number of DR mismatches. Table A7.9 shows total number of A + B ÷ DR mismatches, again differing between highly sensitized and control grafts. 3.5. Antigen sharing Table A7.10 shows that highly sensitized first graft recipients have more shared antigens, that is A + B + D R antigens in common with their donors, than do first graft controls, but for regrafts this enrichment for shared antigens in the highly sensitized is not confirmed. Table A7.11 compares number of shared antigens at the separate HLA-A, B and DR loci between highly sensitized and control first and regrafts. More class

166 I antigens (HLA-A, B) are shared between recipient and donor when the recipient is highly sensitized, especially for first grafts but shown weakly also in regrafts. The number of shared DR antigens does not depend on sensitization status. 3.6. Recipient homozygosity Table A7.12 investigates recipient homozygosity on HLA-A, B and DR loci. For both the A and B loci there is significant increase in the incidence of homozygosity amongst highly sensitized compared to control recipients of either first or regraft, but there is no enrichment of homozygotes on the DR locus; DR homozygotes constitute about 24% of both highly sensitized and control patients. 3.7. Beneficial/DR matching subdivided by donor DR homozygosity Tables A7.13 and A7.14 relate to the 2014 (89%) grafts with DR typing of both donor and recipient. Besides the already familiar tale of better matching in highly sensitized recipients, another feature derived from Table A7.13 is that of the transplants performed with DR homozygous kidneys between 20% (regrafts) and 30% (first grafts) are mismatched for HLA-DR. More efficient organ exchange should reduce these proportions still further, so that a DR homozygous donor kidney would rarely be mismatched. Table A7.13 shows also that half of the recipients with zero A + B + DR mismatches have DR homozygous donors. 3.8. Selected HLA-DR antigens: DR1, DR2 and DR7 In Chapter 5, specific DR antigens were associated with sensitization status, DR1 and DR7 being more frequent in control recipients and DR2 in highly sensitized patients. Table A7.14 hints at the foregoing associations with recipient DR phenotype but shows no perturbation of the frequency of DR1 amongst donors for highly sensitized versus control recipients of either first or regrafts. 3.9. Cold ischaemia time Table A7.15 compares the distribution of ischaemia time between highly sensitized and control patients and gives no cause for concern about there being longer ischaemia time associated with highly sensitized recipients. Average cold ischaemia time is between 24 and 25 hours and about one fifth of kidneys have ischaemic times longer than 30 hours.

167 3.10. Positive crossmatches Table A7.16 documents significantly increased incidence both of B cell positivity and of unseparated lymphocytes (U) or T cell crossmatch positivity amongst highly sensitized recipients as opposed to control transplants, findings which are consistent for first and regrafts.

3.11. Cyclosporin A on day 1 and day I graft function Table A7.17 evidences no difference in Cyclosporin A usage on day 1 between highly sensitized and control patients. Comparison of combined first versus regrafts indicates a different pattern of usage by graft number with 36% of first grafts and 45% of regrafts known to have had Cyclosporin A on day 1. This observation cannot be interpreted necessarily as evidence of different immunosuppression policy between first and regrafts because the mix of transplant centres need not be the same for first and regrafts, thus confounding the picture. Indeed from Appendix III, Table A7.24 we see that immunosuppression details for day 1 were not recorded for 70% of French patients; that whereas two thirds of UK and Swiss patients received Cyclosporin A on day 1, the corresponding percentage is nearer one third in Eurotransplant. Cyclosporin A was used on day 1 for the majority (97%) of patients from North Italy but in a minority (15%) of Spanish patients. In Scandia Transplant, there appears to be a differential usage of Cyclosporin A between controls (42%) and highly sensitized recipients (79%) but the story is, of course, complicated by year of transplant which was significantly more recent for highly sensitized than control patients. Table A7.17 documents significantly higher incidences of day 1 non-function for highly sensitized compared to control first and regrafts. Day 1 non-function was recorded for 29% of highly sensitized first grafts and for 38% of highly sensitized regrafts compared to 22% and 29% in first and regraft controls. The final panel in Table A7.17 crossclassifies patients by Cyclosporin A on day 1 (CYA) and day 1 graft function (DAY 1). The two variables, when reported, are not significantly inter-dependent, the Mantel-Haenszel test of association (;tl2 = 1.40) between CYA (yes or no) and DAY 1 (yes or no) pooled across four strata - highly sensitized and control first and regrafts - being not statistically significant.

3.12. Blood transfusion Table A7.18 shows that the majority of patients (over 90%) have been transfused and gives only weak evidence (p = 0.07) of definitive transfusion being more common amongst highly sensitized first grafts (94%) versus first controls (90%).

168

3.13. Duration of previous graft and wait from previous graft failure to regraft Table A7.19 compares duration of previous graft between highly sensitized and control regrafted recipients. We note that 43% of controls retained their previous graft for at least a year compared with 29% of the highly sensitized. Table A7.20 shows a highly significant increase in the average waiting time between previous graft failure and regrafting for highly sensitized recipients as opposed to controls mean wait being of the order of 39 months for highly sensitized regrafts compared to 22 months for control regrafts. These estimates take no account of the waiting times of patients registered but still ungrafted, and so apply only to those whose wait has ended in retransplantation. 3.14. Subdivision of highly sensitized recipients by latest reaction frequency The final Table A7.21 in Appendix II subdivides highly sensitized patients according to the reaction frequency of their latest serum sample prior to the index transplant. Eighteen percent of first graft highly sensitized recipients are current negative, that is unsensitized, compared to only 10% of highly sensitized regrafts. Approximately one third of highly sensitized recipients, of both first and regrafts, remained highly sensitized in their latest serum sample prior to index graft. 4. Statistical method: stratified piece-wise proportional hazards ( = relative risks)

Multifactorial analysis of transplant survival is effected through stratified, piece-wise proportional hazards (or relative risks) models (see Gilks et al 1986, Gore et al 1987) which account simultaneously for the prognostic influence of several covariates. Such covariates include year of transplant, tissue matching, ischaemia time, whether the graft functions on the day of transplant, recipient sex, recipient or donor homozygosity, B cell positive crossmatch, duration of previous graft, wait from previous graft failure to current graft, and whether highly sensitized recipients whose latest reaction frequency prior to transplant was 0% are a privileged subgroup. Regression coefficients, estimated jointly, are reported on the natural logarithmic scale (written In, for logarithm to the base e) and sum to give a composite risk score for individual patients; the exponential of the risk score gives the relative risk for an individual compared to a patient who satisfies all the baseline characteristics. For example, a composite risk score of 0.5 corresponds to a relative risk of 1.65 (1.65 = exponential 0.5); a composite risk score of 0.2 transforms to a relative risk of 1.22 ( 1.22 = exponential 0.2), indicating a relative increase in hazard of 22% compared to the baseline patient.

169 W h a t does a relative risk o f 1.65 m e a n in terms o f 1 year t r a n s p l a n t survival? Algebraically, 1 year t r a n s p l a n t survival for the i n d i v i d u a l p a t i e n t whose relative risk is 1.65 equals the 1 year t r a n s p l a n t survival for the baseline individual raised to the power 1.65, that is to the power o f the relative risk. T a b l e 7.1 illustrates how increased relative risk t r a n s f o r m s to a reduction in t r a n s p l a n t survival, the r e d u c t i o n being n o n - l i n e a r b u t d e p e n d i n g u p o n the level of baseline survival. Similarly, a reduction in composite risk score, such as - 0 . 2 , t r a n s f o r m s to a reduced relative risk, n a m e l y 0.82 (0.82 = exponential - 0 . 2 ) , a n d increased t r a n s p l a n t survival.

Table 7.1. Relative risk: implications for transplant survival

Composite risk score for index patient (natural logarithmic scale)

Relative risk for index patient

InRR

RR

Transplant survival for BASELINE patient So(t)

Index patient [So(t)]~

I

N C R E

D

0.5

1.65

A

S E D

50% 60% 70%

~

80%

0.2

1.22

In RR

50% 60% 70% 80%

-~

32% 43% 56%

E C R E

69%

A

43% 54% 65% 76%

S E D S U R

V BASELINE

0.0

1.00

50%

-~

80%

D E C

-0.2

0.82

80%

50% 60% 70% 80%

-~

-.

In

50% 60% 70%

RR

80%

R

E A S E D

-0.4

0.67

50%

57% 66% 75% 83%

63% 71% 79% 86%

I N C R

E A S E D S U R

V

170 4.1. Stratification Stratification by registry is introduced to accommodate different (ie nonproportional) hazard patterns between registries. The database of 1291 first grafts was analysed as seven "registry" strata: UK Transplant, Eurotransplant, France Transplant, Scandia Transplant, Swiss Transplant, North Italy Transplant and Hispano/Luso Transplant. The Council of Europe database on regrafts comprises 966 grafts contributed by six registries as follows: UK Transplant, Eurotransplant, France Transplant, Scandia Transplant, Swiss Transplant and North Italy Transplant. The main analyses have considered first and regrafts separately, in acknowledgement that risk factors for transplant survival may differ according to whether the patient is receiving a first or subsequent graft. A complementary analysis of the entire database of 2257 grafts is presented from the standpoint that risk factors have essentially the same relative influence on first as on regrafts; this analysis is especially relevant to a common match grade policy being applied irrespective of graft number. Stratification for this complementary analysis is by registry and graft number (first versus regraft) giving 13 strata. We observe later that stratification by registry is more of a statistical nicety than a necessity, because inferences about other salient covariates are not affected by assuming proportional hazards between registries. 4.2. Piece-wise versus constant proportional hazards ( = constant relative risks) The assumption of constant relative risks across post-operative time is relaxed to allow relative risks to differ in each of several distinct post-transplant epochs. Comparison of regression coefficients across epochs allows us to judge whether indeed prognostic factors have a persistent or a transient effect on the risk of transplant failure. Epochs of follow-up were selected as 1 to 15 days, 16 to 101 days, 101 ÷ days for first grafts; each epoch comprising approximately onethird of the 410 transplant failures. Day 1 is the day of transplant. For regrafts the chosen time intervals were 1 to 8 days, 9 - 101 days and 101 ÷ days. Conveniently, the time intervals correspond roughly to one week, 2 weeks and 3 months post transplant. The respective databases for first and regrafts are limited in their power to assess piece-wise proportionality for all covariates and so the major emphasis was to monitor how the relative risks associated with high sensitization and HLA-mismatching changed with post-operative time. Nonetheless, final regression models are reported for transplant failure in two distinct epochs (up to 3 months post-trav.splant, which accounts for two thirds of observed failures, and subsequently with the latest reported failure occurring after 4 years).

171 4.3. Covariate structure The exploratory nature of the Council of Europe analysis is reflected in the chosen covariate structure (see Appendix IV) which makes maximal use of indicator variables allowing regression coefficients to be estimated for individual levels of prognostic factors. Thus, ischaemic time (ISC 1) is explored by estimating regression coefficients associated with 16-20 hours ischaemia, 21-25 hours, 26-30 hours, 31 or more hours ischaemia time versus BASELINE of less than 16 hours ischaemia and a regression coefficient is associated also with "ischaemia time not recorded". Linear regression (ISCH) of In relative risk of transplant failure against ischaemia time (with imputation of 24 hours for ischaemia time not known), or fitting a linear trend through ordered categories of ischaemia time (see Appendix IV) are other structural possibilities. Only by comparing different coding schemes for covariates can we be assured that nothing is lost in a final, simplified statistical model. In respect of tissue matching an extensive exploratory analysis has been undertaken to compare different matching schemes (see Appendix IV and Figure 7.1). The first three schemes (MMA, MMB, MMDR) simply fit indicator variables for one and two mismatches at each of the HLA-A, B and DR loci separately; and a fourth fits the foregoing six indicator variables simultaneously. The next set of schemes fits indicator variables for the number of B ÷ D R mismatches (MBDR); or (see Figure 7.1) indicator variables for the total number of A ÷ B + DR mismatches (MMS); or substitutes linear regression on the total number of A + B + DR mismatches (MMS trend). A third set of matching schemes addresses beneficial (zero HLA-DR mismatches and at most one A + B mismatch) versus non-beneficial matching fitted as a single indicator variable (BENE) or by fitting indicator variables for 100 ABDR mismatches, 010 ABDR mismatches and non-beneficial matching versus the BASELINE of 000 ABDR mismatches (BEN4), or by further subdivision of non-beneficial matching (see Figure 7.1) according to number of HLA-DR mismatches (BEN6). BEN6 distinguishes five levels of mismatching from the BASELINE of donor compatible for HLA-A, B and DR; alternatively a trend is assumed (BEN6 trend) through the ordered categories of beneficial/DR matching. Finally (see Figure 7.1) regression coefficients are fitted to distinguish all 27 varieties of HLA-mismatching, and alternatively of antigen sharing. Indeed, coding schemes for antigen sharing mirror those for antigen mismatches. Appendix IV lists the full set of matching and other covariates - their names, structure and description - which have been investigated on the Council of Europe database. It defines both the extent and the limits of our data exploration. Appendix IV includes three interactions with high sensitization which were of prior interest. They allow the effect of beneficial matching to differ between

172 TOTAL MISMATCHES A+B+DR Mismatches

Defined as

Called

Mismatches' at A B DR

Trend or Levels

1 2 ;3

0 1 2 3 4 5 6

27 VARIETIES OF MISMATCH

Baseline I II 6

4 5 6 7

IlI IV V vi

MMS TREND

MMS

0

indicator variables

Defined as

0

0

0

1

0

0

0

1 0

*

*

0

* *

* *

1 2

4 5 6 BEN 6 TREND

Called

III IV V

Mismatches at A B OR

Defined as

Called

1 0

1

1

0

IV

2

1 0

V vii vii1

0 1 2

0 0 0

1 1 1

IX x xI

0 1 2

1 1 1

1 1 1

X~l

0 1 2

2 2

1 1

2

1

xv XVl XVII

0 1 2

0 0 0

2 2 2

X]X XX

0 1 2

~ 1 1

2 2 2

2 2 2

2

BEN 6

*

0

0

Baseline

*

1

0

I

*

0

1

I

0 1 2

*

1

1

II

* *

2 0

0 2

II 11

* *

2 1

1 2

III 1II

*

2

2

4

•III

0

indicator variables

Levels

0

2

Defined as

B & DR MISMATCHES

l

II

2

I Baseline 1 I! 5

Baseline

0

Trend or Levels

1 2 3

0

0

020 1 2

B E N E F I C I A L / D R MATCHING Mismatches at A B DR

0

1 0 200

Levels

VI

2 2

X/II

26 indicator variables

xw

XVIII

XXI xxII

XXIII XXlV xxv XXVl

indicator variables

Called

MM27

IV MBDR >g = O, I o r 2 m i s m a t c h e s

Figure 7.1. Different coding schemes for HLA-mismatches.

highly sensitized a n d c o n t r o l recipients ( H C B E ) , p e r m i t sex to have a different influence ( n o t p r o v e n ) on t r a n s p l a n t survival in highly sensitized a n d c o n t r o l recipients ( H C M F ) , a n d a m o n g s t regrafts allow there to be a different p r o g n o s tic influence ( n o t p r o v e n ) a s s o c i a t e d with high sensitization a c c o r d i n g to

173 whether the previous graft failed within 3 months or not (HCP2). A fourth interaction, defined a posteriori, considered whether the improvement in graft survival in later transplant years was different between highly sensitized and control recipients (HCYR). 4.4. Testing interactions or differences between corresponding regression coefficients Besides the formal testing of specific interactions, as above, fitting regression models for transplant survival separately for highly sensitized and control recipients, or for first versus regrafts, constitutes weak (in virtue of the limited number of transplant failures) tests of generalized interaction whereby all covariates are allowed to have a different influence on transplant survival depending upon whether the recipient is highly sensitized or control, first or regraft. How, informally, do we compare corresponding regression coefficients - for example, for ischaemia time in highly sensitized versus control grafts? Answer: the difference between corresponding regression coefficients is assessed by computing an approximate 95% confidence interval (see Chapter 2), which runs from two standard errors below to two standard errors above the observed difference. For ischaemia time we compute: (isch.coeffHs -- isch. c o e f f c o n t r o l )

=

observed difference in regression coeff.

variance of the difference ( = sum of variances) = isch.seZns+isch.se~2ontro~ standard error of difference ( = x/variance)

= x/(isch.se~s q- isch.sec2ontro~)

95% confidence interval runs from: observed difference - 2 . s t a n d a r d error of difference to : observed difference + 2.standard error of difference ie (isch.coeffrts - isch.coeff~ontrol)+ 2*x/(isch.se~s + isch.se~o.t~o~) Remember that the observed difference between corresponding regression coefficients is an estimate only, and always qualified by a standard error. Regression coefficient divided by its standard error gives the z-score (see Chapter 2). Disparate z-scores, the one statistically significant the other not, may deceive us into believing that some clinically relevant phenomenon, rather than random variation, underlies the disparity. Formal tests of interaction, ie comparison of corresponding regression coefficients, are a necessary, but not sufficient, guard

174

against that deception. Other considerations include: 1) whether the subgroups were of prior interest to the investigators, 2) whether the subgroups were discovered by exploratory analysis and 3) whether there is corroboration from independent data sources.

5. Transplant survival: results

Graft failure or death with a functioning graft constitutes failure of the transplant. Table 7.2 summarizes transplant survival for highly sensitized versus control recipients o f first or regrafts. Sixty-eight percent o f highly sensitized first grafts ( n = 6 3 8 ) survive to one year c o m p a r e d to 77% one year survival for control first grafts (n = 653). Corresponding transplant survival rates at one year for regrafts (see Figure 7.2) are 59% and 74% in highly sensitized ( n = 527) and control recipients ( n = 4 3 9 ) . The relative risk o f transplant failure in the first week (regrafts) or two weeks (first grafts) post-transplant (see Figure 7.3) is doubled in highly sensitized versus control grafts. F o r example, 11% o f highly sensitized first grafts failed in the first two weeks post-transplant c o m p a r e d to 6% o f first graft controls. There is accelerated failure o f regrafts with 14% o f highly sensitized regrafts and 7% o f control regrafts failing in the first week post-transplant, equalling the two-week failure rates for first grafts. Table 7,2. Summary survival statistics

1st grafts

HS n=638

Control n=653

% Tx surviving at 3 months 1 year 3 years

76% 68% 58%

86% 77% 68%

% Tx failing on or before day 3 % Tx failing on or before day 8 % Tx failing on or before day 15

4% 8% 11%

3% 4% 6%

Regrafts

Control n=439

HS n= 527

% Tx surviving at 3 months 1 year 3 years

70% 59% 54%

82% 74% 65%

% Tx failing on or before day 3 % Tx failing on or before day 8 % Tx failing on or before day 15

11% 14% 18%

4% 7% 9%

Tx day = day 1

175 COUNCIL OF EUROPE : HS v CONTROL 100'

80>

1st GRAFTS 653 controls

=>60-

638 highly sensitized

n- 4 0 =~ 20-

0 o

0

200

400

600

800

1000

DAYS SINCE TRANSPLANTATION

COUNCIL OF EUROPE : HSv CONTROL lOO

L~iiiii!iiiiiiii~ iiiiiii

'

'

'

,

u

REGRAFTS 439 controls 527 highly sensitized

~ ~o i!i!!~!iiiiiiii!iiiiiiii!i!i~ii~ii~i!i!!'~ 2

v

i"':':+:':+:':':':':':':':'i ':':':':'''''''''"

0

I

I

I

1

200 400 600 800 1000 DAYS SINCE TRANSPLANTATION

Figure 7.2. Transp]ant survival

176

m

£ m

&

::~

•

- -

C & ~

N

~ ~ ~

,.~

-~ ~ ~ 0 ~ ~

~

~

~

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"~

~ ~

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~'-~ ~

~

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177

5.1. Time dependent penalty associated with high sensitization From Table 7.3 (for first grafts and regrafts respectively) we see in the coefficients for HSC (highly sensitized versus BASELINE = control) very significant In relative risks associated with high sensitization in the first week or two weeks post-transplant and continuing up to 3 months. In the third epoch from 101 days onwards the risk associated with high sensitization has dissipated. Figure 7.3 illustrates these finding in terms of relative risks of transplant failure, quoting for the second and third post-transplant epochs 95% confidence intervals for the risk of transplant failure in highly sensitized versus control recipients. Figure 7.3 also shows how closely the ratio of transplant failure rates in the first epoch (ratio=2) agrees with exponentiating the In relative risks estimated in the corresponding regression model: for regrafts In relative risk is 0.7, the exponential of which is 2. This link to the familiar reminds us of the simple notion which underlies piece-wise proportionality of risks. Table 7.4 summarizes for ALL grafts the transient nature of the increased risk in highly sensitized patients; the regression coefficient associated with high sensitization is.59 in the first 3 months (standard error = .09) and reduces to. 11 thereafter (95% confidence interval for the In relative risk regression coefficient from 3 months onwards being from -0.12 to ÷ 0.34). Figure 7.4 illustrates the foregoing In relative risks for ALL grafts; also shown in three distinct post-transplant epochs, separately for first and regrafts, are the natural logarithms of the relative risks given in Figure 7.3. Figure 7.4 conforms to the format which will be used to illustrate results in section 5.2 on model building. Notice that the scale is In relative risk; that the upper diagram (usually ALL grafts) identifies the BASELINE category which applies also in subset diagrams (for example, first versus regraft); that In relative risks which differ from zero ( = BASELINE In relative risk) at the 5% significance level are shown as shaded bars; standard errors are not plotted but can be derived from tabulated In relative risks and z-scores (see Chapter 2). 5.2. Model building: additions to background covariates In the following subsections different covariates are explored individually against a background which at least takes account of high sensitization, year of transplant (as indicator variables) and recipient sex, and may include other covariates also. Each table identifies the background covariates to which a new explanatory variable is being added. If the additional regression ~2 exceeds its 5% or 1% critical value then the exploratory analysis has identified a potential prognostic factor which in sections 5.3 and 5.4 is fitted jointly with the full set of other prognostic factors to judge its contribution multifactorially to the risk score for transplant survival: overall and in distinct epochs.

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181 1.0-

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( relative to Baseline )

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Figure 7.4. High Sensitization: In relative risk by post transplant epoch.

Covariates are explored for first and regrafts separately, and also for ALL grafts with stratification by graft number and registry (RERE) instead of simply by registry (REGS). Whereas diverse matching schemes are explored assuming both constant relative risks (epoch 1-1600 days) and in two distinct epochs (1-101 days and thereafter), initial investigation of other covariates does not extend to piece-wise proportionality. That extension is introduced for the final models in sections 5.3 and 5.4; then, multifactorial risk scores are developed not only for first versus regrafts and ALL grafts but also for highly sensitized grafts separately from control grafts. The only exploratory analysis which focuses on highly sensitized versus control grafts is our assessment of diverse matching schemes, giving initial insight to whether mismatching of HLA antigens penalizes transplant survival in highly sensitized as well as control grafts.

182 5.2.1. Tissue matching: H L A mismatches or antigen sharing (overall and in two distinct epochs). Number of mismatches was determined on broad specificities. Thus the following recipient/donor pair: Recipient Donor

A23 A24

A11 All

B 7 B51

B27 B52

DR1 DR1

DR3

A 9 All A 9 All

B 7 B 5

B27 B 5

DR1 DR1

DR3

has broad specificities: Recipient Donor accounting for likewise for

0 A mismatches 2 A antigens shared

1 B mismatch 0 B antigens shared

and and

1 DR mismatch; 1 DR antigen shared

If recipient or donor is not typed for HLA-DR, one DR mismatch is assumed. Tables 7.3 and 7.4 (for first grafts and regrafts and for ALL grafts) assess a variety of matching schemes having taken account already in the regression model of high sensitization, year of transplant and recipient sex (background covariates). Our discussion focuses on the entire follow-up period, although the tables also summarize the performance of the different matching schemes during the first 3 months of follow-up, and subsequently. Registry stratification is implemented throughout. Table 7.5, which compares matching policies for highly sensitized grafts separately from control grafts, allows the underlying hazard patterns to differ between registries and between first and regrafts from the same registry; likewise Table 7.4 uses stratification by graft number and registry (ie RERE). HLA-mismatches: first grafts. Both DR matching and beneficial matching contribute significantly to the explanation of first transplant survival (see Table 7.3a). The covariate BEN6 (see Figure 7.1) sub-divides non-beneficially matched recipients according to the number of DR mismatches; regression coefficients for BEN6 are jointly significant at about the 2% level and are illustrated in Figure 7.5. Since there is an order in the levels of beneficial/DR matching as defined by BEN6, an alternative model is to assume a linear trend through these levels. That a linear trend exists is confirmed by the regression Z2 for BEN6 trend being significant beyond the 1% level. Moreover, comparison of the trend Z~z (9.74) and the regression Z2 on 5 degrees of freedom (13.20) associated with categorical BEN6 suggests that the trend coefficient conveys most of the available information. Similarly, the total number of mismatches can be modelled (see Figure 7.1) by six indicator variables (MMS), giving ;~ of 19.83, or as a linear regression on the number of mismatches (MMS trend) with chi-squared on 1 degree of freedom of 7.37. Unlike the case of BEN6, there is

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185 a significant non-linearity in the regression on total number of mismatches. This can be appreciated by subtracting 7.37 from 19.83, giving 12.46 as regression ;t 2 (on 5 df) for non-linearity, which is significant at the 5% level. The 27 varieties of mismatch (see Figure 7.1) are associated with a Z2 of 35.21 on 26 degrees of freedom but this most complex matching scheme is not preferred to beneficial matching, DR matching or to beneficial/DR matching (BEN6), any of which conveys essential features of the 27 varieties of mismatch. For BEN6, for example, the information lost to simplification is assessed by subtracting 13.20 from 35.21, giving 22.01 as its regression Z2 on 21 degrees of freedom and is clearly not statistically significant. HLA-mismatches: regrafts. As for the first grafts, Table 7.3b shows significant merit for DR matching, beneficial matching or beneficial/DR matching, and also for mismatches on B + DR. Unlike the case of first grafts, a linear trend on the ordered levels of BEN6 does not adequately describe the coefficients estimated for the individual levels since the regression ;t~2 for nonlinearity, got by subtracting 12.41 from 24.48, is significant beyond the 5% level. From the In relative risk regression coefficients for BEN6 (see Table 7.3b and Figure 7.5) we infer privileged status for completely matched grafts and those with only one A mismatch (000 ÷ 100: ABDR mismatches) together with a stern penalty for 2 DR mismatches. For regrafts, indicator variables for the total number of mismatches (MMS) are inferior to beneficial/DR matching and fail to summarize adequately the 27 varieties of mismatch. This can be appreciated by comparing the information loss of 31.73 (p < 0.05) to critical Z~o values (see Chapter 2 for rule of thumb guide to use without tables). Moreover, the regression coefficients associated with total number of mismatches do not conform to a linear trend since the regression Z52for non-linearity, got by subtracting 3.78 from 15.41, is significant at the 5% level. Likewise B ÷ D R mismatching is inferior to beneficial/DR matching but is equivalent to beneficial matching (BENE). HLA-mismatches: A L L grafts. Utilizing the entire Council of Europe database, matching policies (see Table 7.4) which have the dual merit of 1) prognostic significance at the 0.1% level (DR matching, beneficial matching, beneficial/DR matching and indicator variables for the total number of mismatches (MMS) or for B + DR mismatches (MBDR)) and 2) summarizing adequately the 27 varieties of mismatch (beneficial/DR matching, indicator variables for the total number of mismatches) are reduced to beneficial/DR matching and separate indicators for total number of mismatches. Of these two, our preference is for beneficial/DR matching whose regression coefficients are consonant with a linear trend through the ordered levels of mismatching (see Figure 7.6), whereas regression on the total number of mismatches is non-linear (see Figures 7.7 and 7.8). Mismatching on B ÷ DR

186

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97

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HSC : Highly Sensitized versus Control ~ accounted for TR YR: Year o f transplant J

Figure 7.5.

Beneficial/DR matching:BEN6.

accounts less well for transplant failures in the first three months than do other schemes which involve also A mismatches. Two hundred and eighty two transplants failed in the second post-transplant epoch - sufficient for us to have a 50 : 50 chance of identifying as statistically significant In relative risks for HLA-mismatching of the order of 0.5. Implications for transplant survival when In relative risk is 0.5 are displayed in Table 7.1 and are of clear clinical relevance. There is sound evidence from significant regression Z2 in Table 7.4 that the penalties associated with HLA-mismatching persist beyond three months. Ln relative risk regression coefficients for

99

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TR YR : Year o f transplant accounted for ~¢ = O, 1,or 2 mismatches Figure 7.6. Beneficial/DR matching: BEN 6 trend.

beneficial/DR matching, given separately for distinct post-transplant epochs (up to 101 days and thereafter), show (see Figure 7.9) that the risks associated with mismatching equal that for high sensitization in the first three months; thereafter, when high sensitization contributes minimally up to the risk score 0.11, regression coefficients for HLA-mismatching hold up, at around 0.5. HLA-mismatches: highly sensitized grafts. Table 7.5a argues for beneficial matching, DR matching and beneficial/DR matching or B + DR matching for highly sensitized grafts, each scheme being associated with an overall regression ;(2 which is significant beyond the 1% level.

61

188 Beneficial/ 0 00 DR matching 9%

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Beneficial/DR

matching and 27 varieties of HLA-mismatch:

ALL

grafts.

The trend coefficient for beneficial/DR matching does not absorb fully the information conveyed by the separate levels of beneficial/DR matching, the regression Z4~ for non-linearity being 8.61 which is significant at the 10% (see Figure 7.6); there is information loss when BEN6 trend is compared to the 27 varieties of mismatch, amounting to 32.43 on 21 degrees of freedom, also significant at the 5% level. Inspecting regression coefficients for separate levels of beneficial/DR matching we notice a warning in them against two DR mismatches (In RR=0.93, s e = 0 . 2 0 and z-score of 4.56 compared to the BASELINE of zero ABDR mismatches); also seen in Figure 7.6 is the privileged status for fully matched (000) or at most one A mismatch (100) grafts. HLA-mismatches: control grafts. Table 7.5b gives only modest support to the need for tissue matching in lowly sensitized grafts. The message conveyed by the BEN6 coefficients is that only zero mismatching on all three loci significantly improves transplant survival (see Figure 7.6).

189 Total A,B,DR mismatches 0 (MMS) 9%

1

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3

22%

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• Total A+B+DR mismatches MM27 27 varieties of mismatch • ~e - 0.2 • se - 0.3 ~ se- 0.4 H S C : H i g h l y Sensitized versus C o n t r o l ] T R Y R : Year of transplant "| a c c o u n t e d for J

Figure 7.8. A + B + DR mismatches and 27 varieties of HLA-mismatch: ALL grafts. Antigen sharing. Numbers of shared antigens between donor and recipient on the three loci A, B, D R have been analysed as fully as antigen mismatches but, because antigen sharing is less insightful than antigen mismatches, Tables 7.3 to 7.5 condense the alternative analyses to the 27 varieties of antigen sharing (SH27) for ALL grafts and the entire follow-up interval (see Table 7.4); and, by epoch, (see Tables 7.3 to 7.5) to indicator variables for the number of shared antigens across loci (SHS). Typically the number of shared antigens (SHS) has a lower Z 2 than is associated with the number of mismatched antigens (MMS), except in respect of regrafts or highly sensitized recipients. For ALL grafts (see Table 7.4), the 27 varieties of antigen sharing contribute a Z 2 of 35.05 on 26 degrees of freedom whereas the 27 varieties of mismatch account for a higher Z2 of 53.84, also on 26 degrees of freedom.

190 RISK OF HIGH

•

SENSITIZATION

WITH

TIME

1.0EPOCH 0-101

P
101+ DAYS

HS

HS

0"5"

,,

-~ m

0

• Baseline = Control grafts

C ,-I

-05

RISK OF H L A -- M I S M A T C H I N G W I T H T I M E (BEN 6)

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..~ ,~ .n_- '

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-O.S 1

• Baseline = O O O A B DR mismatched grafts

HSC : Highly Sensitized versus Control~ T R Y R : Year o f transplant I accounted for * = O, I o r 2 mismatches

Figure 7.9. High Sensitization and beneficial/DR matching: by post transplant epoch.

5.2.2. Model building: high-low sensitization. Table 7.6 summarizes the comparison between two models: (a) which differentiates highly sensitized from control recipients (HSC) versus (b) which draws an additional comparison with controls, between formerly highly sensitized patients whose latest reaction frequency prior to grafting was 0% and highly sensitized recipients who are persistently sensitized (PKNG). The comparison between models (a) and (b) is summarized as chi-squared on 1 degree of freedom and in no instance - ALL grafts, first grafts or regrafts - do the data give significant support for drawing

191 Table 7.6. Peak = HS, latest reaction frequency prior to graft = 0% Model comparison

ALL

1st grafts

Regrafts

(a) HSC,TRYR,BEN6, ISCI,CYA,* versus (b) PKNG,TRYR,BEN6, ISC1,CYA,*

x~ = 0.08

~ = 0.90

~ = 0.52

F r o m model (b)

coeff,

z-score

coeff,

z-score

coeff,

z-score

PKNG:

0.41 0.45

2.82 5.82

0.25 0.43

1.28 4.02

0.56 0.41

2.50 3.43

HS, latest R F = 0 % HS, latest R F > 0%

* For A L L and Ist grafts For regrafts

* denotes P R E G * denotes F E M M , PRE2, W A I T

a distinction between highly sensitized recipients on the basis of recent disappearance of sensitization (see Figure 7.10). 5.2.3. Model building: pregnancy. Table 7.7 contrasts model (a) which distinguishes female from male recipients and model (b) which sub-divides female recipients according to pregnancy status: never pregnant, known pregnancy, pregnancy status unknown. Only in respect of first grafts is there modest support (p ~ 0.10) for model (b) versus model (a). The estimated regression coefficients accord privileged graft survival to females who have never been pregnant (see Figure 7.11). Interestingly, the risk factors report highlighted known pregnancy as a risk factor for high sensitization amongst first grafts but not regrafts, so that pregnancy may be not only a risk factor for high sensitization at first graft but also a hazard to the outcome of that graft. 5.2.4. Model building: ischaemia time. Ischaemia time was not recorded for 17% of grafts in the Council of Europe database. Accordingly, to permit regression on ischaemia time, imputation of 24 hours ischaemia has been made for ischaemia time not known (covariate ISCH). The alternative covariate structure (ISC1) estimates relative risks for different levels of known ischaemia time and estimates a regression coefficient also for the sub-group of grafts for which ischaemia time is not recorded (see Figure 7.12). Comparison in Table 7.8 of the regression coefficients for ischaemia time not recorded and the corresponding regression coefficients for 21-25 hours ischaemia versus 26-30 hours ischaemia confirms that imputation of 24 hours (median) for missing ischaemia time is not unreasonable. From Table 7.8 we infer modest penalties associated with longer ischaemia time (more than 25 hours) for regrafts and when all grafts are combined. Notice that this assessment of the influence of ischaemia

192

•

p
1-OA L L GRAFTS PEAK:

HS

CURR:

0%

HS >0% RF

0.5.~ m .~ m ¢ J

• B a s e l i n e = Control grafts

O 166

1092

999

Number of grafts -0.5

1st G R A F T S

REGRAFTS 1-0-

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HS

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HS

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51

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•

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0

653

Number of grafts -0.5

439

Number of grafts -0'5

TR Y R : Year of transplant t a c c o u n t e d for HLA - mismatching J i

Figure 7.10. Latest pre-transplant % Reaction Frequency.

time is made against the background of other explanatory variables: for highly sensitized versus control recipients, year of transplant, being female and beneficial matching. 5.2.5. Model building: transplant year. Comparison of model (a) in which the influence of year of transplant is conveyed by three indicator variables for 1983, '84, '85 versus 1982 and model (b) which postulates a linear trend of In relative risk against year of transplant is reported in Table 7.9. In every instance - ALL grafts, first or regrafts, highly sensitized or control grafts - the trend coefficient

193

Table 7. 7. Pregnancy Model comparison

ALL

1st grafts

Regrafts

(a) FEMM,HSC,TRYR, BENE versus (b) PREG,HSC,TRYR, BENE

~(~z= 0.94

~ = 4.58

~.~ = 1.28

From model (b)

coeff,

z-score

coeff,

z-score

coeff,

z-score

0.10 0.11 0.13

0.88 0.75 1.24

0.23 0.38 0.20

1.63 1.98 1.39

-0.11 -0.27 0.01

-0.58 -1.10 0.09

p ~ 0.10

PREG: f pregnancy YES f pregnancy NK male

is a n a d e q u a t e s u m m a r y o f the i m p r o v e m e n t in g r a f t s u r v i v a l in l a t e r years. T h e t r e n d t o w a r d s i m p r o v e d g r a f t s u r v i v a l is s i m i l a r f o r first a n d r e g r a f t s , a n d s i m i l a r also for h i g h l y sensitized v e r s u s c o n t r o l grafts. 5.2.6. Model building: INTERACTION of high sensitization and non-beneficial matching. T a b l e 7.10 a d d r e s s e s the q u e s t i o n : d o e s m a t c h i n g m a t t e r o n l y in h i g h l y sensitized grafts? Beneficial m a t c h i n g (000, 100, 010 A B D R m i s m a t c h e s )

Table 7.8. Ischaemia time Model comparison

ALL

Background HSC, TRYR, FEMM, BENE plus (a) ISCH: with imputation ;t2~ 6.15 of 24 hrs (b) ISC1 ;~ 7.80 From model (a) ISCH per 10 hrs

From model (b) ISCI 16-20 hrs 21-25 hrs 26-30 hrs 31 + hrs not recorded

coeff,

z-score

0.12

2.49

coeff, 0.02 0.15 0.28 0.32 0.18

1st grafts

Regrafts

0.72

7.37

4.73

6.62

coeff,

z-score

coeff,

z-score

0.06

0.85

0.20

2.74

z-score

coeff,

z-score

coeff,

z-score

0.10 0.99 1.74 2.00 1.14

-0.21 -0.05 0.13 0.11 0.13

-0.94 --0.23 0.59 0.50 0.63

0.24 0.36 0.45 0.57 0.25

0.99 1.56 1.82 2.29 0.98

194

•

1-0-

p<0"05 ( r e l a t i v e to B a s e l i n e )

ALL GRAFTS

O'5-

NK

PAROUS

o

I

]

• Baseline = nulliparous female ~

188

628

466

975

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1"0--

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PAROUS

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111

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158

I

NK

•

77]

564

167

Number of patients

HSC : Highly Sensitized versus Control TRYR : Year of transplant t HLA - mismatching

accounted for

Figure 7.11. Sex and Pregnancy history.

substitutes for tissue matching generally. Model (b) includes an additional term which allows the penalty associated with non-beneficial matching to be different for highly sensitized versus control grafts. The difference between models (a) and (b) is chi-squared on 1 degree of freedom. Only in respect of first grafts is there weak evidence (X~ = 3.56, p = 0.06) that matching matters only in highly sensitized patients. From the Council of Europe database, we do not find convincing support for the abandonment of matching even for lowly sensitized first grafts, albeit the advantage to them seems to come mainly from zero mismatched grafts.

195 5.2.7. Model building: recipient homozygosity. On registry waiting lists (see Chapter 5) there is an excess of HLA-A or B homozygotes amongst highly sensitized patients. In transplanted patients (see Table A7.11) there is likewise an excess of HLA-A or B homozygotes amongst the highly sensitized but similar proportions of HLA-DR homozygotes in highly sensitized and control transplantees. Table 7.11 investigates whether recipient homozygosity compromises transplant survival. Only in respect of HLA-DR homozygosity is there support at the 2% level for a modest increase in the risk of transplant failure associated with recipient HLA-DR homozygosity. Even with due allowance for

•

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31+ 26-30 ~

157

259

163

171

130

Number of grafts

-05 HSC : Highly Sensitized versus C o n t r o l ~ accounted for T R Y R : Year o f transplant H L A - matching

Figure 7.12a. IschaemiaTime(hours).

86

196

•

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( relative to Baseline )

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o ~.J

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26-30

31+

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Ischaemia trend

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HIGHLY SENSITIZED

CONTROLS I'O-

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197

212

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169

194

205

I

256

119

Number of grafts

Number of grafts -0"5

21 - 25

-05

TR YR : Year of transplan t ~ accounted for HLA - matching J

Figure 7.12b. Ischaemia Time (hours).

multiplicity of testing at three loci, the issue of recipient H L A - D R homozygosity being a handicap for transplant survival warrants further investigation. This is done in subsection 5.2.9 in which we analyse transplant survival for the 2014 recipients whose donors, as well as they, were typed for HLA-A, B and D R and so avoid imputation of one D R mismatch when either donor or recipient is not typed for HLA-DR. 5.2.8. Model building: recipient HLA-DR phenotype. In Chapter 5 we saw that antigens DR1 and DR7 are more frequent in the unsensitized and the opposite applies to DR2. Terasaki (1985) and Klouda (1986) have commented upon

197

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198 Table 7.10. INTERACTION of high sensitization and non-beneficial matching Model comparison

ALL

1st grafts

Regrafts

X21= 3.56 p ~ 0.06

;t~ = nil

(a) HSC,TRYR,FEMM,BENE versus

~2l = 1.67

(b) HSC,TRYR,FEMM,BENE,HCBE Covariates from model (b) relating to high sensitization and non-beneficial matching

HSC: highly sensitized BENE: non-beneficial HCBE: highly sensitized and non-beneficial match

coeff,

z-score

coeff,

z-score

coeff,

z-score

0.24 0.15 0.24

1.47 1.03 1.30

0.01 0.02 0.48

0.04 0.10 1.90

0.48 0.32 -0.01

1.97 1.39 -0.05

Table 7.11. Recipient homozygosity Model comparison

ALL

1st grafts

Regrafts

Background HSC,TRYR,FEMM,BENE plus (a) HOMA: YES

.~ 0.82

Z~2 0.56

Zl2 0.26

(b) HOMB: YES

0.14

1.16

0.49

(c) HOMD: YES

5.50

3.99

1.49

From model (c)

coeff,

z.score

coeff,

z-score

coeff,

z-score

recipient D R homozygote

0.20

2.38

0.23

2.03

0.15

1.24

Table 7.12. Recipient D R phenotype ALL

HS

Controls

Background (HSC),TRYR,FEMM,BEN6

~l~0 68.72

~92 36.73

~ 12.95

In addition

Zl2

RDRI RDR2 RDR7

1.33 1.14 0.01

coeff. -0.12 0.09 -0.01

Z~2 0.04 0.33 1.69

coeff. 0.03 0.06 0.16

Zl2 3.59 0.83 1.72

coeff. -0.30 0.12 -0.19

199 enhanced graft survival for DR1 recipients but took no account of possible confounding by sensitization status. The confounding possibility arises because DR1 is more frequent in unsensitized recipients who have better graft survival anyway in virtue of being unsensitized. We investigate the influence on transplant survival of recipient DR antigens - DR1, DR2 and DR7 - separately for control versus highly sensitized grafts against the background of transplant year, recipient sex and beneficial/DR matching (see Table 7.12). The only association is with DR1; only in respect of control grafts is there some evidence (p = 0.06) for DR1 recipients having privileged transplant survival. For ALL grafts, we do not discern a marked advantage for DR1 recipients. Is there evidence for a differential DR1 effect depending upon whether the recipient is highly or lowly sensitized? Although the regression coefficients for recipient DR 1 are not significantly different between controls ( -0.30, se = 0.16) and highly sensitized grafts (0.03, se=0.15), the 95% confidence interval for the DR1 differential (control versus highly sensitized) is wide and essentially negative. It runs from -0.77 to 0.10. The width of the confidence interval testifies to low power for the test of interaction; the location of the confidence interval, only just straddling zero, forces a "not proven" verdict on the issue of recipient DR1 having a differential effect on transplant survival depending upon sensitization status. 5.2.9. Recipients as well as donors fully typed: HLA-DR homozygosity (donor or recipient) and recipient HLA-DR phenotype revisited. Two thousand and fourteen (89%) of the 2257 grafts on the Council of Europe database of highly sensitized/control transplants were DR typed for both donor and recipient. This restricted database is used to reassess the influence on transplant survival of recipient DR homozygosity and phenotype; and to determine whether DR mismatches should be differentiated according to whether the donor is DR heterozygote or homozygote. Quality of DR typing. Table 7.13 shows that France, Scandia and Swiss Transplant have high rates of DR homozygosity (around 40%) compared to UK and Eurotransplant (around 20%). Recipient HLA-DR homozygosity and phenotype. Table 7.14 shows that after exclusion of grafts for which recipient or donor was untyped, the hazard previously associated with recipient DR homozygosity finds less support (ALL grafts: Z~ = 1.21), whereas the transplant survival advantage to lowly sensitized DRI recipients stays the course. Moreover the z-score for comparing the DR1 effect in controls versus highly sensitized recipients is 1.94, bordering on significance at the 5% level, and a platform for suggesting that recipients who are DR1 have privileged transplant survival provided that they have not become highly sensitized (see Figure 7.13).

2OO Table 7.13. Recipients (and donors) fully typed (n=2014): recipient homozygosity Registry

Total

A homozygote

B homozygote

DR homozygote

Euro France N Italy Scandia Spain Swiss UKTS

1050 211 20 160 68 125 380

192 (18%) 47 (22%) 5 (25%) 46 (29%) 13 (19%) 25 (20%) 72 (19%)

124 (12%) 34 (16%) 5 (25%) 30 (19%) 10 (15%) 22 (18%) 49 (13%)

194 (19%) 80 (38%) 8 (40%) 68 (43%) 17 (25%) 50 (40%) 94 (25%)

H L A - D R mismatches according to whether the donor is D R heterozygote or homozygote. Extra explanatory value ( A L L grafts: p < 0.05) is teased out by comparing beneficial/DR matching (BEN6) to beneficial/DR matching subdivided by donor H L A - D R homozygosity (BE11). Table 7.15 displays the In relative risk coefficients associated with 10 levels of beneficial/DR matching subdivided by donor D R homozygosity. The BASEL I N E is zero A, B, D R mismatches when the donor is D R heterozygote. Recipients who have zero A B D R mismatches with a D R heterozygous donor have significantly better transplant survival than do zero mismatched recipients whose donor is D R homozygous. At other levels of mismatching, the subdivision by donor D R homozygosity is unimpressive (see Figure 7.14). 5.2.10. Eurotransplant excluded: positive crossmatch. The Council of Europe follow-up questionnaire did not specify whether crossmatch results were to be given for the most recent serum, peak serum or historical sera (see Chapter 2: Design faults). Reporting practices will have varied between laboratories so that any apparent safety of a positive crossmatch result could not be particularized:

Table 7.14. 2014 recipients (and donors) fully typed: recipient HLA-DR homozygosityand phenotype ALL

HS

Controls

Background (HSC),TRYR,FEMM,BEN6

g~o 56.34

Z9~ 32.79

g~ 15.35

In addition

g~

coff.

RDR1 RDR2 RDR7

1.35 0.90 0.22

--0.12 0.08 -0.05

HOMD

1.21

0.10

~ 0.20 0.28 1.08

coeff, 0.06 0.06 0.13

g~ 4.62 0.64 2.27

coeff. -0.35 0.11 -0.22

201 1"0-

•

ALL GRAFTS P<0'05 ( relative t o Baseline )

0"5-

RDR1

RDR7 • Baseline

331

= non D R 1 / D R 1 recipients

411

Number of patients

HIGHLY SENSITIZED

CONTROLS

1"0-

1"0"

O'5 m

0.5RDR1

RDR7

RDR1

RDR7

-~ ~-

0

N'

.J

151

193

-0.5

180

I 218

-0! Number of patients

Number of patients

HSC : Highly sensitized versus Control ~ TRYR: Year of transplant ~ accounted for HLA matching Figure 7.13. Recipient DR1 or DR7: limited database (2014 recipients, and their donors fully typed).

for example to historical positive, current negative. Nor can any discerned penalty associated with positive crossmatch results be other than a generalized warning. Data on U or T and B cell crossmatches were not available from Eurotransplant; for the rest, Table A7.16 shows positive crossmatches to be many times more common in highly sensitized than control recipients. Accordingly the model sequence in Table 7.16 is for highly sensitized recipients only. Notice that

202

Table 7.15. 2014 recipients (and donors) fully typed: influence on transplant survival of donor DR homozygosity Model comparison

ALL n = 7914

HS n = 1041

Controls n = 973

~ = 10.18 p<0.10

Z~ = 10.07 p<0.10

(a) (HSC),TRYR,FEMM,BEN6 versus

~52= 12.59 p <0.05

(b) (HSC),TRYR,FEMM,BEll (ie BEN6 subdivided by donor DR homozygosity) From model (b) for

ALL 2014 recipients (and donors) fully typed

mismatches ABDR

donor HLA-DR

000

heterozygote

000

homozygote

I00 100 010

heterozygote

010

homozygote

.0

heterozygote

.0

homozygote

.

.

. .

.

.

.

homozygote heterozygote

1

heterozygote

1

homozygote

.2

heterozygote

coeff,

z-score

nil ( = BASELINE) 0.85 0.77 0.67 1.02 0.95 1.05 0.82 1.03 1.28 1.36

2.75 2.29 1.75 3.38 2.95 3.71 2.73 3.86 4.30 4.83

Table 7.16. Positive crossmatch: HIGHLY SENSITIZED RECIPIENTS, excluding Eurotransplant Model sequence

1st grafts 324 Tx

Regrafts 121 fail

295 Tx

146 fail

z-score

coeff (

%)

z-score

0.22 (10%) 0.36 ( 2 % )

0.72 0.50

0.12 (16%) -0.60 ( 3 % )

0.52 - 1.01

coeff(

z-score

coeff(

z-score

Background FEMM,TRYR,BENE coeff(

plus UTCL:

positive not done/recorded

%)

Background FEMM,TRYR,BENE PKTM,LATE plus BCEL:

positive not done/recorded

where % = % covariate frequency

%)

0.20 (15%) 0.22 (38%)

1.28 0.73

%)

0.48 (31%) 0.41 (37%)

2.10 1.83

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204 positive crossmatches are also significantly more frequent amongst regrafted than first graft highly sensitized recipients both for U or T cells and, more especially, B cells. B cell crossmatch was not done, or no result was recorded, for 40% of highly sensitized recipients. Amongst highly sensitized regrafts, B cell positive crossmatch is associated with significantly increased relative risk of transplant failure (In RR = 0.48, se = 0.23); the 95% confidence interval for In relative risk in highly sensitized first grafts runs from -0.11 to 0.51; it therefore does not exclude risks of clinical relevance. Certainly a test for interaction, comparing the B cell positive crossmatch risk between highly sensitized first and regrafts, does not dislodge the hypothesis of equivalent risks (see Figure 7.15). A more detailed follow-up, at least on the 295 highly sensitized regrafts, might be envisaged, in which B cell crossmatch results are requested separately for peak serum, current serum and any dated historic sera other than the two already mentioned. 5.2.11. Registry: to stratify or not? The lower panel in Table 7.17 compares for first grafts the estimated regression coefficients and corresponding standard errors for model (a) stratified by registry and for model (b) in which the underlying hazard patterns by registry are assumed to be proportional. Notice the robustness of the estimated coefficients irrespective of registry stratification or not. This being so, we revert to the upper panel in Table 7.17 and assume proportionality of hazards to make comparison of transplant survival between registries. First grafts show only modest differentials between registries, whereas regraft survival differs markedly between registries - with the best prospects being apparent within Eurotransplant and France Transplant (see Figure 7.16).

5.2.12. Model building: duration of previous graft and waiting time from previous graft failure to regraft. Regrafts enjoy a significant advantage if the recipient retained his/her previous graft for at least 3 months (PRE2); further refining categories for duration of previous graft, as shown by PRE6 in Table 7.18, does not contribute anything of additional note (;~] = 3.03). We observe that retention of the previous graft for more than one year seems particularly auspicious but choose a more liberal cut-off to avoid extreme data dependence. Table 7.19 shows that the longer a recipient has had to wait for regrafting (WAIT), the poorer is the outlook for transplant survival; patients who wait more than 5 years for a regraft may be most at risk (IWAIT). WAIT may be a proxy for degree of sensitization, for uncommon or unmatchable phenotype, for persistent high sensitization, or for recipient well-being. Indicator variables (IWAIT) were fitted to determine the appropriateness of linear regression on WAIT. The small additional ~2 for model (b) which

205

•

Positive U or T cell cross match

1.0-

_~ 0 ' 5 -

n-

P
HS

HS 1"0-

1st GRAFTS

POS 10%

NK 2%

31

5 Number of patients

0"5-

POS 16%

NK 3%

l--I

•

0

REGRAFTS

288

,

,

47 10 Number of patients

, 238

-0.5 • Baseline = Negative U/T cell cross match

Positive B cell cross match HS 1"0-

10-

1st GRAFTS

POS 15%

POS 31%

NK 38%

e-

NK 37%

0'5-

0"5.--

n-

HS REGRAFTS

0 50

• 150

124

0

N 97

•

~0

Number of patients

Number of patients

"0"5 • Baseline = Negative B cell cross match

T R Y R : year o f transplant accounted for Figure 7.15. Eurotransplant excluded: crossmatch positivity in highly sensitized grafts.

94

206 Table 7.17. Registry: to stratify or not? Model comparison Background HSC,TRYR,FEMM,*

1st gratis

Regra~s

g2

(a) stratified (REGS)

24.02

6

73.73

16

(b) proportional hazards (REGS)

40.54

12

107.08

21

Registry (BASELINE = UKTS)

coeff,

z-score

coeff,

z-score

Euro France Scandia Swiss N Italy Spain/Luso

-0.15 -0.32 0.30 -0.61 -0.38 -0.34

- 1.06 - 1.75 1.46 -2.32 - 1.27 - 1.22

-0.72 -4.81 -0.91 -3.19 0.06 0.34 -0.32 -1.27 -0.37 -0.62 not applicable

df

~2

df

Model comparison: 1st grafts

(a) stratified (REGS)

Covariate

coeff,

se

coeff,

se

0.39 -0.13 -0.21 -0.38 --0.02 0.29

0.10 0.14 0.14 0.16 0.11 0.13

0.40 -0.13 -0.22 -0.37 -0.02 0.27

0.10 0.14 0.14 0.16 0.11 0.13

HSC: highly sensitized TRYR: 1983 1984 > 1985 FEMM: female BENE: non-beneficially matched * for regrafts for 1st grafts

(b) proportional hazards (REGS)

plus PRE2,WAIT,MMDR,ISC1,CYA plus BENE

Table 7.18. Duration of previous graft Model comparison

Regrafts

(a) HSC,TRYR,FEMM,BEN6,PRE6 versus (b) HSC,TRYR,FEMM,BEN6,PRE2

Z~=3.03

From model (b)

coeff,

z-score

PRE~

-0.04 0.04 0.13 -0.18 0.44

-0.19 0.21 0.72 -0.92 - 2.60

8 15 days 1 6 40 days 41-100days 101-365 days 366+ days

-

207

Table 7.19. Regrafts: wait from previous graft failure to regrafting Model comparison:

Regrafts

Background HSC,TRYR,FEMM,BEN6,PRE2,CYA,ISCH In addition

total ~2

df

coeff.

Izlscore

(a) WAIT: per 5 yrs

86.95

15

0.22

1.93

91.74

19 -0.02 0.12 0.08 -0.09 0.43

0.12 0.67 0.40 0.39 2.39

or

(b) IWAIT: I-2 yrs 2-3 yrs 3-4 yrs ,~5 yrs > 5 yrs

substitutes indicator variables for successive years of waiting up to the fifth does not justify abandoning simple linearity (WAIT) in so unobvious a covariate. 5.3. Preferred coding for I~LA-mismatches: model preference amalgamating other covariates The various insights, apart from tissue matching, which we have built up in the previous section are amalgamated into a multifactorial background for our choosing between rival schemes for coding H L A mismatches. The front runners (ALL grafts) from subsection 5.2. l, when few other covariates were considered, were number of D R mismatches (MMDR), beneficial matching (BEN4), beneficial/DR matching (BEN6) and total number of mismatches (MMS). We also review MBDR, that is the number of B + D R mismatches. The chosen epoch is l-1600 days giving maximum failures. Back-reference should be made to subsection 5.2.1 where coding schemes were compared against a simpler background but in two distinct epochs post-transplant (up to 3 months and thereafter). Separately for first versus regrafts (see Table 7.20), for ALL grafts without and with restriction to fully typed recipients as well as donors (see Table 7.21), and for highly sensitized versus control grafts (see Table 7.22) we compare likelihood ratios or regression X2 for five models which differ only in how H L A mismatches are coded. Model (c), which invokes beneficial/DR matching (BEN6), is reported in detail; for the others - that is, model (a) beneficial matching (BEN4); (b) D R mismatches ( M M D R ) ; (d) B-t-DR mismatches (MBDR) and (e) total A + B + D R mismatches (MMS) - w e document only the In relative risks for HLA-mismatching. This economy is permissible because no matter how we code HLA-mismatches the regression coefficients for other covariates are essentially undisturbed.

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217

Table 7.22. Model preference: highly sensitized and control grafts (stratified by RERE) Model(c) Epoch

%~equency

Highly sensitized 1165 Tx, 455fail 1 1600 days

Covariate

HS

controls

coeff,

lzlscore

coeff,

lzlscore

TRYR:

21% 30% 33%

23% 32% 27%

-0.32 -0.42 -0.55

2.18 2.98 3.77

-0.02 -0.26 -0.24

0.11 1.54 1.28

30% 8% 43%

26% 9% 43%

0.13 0.11 0.17

0.89 0.51 1.21

0.11 0.16 0.10

0.64 0.71 0.62

BEN6: A B D R 100 010 rest: . .0 rest: . .1 rest: . .2

6% 8% 16% 47% 10%

5% 10% 22% 47% 10%

-0.02 0.31 0.44 0.39 0.94

0.06 1.36 2.22 2.24 4.58

0.74 0.69 0.64 0.80 0.93

1.82 1.89 1.87 2.41 2.53

ISCI: 16-20 hrs 21 25 hrs 26-30 hrs 31 + hrs ischaemia NK

15% 25% 17% 18%

14% 23% 15% 18%

-0.05 0.22 0.19 0.23

0.26 1.14 0.87 1.08

0.01 -0.07 0.26 0.39

0.06 0.29 1.03 1.53

16%

19%

0.04

0.16

0.17

0.68

C Y A on day 1:

YES NK

42% 13%

38% 14%

-0.13 -0.04

1.19 0.18

--0.22 0.39

1.70 2.00

Model (b): MMDR

1 mm 2 mm

47% 10%

47% 10%

0.15 0.71

1.43 4.58

0.20 0.33

1.62 1.64

;(2 30.70 37.44 44.01 34.71 32.30

df 16 15 18 17 19

X2

df 16 15 18 17 19

1983 1984 -> 1985

PREG: f pregnancy f pregnancy male

YES NK

LIKELIHOOD RATIO Model (a) BEN4 Model (b) M M D R Model (c) BEN6 Model (d) M B D R Model (e) M M S MODEL PREFERENCE preference preference

Model (b): Model (c): Model (b)

Control patients 1092 Tx, 326fail 1-1600 days

29.29 26.31 31.23 26.51 45.51

MMDR Model (e): M M S BEN6 over model (a)

218 A panel giving the five likelihood ratios or regression ~2 for models (a) to (e) appears at the end of Tables 7.20 to 7.22 together with a model preference summary which is based on informed comparisons of the above regression ~2 (see Chapter 2) and their degrees of freedom. HLA-mismatches:first grafts. In Table 7.20(a) for first grafts the preferred coding of HLA-mismatches is indicator variables (MMS) for total A ÷ B ÷ DR mismatches, model (e) having regression g 20 = 48.61. Its nearest rival, model (c) for beneficial/DR matching (BEN6), has regression ~29 = 42.08. Although models (e) and (c) are not nested, and so not strictly comparable, the difference of 6.53 between the two regression ~2 can be thought of loosely as a ~ observation. But it exceeds the 5% critical value for ~ and approaches its 1% critical value (6.6) leading to preference for MMS over BEN6. Against MMS, as noted earlier in subsection 5.2.1, is non-linearity in the In relative risk regression coefficients. For first grafts, both beneficial (BEN4) and beneficial/DR matching (BEN6) are superior to MBDR, the number of B-t-DR mismatches, but the verdict between beneficial (BEN4) and DR matching (MMDR) is not proven. HLA-mismatches: regrafts. In Table 7.20(b) beneficial/DR matching (BEN6) is the preferred coding scheme in regrafts. HLA-mismatches: ALL grafts. In Table 7.21 there is no proven preference between beneficial/DR matching (BEN6) and total A + B ÷ D R mismatches (MMS) for ALL grafts except with restriction to fully typed recipients and donors, when MMS comes out ahead - but the four mismatch effect appears oddly privileged, leading to obstinate non-linearity in MMS coefficients. As for first grafts, beneficial/DR matching is preferred to the number of B + DR mismatches and to MMDR, but the verdict between beneficial (BEN4) and DR matching (MMDR) is not proven. It is, of course, hardly surprising that beneficial/DR matching, designed to unite the strengths of BEN4 and MMDR, does so and improves on both. Gilks et al (1987) commented upon there being a slight gradient in In relative risk for non-beneficially matched recipients, which they related to number of mismatches. BEN6 echoes this theme, but with subdivision by number of DR mismatches. HLA-mismatches: highly sensitized grafts. In Table 7.22 the choice between beneficial/DR matching (BEN6) and number of DR mismatches (MMDR) is not determined for highly sensitized grafts, but M M D R is clearly superior to beneficial matching (BEN4) with which is associated a lesser regression ~2 on more degrees of freedom. HLA-mismatches: control grafts. Table 7.22 evinces preference for total A + B + D R mismatches (MMS) in lowly sensitized grafts. But Table 7.5(b) showed that the In relative risks did not conform to a linear trend against number of mismatches. The MMS regression coefficients for ALL grafts (see Table 7.21) showed patients with four A ÷ B ÷ DR mismatches as paradoxically

219 privileged, and so it is for control grafts also (data not shown). The rest of the MMS story is conveyed equally well by any of the other coding schemes: it is simply that zero mismatched control recipients are singularly privileged. Thus in Table 7.22 we read similar In relative risk control coefficients for all levels of beneficial/DR matching compared to the BASELINE of 000 ABDR mismatches, whereas the corresponding coefficients for highly sensitized recipients display trend with increasing levels of mismatch. HLA-mismatches: summary. In summary, model preference against a comprehensive background of other covariates settles on beneficial/DR matching 1) because total A + B + D R mismatches (MMS) lacks orderly progression of In relative risks (see Tables 7.3 to 7.5: MMS trend) and 2) because beneficial/DR matching has immunological credibility as an expression of the interplay between Class I and Class II antigens. 5.4. Final regression models including day 1 graft function All covariates so far assessed in section 5.2 were known before operation or at the time of operation. Whether the graft functions on day 1 (the day of operation) and whether Cyclosporin A is given on that day (CYA is featured in Tables 7.20 to 7.22) are observed after the time of transplant. The decision to use Cyclosporin A or not on day 1 is elective and could be influenced by whether the graft functions on day 1. Day 1 non-function coincident with transplant failure on day 1 reveals the chimeric nature of DAY1 graft function as a covariate, because an element of it is outcome also. The final regression models presented here differ from those in section 5.3 by inclusion of the covariate DAY1, by abbreviating PREG to indicate simply females (FEMM) and parsimoniously substituting linear regression of In relative risk on ischaemia time (ISCH) instead of identifying different levels (ISC1) of ischaemia time. Also we adopt beneficial/DR matching (BEN6) throughout. Before presenting these final regression models, we review which pretransplant or perioperative factors seem to predict day 1 graft function. 5.4.1. Day 1 graft function. Table 7.23 shows DAY1 graft function by registry. Registries differ strikingly in the percentage of patients for whom DAYI graft function is not known or not recorded, data being missing for 90% of French patients, as could be the case if the Council of Europe follow-up questionnaires were not referred back to individual transplant centres, but were completed only from registry data. For the 1918 patients whose DAY1 graft function was reported, there is highly significant variation by registry in the proportion of grafts which function on day 1 ranging from 76% and 67% for Eurotransplant and North Italy Transplant (which have higher percentages of first grafts: 59% and 81%) to

22O Table 7.23. DAY1 graft function by registry

DAY1 graft function Registry

No

Yes

(% reported)

NK

(% total)

Total

Euro France N Italy Scandia Spain Swiss UKTS

255 10 14 82 28 47 215 651

789 14 29 78 24 79 254 1267

76% 58% 67% 49% 46% 63% 54% 66%

34 222 21 16 17 7 22 339

3% 90% 33% 9% 25% 5% 4%

1078 246 64 176 69 133 491 2257

_

_

_

_

Z62= 103.7 (DAY1 no/YES by registry) p = highly significant

49% and 54% in Scandia and UK Transplant (whic h have only 41% first grafts). In Spain, despite all being first grafts, only 46% function on day 1. Table 7.24 explores risk factors for DAY1 graft function in respect of the 1918 reported patients. Whereas 69% of first grafts function on day 1, only 62% of regrafts do so (p = 0.002). Being highly sensitized is another risk factor for DAY1 non-function of the graft, significant at the 1% level in first and regrafts separately. Seventy three percent of control first grafts function on day 1, 66% if highly sensitized; for regrafts, the corresponding DAY1 function rates are 67% for controls and 59% if highly sensitized. Table 7.24 shows, separately for highly sensitized and control re~cipients, that regraft patients whose previous graft functioned for at least three months have a better chance of day 1 graft function than do patients whose previous graft failed before three months. B cell positive crossmatch regrafts (see Chapter 10: Table 10.1) are significantly more vulnerable to DAY1 non-function than are their B cell negative counterparts, after allowance for high sensitization. Year of transplant is another risk factor, 1984 having apparently been a good year for DAY1 functioning grafts. Between highly sensitized and control grafts there is some evidence of beneficially matched (BENE) grafts having a better prospect for DAY1 function: 70% of beneficially matched grafts and 65% of non-beneficially matched grafts functioned on day l o The association of ischaemia time with DAY 1 graft function is essentially an association between ischaemia time not known and a poor (52%) chance of the graft functioning on day 1. Apart from France, UK Transplant and Spain are the two registries with highest rates of ischaemia not known (24% and 40% respectively).

221 Table 7.24. Exploring risk factors for DAY1 graft function: A L L grafts with DAY1 reported

RISK F A C T O R

DAY1 graft function

DAY1 graft function

no

YES

(%)

no

324 327

724 543

69% 62%

382 269

629 638

62% 70%

Graft number 1st graft regraft

Sensitization highly sensitized control

YES

ALL

ZI~ =

9.4

p=0.002

ALL 1st grafts regrafts

X~= ~ = Z~ =

14.1 6.0 6.8

p = 0.002 p =0.01 p = 0.01

(%)

HS regrafts only: duration of previous graft

Control regrafts only: duration of previous graft

up to 100 days 101 + days Z~=5.9 p=0.02

up to 100 days 101 + days Z~2 =

120 80

138 145

54% 64%

HS grafts only: year of transplant 1982 1983 1984 1985 Z32=6.9

70 78 105 129

80 126 202 222

65 62 2.7

ll0 150 p = 0.10

63% 71%

Control grafts only: year of transplant 53% 62% 66% 63%

1982 1983 1984 1985

p=0.08

Z~2 =

48 68 70 83 7.7

103 135 226 174 p=0.05

68% 67% 76% 68%

HS grafts only: beneficial matching ABDR

Control grafts only: beneficial matching ABDR

000,100,010 rest Zl2 = 5 . 8 p = 0 . 0 2

000,I00,010 rest

92 290

196 433

68% 60%

A L L grafts ;t~ = 4.9

Ischaemia time less than 16 hrs 16-20 hrs 21-25 hrs 26-30 hrs 3 1 + hrs NK Z~=18.4 p=0.003

57 212 Z~ = 0.8 p = 0.03

153 485 p =0.36

73% 70%

636 534 97 p = 0.0002

67%) * 63%J 82%

Cyclosporin A on day 1 59 104 178 ll9 114 77

137 200 350 236 262 82

70% 66% 66% 66% 70% 52%

no YES NK

319 311 21 Z~2 = 16.9

*Z21=

2.3

p=0.13

Cyclosporin A on day 1 is associated with DAY1 graft function only insofar as 82% of grafts function on day 1 for which information on Cyclosporin A usage is unrecorded! Reported CYA and reported DAY1 graft function are not significantly interdependent (Z~2 = 2.3, p = 0.13). 5.4.2. Final regression models. DAY1 graft function is added to the list of prognostic factors in Table 7.25 for first versus regrafts; in Table 7.26 for ALL grafts; and in Table 7.27 for highly sensitized versus control grafts. Figures 7.17, 7.18 and 7.19 illustrate selected risk scores for regrafts, ALL grafts (in distinct epochs) and highly sensitized recipients, respectively.

222

Table 7.25a. Including D A Y I function: s u m m a r y model for 1st grafts (stratified by REGS) Epoch

1291 Tx 410 fail 1-1600 days

250 failures 1-101 days

Covariate

coeff,

coeff,

HSC: highly sensitized

lzl score

160 failures 101 + days lzl

coeff, ~ 5 = 8.84

0.37

3.65

0.57

4.25

1983 1984 >= 1985

-0.13 -0.16 -0.37

0.88 1.11 2.32

-0.03 -0.03 -0.42

0.15 0.18 2.08

FEMM: female

TRYR:

-0.02

0.23

0.05

0.38

BEN6: A B D R 100 010 rest: . .0 rest: . .1 rest: . .2

0.64 0.35 0.45 0.58 0.78

2.18 1.32 1.92 2.72 3.08

0.83 0.25 0.65 0.75 0.88

2.21 0.68 2.12 2.60 2.63

ISCH per 10 hrs

0.04

0.56

0.05

0.59

YES NK

0.08 -0.23

0.69 1.27

0.05 0.46

0.34 2.04

DAY I YES function N K

-0.62 -0.30

5.42 1.38

-0.96 -0.47

6.74 1.69

C Y A on day 1

lzl

Table 7.2Yo. Including D A Y 1 function: s u m m a r y model for regrafts (stratified by REGS)

Epoch

966 Tx 371 fail 1-1600 days

249 failures 1 101 days

Covariate

coeff,

coeff,

lzl score

122 failures 101 + days lzl

coeff,

lzl

0.41

3.51

0.48

3.37

0.29

1.44

1983 1984 > 1985

--0.15 -0.36 -0.36

0.94 2.24 2.13

--0.13 -0.54 --0.46

0.70 2.71 2.31

--0.19 --0.09 --0.20

0.69 0.31 0.57

FEMM: female

-0.05

0.42

-0.03

0.20

--0.10

0.53

BEN6: A B D R 100 010 rest: .... rest: .. 1 rest: . .2

-0.30 0.49 0.45 0.40 0.90

0.89 1.92 1.92 1.88 3.64

-0.40 0.08 0.33 0.14 0.65

0.99 0.25 1.25 0.59 2.33

0.11 1.45 0.87 1.12 1.54

0.16 2.83 1.70 2.36 2.87

0.18

2.41

0.16

1.71

0.20

1.54

HSC: highly sensitized TRYR:

ISCH per 10 hrs C Y A on day I

YES NK

-0.31 0.37

2.61 1.61

-0.15 0.68

1.05 2.56

--0.62 -0.38

2.84 0.80

DAY 1 function

YES NK

-0.67 - 0.70

5.79 2.50

-0.86 --0.78

6.06 2.27

--0.28 -0.39

1.31 0.80

PRE2 > 100 days

YES

-0.34

3.04

--0.42

3.03

-0.16

0.81

0.13

1.10

0.13

1.00

0.12

0.53

W A I T per 5 yrs

223

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224 Table 7.27a. Including D A Y 1 function: s u m m a r y model for highly sensitized grafts (stratified by RERE)

Epoch

1165 Tx 455fail 1-1600days

320failures 1-101 days

Covafiate

coeff,

lzlscore

coeff,

lzl

coeff,

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1983 1984 > 1985

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0.13 1.59 2.17 2.14 4.62

0.22 0.05 0.38 0.32 0.81

0.73 0.18 1.62 1.57 3.31

-1.07 0.89 0.59 0.51 1.41

1.38 2.39 1.61 1.62 3.63

0.08

1.16

0.07

0.80

0.11

0.91

ISCH per 10 hrs

135failures 1 0 1 + days

C Y A on day 1

YES NK

-0.10 0.09

0.93 0.41

-0.10 0.24

0.81 0.99

-0.04 -0.26

0.17 0.65

DAY 1 function

YES NK

-0.72 -0.59

6.91 2.30

-0.87 -0.45

6.92 1.51

-0.41 -0.89

2.04 1.90

Table 7.27b. Including D A Y 1 function: s u m m a r y model for control grafts (stratified by RERE)

Epoch

1092 Tx 326 fail 1-1600 days

179 failures 1-101 days

148 failures 101 + days

Covariate

coeff,

lzl score

coeff,

lzl

coeff,

lzl

TRYR:

1983 1985 > 1985

-0.02 -0.23 -0.25

0.10 1.34 1.32

0.06 -0.29 -0.25

0.29 1.25 1.03

-0.11 -0.12 -0.32

0.50 0.51 0.92

FEMM: female

0.00

0.03

0.02

0.11

- 0.04

0.24

BEN6: A B D R 100 010 rest: . .0 rest: .. 1 rest: . .2

0.70 0.60 0.58 0.77 0.88

1.74 1.64 1.70 2.34 2.29

0.30 0.26 0.63 0.60 0.81

0.53 0.54 1.42 1.40 1.72

1.23 1.10 0.52 1.04 0.98

2.02 1.95 0.94 1.99 1.68

ISCH per 10 hrs

0.13

1.64

0.14

1.38

0.09

0.76

C Y A on day 1

YES NK

--0.21 0.44

1.58 2.28

--0.07 0.85

0.42 3.48

--0.37 -0.12

1.86 0.39

DAY 1 function

YES NK

- 0.60 -0.34

4.65 1.51

- 1.04 --0.75

6.23 2.42

0.11 0.43

0.48 1.28

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0 ~. ~" 0 I~.

228 Comparison of regression coefficients (epoch 1-1600 days) for corresponding covariates in Tables 7.20 to 7.23 and Tables 7.25 to 7.27 shows that pre-transplant and peri-operative risk factors act separately from DAY1 graft function: that is, they maintain their influence on transplant survival when account is taken also of whether the graft functions on day 1. First grafts. High sensitization, beneficial/DR matching and DAY1 graft function together with transplant year are the dominant influences on first transplant survival. The regression ;~5 in the second epoch is only 8.84, consistent with random variation, and so the estimated coefficients are not reported. Regrafts. Whereas the relative risks associated with year of transplant, being highly sensitized and DAY1 non-function are short-term, strongly evident only in the first post-transplant epoch up to three months, the benefits of tissue matching persist beyond three months. As does the penalty associated with longer ischaemia times, 0.18 per additional 10 hours' ischaemia. Another prognostic factor for early outcome in regrafts is whether the previous graft survived for more than three months, this being a favourable sign, indeed as favourable as high sensitization is penalizing. Waiting time from previous graft failure to regrafting makes little contribution to the multifactorial risk score overall (see Figure 7.17) or in distinct epochs. Cyclosporin A is associated with improved transplant survival for regrafts. A L L grafts. The central panel of Table 7.26 gives In relative risks in distinct post-transplant epochs (up to 101 days and thereafter) which are illustrated in Figure 7.18. The transient risk associated with being highly sensitized is well illustrated (ln relative risk = 0.56 in first epoch, 0.16 (se = 0.13) thereafter), as is the overwhelming advantage for graft survival in the first three months of DAY1 graft function (In relative risk = - 0 . 9 4 compared to day 1 nonfunction); but DAY1 graft function has little impact on transplant survival after three months (ln relative risk = -0.15, se = 0.14). Over the period 1982 to 1985 transplant survival has improved. Beneficial/ DR matching is influential in both post-transplant epochs, the risks associated with mismatching being persistent, not transient. A modest persistent penalty is associated with longer ischaemia time. The right-hand panel in Table 7.26 relates to transplant survival after the third day and demonstrates that the penalty associated with DAY1 nonfunction is not restricted to what happens in the first few post-transplant days but constitutes a risk factor thereafter for transplant survival up to three months. Highly sensitized versus control recipients. Table 7.27 contains no new insights except for the benefits of tissue matching being reserved for zero mismatched control grafts and the advantage of DAY1 graft function being evident beyond three months, albeit with diminished In relative risk, for highly sensitized recipients (see Figure 7.19 for overall risk score components).

229 5.4.3. Transplant survival: summary. Matching benefits all transplants, zero mismatched transplants from a DR heterozygous donor do best and at the other extreme two HLA-DR mismatches are to be avoided. Amongst lowly sensitized grafts the indication that matching is of lesser importance is qualified by the observation that they derive benefit if zero mismatched. Over the period 1982 to 1985 transplant survival for both first and regrafts has improved. Inter-registry differences in transplant survival are particularly marked for regrafts with the best outcome for Eurotransplant and France Transplant regrafts. Whereas approximately one-third of reported first graft failures occurred within the first two weeks a similar proportion of regrafts had failed within the first week. The risks associated with high sensitization continued beyond the first week or two weeks up to three months, but diminished thereafter. There is a modest risk associated with longer cold ischaemia time beyond 25 hours. Day 1 graft function is as great an advantage for transplant survival in the first post-transplant epoch as two HLA-DR mismatches are a handicap (relative to 000 ABDR mismatches). Unlike the relative risks associated with mismatching, the influence of day 1 graft function applies in the first post-transplant epoch only (up to three months). Other prognostic influences are complemented, not overridden, by post-transplant information about immediate graft function. Amongst control grafts (lowly sensitized) there is confirmation of privileged transplant survival for recipients whose phenotype includes DR1. A corresponding advantage for DR7 recipients is not proven. The implication of a slight advantage for first grafts into nulliparous females warrants further enquiry. Highly sensitized regrafts are prejudiced by a B cell positive crossmatch. The 95% confidence interval for In relative risk in highly sensitized first grafts runs from -0.11 to 0.51; it therefore does not exclude risks of clinical relevance. The decision to give Cyclosporin A on day 1 (or not) was at the discretion of individual clinicians and elective for each patient; estimates of the efficacy of Cyclosporin A from the Council of Europe database are therefore vulnerable to inherent selection biasses.

6. Mortality: results There were 128 deaths amongst the 1291 first graft recipients and 95 deaths reported for the 966 regrafts, the death rates being similar in the control and highly sensitized subgroups. For first grafts, there were 62 deaths out of 638 highly sensitized recipients, and 66 deaths amongst 653 controls. Fifty two deaths occurred out of 527 highly sensitized regrafted recipients and 43 deaths amongst 439 control regrafts.

230 During the seven-week study of registry transactions (see Chapter 6), 76 deaths occurred on the combined waiting list of 16375 patients. This corresponds to an annual death rate on the waiting list of 34.5 deaths per 1000 waiting patients and is similar to the above: 39.5 deaths per 1000 transplanted patients assuming an average follow-up of two and a half years. The above annual death rate for patients with chronic renal disease exceeds expected mortality in the age-sex matched U K population by a factor of at least four. We recall from Chapter 4 that 60% of the waiting list is male; we apply the age-distribution for the transplantees (see Table A7.3: 4% are aged 15 years or less; 28% are aged 16-30 years; 52% aged 31-50 years; and 16% of transplanted patients are 51 years of age or older) and approximate the annual death rate per 1000 individuals for each age-group by the U K 1985 upper limit. This U K upper limit on deaths per 1000 individuals is 0.5 deaths per year for ages 15 years or less - based on U K deaths aged 15-19 years; 0.7 for ages 16-30 years - based on U K deaths aged 25-34 years; 4.6 for ages 31-50 years - based on U K deaths aged 45-54 years; and 36.2 for 600 men + 400 women aged 51 years and over. The last rate was computed as 0.6 x 44.3 ( U K 1985 death-rate per 1000 males aged 65-74 years) + 0.4 x 24.1 ( U K 1985 death-rate per 1000 women aged 65-74 years). Total expected U K deaths per 1000 individuals, (60% male, within the age-distribution of transplantees are certainly fewer than: 0.04 × 0.5 + 0.28 x 0.7 ÷ 0.52 x 4.6 ÷ 0.16 x 36.2 = 8.4 deaths per 1000 per year which is one quarter of the observed deaths in chronic renal failure patients.

7. Discussion

Improved graft survival in recent transplant years was shown for lowly and highly sensitized, first and re-transplants. This improvement was separate from other favourable pre- and peri-operative factors which we identified and is probably attributable to unquantified improvements in post-operative clinical management. High sensitization has diverse aetiologies (see Chapter 3). In this Chapter, patients who were highly sensitized at the time of first transplant were slightly enriched for the proportion of patients transfused; and there was highly significant increase in the proportion of ever pregnant women compared to control first transplants. Both observations are consistent with known aetiologies. In regrafted highly sensitized patients, no such increases were observed, suggesting that alternative aetiologies, perhaps genetically or constitutionally derived, were predominant. Highly sensitized re-transplantees had had first grafts that were rejected faster than were those of patients who remained lowly sensitized at regraft. Whereas levels of panreactivity tended to be more persistent in those

231 awaiting regrafts, they tended to be transient prior to first graft: 64% of retransplantees and 53% of patients who had undergone first graft had levels of panreactivity over 50% at the time of grafting. Finally, the B cell crossmatch test was more often positive prior to regrafting than before first graft in highly sensitized patients; and was more often positive in highly sensitized than in control transplants concordant for graft number (first versus regraft). Highly sensitized patients were distinguished from lowly sensitized transplantees by an increase in recipient HLA-A and B homozygosity. Moreover, with regard to matching, there were more 000 (ABDR) transplants performed in highly sensitized than in lowly sensitized recipients. In order of priority, the factors that were prejudicial to transplant survival included mismatching for HLA-A, B and DR antigens (persistent risk), non-function of the graft on day 1 (the greatest early hazard), high sensitization (transient risk), prolonged ischaemia time (persistent penalty); the previous graft having failed within three months was prejudicial to the survival of retransplants; positive B cell crossmatch was detrimental to the survival of highly sensitized regrafts, especially. Factors favourable to transplant survival were the obverse of the above plus Cyclosporin A usage and the possession ofnon-responder HLA-DR phenotypes such as HLA-DR1 in lowly sensitized recipients. In deciding which of several mismatching schemes is appropriate, model preference as judged by statistical significance is only one aspect; consideration should be given also to the genetic structure of the HLA-region, the quality of typing, especially HLA-DR typing, and the biological role of the mismatched antigens, for example Class II versus Class I. Currently, tissue matching programs take account only of gene products of the HLA-A, B and DR loci yet several other loci mapping in adjacent regions may play a role in graft rejection. Some of these are closely linked to identifiable gene products and are predictable, for example, DR and DQ. The quality of tissue typing varies between loci, and in Chapter 5 the relative inaccuracy of HLA-DR typing compared to HLA-A and B was illustrated using Hardy-Weinberg analysis. The quality of HLA-DR typing in transplantees also varied widely between registries as reflected by the significant differences in proportion of DR homozygotes. For example, France, Swiss and Scandia Transplant had 40% DR homozygotes compared to Eurotransplant and UK Transplant at around 20%. Polymorphic differences relevant to graft rejection may pass unrecognized in serological typing, yet can be revealed by DNA-Restriction-Fragment-Length-Polymorphism (DNA-RFLP) techniques. These differences are often seen in association with particular alleles at adjacent loci. For example, there are two subtypes of DR3; DQw2, one of which is in strong linkage disequilibrium with B18 and the other with B8 (Bidwell et al 1987). The biological function of Class I molecules is different from that of Class II molecules, the former being considered more in terms of target for cell

232 destruction and the latter as an amplifier of cellular immune responses. However speculative this view may ultimately prove to be, it is important not to assume that all mismatches across loci are of equal potency. Thus any statistical model describing mismatches should take account of genetics, typing quality, linkage and function. The most general of the coding schemes considered was the 27 varieties of mismatch but even that assumes interchangeability of the mismatched antigens within a locus: that is, we assume that a mismatch for A1 carries the same penalty as a mismatch for any other A antigen. Nonetheless, the 27 varieties of mismatch are the least prejudiced with regard to all the above features, except quality of HLA-DR typing. Beneficial/ DR matching was shown to convey the essential features of the 27 varieties of mismatch, but further subdivision of beneficial/DR matching according to whether the donor was HLA-DR heterozygote or putative homozygote gave some additional insight, particularly in respect of zero ABDR mismatched transplants. Compared to 000 mismatched transplants in which the donor was HLA-DR heterozygous there was a significant increase in the relative risk of graft failure for 000 mismatched transplants in which the donor was HLA-DR homozygous. The latter putative DR homozygotes are subject to error depending firstly on the unidentified DR gene frequency and secondly upon poor typing sera which make it difficult to recognise certain DR antigens in the presence of others. We speculate that retyping of zero mismatched transplants by non-serological techniques may discern subgroups of clinical and prognostic relevance. High sensitization, defined in terms of lymphocytotoxicity to unseparated lymphocytes, reflects prior immunity to HLA-A and B antigens. But less account is taken of sensitization to HLA-DR antigens since B cell reactions are generally ignored. The relative risk associated with high sensitization is separate from other identified risks including that associated with HLA-A and B mismatching. This being so, highly sensitized transplants of the 00" mismatched category would have significantly poorer graft survival than lowly sensitized 00" transplants. And indeed they do. Of the 347 00" grafts, fully typed for recipient and donor, 260 were highly sensitized and 83 were control grafts. Allowing for DR mismatching, the In relative risk for high sensitization was 0.56 (se = 0.25): 1 year graft survival was 83% for control 00" mismatched grafts and was 66% for their highly sensitized counterparts. Although the majority of transplants studied in this series were negative in the conventional "T cell" crossmatch test, a high proportion (about half of those tested) were performed in the face of a positive B cell crossmatch. Although these positive reactions are in part attributable to IgM antibody or antibody directed at non-HLA targets, a proportion are attributed to IgG anti HLA-A and B and DR mismatches on the donor. That the observed risk associated with B cell positive crossmatches was higher in regrafts is consistent with the view that first graft failure is followed by production of clinically

233 relevant HLA antibody that passes undetected in the conventional crossmatch test. Ischaemia time is confirmed as a significant risk factor in transplant survival. The longer the time, the greater the risk. In this study no significant difference in ischaemia time between highly and lowly sensitized transplants was noted. Retransplants seem more sensitive to ischaemia time than first grafts. Non-immediate graft function was associated in part with high sensitization; was more frequent in regrafts, especially if the previous graft had failed before three months or B cell crossmatch test was positive, and was diminished in beneficially matched grafts. Yet, non-functioning of the graft on day 1 was a potent risk factor for early graft loss which was clearly separable from these other influences on transplant survival. A significant reduction in the incidence of non-immediate function in recent years suggests a greater ability to counteract the important causal factors, which may include cold agglutinin activity in a non-prewarmed kidney and fluid load to the recipient prior to anastomosis (Chapter 1). The use of Cyclosporin A on day 1 varied markedly between registries. Cydosporin A was used electively; moreover, its use on subsequent days was not precluded in patients who did not receive Cyclosporin A on day 1. Hence, this study, which associates with Cyclosporin A on day 1 only a marginal reduction in the risk of graft loss, probably underestimates the beneficial effect of the drug, this being better discerned in randomized trials. In summary, whereas the risk of transplant failure pertaining to high sensitization diminishes after three months, HLA-mismatching continues to influence longer term graft outcome. The accelerated failure of regrafts whereby the proportion failing in the first week is as high as the proportion of first grafts which fail within two weeks, was accommodated throughout by stratification for graft number (first versus regrafts) as well as registry. There is reason to be cautious about transplanting across B cell positive crossmatches, a need to re-evaluate the typing of 000 mismatched grafts particularly when the donor is H L A - D R homozygous, and an incentive to improve the proportion of grafts which produce urine on day 1, this being a potent risk factor for early graft loss.

234

,~,~

Q 0

e, 8

~ •

Q

235

Appendix II. Risk factor report by first versus regraft Table A 7.1. Transplant year FIRST GRAFTS

HS patients

~1982 83 84 ~1985 ~]=3.07 p = 0.38

107 133 193 205

REGRAFTS

HS patients

Control patients

Combined

~ 1982 83 84 ~1985

76 ll0 156 185

73 116 140 I10

149 226 296 295

~

=

17% 21% 30% 32%

14% 21% 30% 35%

Control patients

Combined

128 136 204 185

235 269 397 390

20% 21% 31% 28%

17% 26% 32% 25%

18% 21% 31% 30%

15% 23% 31% 30%

12.24

p = 0.007

Table A 7.2. Recipient sex and pregnancy FIRST GRAFTS

HS patients

Control patients

Combined

f,pregnancy N O f,pregnancy YES f,pregnancy N K male Z3z = 19.53 p < 0.0002

125 268 49 196

174 202 62 215

299 470 111 411

REGRAFTS

HS patients

Control patients

Combined

f,pregnancy N O f,pregnancy YES f,pregnancy N K male Z32= 1.77 p = 0.62

96 80 44 307

71 78 33 257

167 158 77 564

20% 42% 8% 31%

18% 15% 8% 58%

27% 31% 10% 33%

16% 18% 8% 58%

23% 36% 9%" 32%

17% 16% 8% 58%

236 Table A 7,3. Age-group FIRST GRAFTS

HS patients

Control patients

Combined

N 15 yrs 16-30 yrs 31 50 yrs 5 1 + yrs Z~=3.78 p = 0.29

23 154 347 114

27 178 320 128

50 332 667 242

REGRAFTS

HS patients

N15 yrs 16-30 yrs 31-50 yrs 5 1 + yrs Z]=4.22 p = 0.24

20 174 272 61

4% 24% 54% 18%

4% 33% 52% 12%

4% 27% 49% 20%

Control patients

Combined

14 127 232 66

34 301 504 127

3% 29% 53% 15%

4% 26% 52% 19%

4% 31% 52% 13%

Footnote: patients whose age is u n k n o w n are included in the age-group 31-50 years

Table A 7.4. N u m b e r of A mismatches FIRST GRAFTS

HS patients

Control patients

Combined

MMA: 0mm l mm 2mm Z~=31.64 p = highly significant

306 276 56

220 333 100

526 609 156

REGRAFTS

HS patients

Control patients

Combined

MMA: 0 m m l mm 2 mm Z ~ = 10.99 p = 0.004

269 206 52

179 199 61

448 405 113

48% 43% 9%

51% 39% 10%

34% 51% 15%

41% 45% 14%

41% 47% 12%

46% 42% 12%

237

Table A 7.5. Number of B mismatches FIRST GRAFTS

HS patients

Control patients

Combined

MMB: 0 mm 1 mm 2 mm Z~ = 46.63 p = highly significant

256 292 90

148 372 133

404 664 223

REGRAFTS

HS patients

Control patients

Combined

MMB: 0 mm 1 mm 2 mm Xz~=23.15 p = highly significant

206 234 87

113 259 67

319 493 154

40% 46% 14%

39% 44% 16%

23% 57% 20%

26% 59% 15%

31% 51% 17%

33% 51% 16%

Table A 7.6. Number of D R mismatches FIRST GRAFTS

HS patients

Control patients

Combined

M M D R : 0 mm 1 mm 2 mm Z~ =0.37 p = 0.83

266 311 61

271 313 69

537 624 130

REGRAFTS

HS patients

Control patients

Combined

M M D R : 0 mm 1 mm 2 mm ~ = 0.29 p = 0.86

231 242 54

200 196 43

431 438 97

42% 49% 10%

44% 46% 10%

42% 48% 11%

46% 45% 10%

42% 48% 10%

45% 45% 10%

238

Table A 7. 7. Beneficial/DR matching FIRST GRAFTS

HS patients

Control patients

Combined

ABDR: 000 100 010 rest DR = 0 rest DR = 1 rest DR = 2 ~5~ = 16.62 p = 0.005

73 34 50 109 311 61

37 35 61 138 313 69

110 69 111 247 624 130

REGRAFTS

HS patients

Control patients

Combined

ABDR: 000 100 010 rest DR = 0 rest DR = 1 rest DR = 2

75 35 47 74 242 54

24 22 52 102 196 43

99 57 99 176 438 97

11% 5% 8% 17% 49% 10%

14% 7% 9% 14% 46% 10%

6% 5% 9% 21% 48% 11%

6% 5% 12% 23% 45% 10%

8% 5% 9% 19% 48% 10%

10% 6% 10% 18% 45% 10%

~ = 32.27

p = highly significant

Table A 7.8. Number of B + D R mismatches FIRST GRAFTS

HS patients

Control patients

Combined

MBDR: 0 mm 1 mm 2 mm 3 mm 4 mm Z4~ = 22.40 p = 0.0002

115 243 195 68 17

77 208 249 93 26

192 451 444 161 43

REGRAFTS

HS patients

Control patients

Combined

MBDR: 0 mm I mm 2 mm 3 mm 4 mm

112 180 142 78 15

50 178 148 51 12

162 358 290 129 27

z,~ = 22.01

p = 0.002

18% 38% 31% 11% 3%

21% 34% 27% 15% 3%

12% 32% 38% 14% 4%

11% 40% 34% 12% 3%

15% 35% 34% 13% 3%

17% 37% 30% 13% 3%

239

Table A 7.9. Total number of mismatches FIRST GRAFTS

HS patients

Control patients

Combined

MMS: 0 m m [ mm 2 mm 3 mm 4 mm 5 mm 6 mm Z62= 45.07 p = highly significant

73 158 196 132 53 21 5

37 114 177 195 86 39 5

110 272 373 327 139 60 10

REGRAFTS

HS patients

Control patients

Combined

MMS: 0 mm 1 mm 2 mm 3 mm 4 mm 5 mm 6 mm Z~ = 35.10 p = highly significant

75 138 139 84 59 28 4

24 96 153 93 56 11 6

99 234 292 177 115 39 10

11% 25% 31% 21% 8% 3% 1%

14°/~.. 26% 26% 16% i 1% 5% 1%

6% 18% 27% 30% 13% 6% 1%

6% 22% 35 % 21% 13% 2% 1%

8% 21% 29% 25% 11% 5% 1%

10% 24% 30% 18% 12% 4% 1%

Table A 7.10. Total number of shared antigens FIRST GRAFTS

HS patients

Control patients

Combined

SHS: share 0 1 2 3 4 5 6 Z26= 23.49 p = 0.0006

15 43 110 226 173 64 7

25 64 164 197 138 57 8

40 107 274 423 311 121 15

REGRAFTS

HS patients

Control patients

Combined

SHS: share 0 1 2 3 4 5 6 Z~ = 6.73 p=0.35

23 44 93 143 151 62 11

16 32 87 141 120 37 6

39 76 180 284 271 99 17

2% 7% 17% 35% 27% 10% 1%

4% 8% 18% 27% 29% 12% 2%

4% 10% 25% 30% 21% 9% 1%

4% 7% 20% 32% 27% 8% 1%

3% 8% 21% 33% 24% 9% 1%

4% 8% 19% 29% 28% 10% 2%

240

Table AT.11a. Antigen sharing at A, B, D R loci FIRST GRAFTS # shared A antigens SHA: share 0 1 2 X22= 20.20 p = highly significant CA shared B antigens SHB: share 0 1 2 ;~22= 26.97 p = highly significant # shared DR antigens SHDR: share 0 1 2 X~ = 0.91 p = 0.63

HS patients

Control patients

Combined

78 411 149

12% 64% 23 %

141 380 132

22% 58% 20%

219 791 281

17%o 61% 22%

108 380 150

17% 60% 23%

158 408 87

24% 63% 13%

266 788 237

21% 61% 18%

176 318 144

28% 50% 23%

173 318 162

27% 49% 25%

349 636 306

27% 49% 24%

Table A7.11b. Antigen sharing at A, B, D R loci REGRAFTS CA shared A antigens SHA: share 0 1 2 Z~2 = 6.26 p = 0.04 # shared B antigens SHB: share 0 I 2 x~ = 5.85 p =0.05 CA shared DR antigens SHDR: share 0 1 2 Z2: = 0.86 p = 0.65

HS patients

Control patients

Combined

70 332 125

13% 63% 24%

83 250 106

19% 57% 24%

153 582 231

16% 60% 24%

105 303 119

20% 57% 23%

86 280 73

20% 64% 17%

191 583 192

20% 60% 20%0

137 264 126

26% 50% 24%

113 210 116

26% 48% 26%

250 474 242

26% 49% 25%

241

Table A 7.12a. Recipient homozygosity F I R S T GRAFTS

HS patients

Control patients

Combined

167 471

26% 74%

111 541

17% 83%

278 1013

22% 78%

123 515

19% 81%

87 566

13% 87%

210 1081

16% 84%

151 487

24% 76%

165 488

25% 75%

316 975

24% 76%

A homozygosity HOMA:

yes other

;t2~= 16.08 p = 0.0001

B homozygosity HOMB:

yes other

Z~ = 8.40 p = 0.004

DR homozygosity HOMD:

yes other

Z~ = 0.45 p = 0.50

Table A 7.12b. Recipient homozygosity REGRAFTS

HS patients

Control patients

Combined

118 409

22% 78%

60 379

14% 86%

178 788

18% 82%

76 451

14% 86%

37 402

8% 92%

113 853

12% 88%

120 407

23% 77%

100 339

23% 77%

220 746

23% 77%

A homozygosity HOMA:

yes other

;tt~ = 12.13 p = 0.005

B homozygosity HOMB:

yes other

~=8.33 p = 0.004

DR homozygosity HOMD:

z~, = o . o p = 1.0

yes other

242

Table A 7.13. Beneficial/DR matching subdivided by donor H L A - D R homozygosity (2014 grafts D R typed for both donor and recipient) FIRST GRAFTS mismatches

rest rest rest rest rest

ABDR; 000 000 100 100 010 010 • .0 • .0 • .1 •. 1 • .2

-~ donor D R het homoz. het homoz. het homoz. bet homoz. her homoz. bet

HS patients

Control patients

33 36 19 14 24 25 68 41 202 52 61

17 15 25 9 34 25 87 49 219 34 69

6% 6% 3% 2% 4% 4% 12% 7% 35% 9% 11%

Combined

3% 3% 4% 2% 6% 4% 15% 8% 38% 6% 12%

50 51 44 23 58 50 155 90 421 86 130

4% 4% 4% 2% 5% 4% 13% 8% 36% 7% 11%

m

575

583

1158

HS patients

Control patients

Combined

35 38 24 11 27 18 40 30 167 22 54

9 13 12 10 35 17 62 38 131 20 43

44 51 36 21 62 35 102 68 298 42 97

Z~o = 25.33 p = 0.005 REGRAFTS mismatches

rest rest rest rest rest

ABDR: 000 000 I00 100 010 010 . .0 . .0 . .I .. 1 . .2

donor D R het homoz. bet homoz. bet homoz. het homoz. het homoz. bet _

_

466 Z~o ~ 37.65 p = highly significant

8% 8% 5% 2% 6% 4% 9% 6% 36% 5% 12%

2% 3% 3% 3% 9% 4% 16% 10% 34% 5% 11%

m

m

390

856

5% 6% 4% 3% 7% 4% 12% 8% 35% 5% 11%

243

Table A 7.14a. Selected H L A D R antigens: DR1, D R 2 and D R 7 (2014 grafts D R typed for both donor and recipient) FIRST GRAFTS

HS patients

Control patients

Combined

Recipient DR1 yes no

74 501

13% 87%

103 480

18% 82%

177 981

15% 85%

yes no

213 362

37% 63%

170 413

29% 71%

383 775

33% 67%

yes no

115 460

20% 80%

130 453

22% 78%

245 913

21% 79%

yes no

94 481

16% 84%

96 487

16% 84%

190 968

16% 84%

z~ = 5.15

p = 0.02

Recipient DR2

Zl~= 8.13 p =0.004

Recipient D R 7

z, ~ =

0.92

p = 0.34

Donor DR I

x,~ =

o.oo

p = 1.00

Table A 7.14b. Selected H L A D R antigens: DR1, D R 2 and D R 7 (2014 grafts D R typed for both donor and recipient) REGRAFTS

HS patients

Control patients

Combined

yes no

77 389

16% 84%

77 313

20% 80%

154 702

18% 82%

yes no

139 327

30% 70%

104 286

27% 73%

243 613

28% 72%

yes no

78 388

17% 83%

88 302

23% 77%

166 690

19% 81%

yes no

86 380

18% 82%

74 316

19% 81%

160 696

19% 81%

Recipient D R I

Z~ = 1.49 p = 0.22

Recipient DR2

z, ~ =

1.o4

p=0.31

Recipient D R 7

Z~=4.61 p = 0.03

Donor D R 1

x~ = o.o4

p =0.85

244

Table A 7.15a. Cold ischaemia time FIRST GRAFTS

HS patients

Control patients

Combined

Not recorded _< 15 hrs 16-20 hrs 21-25 hrs 26-30 hrs 31+ hrs Z5~ = 4.47 p = 0.48

123 60 81 148 112 114

138 73 85 145 91 121

261 133 166 293 203 235

REGRAFTS

HS patients

Control patients

Combined

Not recorded =<15 hrs 16-20 hrs 21-25 hrs 26-30 hrs 31 + hrs

63 40 93 148 85 98

67 46 64 111 78 73

130 86 157 259 163 171

19% 9% 13% 23% 18 % 18%

12% 8% 18% 28% 16% 19%

21% 1I % 13% 22% 14% 19%

15% 11% 15% 25% 18% 17%

20% 10% 13% 23% 16% 18%

14% 9% 16% 27% 17% 18%

x~=7.18 p = 0.21

Table A 7.15'o. Cold ischaemia (hours) FIRST GRAFTS

HS patients

Control patients

Combined

mean sd n # missing

24.9 7.7 638 "nil .

24.4 8.3 653 nil .

24.7 8.0 1291 nil"

REGRAFTS

HS patients

Control patients

Combined

mean sd n # missing

24.8 7.8 527 "nil .

24.4 8.3 439 nil .

24.7 8.0 966 nil"

.

.

.

.

.

.

24 hours was assumed for missing ischaemia time

.

.

.

.

.

.

245 Table A 7.16. Positive crossmatch (Eurotransplant excluded)

HS patients

Control patients

Combined

150 50 124

46% 15% 38%

175 7 153

52% 2% 46%

325 57 277

49% 9% 42%

No YES both Not tested ~ = 27.02 p = highly significant

288 31 5

89% 10% 2%

330 3 2

98% 1% 1%

618 34 7

94% 5% I%

REGRAFTS

HS patients

Control patients

Combined

94 91 110

32% 31% 37%

112 25 88

50% 11% 39%

206 116 198

40% 22% 38%

238 47 10

81% 16% 3%

215 3 7

96% 1% 3%

453 50 17

87% 10% 3%

FIRST GRAFTS

B cell + ve crossmatch

NO YES Not tested Z2z = 37.22 p = highly significant U or T + ve crossmateh

B cell + ve crossmateh

NO YES Not tested ~ = 32.74 p = highly significant U or T cell + ve crossmatch

NO YES both N o t tested ~z = 31.57 p = highly significant

246

Table A 7.17a. Cyclosporin A on day 1 (CYA) and D a y 1 graft function ( D A Y 1)* FIRST GRAFTS

HS patients

Control patients

Combined

291 239 108

46% 38% 17%

309 228 116

47% 35% 18%

600 467 224

47% 36% 17%

182 346 110

29% 54% 17%

142 378 133

22% 58% 20%

324 724 243

25% 56% 19%

27% 22% 5% 14% 13% 1% 17%

193 153 32 81 52 9 133

30% 23% 5% 12% 8% 1% 20%

366 292 66 173 137 14 243

28% 23% 5% 13% 11% 1% 19%

Cyclospor~ A on day 1 NO YES NK X~=0.91 p = 0.63

Day lfunction NO YES NK ~=8.36 p = 0.015

Cyc~spor~ A and Day lfunction NO YES NK NO YES NK

YES YES YES NO NO NO NK

173 139 34 92 85 5 110

Z~ = 13.62 p = 0.03 * Mantel-Haenszel test of association (~12= 1.40) between C Y A (yes or no) and Day 1 (yes or no) pooled across 4 strata - HS/control first grafts; HS/control regrafts - is not statistically significant. Nor is there significant heterogeneity (2(3~ = 3.64) of association between strata.

Table A7.17b. Cyclosporin A on day 1 (CYA) and D a y 1 graft function ( D A Y 1)* REGRAFTS

HS patients

Control patients

Combined

Cyclosporin A on day 1 NO YES NK ;t~ = 3.21 p = 0.20

235 250 42

45% 47% 8%

217 183 39

49% 42% 9%

452 433 81

47% 45% 8%

200 283 44

38% 54% 8%

127 260 52

29% 59% 12%

327 543 96

34% 56% 10%

25% 25% 3% 16% 21% 1% 8%

136 111 13 61 62 4 52

31% 25% 3% 14% 14% 1% 12%

270 242 31 146 174 7 96

28% 25% 3% 15% 18% 1% 10%

Day l function NO YES NK ;(~ = 10.00 p = 0.007

Cyclosporin A and Day I function NO YES NO NO YES NK Z ~ = 13.69 p = 0.03

YES YES YES NO NO NO NK

134 131 18 85 112 3 44

247 Table A 7.18. Transfused FIRST GRAFTS NO YES NK

HS patients

Control patients

Combined

8 597 33

18 591 44

26 1188 77

1% 94% 5%

3% 90% 7%

2% 92% 6%

x~ = 5.27

p = 0.07 REGRAFTS

HS patients

Control patients

Combined

NO YES NK Z~ : 1.19 p =0.55

8 500 19

6 411 22

14 911 41

2% 95% 4%

1% 94% 5%

1% 94% 4%

Table/17.19. Duration of previous graft (PRE6) REGRAFTS

HS patients

Control patients

Combined

g 7 days 8-15 days 16-40 days 41 I00 days 101 365 days 366+ days ;~52= 26.78 p = 0.0001

81 42 61 95 94 154

53 34 58 56 49 189

134 76 119 151 143 343

15% 8% 12% 18% 18% 29%

12% 8% 13% 13% 11% 43%

Table A 7.20. W A I T (months) from previous graft failure to regraft REGRAFTS

HS patients

Control patients

Combined

mean sd n

38.7 29.8 527

22.3 24.1 439

32. I 28.5 966

14% 8% 12% 16% 15% 36%

248 Table A 7.21. Classification by peak and latest reaction frequency (PKLA) FIRST GRAFTS Level of latest reaction frequency

# patients

% of HS

653 115 183 142 198

18% 29% 22% 31%

REGRAFTS Level of latest reaction frequency

# patients

% of HS

controls HS: 0% RF 1-50% RF 51-80% RF 81+ % R F

439 51 140 144 192

10% 27% 27% 36%

controls HS:

0% 1 50% 51-80% 81 + %

RF RF RF RF

Appendix III. Risk factor report by registry Table A 7.22. Transplant year (TRYR)

REGISTRY TRYR

HS patients

Control patients

Combined

UKTS

~1982 83 84 ~ 1985

34 42 88 108

12% 15% 32% 40%

31 48 93 47

14% 22% 42% 21%

65 90 181 155

13% 18% 37% 32%

EURO

~ 1982 83 84 ~ 1985

92 122 160 172

17% 22% 29% 32%

91 119 166 156

17% 22% 31% 29%

183 241 326 328

17% 22% 30% 30%

FRANCE

~1982 83 84 ~ 1985

26 35 31 31

21% 28% 25% 25%

26 36 35 26

21% 29% 28% 21%

52 71 66 57

21% 29% 27% 23%

SWISS

~ 1982 83 84 ~1985

17 12 15 23

25% 18% 22% 34%

15 15 13 23

23% 23% 20% 35%

32 27 28 46

24% 20% 21% 35%

N. ITALY

< 1982 83 84 >- 1985

1 9 11 11

3% 28% 34% 34%

1 8 11 12

3% 25% 34% 38%

2 17 22 23

3% 27% 34% 36%

SPAIN

=<1982 83 84 =>1985

nil 5 12 14

16% 39% 45%

nil 4 14 13

13% 45% 42%

nil 9 26 27

14% 42% 44%

=<1982 83 84 > 1985

13 18 32 28

14% 20% 35% 31%

37 22 12 14

44% 26% 14% 16%

50 40 44 42

28% 23% 25% 24%

SCANDIA

249

Table A 7.23. Number of D R mismatches ( M M D R ) REGISTRY MMDR

HS patients

Control patients

Combined

UKTS

mm mm mm

83 156 33

31% 57% 12%

66 121 32

30% 55% 15%

149 277 65

30% 56% 13%

EURO

0mm mm 2mm

319 192 35

58% 35% 6%

297 199 36

56% 37% 7%

616 391 71

57% 36% 7%

FRANCE

0 mm mm 2 mm

31 75 17

25% 61% 14%

28 75 20

23% 61% 16%

59 150 37

24% 61% 15%

SWISS

0 mm mm mm

16 38 13

24% 57% 19%

16 37 13

24% 56% 20%

32 75 26

24% 56% 20%

N. ITALY

0 mm mm mm

3 26 3

9% 81% 9%

1 28 3

3% 87% 9%

4 54 6

6% 84% 6%

SPAIN

mm mm mm

17 14 nil

55% 45% 0%

21 9 1

68% 29% 3%

38 23 1

61% 37% 2%

SCANDIA

mm mm mm

41 37 7

48% 44% 8%

27 51 13

30% 56% 14%

68 88 20

39% 50% 11%

D R typing was not available for the majority of N. Italy donors and 1 D R mismatch is assumed.

250

Table A 7.24. Cyclosporin A (CYA) on day 1? REGISTRY CYA

HS patients

Control patients

Combined

UKTS

No Yes NK

88 178 6

32% 65% 2%

85 124 10

39% 56% 5%

173 302 16

35% 62% 3%

EURO

No Yes NK

334 157 55

61% 29% 10%

317 166 49

60% 31% 9%

651 323 104

60% 30% 10%

FRANCE

No Yes NK

35 1 87

28% 1% 71%

34 3 86

28% 2% 70%

69 4 173

28% 2% 70%

SWISS

No Yes NK

23 43 1

34% 64% 2%

19 45 2

29% 68% 3%

42 88 3

32% 66% 2%

N. ITALY

No Yes NK

1 31 nil

3% 97%

1 31 nil

3% 97%

2 62 nil

3% 97%

SPAIN

No Yes NK

26 5 nil

84% 16%

25 4 2

81% 13% 6%

51 9 2

82% 15% 3%

SCANDIA

No Yes NK

18 72 1

20% 79 % 1%

43 36 6

51% 42 % 7%

61 108 7

35% 61% 4%

A p p e n d i x I V . C o v a r i a t e n a m e s , s t r u c t u r e and d e s c r i p t i o n

Covariate

Type*

:

# indicators

Description

Tissue antigens: mismatching MMA MMB MMDR MBDR MMS MMS trend BENE BEN4 BEN6

I I I I I T I I I

BEN6 trend

T

MM27

I

2 2 2 4 6 0 1 3 5

26

mismatches on A locus (0;1;2) mismatches on B locus (0;1;2) mismatches on DR locus (0;1;2) mismatches on B + DR (0;1;2;3;4) total mismatches (0; 1;2;3;4;5;6) linear regression on # mismatches beneficial matching (ABDR: 000 + 100 + OlO;rest) beneficial matching (ABDR: O00;lO0;OlO,~est) beneficial/DR matching (ABDR: 000; lO0;OlO,'rest..O;rest.. 1;rest..2) linear trend thro' ordered categories of beneficial/DR matching 27 varieties of mismatch ( ~ MMA + 3 # MMB + 9 # M M D R + I)

251

Covariate

Type*

:

# indicators

Description

2 2 2 4 4 6 26

sharing on A locus (0; 1;2) sharing on B locus (0;1;2) sharing on D R locus (0;I;2) sharing on A + B (0;1;2;3;4) sharing on B + DR(0;1;2;3;4) total shared antigens (0;1;2;3;4;5;6) 27 varieties of antigen sharing ( # S H A + 3 # S H B + 9 # S H D R + 1)

Tissue antigens: sharing SHA SHB SHDR SHAB SBDR SHS SH27

I 1

I I I

I I

Ischaemia time ISCH

L

ISC 1

I

ISC0

I

linear regression on ischaemia time (hrs) with imputations of 24 hrs for not recorded ischaemia time ( =<15hrs; 16-20 hrs; 21-25hrs;

26-30 hrs; 31 4-hrs; not recorded) Recipients for whom ischaemia time is not recorded are deleted from data base.

Homozygosi~andrec~ntantigens HOMA HOMB HOMC

I 1 I

HOMD HDDR HMDR

1 I I

BEI 1

I

1 I 1

recipient A-hetero versus A-homozygote recipient B-hetero versus B-homozygote recipient A and B-hetero versus

A or B homozygote recipient DR-hetero versus DR-homozygote donor DR-hetero versus donor DR-homozygote DR-mismatches with donor DR homozygosity (0, donor D R het; 0, donor DR horn;

1, donor DR het; 1, donor DR horn; 2)

RDR I RDR2 RDR7 DDR1 DDR6

10

I

1

I I I I

1 1 1 1

Beneficial/DR matching with donor D R homozygosity (ABDR: 000, donor D R het; 000, donor DR hom;

I00, donor DR het; 100, donor DR horn; 010, donor DR het; 010, donor DR horn; ..0, donor DR het; ..0, donor DR hom; ..1, donor DR her; ..1, donor DR hom; ..2) recipient DRI? (no or NK; YES) recipient DR2? (no or NK; YES) recipient DR7? (no or NK; YES) donor DRI? (no or NK; YES) donor DR6? (no or NK; YES)

A PRIORI INTERACTIONS with high sensitization HCP2 HCMF HCBE

I I I

1 1 1

see previous grafts rest versus highly sensitized female rest versus highly sensitized and non-

beneficially matched Year of transplant TRYR I TRYR trend T

year of transplant ( =<1982; 1983; 1984; _>_1985) linear trend thro' year of transplant

252

Covariate

Type*

:

# indicators

Description

High sensitization control versus highly sensitized control; HS current O%RF; HS current positive % R F peak/latest % R F (control; HS,0%; HS, 1-50%;

HSC PKNG PKLA

I I I

DURP

I

PKTM LATE

L L

per year from peak R F to grafting per month from latest R F to grafting

PREV

L

PRE6

I

linear regression on time to failure of previous graft (days) fail interval of previous graft (0-7 days;

HS, 51-80%; HS,HS) when most recently HS? (control; HS, >=25m;

HS, 13-24m; HS, 4-12m; HS, < 3m)

Previous graft

8-15 days; 16-40 days; 41-100 days; 101-365 days; 366 4- days) PRE2

I

HCP2

I

WAIT

L

IWAIT

I

fail interval of previous graft (0-100 days; 1014- days) INTERACTION: HSC x PRE2 (rest;

HS and previous fail time 1014- days) linear regression on waiting time for regraft: from previous failure to index graft (months) waiting interval for regraft ( < 1 yr;

l-2yrs; 2-3yrs; 3-4yrs; 4--5yrs; > 5yrs) Recipient sex FEMM PREG

I I

male versus female pregnancy/male (female, pregnancy NO;

f, pregnancy YES; f, pregnancy NK; male) Other covariates BCEL J UTCL l CYA DAY1 AGEO 2 DIAB

I I I I I I

B cells crossmatch? (neg; +re; NK) U or T cells crossmatch? (neg; some 4.ve; both NK) day 1 cyclosporin? (no; YES; NK) day 1 graft function? (no; YES; NK) adult age (15-30yrs; 31-50yrs; 51 + yrs) diabetic? ( no; YES: NK)

* Type I = indicator variables L = linear regression on continuous covariate T = linear trend on ordered categories t restricted to non-Eurotransplant recipients 2 restricted to adults; and age not recorded included as 31-50 years

8. Distinct Post-Transplant Course for Highly Sensitized Recipients? (Kalman Filter Monitoring)*

1. Introduction

Early in 1984, UK Transplant Service (UKTS) initiated its multi-centre 'Save our Sensitized (SOS)' scheme (see Chapter 9) to reduce the accumulation of highly sensitized patients on the UK renal transplant waiting list. Sera with more than 85% reaction frequency against the national reference panel were selected for distribution in Terasaki trays to make up what was called the "SOS serum set". Under the scheme, donor kidneys that were crossmatch negative with both plated ('peak') and 'current' sera (tested pre-operatively) were transplanted. No mismatches of former grafts were repeated, but transplants were otherwise performed irrespective of HLA mismatches. To answer the question: is the post-transplant course for highly sensitized patients distinct from that of other recipients, the UKTS Management Committee proposed a prospective study of SOS and matched control recipients in which post-transplant course would be recorded daily until discharge, dates of onset of subsequent rejection episodes would be reported, and immunological and genetic data would be provided. Appropriate forms (see Chapter 2) were designed by the UKTS Management Committee and piloted in a retrospective study of highly sensitized(SOS)/ matched control pairs. Collaborating centres were those represented on the UKTS Management Committee. We also report a subsequent validation exercise in the same centres plus Bristol.

2. Study method and Kalman filter

Seven centres completed retrospectively the specially designed records for their highly sensitized patients who had been transplanted under the UKTS SOS * Co-authors: K. Gordon & A. F. M. S. Smith, Department of Mathematics, Nottingham University.

254 Table 8.1. Fifteen SOS/Control pilot pairs SOS or control

Graft number

Cyclosporin

1 2 3 4 5 6 7 8

S

1 1 1 2 1 1 2 2

Y Y Y Y no data Y Y Y

F F M M

9 10 11 12

S

C

1 1 2 2

N N N N

10 10 84 20 02 85

F F

13 14

S C

3 3

Y Y

04 24 13 28

05 05 11 11

84 84 84 84

F F M M

15 16 17 18

S

1 1 1 1

Y Y Y Y

03 04 03 25 23 28

05 05 11 11 08 08

84 84 84 84 84 84*

M M F F F F

19 20 21 22 23 24

S

C

2 2 3 3 1 1

Y N Y Y N N

08 03 84 03 08 84

F F

25 26

S C

1 1

Y Y

19 19 20 28

F F M M

27 28 29 30

S

1 3 3 3

N Y Y Y

Centre

Date of index transplant dd mm yy

Sex

2

20 19 29 26 03 08 24 29

07 08 07 08 I1 11 11 11

84 84 84 84 84 84 84 84

M M M M F F M M

11 22 15 13

03 03 03 04

84 84 84 84

3

5

8

10

19

25

02 02 08 08

84 84 84 84*

Patient

C S C S C S C

C S

C S C

C S C S

C S

C

* Registered with UKTS as having more than 50% reactive antibody.

scheme and for matched control patients. Control patients were matched for centre, sex, graft number matched, and next recipient on UKTS records. The pilot study included 15 SOS/control pairs. A further 23 SOS/control pairs transplanted up to January 1986 in Management Committee centres and in Bristol were collected coincidentally through the Council of Europe Study.

255 Table 8.1. (continued) Kidney function: onset day

Reciprocal creatinine Grumbling or Upward slope

OUTCOME: DAYS POST-TRANSPLANT

-

G G

10 0 -

G

-

G

6

9 3

G G

49

I

G

Rejection therapy

I F

G

13

G

10

U U

6 8

1

U

11

0 0 0 0

U U

no data 0 0 no data no data 0

U U U U U G G G G

0 23 19

G G

1

F

0

F

10

29 37 11

I

*

D

**

U

9 2, 17

U

5

22 20 18 17 _

_

12 11 30 21

2, 17, 23, 28 6

9 4,8 19 no data 6

_

14 9

2

G

0

5 4

3, 9

U no data

0 9 0

Discharge

F Failure (non-immun.) I Immunological failure D Death

I

***

F

1

11 14 14 28 29 27 62 20 16 24 27 23

* Immunological failure at 5 months. ** Patient died at 2 months. *** Immunological failure at 3 months.

Serum creatinine, recorded daily from the day of transplant until discharge, was taken as the marker of post-transplant events and used for the statistical identification of rejection. The methodology (Kalman filter) exploits the fact that renal function, expressed as reciprocal serum creatinine, behaves as a series of approximately linear trends, whose direction (i.e. slope) changes as function

256 moves from improvement to deterioration. Other changes in renal function are induced by external events such as dialysis, which alters creatinine level, and rogue laboratory measurements, which result in transient changes, both of which the Kalman filter has been designed to 'filter' out analytically so that instabilitY in the series does not obscure salient slope changes indicative of rejection. The Kalman filter identification of rejection episodes is compared with the date of clinical intervention to increase immunosuppression. Weightcorrected reciprocal creatinine corrects for distortion of serum creatinine induced by changes of hydration and was analysed whenever possible in preference to unadjusted reciprocal creatinine.

3. Pilot study results

3.1. Design and data quality Two design problems were identified. Graft number matching failed in two pilot study pairs, whether by reason of carelessness, error in UKTS records or misreporting by centres, with the result that two first transplant SOS patients were paired with retransplant control recipients. We also failed in the pilot study (rectified at validation: see later) to exclude as potential control transplants other sensitized recipients with more than 50% reactive antibody against local panels. Table 8.1 documents the 15 SOS/control pilot pairs: three of the 15 control patients were registered with UKTS as having more than 50% reactive antibody. Nine of the 15 SOS patients received a first transplant and 21 of the 30 transplantees were given Cyclosporin. Missing data were few in respect of serum creatinine and immunosuppression; weight was less well documented because not all centres weigh recipients daily. Serum creatinine was taken as the marker of post-transplant events, because it is a generally accepted criterion and because Kalman filter analysis of (weight-corrected) reciprocal serum creatinine has been shown in one centre to be a successful monitor of rejection episodes in renal transplantation (Knapp M S et al, 1983).

3.2. Distinct post-transplant course for SOS patients: "grumbling start" Figure 8.1 shows the time series of reciprocal creatinine ( x 1000) for two SOS/ control pairs (A and B) from a single centre. Notice that as serum creatinine decreases from 500 to 200, reciprocal creatinine ( x 1000) increases from 1000/ 500 = 2 to 1000/200 = 5; thus increasing reciprocal creatinine is good for the patient. In Figure 8.1, both highly sensitized patients have a grumbling start to their creatinine profile, whereas reciprocal creatinine is increasing from the

257

11_

91

KEY ....

10_

.

.

control .

SOS

.

* rejection treated _

111 125

,,.;~/',,,.....-"

143 ~ -I-

.~

167 ~:

.~6_ 0

E

/,,,.,:

5_ ~

o o

,,,-, •

/ 3_ ,"

~

o',

s

,(

,¢~°"

,'-... ~

,,;:;8

,,

/,, .- • %.- /

~ ...

,' .;A

~,-

/

200 ~

?

/ "

OC

250 ~ 333

500

_ _

1000

, ~ . . , . ~ ; : •~ . ~ ' ~ - ~ . . . ~ . - ~

2

4

6

8

10 12 14 16 18 20 22 24 26 28 DAYS POST TRANSPLANT

Figure 8.1. Post transplant reciprocal creatinine.

11_

91

KEY ....

10.

control

SOS * rejection treated .

.

.

.

.

.

125

_z 8 #7

~6 O

==5 o o

111

/

/

!

-I-

167 ~:

/B

I,,,""'"~ •

.

. . . . .

4

6

8

10 12 14 16 18 20 22 2 4 2 6 DAYS POST TRANSPLANT

Figure 8.2. Post transplant reciprocal creatinine.

~

~-~

~50

~,

333

. . . . ;,,, . / ;<",..'",, ':,':'-- ,,':,,.,,"" ...... ,,~.:~,L ~ , # : ' - : ....... -~ '. ~ : L . , : : < " L . ) < Z . 3 . < > . ~ : z , ........ ~.................. 9

2

...

143 ~_

1000 28

258 Table 8.2. Reciprocal creatinine initial profile: Grumbling start or Upward slope

SOS/control

Number of pairs

1st transplants

Retransplants

Mismatched

U

1~" p = 0.12

0

0

I

4 3

3 1

I 2

0 0

1

1

0

0

G

G G U U missing G

outset for both control patients. Figure 8.2, which relates to a second centre, is included to show that the so-called "grumbling start" can be associated with control recipients too, and to illustrate the spiked pattern associated with intermittent dialysis support (reciprocal creatinine increases after dialysis). Table 8.2 summarises the reciprocal creatinine profile for the 15 SOS/control pairs; in six of the seven pairs in whom the pattern was different between SOS and control recipient, a "grumbling start" was associated with the SOS patient (p = 0.12). Overall, grumbling start characterised 10 out of 14 SOS patients (creatinines were not recorded for one SOS patient whose graft never functioned) but only six of 15 control patients. These preliminary data suggested a different posttransplant course for highly sensitized and control patients, be they first or re-transplant (see Table 8.2). Our data were consistent with those reported from the UCLA International Transplant Registry (Iwaki Y et al, 1985) where nonimmediate function occurred twice as often in patients with more than 75% reactive antibody than in graft number matched unsensitized recipients. 3.3. Kalman filter: analysis 1 on nine SOS~control pairs Figure 8.3 shows reciprocal serum creatinine for a control patient who was treated for rejection on day 8 post-transplant. The Kalman filter signals instability on day 4, which on day 5 is interpreted as an unfavourable slope change, confirmation being given on day 6 when the 2-step probability of negative slope change exceeds 0.2, the Nottingham threshold (Knapp M S e t al, 1983): The intervention interval, or lag between Kalman filter 2-step probability of unfavourable creatinine slope change and initiation of rejection therapy, is two days for this patient. Figure 8.4 shows an SOS patient from a different centre who was treated for rejection on day 2 post-transplant and who received intermittent dialysis support. The Kalman filter signals frequent instability but gives no 2-step warning of unfavourable slope change. In particular, the initially decreasing reciprocal creatinine is not flagged. Initial Kalman filter analysis was made on nine SOS/control pilot pairs from four centres which returned completed forms most quickly. Table 8.3 summarizes the Kalman filter performance on the nine control patients in these four

259

91

11 10

111

9

125

8

_~ )-

.o"'...,

<

~:7

143 ~ --

,... -,°

F--

~

s'

~6 C3 ~,~. ,s~ -~.

E 5 -o

.~

., ~

.°

~

,~

C.)

~ 4

167 ~ n-

.~

'\

/ ~,

•

; ~

21111 ~

,

/ ~s

~.

~50

.~

\

:

_o 3

~

333

-~ _ __

2

4

6

8

rejection treated

¢0ntro

I

500 1000

10 12 14 16 18 20 22 24 26 28 DAYS POST TRANSPLANT

-8

THRESHOLD

-6

E

.4

~,

g~ ~ .~ x~ ~

lnn _

~

"8

~.4 o

C3

~o, .2 ~

~3

.8 _-- <

~ :~ .2

~ ~

.

.

.

.

.

THRESHOLD

.

~)

2

4

6

8

10 12

14 16 18 20 22 24 26 28

DAYS POST TRANSPLANT

Figure 8.3. Kalman filter analysis of reciprocal creatinine.

260

51111 667

1.5 '

~Z

-

"

-

•

1000

1 x 4

Magnification

~

2000

0.5

~ w ~, _

F<

~3 ¢~ ~.

D

D

D

D

D

D

333

3

~l

x:~'/'~~/'/~'t'~ D

2

4

.........

D

O

d

8

D

/'\...../'~-.~ .......... D

•

DAYS

1000

D

10 12 14 16 18 20 22 24 26 28 POST TRANSPLANT

~,v ]

~ r~i~-~ t~te~ D

--

.8

.

.

.

.

.

-

"

di~lyfi| SOS

THRESHOLD

-

-6

E

~>-z~ -4 ~<

~.~. ~

__, nn ~

n_[~_ ~]_

l

~

~

"8

.~ ~ - 4 o ~ ~'m ~. "2 ~o, ~

n

r~

.8, >Q0

THRESHOLD

2

4

6

8

FI

10 12 14 16 18 20 22 24 26 28 DAYS

Figure 8.4.

POST TRANSPLANT

Kalmanfilteranalysisof reciprocalcreatinine.

261 Table 8.3. Original Kalman filter: control patients

Centre

2

3 5 25

Patient

Reciprocal creatinine

Instability signalled

-

Unfavourable slope change: 2-step

Rejection therapy initiated

2

G

-

-

4

U

9

-

-

6

G

6,8

-

-

8

G

5, 6, 8, 11, 15, 18, 19

-

10 12 14 28 30

U U U G U

4, 10, 16 12 6, 9,11, 14, 15, 16, 18, 19 3, 7, 14, 16, 19, 21

6 renal artery thromb.

10 6 6 5, 6

8 11 2, 17 5

centres, o f w h o m seven h a d received C y c l o s p o r i n a n d f o u r (see T a b l e 8.1) h a d a n initially g r u m b l i n g c r e a t i n i n e profile. T h e K a l m a n filter p e r f o r m s well. O n l y o n c e d i d it fail to signal at o r b e f o r e a n t i - r e j e c t i o n t h e r a p y was i n i t i a t e d . T h e failure c o i n c i d e s w i t h a n initially g r u m b l i n g c r e a t i n i n e profile; the o t h e r three such profiles p r o g r e s s e d to n o n - i m m u n o l o g i c a l graft failure, i n o n e case associa t e d w i t h r e n a l a r t e r y t h r o m b o s i s for w h i c h a n t i - r e j e c t i o n t h e r a p y was s t a r t e d i n a p p r o p r i a t e l y o n d a y 6. T h e r e are i n d i c a t i o n s t h a t the i n t e r v e n t i o n i n t e r v a l differs b e t w e e n centres, c e n t r e 25 i n t e r v e n i n g early w h e r e a s c e n t r e 3, for e x a m ple, i n t e r v e n e d late. F r o m T a b l e 8.4 for SOS p a t i e n t s we n o t e the f r e q u e n t failure o f the K a l m a n filter to w a r n o f a n i n i t i a l l y t r o u b l e s o m e graft. T h i s difference in K a l m a n filter p e r f o r m a n c e b e t w e e n c o n t r o l a n d S O S p a t i e n t s is a f u r t h e r i n s i g h t i n t o the a t y p i c a l p o s t - t r a n s p l a n t c o u r s e o f h i g h l y Table 8.4. Original Kalman filter: SOS recipients

Centre

2

3 5 25

Patient

Reciprocal creatinine

Instability signalled

Unfavourable slope change: 2-step

Rejection therapy initiated

1 3 5 7 9 11 13 27 29

G G no data G G G G U G

-

24 not fitted

-

6, 8, I0, 17, 22 not fitted 3, 5, 7, 10, 11, 21, 22 6 6, 9, 12, 13 4, 7, 9, 17, 20, 17 12 4, 6, 9, 11, 15-17, 22

17 8 -

3, 9 not fitted 10 6 2 9 -

262 sensitized patients. That not all such patients follow the same pattern suggests that we should seek refinement of the crossmatch test or immunological and genetic windows through which some SOS patients have seen their way to an auspicious post-transplant course.

3.4. Retuned Kalman filter: analysis 2 Prior estimates derived from non-Cyclosporin treated, and mainly non-highly sensitized, Nottingham patients were adequate for the Kalman filtering of reciprocal creatinines from control patients in other centres. Failure in respect of SOS recipients was principally failure to signal an initially unfavourable slope, and so retuning concentrated on the slope parameter. Using one SOS/control pair as training set, in which only the SOS patient has a grumbling creatinine profile, resetting the prior slope information from expectation zero to ÷ 3 (on the scale of reciprocal creatinine x 1000) resulted in signals which correctly discriminated between the SOS and control profile. Shallower slope changes, such as + 1, failed to induce the signalling of initially unfavourable slope in the SOS test patient, presumably because the prior slope to variance ratio was weak. The retuned Kalman filter (prior expectation of slope equal to ÷ 3) was then evaluated on all 15 SOS patients, as reported in Table 8.5. Comparing the original and retuned Kalman filters on SOS and control patients confirms the improved performance on SOS recipients of the retuned Kalman filter which now correctly signals an initally unfavourable slope. Whereas the principle of reflecting prior knowledge advocates use of the retuned Kalman filter only for known SOS recipients, the good global performance of the retuned filter on both SOS and control patients, if sustained in a more extensive validation exercise, could seem to promote its use in ignorance of the patient's sensitization status.

4. Validation exercise

To validate the observation that a grumbling initial creatinine profile, described as non-immediate function by Iwaki et al (1985), predominates amongst SOS recipients, 23 further SOS/control pairs, transplanted up to January 1986 in UKTS Management Committee centres and in Bristol, were reviewed. Basic data for the validation pairs are given in Table 8.6 and reciprocal creatinine initial profiles are summarised in Table 8.7, together with whether kidney dysfunction was recorded on the day of transplant, and the need for dialysis on the day of transplant or the next day. In Table 8.7 the symbol ? denotes an initial reciprocal creatinine profile which was rescued only by intermittent dialysis. Patients whose serum creatinine increased (two consecutive increments) despite dialysis were characterised as having a grumbling start. Out of 14 pairs discordant for initial

263

Table 8.5. Retuned Kalman filter: SOS recipients and control patients SOS recipients Centre

2

Patient

1 3 7 9 11 13 15 17 19 21 23 25 27 29

3 5 8 10

19 25

Reciprocal creatinine profile

Unfavourable slope change: 2-step Original

Retuned

(* if preferred)

G G G G G G U G U U G G U G

24 23, 24 6 19 8, 10 6 4 8, 9 -

3 2, 24 2, 23, 24 3 3, 17 3 4,6 2, 9 2, 6 2, 4 2 8, 9 2

* *

Rejection therapy or graft failure

3,9 49 10 6 2

* * * * * * ? Dialysis * * ? Dialysis

2, 17, 23, 28 4,8 no data F~ 9

CONTROL patients Centre

2

3 5 8 I0

19 25

Patient

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Reciprocal creatinine profile G U G G U U U U U U U G G G U

Unfavourable slope change: 2-step Original

Retuned

( # if not preferred)

Rejection therapy or graft failure

10 6 6 not not not not not not 5

fitted fitted fitted fitted fitted fitted

15 10 6, 6, 5 6, 4, 6 5,

7 9, 20 10 6

6

c r e a t i n i n e p r o f i l e ( G o r ? v e r s u s U ) in t h e c o m b i n e d

8

11 6 9 19 6 2, 17 5

(pilot +validation)

series,

t h e g r u m b l i n g o r e q u i v o c a l s t a r t w a s a s s o c i a t e d w i t h t h e S O S p a t i e n t i n 10 p a i r s ( p ,,~ 0.18). A t l e a s t 32 d i s c o r d a n t

p a i r s n e e d t o b e s t u d i e d t o h a v e e v e n a 50 : 50 c h a n c e

of identifying statistically a 2 to 1 more frequent association of grumbling with the SOS patient; and 64 discordant

start

pairs give a respectable 80% power.

264

8

8 ©

265

266 Table 8. 7. Reciprocal creatinine initial profile (Grumbling start Or Upward slope); kidney dysfunction on the day of transplant; initial dialysis (on day of transplant or next day) Reciprocal creatinine

NUMBER OF PAIRS

SOS/control Pilot G U U G G G ? G U U ? U U ? missing data

6 1 4 0 3 0 0 1

Kidney dysfunction on day of transplant

NUMBER OF PAIRS

SOS/control

Pilot

Y

Validation 2 3 5 4 2 2 0 2

Validation

8 [. 4 9 4 p =0.18 5 2 ~ 0 3

J

Pilot ÷ validation

N

5

1

4

N N Y Y missing data

4 2 3

12 0 1

16 2 4

Validation

Pilot + validation

N

Y

Dialysis on day of transplant or next day

NUMBER OF PAIRS

SOS/control

Pilot (common centres)

Y N N Y N N Y Y missing data

6

Pilot ÷ validation

3(1) 0

5(5) 4(3)

2(2) 9(8) 1(1)

4(4) 8(5) 2 ( 2 ) both dialysed

.IJ p = 0.21

84}

p = 0.39

6 17 3

Footnote: In brackets are shown the dialysis counts for Centres with patients in both the pilot and validation series. For these centres initial use of dialysis differs significantly between pilot and validation phases Zl2 = 4.3.

Thus, even the combined series lacks power and follow-up of at least as many pairs again is needed. Our intention in the Council of Europe Study had been to achieve this follow-up but in practice mailing was restricted to those centres already familiar with the pilot study so that non-initiated centres were not burdened with requests to complete at once two unfamiliar sets of Council of Europe forms: follow-up and post-transplant course. In the validation series there was frequent failure of the Kalman filter to signal treated rejection episodes in highly sensitized patients. In six SOS

267 patients there were Kalman filter signals, which accorded with the clinical management in three cases; in the remaining 17 SOS patients, unsignalled clinical interventions were recorded in 12 patients, in four of whom graft failure was, with hindsight, the day of transplant, yet various clinical manoeuvres were attempted. The more frequent early dialysis in the validation series may have limited the value of Kalman filter predictions.

5. Discussion Retrospective study of 15 highly sensitized SOS/control pairs suggested that the post-transplant course of highly sensitized and control recipients differed initially, there being more often a grumbling start to the creatinine profile of highly sensitized patients, which accorded with a doubling in the incidence of non-immediate function in highly sensitized patients, as reported by the UCLA International Transplant Registry. The pilot study, besides reaffirming the value of plotting reciprocal serum creatinine, was independent validation of the Kalman filter for monitoring rejection episodes in control patients. By resetting prior estimates so that they are appropriate for highly sensitized SOS patients, the retuned Kalman filter's performance on SOS patients was also creditable (see pilot study) but was vulnerable when intermittent dialysis was instigated early in the post-transplant course (see validation exercise). Extending the pilot study to include 23 further SOS/control pairs transplanted up to January 1986 in the Management Committee centres and in Bristol was inadequate; power was lacking still. Overall, there were sixteen pairs which were discordant in respect of kidney dysfunction on the day of transplant and, in 11 of these, the SOS kidney did not function when the control kidney did (sign test: p ~ 0.21). Early dialysis (on the day of transplant or on the next day) was more commonly a feature of transplant management in validation than pilot pairs from centres which contributed to both series. Early dialysis may have compromised Kalman filter performance.

Acknowledgement We are grateful to the members of UKTS Management Committee for their collaboration.

9. Special Schemes for Transplanting Highly Sensitized Patients

1. Introduction to 11 special schemes

Eleven special schemes for finding crossmatch negative kidneys for highly sensitized patients were initiated in Europe between 1976 and 1986. The schemes are listed in Table 9.1 in order of initiation date. The first scheme for highly immunized patients (ET:HIP) was started by Eurotransplant in 1976 with eligibility defined as peak reaction frequency of at least 60%. Up to mid 1986 approximately 2,000 transplants had been performed under this scheme, at the rate of 200 transplants per year; 10% to 15% of donor kidneys in Eurotransplant were diverted to these highly immunized patients. The next scheme initiated by Eurotransplant is known as the European highly immunized file (ET: EIF). It started in 1978 and admitted patients with peak reaction frequency of at least 85%. Before 1986, 88 transplants in highly immunized patients had been performed, at a rate of 11 transplants per year, and utilising from 1% to 2% of all kidneys in Eurotransplant. On 1 January 1978, North Italy Transplant introduced a special scheme (NIT:l) for patients whose peak reaction frequency was more than 30%. Whereas the preceding two schemes had only advised removal of auto-antibodies by absorption, since 1985 the North Italy Transplant scheme has required the treatment of auto-antibodies by Dithiothreitol (DTT), which removes all IgM activity- both allo and auto antibody. About 29 patients per year were transplanted under the North Italy scheme which appropriated approximately 6% of donor kidneys. In May 1981 Scandia Transplant introduced a special scheme (SKT: 1) for patients with more than 90% peak reaction frequency. No treatment of autoantibodies was advised. Approximately six patients were transplanted per year, using 1% of kidneys. The first Swiss Transplant scheme (SWT : 1) was introduced on 1 January 1982 and applied to patients who had more than 50% peak reaction frequency.

269 Table 9.1. Special schemes: their initiation, eligibility and transplant statistics

Scheme

Eurotransplant highly ~mmunized patient (ET HIP) European Immunized File (ET ElF) North Italy Transplant Scheme 1 (NIT 1) Scandia Transplant Scheme 1 (SKT:I) Swiss Transplant Scheme 1 (SWT:I)

Initiation date

ElJgibilit y peak reaction treatment frequency and of unless stated other wise auto antibodies

NO transplants to date ( )

>60%

absorption advised

200 approx

20 approx )assuming 16% or 1000 Eurotransplant patients are eligible for ET HIP)

10%to 15%

200Oapprox

1978

>85% (or high clinical urgency)

absorption advised

11 approx

2 approx )assuming 9% or 570 Eurotransplant patients are eligible for ET ElF)

0 6% ~n 1984 2O% ~n 1985

88 to (01 01 86)

1st Jan 1978

,3O%

DTT since 1985

29 approx

7 5 appro× (assuming 20% or 370 N Italy Transplant patients are eligible for NIT:I )

6%

NO data

6 approx

5 approx (assuming 11% or 120 Scandia Transp}ant patients are eligible for SKT 1)

1%

30

28 approx

23 approx (assuming 38% or 120 Swiss Transplant patients are eligible for SWT:I )

15%

110 to (01 01 86) approx

May 1981

1st Jan 1982

> 90%

50%

none

all sera tested on patient's own lymphocytes DTT or heating at 37°C for 3 0 mins

UK Transplant SOS scheme (UKT: SOS)

Feb 1984

Collaborative Transplant Study Highly immunized trial (CTS HIT)

Early 1985

North Italy Transplant Highly immunized trial (NIT 2)

Swiss Transplant Scheme 2 (SWT:2)

Transplant statistics for the special scheme NO transplants Transplants per 100 eligible patients as %kidneys°f donor per year

1976

France Transplant Scheme 1 (FR1)

Eurotransplant acceptable HLA A and 8 mismatches (ET:ACMM)

NO transplants per year

20 approx

5 5 approx (assuming 20% or 38O 2% (of transplants patients from 6 French centres are eligible for FR:I ) )e~formed in 6 centre~

39

none until 1st June'87 DTT since 1st June 87

46 approx

95 approx (assuming 16% or 480 UK Transplant patients are eligible for UKT SOS)

> 80% in latest serum )within 2 months)

none

52 approx

no data

no data

61 to (21 O4 86)

Sept 1985

>85%

DTT

4 approx

2 approx lassuming 10% or 185 N Italy Transplant patients are eligible for NIT2)

1%

3 to (01 07.86)

Oct 1985

> 85%

absorption PEG

27 approx

88 approx (assuming 9% of patients from 2 centres are eligibte for ET: ACMM, assuming these 2 centtes have 340 waiting patients approx and so 31 eligible patients)

1% approx (of Eurotransplant kidneys)

lslAug 1986

~ 80% in latest serum

all antibodies are tested on patient's own lymphocytes

40 approx

38 to 80 approx (prevalence of high sensitizatior is 23% assuming lower proportion say 8% to 17% with ~ 80% latest reaction frequency, 24 - 59 patients)

17%

~ 85%

3%

135 to (31 12 86)

32 to )31 12 86)

27 to (31 0387)

All sera in the Swiss scheme were tested on the patient's own lymphocytes. Up to 1 January 1986 approximately 110 transplants had been performed under this scheme, at a rate of 28 transplants per year, and utilising 15% of kidneys. These first five schemes evidenced a wide variation in eligibility criteria ranging from more than 30% to more than 90% peak reaction frequency. Treatment of auto-antibodies was not standardised but the majority at least advised absorption. The three schemes with more liberal eligibility criteriaET:HIP (more than 60% peak reaction frequency), NIT: 1 (more than 30% peak reaction frequency) and SWT:I (more than 50% peak reaction frequency) -claimed a greater proportion of donor kidneys, up to 15%, than did schemes which recruited only patients satisfying the Council of Europe Study's definition of high sensitization, that is patients having at least 80% peak reaction frequency. The latter schemes appropriated between 1% and 2% of donor kidneys.

270 The efficiency of special schemes for finding crossmatch negative kidneys for highly sensitized patients is summarised more appropriately if we take account of the pool size of waiting, eligible patients. Thus it is better to compare number of transplants performed per 100 eligible patients per year than simply the number of transplants per year. By the criterion of number of transplants per 100 eligible patients per year, the Eurotransplant scheme for highly immunized patients (ET:HIP) and the Swiss Transplant scheme (SWT: 1) for patients whose peak reaction frequency is at least 50% perform similarly at a rate of over 20 transplants per 100 eligible patients per year, and in both cases appropriate a similar proportion of donor kidneys. Comparing the Scandia Transplant scheme (SKT : 1) for patients who have more than 90% peak reaction frequency and the European immunized file (ET:EIF) which relates to patients who have more than 85% peak reaction frequency, the Scandia Transplant scheme achieves approximately 5 transplants per I00 eligible patients per year whereas the European immunized file performs at a rate of 2 transplants per 100 eligible patients per year. An important difference between these two schemes is revealed in Table 9.4 in that patients on the European highly immunized file require a compatible donor whereas the Scandia Transplant scheme permits two DR mismatches: the different matching criteria alone could account for the different transplantation rates per 100 eligible patients per year. The remaining six schemes were introduced as follows. In 1984 exchange of sera between six French (FR: 1) centres was initiated for patients whose peak reaction frequency was at least 75%; treatment by DTT or by heating at 57°C for 30 minutes was mandatory. To date, approximately 20 transplants per year have been performed under the scheme and this works out at about 5.5 transplants per 100 eligible patients per year, accounting for 2% of the transplants performed in the six collaborating centres. UK Transplant's Save Our Sensitized (UKT:SOS) scheme was introduced in February 1984. Patients were eligible if their peak reaction frequency was more than 85% when verified in the National Tissue Typing Reference Laboratory at UK Transplant Service. There was no treatment of auto-antibodies until 1 June 1987, when DTT was recommended but remained optional. The served 32 centres were limited to two SOS patients on each 4-monthly cycle of serum distribution. Validation of peak reaction frequency in national reference centres was a feature also of the earlier European immunized file. One hundred and thirty five patients had been transplanted under the SOS scheme in the United Kingdom up to 31 December 1986, at a rate of 46 transplants per year. Approximately 16% of patients on the UK Transplant waiting list are registered with peak reaction frequency of more than 85%; if they are taken to represent eligible patients, then 46 transplants per year approximates to 9.5 transplants per 100 eligible patients per year. In practice, eligibility for the SOS

271

scheme is limited to 115 patients per 4-month cycle (see Table 9.2) - on this basis the number of transplants per 100 eligible patients per year approximates 13. Early in 1985 the Collaborative Transplant Study introduced the Heidelberg Highly Immunized Trial (CTS : HIT). Eligibility is determined by latest reaction frequency being at least 80% and there is no treatment of auto-antibodies. Sixty-one transplants were performed up to 21 April 1986 constituting a rate of 52 transplants per year. Twenty-four centres, some of which are within the Eurotransplant area, collaborate in the scheme (CTS:HIT). Without knowing how many patients awaiting transplantation in these 24 centres have greater than 80% latest reaction frequency we cannot compute the number of transplants per 100 eligible patients per year under this scheme. Table 9.2. Special schemes: logistics

I Scheme

Number of transplant centres served by by scheme

Materials distributed to centres (or from centres: see N. italy Transplant)

Frequency of distribution

ET : HIP

97

lists sera

ET : ElF

98

lists* monthly sera 3 monthly * circulated also to other transplant registries

NIT : 1 SKT : 1

8 transplant centres and 225 dialysis centres 8

lists centralised sera in Milan sera up to May 1985. now only online matching:HLA-A, B identical or compatible donors

monthly 3 monthly

2 monthly cycle for sending sera to Milan no data

SWT : 1

6

lists sera

monthly 4 monthly

FR : 1

6

lists sera

6 monthly 6 monthly

UKT : SOS

32

duplicate serum sets one scrambled and phone call to UKTS needed to confirm duplicate negative

4 monthly

CTS : HIT

24

duplicate serum sets Consult lists to check duplicate negative

2 montt-'ly

NIT : 2

8

lists centralised sera in Milan

ET : ACMM

SWT : 2

Leiden and Rotterdam originally: Now 6 Dutch centres 6

2 monthly cycle for sending sera to Milan

documentation sera centralised in Leiden

3 monthly 3 monthly

list sera (duplicated in only 1 centre)

monthly 4 monthly

272 North Italy Transplant's Highly Immunized Trial (NIT : 2) was introduced in September 1985. Peak reaction frequency of more than 85% was mandatory, as was treatment by DTT. Only three transplants have been performed to 1 July 1986, a transplant rate of approximately 4 per year or 2 per 100 eligible patients per year, and appropriating 1% of kidneys. Unlike the European immunized file (ET : ELF), which has a similar efficiency profile, the North Italy Transplant Highly Immunized Trial (NIT:2) permits mismatching of donor and recipient and so would have been expected to have had a higher transplantation rate. In October 1985 Eurotransplant introduced its third scheme (ET:ACMM) which entailed identifying acceptable HLA-A and B mismatches for highly sensitized patients whose peak reaction frequency was more than 85%. Acceptable mismatches are defined as HLA antigens foreign to the patient, against which the patient fails to produce antibody. Definition of these antigens requires sophisticated serological analysis using selected panels of carefully typed reference cells from over 25,000 blood donors. Treatment of auto-antibodies was by absorption of Polyethylene Glycol (PEG). Two weeks' work is entailed for each patient to identify acceptable mismatches and a program written on a personal computer aids their laboratory definition. Acceptable HLA-A and B mismatches for the patient are then programmed into the Eurotransplant donor selection scheme, effectively augmenting the recipient's HLA-A/B phenotype. Initially, the scheme was limited to two Dutch centres: Leiden and Rotterdam. Up to 31 December 1986, 32 transplants were performed in these two centres, a rate of approximately 27 transplants per year. In Eurotransplant as a whole, 9% of patients have more than 85% peak reaction frequency. Assuming 9% prevalence in Leiden and Rotterdam also, where there are approximately 340 waiting patients, the efficiency of Eurotransplant's acceptable HLA-A/B mismatch scheme (ET : ACMM) is around 88 transplants per 100 eligible patients per year. This is phenomenal! Besides kidneys, the clear limitation is laboratory time; the services of one senior laboratory technician are required to work up 27 patients per year. However, the success rate of these transplants is currently high, estimated to be 90% at one year, when other schemes report 70% or less. In the United Kingdom, Wood et al. (1987) calculated that each successful graft saves the National Health Service £30,000 (at 1982 prices) so that even one extra successful graft compensates for the laboratory investment. Approximately 1% of the Eurotransplant kidneys, a high proportion, have been assigned to Leiden and Rotterdam for the transplantation of these highly sensitized patients. The latest scheme, introduced by Swiss Transplant (SWT:2) on 1 August 1986, accepted patients who had 80% or more reaction frequency in their latest serum. As in the earlier scheme by Swiss Transplant, all sera were tested for auto-antibody on the patient's own lymphocytes. Up to 31 March 1987, 27

273 transplants had been performed at a rate of 40 per year. The prevalence of high sensitization is of the order of 23% in Swiss Transplant. Assuming a lower proportion, say between 8% (ie one third as in transplanted database in Chapter 7) and 17%, with current reaction frequency of 80% or more, we estimate between 24 and 50 eligible patients throughout Swiss Transplant and so the efficiency index for the latest scheme is between 38 and 80 transplants per 100 eligible patients per year; 17% of donor kidneys are appropriated under the Swiss Transplant scheme. In summary (see Table 9.1), we note considerable variation in eligibility criteria for earlier schemes, with 80% to 85% pea k or latest reaction frequency emerging as criteria for entering the later schemes. Four of the schemes originally advised no treatment of auto-antibodies; North Italy Transplant's first scheme (NIT: l) and UK Transplant's SOS scheme (UKT : SOS) both revised policy to include DTT. Moderately successful schemes for highly sensitized patients use between 2% and 3% of donor kidneys but, as the Eurotransplant scheme for finding acceptable HLA-A/B mismatches ( E T : A C M M ) and second Swiss Transplant scheme (SWT : 2) show, there is potential for a considerably higher proportion of donor kidneys to be appropriated by a highly efficient scheme. The UK Transplant SOS scheme approximately doubles the efficiency of the French and Scandia Transplant schemes but is eight-fold lower than the Eurotransplant acceptable HLA-A/B mismatches ( E T : A C M M ) and Swiss Transplant (SWT : 2) schemes.

2. Special schemes: logistics Table 9.2 summarises the logistics of the 11 special schemes. The number of transplant centres served by each scheme varies from two to 98. Materials distributed are lists of highly sensitized patients, giving their HLA-type and forbidden antigens, and corresponding sera. Scandia Transplant ceased circulation of sera in May 1985; since then, on-line computer matching has been available which selects HLA-A/B identical or compatible donors for highly sensitized recipients. The European immunized file is circulated to other transplant registries as well as to some 98 transplant centres. In most schemes lists are sent out monthly, with sera being distributed in two, three, four or sixmonthly cycles, for organizational convenience or having regard to the availability of serao North Italy Transplant has the special feature of centralizing all laboratory screening and crossmatch activity in Milan whereas other schemes collect in sera and redistribute them in Terasaki trays to participating centres. In the case of UK Transplant's SOS scheme and the Collaborative Transplant Study (HIT scheme) serum sets are duplicated in known and scrambled order. A donor kidney is assigned only if crossmatch tests are negative in both

274 the "known order" and "scrambled" sets. Whereas the CTS:HIT scheme sends lists to tissue typing laboratories from which to identify in the "scrambled" set the duplicate serum for a given patient, the SOS protocol requires a telephone call to UK Transplant to ascertain whether the duplicate is correspondingly negative, laboratories being blind to the identity of sera in the second, "scrambled" UK tray. Scandia Transplant and Eurotransplant's scheme for identifying acceptable HLA-A/B mismatches (ET: ACMM) avoid the distribution of serum trays to many centres. In the latter scheme, the laboratory work-up on a patient requires two weeks and currently is performed only in Leiden, but other tissue typing laboratories could be trained in these methods.

3. Crossmatches and other features of special schemes

With the exception of three French centres, pre-operative positive B cell crossmatches are ignored in all schemes (see Table 9.3). Only UK Transplant's SOS scheme claims that crossmatches with donor tissue are performed for all pre-operative serum samples. Other schemes admit that such testing is discretionary, depending on the transplant centre, but all except SWT:2 recommend testing against peak and most recent sera. In North Italy Transplant, because the selected sera are held in Milan itself, there is uniformity in the practice of crossmatch testing against peak, current and certain historical sera. The recent scheme introduced by Swiss Transplant (SWT:2) requires only that the current serum be tested in addition to the distributed serum. Eurotransplant's acceptable HLA-A/B mismatch scheme (ET : ACMM) aims to identify antigens which would not give rise to clinically relevant positive crossmatches (ie IgG anti HLA) and so crossmatches should be unnecessary if the program has performed well; in practice, selected historical sera are crossmatched with donor tissue, the selection being guided by the analysis program. Special features associated with one or more of the schemes for finding crossmatch negative kidneys for highly sensitized recipients include reference laboratory verification that peak (or latest) reaction frequency exceeds the scheme-designated cut-off; avoidance of forbidden antigens, these being mainly mismatches from previous failed transplants but might include the husband's mismatches in the case of sensitized multiparous women; duplicate negative crossmatch being required before organ allocation; and advice to type the patient's family, this being a feature of Eurotransplant's scheme for highly immunized patients (ET : HIP) and a theme underlying the scheme for finding acceptable HLA-A/B mismatches.

275 Table 9.3.

Special schemes: crossmatches and other special features

Scheme

Crossmatch practices are crossmatches with donor tissue performed with all pre~)p serum samples ?

Special features

are preoperative positive B cell crossmatches ignored?

ET : HIP

No : depends on centre

Yes

advice to type patient's family

ET : ElF

No : depends on centre but is strongly advised

Yes

reaction frequency is checked to be >85% by a national reference laboratory

NIT : 1

No : with peak and recent sera selected by Milan protocol

Yes

SKT : 1

No : centre discretion

Yes

SWT : 1

No : advised testing with peak and current sera

Yes

None

No : selected historic most positive and most recent sera

Yes (3 centres) No (3 centres)

None

FR : 1

UKT : SOS

Yes

Yes

None forbidden antigens veto offer of donor kidney to potential recipient

(i) reaction frequency is checked to be >85% by the national reference laboratory (ii) blind duplicate crossmatch must accord with negative in ordered set (iii) avoidance of mismatches from previous grafts and forbidden antigens

CTS : HIT

No : discretionary

Yes

duplicate crossmatch in 'scrambled' sat must accord with negative in ordered set.

NIT : 2

No : crossmatches with peak and recent sera selected by Milan protocol

Yes

avoidance of previous mismatches

ET : ACMM

No : only selected historical sera since crossmatches are predicted from analysis

Yes

personal computer program aids analysis of acceptable H L A - A and B mismatches

sw'r : 2

No : only most recent serum must be tested

Yes

None

4. Maximum acceptable donor HLA-A, B or DR mismatches for highly sensitized patients and priority in recipient selection Table 9.4 shows a lack of consensus on the number of acceptable HLA mismatches per locus for highly sensitized patients. The range, for HLA-A and B and for HLA-DR, is complete - from no acceptable mismatches for patients on the European immunized file (ET : EIF) to complete mismatching as permitted by U K Transplant's SOS scheme and by North Italy Transplant's second scheme ( U K T : SOS and NIT : 2). Eurotransplant has required zero D R mismatches in all three of its schemes. The second Swiss Transplant scheme (SWT:2) accepts one A and one B mismatch and up to one D R mismatch. This is a change of emphasis from SWT : 1 by liberalising acceptable class I mismatches and restricting acceptable D R

276 Table 9.4. Special schemes: maximum acceptable HLA mismatches and priority for receiving donor kidneys

I

Scheme ET : HIP

Maximum acceptable HLA mismatches CLASS I CLASS II 2 mm

0 mm

organ exchange priority no data 1st priority, but listed after high urgency patients who are full house A, B. DR identical with donor.

ET : ElF

0 mm

0 mm

NIT : 1

1 mm

2 mm

SKT : 1

0 mm

2 mm

1st priority

SWT : 1

1 mm

2 mm

1st priority

FR:I

1 mm

1 mm

1st priority

UKT : SOS

4 mm

2 mm

CTS : HIT

1 mm (A locus) and 1 mm (B locus)

no data no data

4 mm

2 mm

ET : ACMM

4 mm

0 mm

1 m m ( A locus) and 1 mm (B locus)

3rd priority after recipients with zero DR mismatches and at most I A + B mismatch.

1 mm

NIT : 2

SWT :2

1st priority

1 mm

2nd priority after 000 A, B, DR mismatches 2nd priority after "patients who have rejected a graft within 6 months of grafting, have negative crossmatch with latest and peak serum and are mismatched for at most one B and at most one DR antigen"

mismatches from two (see SWT: 1) to one. Likewise, the second North Italy Transplant scheme (NIT:2) liberalises HLA-A and B mismatches but continues to impose no DR restriction. For the most part, national organ exchange organizations accord first or second priority to highly sensitized patients identified by their special schemes. Second priority after full-house A,B,DR match is accorded to patients on Eurotransplant's acceptable HLA-A/B mismatch scheme (ET:ACMM). On the second Swiss Transplant scheme (SWT:2) highly sensitized patients are accorded second priority for organ exchange after "patients who have rejected a graft within six months of grafting and have negative crossmatch with latest and peak serum and are mismatched for at most one ~B+ DR antigen". European immunized file patients (ET:EIF) have second priority within

277 Eurotransplant after high urgency patients who are full-house A,B,DR identical with the donor; their priority in other national registries is at the discretion of the registry. In UK Transplant SOS patients have third priority after zero mismatched and 100 and 010 ABDR mismatched recipients.

5. Organ exchange hierarchy Table 9.5 shows the first four priority levels in the organ exchange hierarchy for different European registries. Eurotransplant, France Transplant, North Italy Transplant and Scandia Transplant accord first priority to special scheme patients. Swiss Transplant and North Italy Transplant accord high priority to patients awaiting regrafts provided that fairly stringent matching criteria are also met. Children are accorded second or third priority in France and North Italy Transplant. Luso Transplant gives preference to highly immunized recipients (more than 75% reaction frequency) and to children (less than 16 years old) within each of its four recognised priority levels which are described in terms of urgency and minimal matching requirements; this is the reverse of priorities selected by other registries. In UK Transplant, since January 1987, competition for a donor kidney within any priority level is resolved by other considerations such as a centre's "balance of trade figures" (that is, trade of imported kidneys to kidneys exported from the centre) and transportation times between centres.

6. Discussion Whereas all special schemes aim to find crossmatch negative kidneys for highly sensitized patients, the search is directed differently in the various registries, and the stringency of crossmatch testing with all historical sera varies also. Thus Scandia Transplant (SKT : 1) directs its donor search by requiring zero mismatches for HLA-A and B antigens. In Eurotransplant, the donor search is narrowed by requiring zero DR mismatches; a more stringent matching grade still, zero mismatches for HLA-A, B and DR, is enforced in the search for donors for patients on the European immunised file (ET : ELF). Avoidance both of previous mismatches and forbidden antigens, as in the UK Transplant scheme (UKT : SOS), is a third strategy for refining the donor search; a fourth is by Eurotransplant's scheme for identifying acceptable HLA-A/B mismatches. Schemes differ in other philosophical aspects besides how to narrow the search for potential donors. With the important exception of three French centres, the general, but dubious, assumption is made that a B-cell positive crossmatch can be safely ignored. The assumption is especially doubtful when no account is taken of DR mismatches.

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279 Alloantibodies of IgM type are susceptible to D T T treatment; these are currently removed in the schemes run by North Italy Transplant, in six French centres and is recommended in UK, where it was introduced to the SOS scheme as recently as June 1st 1987. All other schemes respond to crossmatch results which could be attributable to IgM or IgG and failing to distinguish them effectively vetoes otherwise acceptable donor kidneys. IgM antibodies are considered to be safe with the one proviso that IgM cold agglutinins can cause damage if the kidney is not pre-warmed prior to anastomosis. Immune complexes are removed in one scheme (ET-ACMM) using Polyethylene Glycol to avoid spurious results attributable to antiglobulin reactions. Autoantibodies are tested directly on the patient's own cells in the two schemes from Swiss Transplant. Absorption to remove autoantibodies is advised in two other schemes, under the auspices of Eurotransplant ( E T : H I P and ET : EIF). Since autoantibodies are widely assumed to be IgM, D T T treatment, as instituted by North Italy Transplant, the six French centres and U K Transplant, may be sufficient to reduce them. Eligibility, in respect of (1) minimum level of panreactivity and (2) peak versus latest serum, varies widely between schemes. The earlier schemes such as NIT : 1, SWT : 1, FR : 1 and ET : HIP had low thresholds of 30% to 75% peak reaction frequency, thereby admitting moderately as well as highly sensitized patients, rather than concentrating attention on the highly sensitized. The choice of peak or latest serum on which to base eligibility determines the type of patient accepted onto special schemes. Patients with a transient spike or falling reactivity against the panel will tend to be excluded from schemes such as CTS : HIT and SWT : 2 in which 'latest' sera are the credential for acceptance. In this context 'latest' usually refers to blood taken within the preceding two or three months. On the other hand, schemes defined by peak sera admit patients with transient as well as sustained panreactivity. Only one scheme ( U K T : SOS) recommends the philosophy that a positive crossmatch with any historical serum should veto transplantation; it requires that all pre-operative sera are crossmatched with the donor tissue. Four schemes (NIT : 1 and 2, SWT : I and FR : 1) opt pragmatically for crossmatch testing with peak and current sera; but the North Italy and French schemes also include other selected past sera. In the case of North Italy Transplant, the selected historical sera are held centrally in Milan, thus facilitating their testing. Other schemes leave crossmatch testing to the discretion of participating centres. The philosophy underlying Eurotransplant's scheme for finding acceptable HLA-A/B mismatches for highly sensitized patients makes crossmatch testing superfluous in theory, since crossmatch results are anticipated by avoiding HLA-A/B antigens to which the recipient specifically reacts. In practice, the evaluation of E T : A C M M would include documenting the frequency with

280 which positive crossmatches occurred when analysis had predicted that they would be negative. The goal of schemes for finding crossmatch negative kidneys for highly sensitized patients must be to equal unsensitized recipients in terms both of transplantation rate and graft survival rate. From Chapter 6, we estimate that 57 transplants per year are performed per 100 unsensitized patients on the waiting lists of Eurotransplant, France Transplant, North Italy Transplant, Swiss Transplant and UK Transplant. From Chapter 7, the increased risk of transplant failure associated with high sensitization is expressed predominantly within the first three months post-transplant when the relative risk is about 1.5, of the same order as the differential between non-beneficially and beneficially matched (zero DR mismatches and at most one A + B mismatch) grafts. Diverting a kidney to a non-benefically matched recipient when it could have been used for a beneficially matched patient sacrifices one to two years of extra graft function. Likewise diverting a kidney from a non-beneficially matched unsensitized recipient to a mismatched highly sensitized patient also sacrifices graft function- but to a more limited extent because the risks associated with high sensitization are not persistent. Of the 11 schemes for highly sensitized patients only Eurotransplant's scheme for finding acceptable HLA-A/B mismatches (ET : ACMM) exceeds the estimated transplant rate in unsensitized patients; to justify its appropriation of kidneys, ET : ACMM must demonstrate a graft survival profile which is comparable to that for unsensitized patients. No other scheme achieves a transplantation rate per 100 eligible patients per year which challenges the rate for unsensitized recipients. The nearest other contender is SWT : 2 but Swiss Tranplant achieves more than twice as many transplant per year per 100 unsensitized patients as do other registries (see Chapter 6). Published results are available for three schemes only (CTS:HIT, UKT:SOS and ET:ACMM). Chapter 7 shows that transplant survival for highly sensitized recipients is improved by DR matching. Of the foregoing three schemes UK Transplant's SOS scheme permits two HLA-DR mismatches. The Collaborative Transplant Study HIT scheme allows a maximum of one DR mismatch and the Eurotransplant scheme (ET:ACMM) insists on zero DR mismatches. On the basis of DR matching alone, patients transplanted under Eurotransplant's scheme for finding acceptable HLA-A/B mismatches would be expected to have a better transplant survival than patients in either of the other schemes. To date, based on follow-up ranging from one month to two years, there have been five transplant failures amongst 35 patients transplanted according to the ET:ACMM scheme. One year graft survival is 70% for 113 patients transplanted in the Collaborative Transplant Study Hit programme and is 57% for SOS patients. When the Eurotransplant scheme for finding acceptable HLA-A/B mis-

281 matches for highly sensitized patients extends from two to all centres, it may be necessary to revise the priority which is given within Eurotransplant to such patients, to safeguard transplantation rates for other recipients. Only schemes which do not redistribute sera are capable of being generalised to many centres. The two schemes which qualify are Scandia Transplant's kidney allocation on the basis of zero HLA-A and B mismatches (SKT: 1) and the Eurotransplant scheme for finding acceptable HLA-A and B mismatches for highly sensitized patients (ET : ACMM). The latter scheme on the one hand is more restrictive than the Scandia Transplant scheme because it insists on zero DR mismatches, and on the other hand is more liberal, because it extends the recipient's repertoire of acceptable HLA-A and B antigens, in effectively expanding the recipient's Class I phenotype. The laboratory work-up on a particular patient requires two weeks and is currently performed only in Leiden but other tissue typing laboratories could be trained in these methods. Further advantages of centralized laboratory effort are quality control on crossmatching and assurance that all relevant sera have been analysed; North Italy Transplant schemes incorporate these benefits. Besides laboratory time, another essential resource for ET:ACMM is indexed phenotypes and accessible sera from a large number of individuals, covering a wide range of HLA-phenotypes. In Leiden, 25,000 blood donors are indexed! Individual tissue typing laboratories in association with the local blood transfusion services can now equal the Leiden resources and so effort could usefully be put into establishing a European reference bank of rare cells, perhaps established as EBV transformed lines, for use by local laboratories. The Leiden technique could then be evaluated multi-nationally. This would allow numbers of transplants into highly sensitized patients to accumulate quickly with a view to: 1) comparing their survival with corresponding controls matched for centre and graft number 2) estimating how frequently anticipated negative crossmatches with historical sera turn out to be positive 3) monitoring the uptake of kidneys by highly sensitized recipients whose extended repertoire of HLA-A and B antigens includes acceptable mismatches and 4) evaluating whether the HLA-A/B transplant windows are inherited, for example coinciding with maternal antigens, or are not fixed and so the laboratory work-ups might need to be repeated at suitable intervals.

10. Making Sense of Sensitization

1. Introduction

"And time that takes survey of all the world Must have a stop"

Shakespeare

There is also a Scots proverb that "Guid gear gangs intae sma' bulk". This brief concluding chapter draws together the central issues making sense of sensitization. 2. Definition of high sensitization

Peak reaction frequency underpinned the Council of Europe Study definition of highly sensitized (see Chapter 2) because registries rely upon it to identify highly sensitized patients. The practical incentive of using the European Transplant Organizations as liaison centres for the Council of Europe Study, much relevant information being held already on their confidential, computerized databases, was overwhelming. So pragmatic a decision was not taken uncritically, however. "Give me fruitful error any time, full of seeds, bursting with its own corrections You can keep your sterile truth for yourself" Pareto In effect, Chapter 3 explored the speciousness of peak reaction frequency as a summary of sensitization status. It revealed monitoring gaps of at least six months between consecutive reaction frequencies in nearly half the eligible patients whose antibody fluctuation was charted. Communication lapses between physician and tissue typing laboratory were evident in incomplete blood transfusion histories and missing dates of transplant failure for previously transplanted patients who were now awaiting a regraft. Laboratory practice was not standardized. Failure to monitor B cells on platelet absorbed sera

283 allowed anti HLA-A and B reactions to masquerade as B cell reactions and failing to remove autoantibodies was another source of laboratory error. Changes in panel size and composition were perhaps inevitable but they made it all the more difficult to discern patterns underlying antibody fluctuations. However, a salient pattern was the greater frequency of high niveaux in patients awaiting.regrafts than for untransplanted patients. Reaction frequency spikes were a common phenomenon but their antecedents were unclear. In Chapter 7, highly sensitized patients with 0% latest reaction frequency (prior to transPlant) were not proven to have enhanced transplant survival. Consequently, until laboratories conform to agreed monitoring standards, it would be unwise for registries to abandon their cautious position of classifying patients according to peak reaction frequency.

3. Promoting inter-registry collaboration The Study established the feasibility of co-ordinated research through the European Transplant Organizations, and made novel use of registry waiting lists as a research database. Registry waiting lists identified sources of sensitization, responder phenotypes and were the basis for our investigation of transplantation rates. For the future, inter-registry collaboration, in organ exchange or research, would be made simpler by establishing a suite of programs to convert to a common format recipient HLA-phenotypes and forbidden antigens for which registries have different data-storage conventions, as became apparent during the Study - and ad hoc programs had to be written to align the diverse conventions. Another obstacle to collaboration was the practice of rounding peak reaction frequency to the nearest 5%, or more crudely still, without there being agreement amongst registries on cut-offs between sensitization levels. Other registry practices such as scheduling new registrations and reaction frequency updates, so that these were synchronized at regular reporting times, and how sensitization status of new registrants was handled emerged as points of difference but were not recognized as such before the study began. For these reasons our attempt at estimating the rate of increase of highly sensitized patients was confounded because several registries initially labelled new registrants as "sensitization status unknown". "Experience is the name everyone gives to their mistakes" Wilde and "He that leaveth nothing to chance will do few things ill but he will do very few things" Savile

284 4. Antecedents of high sensitization In absolute numbers first graft females, followed by regraft males, dominated the waiting list of highly sensitized patients; thereafter, nearly equal numerically, came first graft males and female regrafts. But the prevalence of high sensitization was greatest for patients awaiting regrafts (28% compared to 8% for first grafts). Chapter 4 produced evidence for Class I mismatches at first graft having a role in determining sensitization at regraft. Moreover, early graft failure (within a year of transplantation) was shown in Chapter 7 as a trigger for high sensitization at regraft. Next to graft failure came parity as an antecedent of high sensitization amongst females awaiting a first graft. In Chapter 7 highly sensitized and control first grafts were matched for sex but not parity: amongst highly sensitized female first transplantees a higher proportion were parous than amongst female controls (see also Chapter 4: Discussion). Moreover, whereas 14% of females awaiting first grafts were highly sensitized, only 5% of males were. The role of blood as a primer for high sensitization was poorly defined, not least because blood transfusion histories were poorly documented. In Chapter 3 the majority of patient charts showed equivocal response to blood or were unclassifiable because too few transfusions were charted. As many patients were apparent responders to blood as there were provoked non-responders. We conclude as Goethe did: "The history of science is science itself: the history of the individual, the individual" Individuals responded differently to individual transfusions. However, in Chapter 7 we discovered a somewhat higher proportiton of never transfused patients amongst lowly sensitized than highly sensitized first transplants. 5. Exploding the myth of sensitization being confined to HLA-A and B antigens "The poets were not alone in sponsoring myths" Strabo

The evidence against sensitization being confined to HLA-A and B antigens is as follows. In Chapter 3, reaction frequency patterns defined by unseparated lymphocytes and B cells were discordant, even after graft failure. In Chapter 5, Class I, but not HLA-DR, homozygosity was shown to be associated with high sensitization; but HLA-DR antigens were heterogeneous in their association with sensitization with DR1 and DR7, for example, favouring low and

285 Table 10.1. B cell positive crossmatch and DAY1 non-functioning regrafts FIRST GRAFTS:

Controls

Highly sensitized

DAYI graft function

DAY1 graft function

B cells crossmatch -ve +ve

no 27 2 (7%) 29

REGRAFTS:

Controls

Highly sensitized

DAY 1 graft function

DAY 1 graft function

B cells crossmatch -ve +ve Zl2 = 2.36 p=O.12

no 28 1~0(26%) 38

yes 59 __4(6%) 63

yes 55 ~9 (14%) 64

no 39 18 (32%) 57

no 30 49 (62%) 79 ;~ = 5.35 p =0.02

yes 37 21 (36%) 58

yes 44 34 (44%) 78

D R 2 favouring high sensitization. In Chapter 7, highly sensitized regrafts with a positive B cell crossmatch were shown to have poorer transplant survival. Yet all special schemes for highly sensitized patients (see Chapter 9) pay no heed to B cell positive crossmatches except in three French centres. Insofar as zero H L A - D R mismatches are mandatory in it, Eurotransplant's scheme for acceptable H L A - A and B mismatches was exemplary if we can assume H L A - D R to be the main target. Yet, in Chapter 7 highly sensitized 00" mismatched grafts had poorer transplant survival than control 00" mismatched grafts. Moreover, the In relative risk associated with high sensitization was 0.47 (95% confidence interval: from -0.26 to 1.20) based on the 196 fully typed 000 mismatched grafts (142 of them highly sensitized). A final link in the chain of evidence against sensitization being confined to H L A - A and B antigens is the increased frequency of B cell positivity in regrafts which failed to function on DAY1 (see Table 10.1). Sixtytwo percent of DAY1 non-functioning highly sensitized regrafts had B cell positive crossmatch compared to 44% of highly sensitized regrafts which functioned on the day of transplant. "The great tragedy of Science - the slaying of a beautiful hypothesis by an ugly fact" Huxley

286

6. Quality of tissue-typing Quality of HLA-DR typing was variable between registries. Table 10.2 compares Hardy-Weinberg estimates of missing HLA-DR gene frequency in waiting-listed patients in four registries. Missing HLA-DR gene frequency ranged from 5% in Eurotransplant unsensitized patients to 18% for Scandia Transplant's highly sensitized patients awaiting transplantation. Further evidence came from comparing the proportion of homozygotes for HLA-A, B and DR amongst transplantees. The proportion of HLA-DR homozygotes ranged from around 40% in Swiss, France and Scandia Transplant patients to nearer 20% in Eurotransplant and UK patients; however, it should be stressed that these percentages relate to recipients who were transplanted between 1982 and 1985, not more recently (see Chapter 7). Half of 000 HLA-A, B and DR mismatched grafts had HLA-DR putative homozygous donors; for them, transplant survival was significantly worse than for zero mismatched grafts whose donor was HLA-DR heterozygous.

Table 10.2.

Registrywaitinglists: missingHLA-DRgenefrequency(Hardy-Weinberganalysis)

Registry

Eurotransplant France Transplant Scandia Transplant UKTransplant

HLA-DRmissinggenefrequency Unsensitized

Highlysensitized

5% 15% 16% 11%

6% 14% 18% 7%

7. Responder phenotypes Quality of tissue-typing was accounted for in analysis of responder phenotypes by checking that the main findings were sustained for patients from Eurotransplant and UK Transplant, which had the lowest rates of missing HLA-DR gene frequency. Class I homozygosity was associated with high sensitization in waiting-listed and transplanted patients. It seemed to represent an excess of unidentified HLA-A and B phenotypes rather than of genuine homozygotes, as evidenced by increased missing HLA-A and B gene frequencies in highly sensitized compared to control patients. Specific antigens favouring high sensitization in waiting listed and transplanted patients were DR2 and B14. Specific antigens associated with being unsensitized were DR1, DR7, DR3, A2 and to a lesser extent A1.

287

8. International variation in gene frequency International variation in gene frequency was revealed between Eurotransplant and U K Transplant patients and is further illustrated for the four registries Eurotransplant, U K Transplant, France Transplant and Scandia Transplant in Table 10.3 for specific antigens HLAoDR6 and DR1. H L A - D R 6 was identified more frequently in Eurotransplant patients than in the other registries; HLAD R 1 was less frequent in U K unsensitized patients than amongst their counterparts in other registries, but U K Transplant's waiting list comprised a higher proportion of regrafts than other registries and may have been thereby depleted of low responder phenotypes. Table 10.3. Registrywaiting lists: HLA-DR6 and HLA-DR1 normalizedgene frequencies(HardyWeinberg analysis)

Registry

HLA-DR6

HLA-DR1

Gene frequencyas a proportion of the identifiedgene frequency Unsensitized Highly Unsensitized Highly sensitized sensitized Eurotransplant France Transplant ScandiaTransplant UKTransplant

15% 12% 5% 13%

18% 10% 5% 13%

11% 12% 12% 8%

8% 9% 8% 7%

9. Transplantation rates and special schemes Highly sensitized patients had one third the transplantation rate which applies to unsensitized patients. But the impact of special schemes for highly sensitized recipients was evident from a higher than expected transplantation rate when related to the number of crossmatch negative waiting patients. The schemes worked. Some were so successful that they risked appropriating kidneys from unsensitized or lowly sensitized recipients! There was no evidence of differential death rates on the waiting list between unsensitized and highly sensitized recipients, nor amongst transplantees. Patients in end-stage renal failure die before and after transplantation at a rate which is conservatively estimated at more than four times the expected mortality for age.

I0. Transplant survival One in three (32%) highly sensitized first grafts were lost in the first year compared to one in five (23%) control first grafts. Two in five (40%) highly

288 sensitized regrafts failed within one year of transplant compared to one in four (26%) control regrafts. The accelerated rates of failure of regrafts, whereby the same proportion of regrafts failed within one week as first grafts within two weeks, continued up to three months post-transplant. Thereafter, 10% of all grafts which survived to three months failed by one year, and after one year grafts were lost at a constant annual rate of 5% per annum (see Table 7.2). The accelerated failure of regrafts, and hence non-proportionality of hazards between first and regrafts, was thus a transient phenomenon. Other transient (up to three months) and persistent (beyond three months) influences on transplant survival were identified. Foremost amongst the transient factors were: DAY1 non-function of the graft, high sensitization of the recipient and the risk on regrafting for patients who rapidly rejected first grafts. That DAY1 non-function was more frequent in the highly sensitized was corroborated in a "grumbling start" to their creatinine profile during the immediate posttransplant period, sometimes disguised by dialysis intervention (see Chapter 8). The DAY1 non-function risk was separate from the risk associated with high sensitization. The major persistent influence on transplant outcome was HLA-mismatching. Beneficial/DR matching was the most appropriate model, statistically and immunologically, to describe the relationship between mismatching and graft survival. Subdivision of zero mismatched grafts according to whether the donor was homozygous or heterozygous for H L A - D R indicated that the major advantage was conferred when the donor was H L A - D R heterozygous, but it is likely we could not rule out the possibility that this was a reflection of DR typing quality. The persistent influence of prolonged ischaemia time on transplant survival was most clearly evident in regrafts. Other favourable prognosticators for transplant survival were: for first grafts, nulliparity in females; for lowly sensitized grafts, recipient DR1. But in regrafts, the risk of transplant failure was increased with B cell positive crossmatches. "... all our science, measured against reality, is primitive and childlike and yet it is the most precious thing we have" Einstein and "Science says the first word on everything and the last word on nothing" Irictor Hugo

289 11. Clinical implications and future studies "The great end of life is not Knowledge, but Action"

Thomas Huxley We end with a 5-point action list: 1. to monitor sensitization at 4 monthly intervals 2. to improve communication between clinic and laboratory in respect of immunizing events 3. to introduce beneficial/DR matching for all patients 4. to give extra vigilance to typing apparent DR homozygotes 5. to give due respect to B cell positive crossmatches. In the absence of more refined monitoring of sensitization, we cannot yet recommend abandonning peak reaction frequency as a marker of high sensitization. Future study proposals include the following: As reviewed in Chapter 1, the literature suggested that the most clinically relevant antibody was of IgG type directed to HLA targets. This conjecture needs to be investigated in a prospective study. The principle of defining acceptable HLA-A and B mismatches and so indicating the range of donor HLA-A and B phenotypes acceptable to highly sensitized patients (when also zero HLA-DR mismatched) should be subject to international evaluation. Eurotransplant's scheme for finding acceptable HLAA and B mismatches is feasible logistically; its characteristics in terms of transplantation rate, missed positive crossmatches and transplant survival could be established most expediently by international collaboration. Does it, for example, answer for non-HLA targets?

References

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296 131. Sanfillippo F, Goeken N, Niblack Get al, 1987: The Effect of First Cadaver Renal Transplant HLA A, B Match on Sensitization Levels and Re-transplant Rates Following Graft Failure. Transplantation 43, 240-244. 132. Sanfillipo F, Vaughan WK, Bollinger RR and Spees EK, 1982: Comparative Effects of Pregnancy, Transfusion, and Prior Graft Rejection on Sensitization and Renal Transplantation Results. Transplantation 34, 360-366. 133. Sanfillipo F, Vaughan WK, Spees EK and Lucas BA, 1985: The Effects of Delayed Graft Function on Renal Transplantation. Transplantation Proceedings 17, 13-15. 134. Schr6der J and de la Chapelle A, 1972: Feotal Lymphocytes in the Maternal Blood. Blood 39, 153-162. 135. Schr6der J, Tiilikainen A and de la Chapelle A, 1974: Feotal Leucocytes in the Maternal Circulation after Delivery. Transplantation 17, 346-354. 136. Schuurman RKR, Van Rood JJ, Vossen JJ et al, 1979: Failure of lymphocyte-membrane HLA-A and -B expression in two siblings with combined immunodeficiency. Clinical Immunology and Immunopathology, 14, 418-434. 137. Schweizer RT, Bartus SA, Perkins HA and Belzer FO, 1982: Renal Allograft Failure and Cold Red Blood Cell Auto-agglutinins. Transplantation 33, 77-79. 138. Scornik JC, Ireland JE, Howard RJ and Pfaff WW, 1984: Assessment of the Risk for Broad Sensitisation by Blood Transfusions. Transplantation 37, 249-253. 139. Simonsen M, 1965: Strong Transplantation Antigens in Man. Lancet I, 415-418. 140. Simonsen M, 1953: Biological Incompatability in Kidney Transplantation in Dogs II, Serological Investigations. Acta Pathologica Microbiologica Scandinavica 32, 36-84. 141. Simonsen M, Buemann J, Gammeltoft A et al, 1953: Biological Incompatability in Kidney Transplantation in Dogs I, Experimental and Morphological Investigations. Acta Pathologica Microbiologica Scandinavica 32 (supplement 94-96) 1-35. 142. Singal DP, Mickey MR and Terasaki PI, 1969: Sero-typing for Homo Transplantation XXIII Analysis of Kidney Transplants from Parental versus Sibling Donors. Transplantation 7, 246-258. 143. Sirchia G, Scalamogna M, Mercuriali F et al, 1981: Evaluation of the Blood Transfusion Policy of the North Italy Transplant Programme. Transplantation 3 i, 388-394. 144. Slapak M, Naik RB, Lee HA, 1981: Renal Transplant in a Patient with Major Donor-Recipient Blood Group Incompatibility. Transplantation 31, 4-7. 145. Smith RN, Margolies RT and Sternlicht M, 1982b: The Alloantibody Response in the Allogeneically Pregnant Rat. II Primary Pregnancy Induced Anti RT1A Alloantibodies Are Not as Cross-reactive as Secondary Pregnancy or Conventionally Raised Alloantibodies. Journal of Immunology 192, 777-782. 146. Smith RN, Sternlicht M and Butcher DGW, 1982a: The Alloantibody in Response in the Allogeneically Pregnant Rat: (1) The Primary and Secondary Responses and Detection of Ir Gene Control. Journal of Immunology 129, 771-776. 147. Soulillou G, Peyrat MA and Guenel J, 1978: Association Between Treatment-resistant Kidney Allograft Reflection and Post-transplant Appearance of Antibodies to Donor B-Lymphocyte Alloantigens. Lancet 1,354-356. 148. Soulillou JP, Bignon JD, Peyrat MA et al, 1980: Systematic Transfusion in Haemodialised Patients Awaiting Grafts. Transplantation 30, 285-289. 149. Soulillou JP, de Mouzon A and Peyrat MA, 1981: Relevance and Specificity of Recipient Anti-donor Antibodies in Kidney Allograft: Evidence for Recognition for MHC Coded by Antigens. Transplantation Proceedings 13, 1737-1738. 150. Springer GF, and Horton RE, 1969: Blood group isoantibody stimulation in man be feeding blood group active bacteria. Journal of Clinical Investigation 48, 1280-1291. 151. Starzl TE, Marchioro TL, Holmes JH, et al 1964: Renal homografts in patients with major donor recipient blood group incompatibilities. Surgery 55, 195-200. 152. Stimpfling JH and Reichert AE, 1971: Male Specific Graft Rejection and the H2 Locus. Transplantation 12, 527-530.

297 153. Suciu-Foca N, Rohowsky-Kochan C, Reed E et al, 1985: Idiotypic Network Regulation of the Immune Response to HLA. Transplantation Proceedings 17, 2483~2487. 154. TakiffH, Mickey MR and Terasaki PI, 1986: Factors important in 10 - year kidney transplant survival. Clinical Transplants 1986, PI Terasaki, (editor), UCLA Tissue Typing Laboratory (publisher), Los Angeles 157-163. 155. Taylor AI and Polani PE, 1965: XX/XY Mosaicism in Man. Lancet 1, 1226. 156. Ting A and Morris PJ, 1979: Development of Donor-specific B Lymphocyte Antibodies after Renal Transplantation. No Correlation with Graft Outcome. Transplantation 28, 13-17. 157. Tiwari JL, 1985: Review: Kidney Transplantation and Transfusion, Clinical Kidney Transplants, 1985, PI Terasaki (editor), UCLA Tissue Typing Lab (publisher), 257-271. 158. Tongio MM and Meyer S, 1977: Narrowing of Feoto-maternal Immunisation at Time of Delivery. Tissue Antigens 9, 174-176. 159. Tongio MM, Berrebi A and Meyer S, 1973: Anti-HLA Feoto-maternal Immunisation Persistence of Antibodies. Tissue Antigens 3, 115-122. 160. Touraine JL, Betuel H, Souillet G and Jeune M, 1978: Combined immunodeficiency disease associated with absence of cell surface HLA-A and -B antigens. Journal of Pediatrics 93, 47-51. 161. Van Loghem JJ, Sauer AJ, Van der Hart M e t al, 1956: Zeldzame Immunologische Afwijkingen als Oorzaak van Bloed Transfusie-reacties bij een Lijder aan Verworven Hemolytische Anemie (English title: Rare immunologic abnormalities as cause of blood transfusion reactions in patient with acquired hemolytic anemia). Nederlandse Geneeskunde I00, 314-323. 162. Van Rood JJ, Van Leeuwen A and Eernisser JG, 1959: Leucocyte Antibodies in Sera of Pregnant Women. Vox Sanguinis 4, 427-444. 163. Van Rood JJ, Van Leeuwen A, Brunning JW and Porter KA, 1968: The Importance of Leukocyte Antigens in Renal Transplantation A Study of Patients of GRJ Alexandre et al In: Dausset J (ed) Advances in Transplantation, Munksgaard, Copenhagen, 213-223. 164. Vives J, Gelabert A and Castillo R, 1976: HLA antibodies and period of gestation: Decline in Frequency of Positive Sera during Last Trimester. Tissue Antigens 7, 209-212. 165. Wakerley E, Shelby J and Corrie RJ, 1985: The Effect of Peripheral Blood Components on Allograft Survival. Transplantation 40, 113-114. 166. Watson MA, Briggs JD, Diamandopoulos AA et al, 1979: Endogeonous Cell-mediated Immunity, Blood Transfusion, and Outcome of Renal Transplantation. Lancet 2, 1323-1326. 167. Werneberg B e t al, 1984: Feotal Immunisation Against Mother's Lymphocytes. A report of two cases. Vox Sanguinis 46, 107-I 10. 168. Williams GM, Hulme DM, Hudson RP et al, 1968: "Hyper-acute" Renal Homograft Rejection in Man. New England Journal of Medicine 279, 611~18. 169. Wilson DB, and Fox DH, 1971: Quantitative studies of the mixed lymphocytic interaction in rats VI Reactivity of lymphocytes from conventional and germ-free rats to allogeneic and xenogenic cell surface antigens. Journal of Experimental Medicine 134, 857-867. 170. Wood IJ, Malik NP and Wing AJ, 1987: Prediction of Resources Needed to Achieve the National Target for Treatment of Renal Failure. British Medical Journal 294, 1467-1470. 171. Wood KJ, Evins J and Morris PJ, 1985: Suppression of Renal Allograft Rejection in the Rat by Class I Antigens on Purified Lymphocytes. Transplantation 39, 56~i2. 172. Woods JE, Leary FJ and Deweerd JA, 1972: Renal transplantation without oliguric acute tubular necrosis. Arch. Surgery 105, 427~,30. 173. Wunderlich JR, Rogentine GN and Yankee RA, 1972: Rapid In-vitro Detection of Cellular Immunity in Man Against Freshly Explanted Allogeneic Cells. Transplantation 13, 31-37. 174. Yamaguchi N, Shimizu S, Hara A e t al, 1983: The Effect of Maternal Antigenic Stimulation upon the Active Immune Responsiveness of their Offspring. Immunology 50, 229-238. 175. Zinkernagel RM and Doherty PC, 1979: MHC Restricted Cytotoxic T-cells: Studies on the Biological Role of Polymorphic Major Transplantation Antigens Determining T-cell Restriction Specificity, Function and Responsiveness. Advances in Immunology 27, 51-17.

Index

% frequency ratio 107, 111 27 varieties of antigen sharing 171 of HLA-mismatching 16, 171, 188, 189, 132 ABO (see also blood group) 15 accelerated failure of regrafts 174, 233, 288 acceptable HLA-A and B mismatches special scheme 272, 277, 289 age 165, 236 expected mortality for age-sex matched UK population 230 alloantibody in hyperacute rejection 12 of the IgM type 279 practical monitoring 20 production and sensitization 10 to donor class II mismatches 9 alloimmunity (see also anti-donor alloantibody) 1 blood transfusion donor specific blood transfusions and renal allograft protection 7 immune responses to blood transfusion 4 third party blood party blood transfusions and renal allograft protection 8 unwanted effects of tranfusion in renal transplantation 6 natural phenomena individual variation in responsiveness 2 maternal immunity to neonatal-alloantigens 3 multiparity and paradoxical allograft protection 4

neonatal immunity to maternal alloantigens 3 -~ansplants cellular immunity as cause of hyperacute rejection 9 chronic graft failure and alloantibody to donor class II mismatches 9 donor specific T killer cells 9 panreactive anti-HLA antibody 9 second set phenomenon 8 allotypic variants of HLA 1 analysis exploratory 104, 111, 171 strategy 33 anamnestic response 13, 59 anti-donor alloantibody clinically relevant 12 cold reactive and sensitization 13 (see also alloimmunity) anti-idiotype antibodies 5 antibody fluctuation chart 23 antigen sharing 165, 189, 239, 240 27 varieties 171 1st grafts 178 regrafts 179 ALL grafts 180 control grafts 184 highly sensitized grafts 183 antigens common 124 counts (for orientation) 112, 113, 114, 127 indicator variables for specific antigens 109 regulatory effects 143 sex-specific antigen association with high sensitization 128 specific HLA-A locus antigens 119, 120, 128

300 specific HLA-B locus antigens 119, 120, 128 specific HLA-DR locus antigens 119, 120, 128, 143 (see also HLA-A1, A2, A3, A9, A10, A11, A28, A19) (see also HLA-B5, B7, B8, B12, B14, B27, B35) (see also HLA-DR1, DR2, DR3, DR4, DR5, DR6, DR7, DR8) auto-antibodies absorption advised 269 Dithiothreitol (DTT) 269 Polyethylene Glycol (PEG) 272 sera tested on patient's own lymphocytes 269 treatment of 269, 270, 271,273, 279 avidity 1

sensitization prevalence 1986 by blood group 74, 88 sensitization with blood group (lst order interaction) 93 blood transfusion 3, 167, 247, 284 differential rates between the sexes 92 donor specific blood transfusions and renal allograft protection 7 immune responses to blood transfusion 4 immunizing event 58 non-specific polyclonal B cell activator 59 sensitization 10 third party blood transfusions and renal allograft protection 8 unwanted effects of tranfusion in renal transplantation 6 variation in 86

B cells 2 charts 54 concordance with T cell pattern 58 monitoring failure 282 of maternal origin 3 sensitization 58 B-cell crossmatch 167, 201, 202, 204, 220, 231, 233, 245, 274, 277 transplant penalty associated with B cell positive crossmatch 21, 92, 284, 288, 289 BASELINE 35 individual 115 Bayes Bayes factor 107 Bayes Theorem 107, 108 naive Bayes risk score 120 naive Bayes rule ( = independence or idiot) 107 beneficial matching 18, 165, 220, 280 beneficial/DR matching 166, 188, 232, 238, 288, 289 subdivided by donor DR homozygosity 166, 200, 203, 242 bias (see centre effect bias, selection bias) binary outcome 35 biological function class I molecules 231 class II molecules 231 blood group: ABO 15 blood group 0 low responders 95 diversion of 0 kidneys 95, 99 graft number with blood group (1st order interaction) 97 registry specific variation 88

Calibration In odds 111 Cell destruction target for 231 cellular immune response amplifier of 231 cellular immunity transplants 9 centre effect bias 8 charts antibody fluctuation ( = serological) 23, 284 B cell 54 received 55 sensitization 53 tally (registry transactions) 24, 149, 150 chimerism (see also microchimerism) 3 chronic graft failure 9 clinically relevant anti-donor alloantibody 12 coding scheme for covariates 36, 171 for HLA-mismatches 172 preferred coding for HLA-mismatches 208 coefficients comparison of 37 regression 35 cold agglutinating antibodies 12 cold agglutinin activity 233 IgM cold agglutinins 279 cold IgM antibodies 14 cold ischaemia time 15, 164, 166, 171,191,195, 196, 220, 233, 244, 288 cold reactive anti-donor antibodies sensitization 13 Collaborative Transplant Study Highly Immunized Trial (CTS:HIT) 272

301

communication between clinic and laboratory 289 comparison of goodness of fit :t 2 39 percentages 37 regression coefficients 37 regression X2 40 COMPOSITE combined registries' diagrams 74, 86, 88 sensitization-specific transplantation rates diagram 153 conclusions 60 concordance HLA-phenotype relationship to panreactivity between waiting and transplanted databases 143 of B and T cell patterns 58 transplantation rates for sensitization levels 2 and 3 155 confidence interval for regression coefficient 36 controls for highly sensitized grafts 163 counts 34 covariate counts (orientation) 112, 113, 114, 127 Freeman-Tukey deviates 80 goodness of fit 80 observed counts (Poisson random variable) 80 regression model for true or expected count 80 covariates alias explanatory variables 35 alias indicator variables 35, 171 coding (eg for HLA-mismatching) scheme for 36, 171 coding for trend 36 counts (for orientation) 112, 113, 114, 127 for transplant survival 168 names, structure and description 250 structure 35, 171 weighted sum of 35, 109 creatinine post-operative course 26, 254 reciprocal 21 weight-corrected reciprocal creatinine 254 cross reactions 2 cross-classification for each registry 79 multi-way tables 78 crossmatch B cell 13, 92, 167, 201, 202, 204, 220, 231, 233, 245, 277, 288, 289

latest serum (=current serum) 279 special schemes 274, 275 test 6, 12, 13, 74, 149 lack of sensitivity 13 U or T cell 167, 201, 202, 245 (see also positive crossmatch) current serum (see also latest reaction frequency) 92, 279 Cyclosporin A 167, 246, 250 and day I graft function 221,233, 247 and organ sharing 17

data management strategy 33 program 26 data checking program 26 missing data 254 quality 254 day 1 graft function 21, 33, 167, 219, 247, 284, 288 B cell positive crossmatch 220 beneficial matching 220 cold ischaemia time 220 Cyclosporin A 221, 233, 247 dialysis on day of transplant or next day 262 grumbling start 256, 262, 263 in highly sensitized grafts 220 included in final regression models for transplant survival 220 in regrafts 220 year of transplant 220 (see also immediate graft function) deaths (see also mortality) 229, 230 definition of control transplant 163, 254 of highly sensitized 19, 282 of HLA-mismatches (determined on broad specificities) 182 of transplant failure 174 dependence between loci 16 design faults 30, 128 discordance for A locus matching at first graft 94 of T and B cell patterns 58, 284 within highly sensitized/control pairs 164 Dithiothreitol (DTT) 268, 269, 270 and alloantibody of IgM type 279 DNA hybridization studies 16 DNA restriction fragment length polymorphism 231 DNCB skin reactivity 6

302

donor kidneys 269, 273, 280 diversion of blood group 0 kidneys to A or B recipients 95, 99 priority for 276 donor specific lymphocytotoxic antibody production 12 donor specific alloantibodies sensitization 11 DTT (see Dithiothreitol) duplicated serum sets special schemes 268, 269, 270, 279 duration of previous graft 94, 167, 204, 206, 247

elficiency of special schemes 270 eligibility for responder phenotype analysis 105 for special schemes 269, 273, 279 epitopes 2, 16 epochs distinct post-transplant epochs 170, 177, 181 European Immunized File (ET:EIF) 268 Eurotransplant's Acceptable HLA-A, B mismatch scheme (ET:ACMM) 272 Eurotransplant's Highly Immunized Patients (ET:HIP) 268 exploratory analysis individual antigens 104, 111 tissue matching 171 exponential of risk score 168 failed transplants forbidden antigens 274, 277 immunizing events 59 familial typing 274 faults in study or questionnaire design 30 female (see sex, parity, pregnancy, X-linked) fibrin deposits 14 floppy disk 22, 23, 24 flow cytometry technique to detect IgG alloantibodies 11 fluorescence activated cell sorter 14 foetal lymphocytes sensitization 10 forbidden antigens (eg from previous failed graft, husband's mismatches) 274, 277 Freeman-Tukey deviates 80 French centres scheme 1 (FR:I)270

fully typed HLA-DR homozygosity 199 HLA-DR phenotype 199 recipients and donors (2014 grafts) 199, 215 gene frequency Bernstein estimates 132 donor reference (Eurotransplant and UK Transplant) 134 estimation 129 maximum likelihood estimates 132 normalization to 100% 132 standard error 135 unidentified or missed gene frequency HLA-A 130, 132, 143 HLA-B 130, 134, 143 HLA-DR 130, 132, 134 genetic structure of the HLA-region 231 goodness of fit Z2 39 (see also statistical methods) graft failure which failures risk high sensitization? 93 graft function non-immediate 233 on day 1 167, 246 graft nephrectomy 11 graft number first versus regraft 74, 88, 97 graft number with blood group (1st order) 97 HLA-phenotype and panreactivity 120 registry-specific variation 88 sensitization with graft number (lst order interaction) 93 sensitization with sex and graft number (2nd order interaction) 95 sex with graft number (lst order) 97 transplantation rates (first versus regraft in Eurotransplant) 158 graft versus host antibody reactions 11 grumbling start reciprocal creatinine profile 256, 262, 263, 267, 288 Hardy-Weinberg analysis donor phenotype 33, 105, 134 estimation 129, 132 Eurotransplant and UK Transplant 134,144 missed gene frequency 130, 132, 286 recipient phenotype 129 waiting list and transplanted databases 34, 105

303

hazard non-proportional 170 high sensitization antecedents 284 controls for highly sensitized grafts 163 day 1 graft function 220 definition 19, 282 difficulty in assessing rate of accumulation of highly sensitized patients 159 diverse aetiologies 230 duration of previous graft 93 high-low sensitization (see also sensitization) 190, 248 HLA-A and B mismatches at previous graft 93, 94 interactions 171 interaction with non-beneficial matching 193 prevalence 284 prior immunity to HLA-A and B antigens 232 responder phenotype (waiting list) 104 responder phenotype (transplant database) 127 sex-specific antigen association with high sensitization 128 special schemes 19, 21, 26 time-dependent penalty associated with 177 transplant penalty 19 which graft failures risk high sensitization 93

Histocompatibility ABO 15 DNA hybridization studies 16 HLA 16 barriers 4 epitopes 16 major aims of tissue matching 15 monoclonal antibodies 16 tissue typing accuracy 16 international monitoring 16 27 varieties of HLA-mismatch 16 historic(al) sera crossmatch 92, 279 sensitization 13 HLA 16 HLA-A + B + DR mismatches 165, 171,189, 239 HLA-A and B mismatches 93 HLA-A homozygosity 109, 115, 128, 143, 195, 231, 241 HLA-A 1 113, 119, 120, 124, 128, 134, 139,

143, 145, 286 HLA-A 2 113, 119, 120, 124, 128, 134, 139, 143, 145, 286 HLA-A 3 134 HLA-A 9 139, 140 HLA-A10 113, 128, 139, 140 HLA-All 123, 128, 139, 144 HLA-A19 113, 140 HLA-A28 119, 120, 139, 140, 145 HLA-B homozygosity 109, 115, 128, 143, 195, 231, 241 HLA-B 5 120 HLA-B 7 124 HLA-B 8 119, 124 HLA-B12 120 HLA-B14 113, 120, 124, 144, 145, 286 HLA-B27 113, 119, 120, 128, 144, 145 HLA-B35 120 HLA-broad specificities determine HLA-mismatches 182 HLA-B+ DR mismatches 165, 171, 238 HLA-Class I molecules biological function 231 HLA-Class I homozygosity 115, 120, 128, 143, 195, 231, 241, 284, 286 HLA-Class II molecules biological function 231 HLA-DR homozygosity 109, 115, 128, 143, 144, 195, 198, 199, 231,241,284, 289 recipient 109, 115, 144, 169, 195, 241 donor 166, 288 subdividing beneficial/DR matching 166, 200, 203, 242 HLA-DR mismatches 237, 249 HLA-DR phenotype common format between registries 283 recipient 196, 198, 199, 243 HLA-DR1 3, 113, 119, 120, 130, 132, 135, 137, 143, 144, 147, 166, 196, 199, 201, 143, 284, 286 HLA-DR2 113, 119, 120, 128, 132, 137, 144, 145, 166, 196, 243, 285, 286 HLA-DR3 113, 119, 120, 124, 135, 137, 143, 145, 286 HLA-DR4 135, 137, 139, 144 HLA-DR5 137, 139, 144, 145 HLA-DR6 3, 124, 130, 132, 135, 137, 139, 145, 187 HLA-DR7 113, 119, 130, 143, 144, 145, 166, 196, 201,243, 284, 286 HLA-DR8 137 HLA-DR homozygotes 166, 196, 199, 201, 243 sensitization to HLA-DR antigens 232

304

type unknown 164 HLA-mismatch 27 varieites 16 (see also mismatching of HLA-antigens) homozygosity 20, 104, 289 locus, not antigen, specific 113, 115 recipient 104, 109, 128, 143, 198, 199, 241 (see also HLA-A, B, DR, Class I, Class II homozygosity) humoral immune responses 2 hydration 15, 233 hyperacute rejection 6, 12 idiotype true 5 IgG anti-HLA-DR antibody 13 IgG 10 anti-HLA-A and B and DR mismatches on the donor 232 IgM 10 alloantibody (susceptible to Dithiothreitol) 279 antibody 92, 232, 279 cold agglutinins 279 illustration format 177 immediate graft function as prognostic indicator of graft outcome 15 cold IgM antibodies 14 cold ischaemia time 15 factors affecting 14 fibrin deposits 14 hydration of recipient and donor 15 non-immediate graft function 233 segmental glomerular-capillary aggregates 14 (see also day I graft function) immunizing events 20, 23, 53, 289 blood transfusions 58 failed transplants 59 multi-parity 57 multi-transfusion 56 immunosuppression clinical intervention 254 discontinuation of 11 (see also Cyclosporin A) independence conditional independence assumption 108, 109 indicator variables 35, 109, 171 initial non-function (see also day 1 graft function) 14 instructions how to sample matched controls 25

intended sensitization levels 75, 77, 78 interactions first order 80, 86-97 high sensitization with non-beneficial matching 193, 198 involving registry 78 other, with high sensitization 171 second order 83, 86-97 testing of 173 sensitization with blood group (lst order) 93 sensitization with graft number (lst order) 93 sensitization with sex (1st order) 88 international variation in gene frequency 287 (see also registry) intervention interval see lag 258 iterative cycle design, experimental data, analysis, discussion 33 of research 34 Kalman filter 2-step probability of unfavourable creatinine slope change 258 analysis 1 258 analysis 2 262 failure to signal treated rejection episodes 266 and early dialysis 267 grumbling start 256 instability 258 monitoring of reciprocal creatinine posttransplant 253 kidneys priority for 276 proportion of donor kidneys claimed by special schemes 269 killer T cells 2 laboratory error (failing to remove autoantibodies) 283 rogue laboratory measurement 254 validation of peak reaction frequency in national reference laboratories 270 variation in laboratory procedure 56 lack of sensitivity of conventional crossmatch test 13 lag between Kalman filter 2-step probability and initiation of rejection therapy 258

305 latest reaction frequency 92, 167, 248 special schemes 271,272, 279 likelihood ratios 107, 111, 112, 208 linear-logistic final regression model 120 model sequence 115 motivation for linear-logistic regression 106 regression 35, 104 linkage disequilibrium 104, 231 In odds addition 109 calibration 111 observed 111 predicted 110 risk score contributions 121 transform 110 In relative risk 177 log-linear regression 35 logistics special schemes 271,273 logrank comparison of transplantation rates by sensitization status 153 low responder status 3 lymphocytotoxic antibody sensitization 10 magnetic tape 22, 23, 24, 26, 74, 104 male (see sex) match grade policy common, irrespective of graft number 170 maternal immunity to neonatal alloantigens 3 microchimerism (see also chimerism) 10 mismatching of HLA antigens acceptable HLA-A and B mismatches (special scheme) 272 at first graft 94 A+B+DR mismatches 165, 171, 189, 23(~239 B + DR mismatches 165, 171,238 beneficial/DR matching 165, 171, 188, 203, 232, 238, , beneficial/DR matching subdivided by donor HLA-DR homozygosity 166, 200, 203, 242 beneficial matching 165, 220 determined on broad specificities 182 different coding schemes 172 HLA-A and B mismatches 93, 94 interchangeability within a locus 232

inter-registry differences in stringency of HLA-matching 99 matching schemes 171 model preference 1st grafts 178, 182, 209, 218 regrafts 179, 185, 211, 218 ALL grafts 180, 185, 213, 215, 218 control grafts 184, 188, 217, 218 highly sensitized grafts 183, 217, 218 penalizes transplant survival 181, 187 special schemes 275, 276 summary 219 27 varieties of HLA-mismatching 171, 188, 189 missing gene frequency (see gene frequency) monitoring alloimmunity 20 failure (B cells on platelet absorbed sera) 282 gaps (reaction frequency) 282 post-transplant course 253 sensitization 53, 162, 289 monoclonal antibodies histocompatibility 16 mortality 229 annual death rate on the waiting list 161, 230 expected mortality in age-sex matched UK population 230, 287 multi-way tables cross-classification 78 multi-gravidae 4, 10 multi-parity immunizing events 57 multifactorial analysis 34 of responder phenotype 104 of transplant survival 168 myth of sensitization being confined to HLA-A and B antigens 284 naive Bayes (see Bayes) national reference laboratories validation of peak reaction frequency 270, 274 natural logarithmic scale addition of In odds 109 natural phenomena individual variation in responsiveness 2 maternal immunity to neonatal alloantigens 3 multiparity and paradoxical allograft protection 4 neonatal immunity to maternal alloantigens 3

306 neonatal immunity to maternal alloantigens 3 nested regression models 40 niveaux high niveaux 283 non-HLA targets 289 non-immediate graft function 233, 258, 284 (see also day 1 graft function and immediate graft function) non-specific polyclonal B cell activator blood transfusion 59 North Italy Transplant scheme 1 (NIT: 1) 268 North Italy Transplant's Highly Immunized Trial (NIT:2) 272 nulliparity 288 (see also parity) odds In odds 109 on being unsensitized 106 posterior odds 108 prior odds 107 offspring-to-mother and offspring-to-father transplants 4 organ exchange organizations (see transplant organizations) organ exchange hierarchy 21, 31, 86, 92, 95, 156, 276, 277, 278 balance of trade figures 277 favouring regrafts 91, 99 organ sharing "Cyclosporin era" 17 polymorphism of genetic system 17 pool size 18 potential for 17 rational basis for 18 simulation studies 17 outcome binary 35 counts 34 survival 35 panreactivity 5 panreactive anti-HLA antibody 9, 104 (see also sensitization) robustness of HLA phenotype relationship with panreactivity 120 parity 3, 97, 284, 288 (see also multi-gravidae and multiparity) patterns associated with intermittent dialysis 258 concordance of B and T cell patterns 58, 284

niveaux 53 of response (serological) 20, 22, 56 plateaux 53 related to immunizing events 20 slope change in reciprocal creatinine 258 spikes 53, 283 peak reaction frequency 19 speciousness of 282 underestimation of 156 PEG (see Polyethylene Glycol) penalty associated with high sensitization 19 mismatching of HLA antigens 181 time-dependent associated with high sensitization 177 transplant 19 percentages comparison of 37 persistent risk 170, 288 phase of gestation sensitization 10 phenotype donor 33 Hardy-Weinberg analysis 33 HLA-phenotype 104 (see also responder phenotype) responder 20, 23, 33 robustness of HLA-phenotype relationship with panreactivity 120 pilot studies 30, 254 plateaux 53 platelet absorption 13 failure 282 Poisson random variable 80 Polyethylene Glycol (PEG) absorption by 272 polymorphism of HLA system organ sharing 17 pool size organ sharing 18 positive crossmatch 149, 200, 245 B cell 13, 92, 167, 201, 202, 204, 220, 231, 233, 245, 277, 285, 288, 289 in highly sensitized recipients 202 U or T cell 167, 201, 202, 245 (see also crossmatch) post-operative course Kalman filter monitoring 253 serum creati.nine 26 postulates Eurotransplant medians for peak reaction frequency applied across registries 158 underestimation of peak reaction frequency

307

for patients registered with 1-50% peak 156 power statistical 95, 263 pragmatism pragmatic definition of highly sensitized 19 study plan 30 precoding 75, 86, 151 (see also rounding) pregnancy 3, 92, 93, 164, 191, 230, 235 associated humoral immunity 11 induced leukoagglutinins 3 (see also multi-gravidae, multiparity, primiparous, nulliparous) presentation of results graphics programs 33 strategy 33 prevalence 19 of high sensitization 86, 284 sensitization 1986 74, 76, 77, 78 previous graft duration of 93, 167, 204, 206, 247 wait from failure to regraft 167,204, 208,247 primiparous 10 (see also pregnancy) profiles reciprocal creatinine 21, 26 sensitization 53 serological 53 prognostic factors 35 program for data management 26 proportional hazards constant 170 piece-wise 170, 177 regression 35 quality control on crossmatches 281 of DR tissue typing 199, 23t of tissue typing 105, 286 questionnaires 25, 26, 31, 42-52, 197 questions posed 20 rabbit anti-thymocyte serum 13 reaction frequency ascending %RF 56 descending %RF 57, 59 latest 167, 248, 271 % reaction frequency (%RF) 53 realization of study design 163, 234 recipient HLA-DR phenotype 196, 198, 199, 243

recipient homozygosity 115, 198, 199, 241 (see also HLA-A, B, DR, Class I, Class II homozygosity) reciprocal creatinine profile 21, 26, 254 weight-corrected 254 reference laboratory validation of peak reaction frequency 270, 274 registry collaboration 283 common format for HLA-phenotype 283 comparison (rates of change in reaction frequency) 159 comparison (sensitization prevalence 1986) 74, 88 comparison (transplantation rates) 153-155 cross-classifications for each registry 78, 79 database accuracy 92 directors 22, 24, 25, 26 interactions involving registry 78, 86 registry-specific variation sex 86 graft number 86 blood group 88 risk factor report 248 stratification 170, 204, 207 variation in frequency of HLA-phenotype 105 variation in gene frequency 287 variation in registry practice 159 waiting lists 20, 104, 283 (see also waiting lists) regraft (see graft number) 74, 93 preferment in recipient selection policy 92 proxy for responder phenotype 93 regression ,~2 177 regression coefficients 37, 110 comparison of coefficients 37 linear-logistic 35, 109 log-linear 35 nested regression models 40 models 34 proportional hazards 35 relative risks 35 ~(2 38 regulatory effects of specific antigens 143 rejection clinical intervention 254 statistical identification of 254 relative risk implications for transplant survival 169 regression 35

308 research iterative cycle 33, 34 responder phenotype 20, 23, 33, 93, 95, 97, 104, 127, 286 risk factor report 164, 234, 248 antigen sharing 165, 239, 240 beneficial/DR matching subdivided by DR homozygosity 169, 242 blood transfusion 167, 247 by registry 248 cold ischaemia time 166, 244 Cyclosporin A 167, 247, 250 day 1 graft function 167, 246 duration of previous graft 167, 247 latest reaction frequency 167, 248 mismatching of HLA antigens 165, 236239, 249 positive crossmatches 167, 245 recipient age 165, 236 recipient homozygosity 166, 241 recipient sex and pregnancy 164, 235 selected HLA-DR antigens DR1, DR2 and DR7 166, 243 transplant year 164, 235, 2;48 wait from previous graft failure to regraft 167, 247 risk score for In odds on being unsensitized 121 summation 35 risk persistent 170, 288 piece-wise proportionality of 177 transient 170, 288 robustness of relationship of HLA phenotype with panreactivity 120 rounding of reaction frequency to nearest 5% 75, 86, 151,292 Save Our Sensitized (SOS) scheme 253, 270 Scandia Transplant scheme 1 (SKT:I) 268 second set phenomenon 8 segmental glomerular capillary aggregates 14 selection bias 105, 128 sensitivity, lack of in conventional cross-match test 13 sensitization alloantibody in hyperacute rejection 12 anamnestic response 13 antecedents 284 assessing sensitization 10 B cells 58 blood transfusions 10 charts 53

clinically relevant anti-donor alloantibody of IgG class directed towards HLA mismatches 12 cold agglutinating antibodies 12 cold reactive anti-donor antibodies 13 cross-match test 12 deaths on waiting list 161 declining sensitization 13 de-registrations other than death or transplant 162 donor-specific alloantibodies 11 flow cytometry to detect IgG alloantibodies 1l fluorescence activated cell sorter 14 foetal lymphocytes 10 graft versus host antibody reactions 11 high-low 190, 248 historic(al) sera 13 IgG 10 IgG anti-HLA-DR antibody 13 IgM l0 intended sensitization levels 75, 77, 78 lymphocytotoxic antibody l0 microchimerism 10 monitoring 53 myth 284 parity 10 phase of gestation 10 platelet absorption 13 positive cross-match test against B lymphocytes 13 post-natal decline 10 pregnancy 10 pregnancy associated humoral immunity 10, 11 prevalence 1986 74, 76 profiles 53 rabbit anti-thymocyte serum prior to transplantation 13 registry-specific variation 86 registry transactions by sensitization status 149 responder phenotype 23, 283 responder phenotype (transplant database) 127 responder phenotype (waiting list) 104 sensitivity of conventional cross-match test 13 sensitization with blood group (lst order interaction) 93 sensitization with graft number (lst order interaction) 93 sensitization with sex (lst order interaction) 88

309

sensitization with sex and graft number (2nd order interaction) 95 sequential studies of alloantibody production 10 sex-specific antigen association with high sensitization 128 sources of 23 s t a t u s not known 30 to HLA-DR antigens 232 (see also high sensitization) transplantation 10 transplantation rates 149, 153 sequential studies sensitization 10 serum creatinine reciprocal profile 21 post-operative course 26 sex 164, 235, 284 differential transfusion rate by sex 92 expected mortality for age-sex matched UK population 230 HLA-phenotype and panreactivity 120 hyper-responsiveness of females 92 registry-specific variation 86 sensitization prevalence 1986 by sex 74, 86 sensitization with sex (lst order interaction) 88 sensitization with sex and graft number (2nd order interaction) 95 sex-specific antigen association with high sensitization 128 sex with graft number (lst order) 97 simulation studies 17 special schemes alloantibodies of IgM type 279 Collaborative Transplant Study Highly Immunized Trial (CTS:HIT) 271 crossmatches (including B cell) 274, 275, 277, 279 donor search 277 duplicated serum sets (known and scrambled order) 273 efficiency 270 European Immunized File (ET:EIF) 268 Eurotransplant's Acceptable HLA-A, B mismatch scheme (ET:ACMM) 272 Eurotransplant's Highly Immunized Patients (ET:HIP) 268 for transplanting highly sensitized patients 19, 21, 26, 86, 268, 287 forbidden antigens 274, 277 French centres scheme 1 (FR1) 270 initiation, eligibility, transplant statistics 269, 279, 280

logistics 271 North Italy Transplant scheme 1 (NIT:l) 268 North Italy Transplant's Highly Immunized Trial (NIT:2) 272 priority in organ exchange hierarchy 156, 276 proportion of donor kidneys claimed by 269, 280 quality control on crossmatches 281 reference laboratory verification 270, 274 Scandia Transplant scheme 1 (SK:TI) 268 Swiss Transplant scheme I (SW:T1) 268 Swiss Transplant scheme 2 (SW:T2) 272 treatment of auto-antibodies 269, 279 zero DR mismatches 277, 280 UK Transplant's SOS scheme (UKTS:SOS) 270 spikes 53, 58 declining, ascending 58 in reciprocal creatinine profile with intermittent dialysis 258 multiple 57 sporadic 59 standard error 36, 135, 173 statistical methods % frequency ratio 107, 111 BASELINE 35, 115 Bayes factor 108 calibration 111 conditional independence assumption 108, 109 confidence interval 36 comparison of percentages 37, 38 comparison of regression coefficients 37 counts 34 covariates (alias explanatory variables alias prognostic factors) 35 cross-classification 78 Freeman-Tukey deviates 80 general notes on 34 goodness of fit ~2 38, 109 interactions (lst order, 2rid order) 80, 83 Kalman filter identification of rejection episodes 254 likelihood ratio 107, l l l , 112, 208 linear-logistic regression 35, 106, l l0, 120 In odds 109, ll0 log-linear regression 35 logrank comparison of transplantation rates 153 main effects 80, 86 multifactorial 34, 78, 104, 168 multi-way tables 81

310

naive Bayes rule 107, 120 odds (on being unsensitized) 106 non-linearity in transplantation rates by sensitization status 153 Poisson random variable 80 posterior odds 108 power 95, 263 prior odds 107 proportional hazards regression 35 ratio of observed to expected transplants (O/E) 153 regression coefficients 35 regression models 34 regression )~2 38 relative risks regression 35 risk score summation 35 standard error 36, 135 stratified piece-wise proportional hazards 168 variance of a percentage 38 z-score 36 statistical reasoning general notes on statistical thinking 34 goodness of fit 22 38, 39 nested regression models 40 regression Z2 38 strategy for data management, analysis, presentation of results 33 stratification by graft number and registry 181 by registry 170 stratified piece-wise proportional hazards 168 study design 163 and data quality 254 realization 234 study plan pragmatism 30 survival outcome 35 Swiss Transplant scheme 1 (SWT:I)268 Swiss Transplant scheme 2 (SWT:2) 272 T cell differentiation 1 regulation of B cell differentiation 2 T helper cells 2, 7 T killer cells 2, 9 tally chart (see chart) target for antibody HLA-DR antigens 143 test panel 53 time-dependent penalty associated with high sensitization 177

time constraint 25 tissue matching (see also mismatching of HLA antigens) 15 tissue typing accuracy 16 international monitoring 16 of patient's family 274 quality 286 quality of DR typing 199 untyped for any of the three loci A, B, or DR 105 transfusion benefit 7 (see also blood transfusion) transient risk 170, 288 transplant databases 34, 127, 163 Hardy Weinberg 129 transplant failure definition (graft failure or death with a functioning graft) 174 hazard 170 ratio of failure rates 177 risk of 170 transplant organizations 22, 276 transplant penalty associated with B cell positive crossmatch 21 associated with high sensitization 19 transplant rates 20, 24, 149, 153, 155, 279, 287 first versus regrafts (Eurotransplant) 158 transplant statistics special schemes 269, 280 transplant survival 21, 164, 287 covariates 163 model building additions to background covariates 177 cold ischaemia time 191 duration of previous graft 204 high-low sensitization 190 HLA-DR homozygosity (donor or recipient and recipient HLA-DR phenotype revisited 199 interaction of high sensitization nonbeneficial matching 193 positive crossmatch 200 pregnancy 191 recipient HLA-DR phenotype 196, 198 recipient homozygosity 195, 198 tissue matching HLA mismatches or antigen sharing 182 transplant year 192

311

registry 204 waiting time from previous graft failure to regraft 204 multifactorial analysis 168 relative risk implications 169 results 174 risk score 177 summary statistics 174 final models including day 1 graft function 21 summary model for 1st grafts 222, 228 summary model for regrafts 222, 228 summary model for ALL grafts 223, 228 summary model for highly sensitized grafts 224, 228 summary model for control grafts 224, 228 model preference 208 summary 229 prejudicial factors 231 favourable factors 231 transplant year 192, 220, 248 transplants cellular immunity as cause of hyperacute rejection 9 chronic graft failure and alloantibody to donor class II mismatches 9 donor specific T killer cells 9 panreactive anti-HLA antibody 9 second set phenomenon 8 trend coding for 39 variables 39 true idiotype 5 U or T cell crossmatch 167, 202, 245 UK Transplant Service Management Committee 26, 253 UK Transplant's SOS scheme (UKT:SOS) (see also Save Our Sensitized) 270 validation exercise (Kalman filter monitoring) 254, 262 of peak reaction frequency by national reference laboratories 270, 274 variables binary 35

counts 35 explanatory 35 indicator 35 Poisson random variable 80 survival 35 trend 35 variance of a percentage 38 variation in gene frequency internationally 287 in laboratory procedure 54, 56 verification by reference laboratory 270, 274 of data 31 wait from failure to regraft 167, 204, 208, 247 waiting lists for renal transplantation 20, 74, 104, 149, 283 composition 87, 149 deaths by sensitization 161 deregistrations other than by death or transplantation 162 dynamics 100, 149 Hardy Weinberg estimation of gene frequencies 129 transactions 149, 153, 161, 162 transplantation rates by sensitization status 153 warm perfusion fluid 14 weight-corrected reciprocal creatinine 254 X-linked 92 ~(2 comparison of goodness of fit ~(2 39 goodness of fit ,~2 38, 39 properties of X2 distribution 39 regression X2 40 year of transplant 192, 220, 248 z-score 36, 170 zero HLA-A + B + DR mismatched transplants 231,232, 284 zero HLA-DR mismatches special schemes donor search 99, 277, 280 zero HLA-A + B mismatched transplants 285

Developments in Nephrology 1. Cheigh, J.S., Stenzel, K.H. and Rubin, A.L. (eds.): Manual of Clinical Nephrology of the Rogosin Kidney Center. 1981 ISBN 90-247-2397-3 2. Nolph, K.D. (ed.): Peritoneal Dialysis. 1981 ISBN 90-247-2477-5 3. Gruskin, A.B. and Norman, M.E. (eds.): Pediatric Nephrology. 1981 ISBN 90-247-2514-3 4. Schiick, O.: Examination of the Kidney Function. 1981 ISBN 0-89838-565-2 5. Strauss, J. (ed.): Hypertension, Fluid-electrolytes and Tubulopathies in Pediatric Nephrology. 1982 ISBN 90-247-2633-6 6. Strauss, J. (ed.): Neonatal Kidney and Fluid-electrolytes. 1983 ISBN 0-89838-575-X 7. Strauss, J. (ed.): Acute Renal Disorders and Renal Emergencies. 1984 ISBN 0-89838-663-2 8. Didio, L.J.A. and Motta, P.M. (eds.): Basic, Clinical, and Surgical Nephrology. 1985 ISBN 0-89838-698-5 9. Friedman, E.A. and Peterson, C.M. (eds.): Diabetic Nephropathy: Strategy for Therapy. 1985 ISBN 0-89838-735-3 10. Dz~rik, R., Lichardus, B. and Guder, W. (eds.): Kidney Metabolism and Function. 1985 ISBN 0-89838-749-3 11. Strauss, J. (ed.): Homeostasis, Nephrotoxicity, and Renal Anomalies in the Newborn. 1986 ISBN 0-89838-766-3 12. Oreopoulos, D.G. (ed.): Geriatric Nephrology. 1986 ISBN 0-89838-781-7 13. Paganini, E.P. (ed.): Acute Continuous Renal Replacement Therapy. 1986 ISBN 0-89838-793-0 14. Cheigh, J.S., Stenzel, K.H. and Rubin, A.L. (eds.): Hypertension in Kidney Disease. 1986 ISBN 0-89838-797-3 15. Deane, N., Wineman, R.J. and Benis, G.A. (eds.): Guide to Reprocessing of Hemodialyzers. 1986 ISBN 0-89838-798-1 16. Ponticelli, C., Minetti, L. and D'Amico, G. (eds.): Antiglobulins, Cryoglobulins and Glomerulonephritis. 1986 ISBN 0-89838-810-4 17. Strauss, J. (ed.), with the assistance of L. Strauss: Persistent Renalgenitourinary Disorders. 1987 ISBN 0-89838-845-7 18. Andreucci, V.E. and Dal Canton, A. (eds.): Diuretics: Basic, Pharmacological, and Clinical Aspects. 1987 ISBN 0-89838-885-6 19. Bach, P.H. and Lock, E.H. (eds.): Nephrotoxicity in the Experimental and Clinical Situation, Part 1. 1987 ISBN 0-89838-977-1 20. Bach, P.H. and Lock, E.H. (eds.): Nephrotoxicity in the Experimental and Clinical Situation, Part 2. 1987 ISBN 0-89838-980-2 21. Gore, S.M. and Bradley, B.A. (eds.): Renal Transplantation: Sense and Sensitization. 1988 ISBN 0-89838-370-6