1. Allocation of deceased donor kidneys Phil Clayton NSW Renal Group 14 June 2012

2. Outline Why study kidney allocation? Equity vs utility Current Australian model Previous work in Australia US allocation research Allocation simulations – Current model – A utility-based model Future directions

4. Equity vs utility trade-off Equity – Everyone has opportunity to benefit from transplantation Utility – Get most out of precious resource – Patient survival – Graft survival – QALYs? – Cost effectiveness?? Whose perspective? – Patient / Donor / System / Society

5. Current Australian model

6. Major listing criteria: – ESKD on dialysis – 80% likelihood of surviving for at least 5 years after transplantation Major allocation criteria: – Blood groupEquity/Utility – Waiting timeEquity – HLA matchUtility – Highly sensitisedEquity/Utility – ChildhoodUtility

8. NSW formula

9. Other states VIC – HLA-B, -DR, waiting time SA – HLA-A, -B, -DR, waiting time QLD – HLA-A, -B, -DR, waiting time, paediatric status WA – HLA-A, -B, -DR; HLA-DR homozygous; waiting time

10. ABO rules DonorRecipientAustraliaNSWVICSAQLDWA AA ✔✔✔✔✔✔ AAB ✔✔✔✔✔✔ ✔✔✔✔✔✔ B ✔✔✔✔✔✔ BB ✔✔✔✔✔✔ OA ✖✖✖✔✖✖ O ✖✖✖✔✖✔ OB ✖✖✖✔✔✔ OO ✔✔✔✔✔✔

11. ABO rules DonorRecipientAustraliaNSWVICSAQLDWA AA ✔✔✔✔✔✔ AAB ✔✔✔✔✔✔ ✔✔✔✔✔✔ B ✔✔✔✔✔✔ BB ✔✔✔✔✔✔ OA ✖✖✖✔✖✖ O ✖✖✖✔✖✔ OB ✖✖✖✔✔✔ OO ✔✔✔✔✔✔

17. Old donors Young patients Young donors Old patients

18. More benefit from age matching if higher proportion of older donors

24. US allocation research Extensive modelling and simulations Proposed algorithm: – Scores for kidney donors and wait-listed patients – Top 20% kidneys -> top 20% recipients – Other 80% kidneys -> recipients aged within 15 years of donor

25. Allocation simulations Waiting list Donor pool Allocation algorithms Outcomes Equity of access Utility Patient survival Graft survival ? others Simulation software

26. NOMS-ANZDATA link NOMS – National database of kidney allocation – Since 2000 (Y2K) – Detailed waiting list data since 28 June 2006 – Data extract 2001-2010 – Demographics, serology, antibodies, crossmatch results, organ match results – Probabilistic linkage with ANZDATA

27. Simulation modelling Visit to Ann Arbor, Michigan – Great people – Spring snow (new jacket) – Bad coffee Simulation software – Developed for US – Adapted for ANZDATA/ANZOD/NOMS data – Australia allocation rules – Validation against actual allocation

30. Actual StateOutInBalance NSW927814 VIC5558-3 QLD4447-3 SA3444-10 WA32302 State balance Simulated StateOutInBalance NSW69627 VIC684523 QLD3454-20 SA3847-9 WA2728

31. Utility-based allocation Factors affecting transplant benefit – Age – Sex – Race – Primary disease – Co-morbidities Diabetes Coronary disease Peripheral vascular disease Cerebrovascular disease Chronic lung disease Smoking history – Body mass index – Time on RRT – Waiting time – Donor age – Ischaemic time – HLA mismatch – Peak PRA

42. Utility-based allocation Factors affecting transplant benefit – Age – Sex – Race – Primary disease – Co-morbidities Diabetes Coronary disease Peripheral vascular disease Cerebrovascular disease Chronic lung disease Smoking history – Body mass index – Time on RRT – Waiting time – Donor age – Ischaemic time – HLA mismatch – Peak PRA Benefit = 26 – (age/4) - (donor age/20) - (HLA mismatch/3) - (peak PRA/20)

48. Current StateOutInBalance NSW69627 VIC684523 QLD3454-20 SA3847-9 WA2728 State balance Utility-based StateOutInBalance NSW282474-192 Vic36634026 QLD28627214 SA250112138 WA14112714

49. Future directions More sophisticated allocation models Sensitivity analyses – Changes in waiting list – Changes in donor pool QALYs Economic analyses

50. Acknowledgements ANZDATA – Blair Grace – Stephen McDonald – Steve Chadban NOMS – Jeremy Chapman – Jenni Wright – Paul Slater Arbor Research Collaborative for Health – Keith McCullough – Bob Merion – Alan Leichtman Australian and New Zealand patients and staff for their cooperation and contributions to ANZDATA and ANZOD