Allocation of deceased donor kidneys. Phil Clayton NSW Renal Group 14 June 2012
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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
3 Demand vs supply Australian dialysis patients vs transplants performed Count Dialysis patients Transplants performed Year
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 Current Australian model Major listing criteria: ESKD on dialysis 80% likelihood of surviving for at least 5 years after transplantation Major allocation criteria: Blood group Equity/Utility Waiting time Equity HLA match Utility Highly sensitised Equity/Utility Childhood Utility
7
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 Donor Recipient Australia NSW VIC SA QLD WA A A A AB AB AB B AB B B O A O AB O B O O
11 ABO rules Donor Recipient Australia NSW VIC SA QLD WA A A A AB AB AB B AB B B O A O AB O B O O
12 Patient survival Australian DD grafts Years HLA mismatch
13 Death-censored graft survival Australian DD grafts Years HLA mismatch
14 Death-censored graft survival Australian DD grafts Years Peak PRA
15 Patient survival Australian DD grafts Years Age
16 Death-censored graft survival Australian DD grafts Years Donor age
17 Frequency Age difference (donor minus recipient age) Australian DD grafts Young donors Old patients Old donors Young patients
18 More benefit from age matching if higher proportion of older donors
19 Patient survival Australian DD grafts Years Primary disease Other Diabetic nepropathy
20 Deceased kidney donor age Australia Age (years)
21 Deceased kidney donor age Australia Age (years)
22 60 Donor co-morbidities Australian deceased kidney donors % History of smoking Hypertension Diabetes Year 3-year moving average
23 100 Deceased kidney donor profile Australia % Brain death, standard criteria Brain death, expanded criteria Cardiac death Year
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 Simulation software Allocation algorithms Outcomes Equity of access Utility Patient survival Graft survival? others
26 NOMS ANZDATA link NOMS National database of kidney allocation Since 2000 (Y2K) Detailed waiting list data since 28 June 2006 Data extract 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
28 Age Actual Simulated Sex Female Male GN Analgesic Polycystic Reflux Hypertension Diabetes Other Uncertain ,000 1,500 Frequency Age (years) ,000 Frequency ,000 1,500 Frequency Race White ATSI Asian Other Primary disease Actual Simulated
29 Waiting time Actual Simulated HLA mismatch Peak PRA Waiting time (months) ,000 1,500 Frequency Frequency Peak PRA Actual Simulated Graft number Actual Simulated
30 State balance Actual State Out In Balance NSW VIC QLD SA WA Simulated State Out In Balance NSW VIC QLD SA WA
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
32 Projected survival after deceased donor transplant Age 65, male, white, diabetic nephropathy, overweight Patient survival Deceased donor transplant Median 12.0 years Years
33 Projected survival on waiting list Age 65, male, white, diabetic nephropathy, overweight Patient survival Median 5.5 years Waiting list Years
34 Projected benefit from deceased donor transplant Age 65, male, white, diabetic nephropathy, overweight Patient survival Deceased donor transplant Waiting list Median benefit 6.5 years Years
35 Projected benefit from deceased donor transplant Age 20, male, white, GN, normal weight Patient survival Deceased donor transplant Waiting list Median benefit 21.0 years Years
36 Projected benefit from deceased donor transplant Age 30, male, white, GN, normal weight Patient survival Deceased donor transplant Waiting list Median benefit 17.6 years Years
37 Projected benefit from deceased donor transplant Age 40, male, white, GN, normal weight Patient survival Median benefit 14.3 years Deceased donor transplant Waiting list Years
38 Projected benefit from deceased donor transplant Age 50, male, white, GN, normal weight Patient survival Median benefit 11.2 years Deceased donor transplant Waiting list Years
39 Projected benefit from deceased donor transplant Age 60, male, white, GN, normal weight Patient survival Median benefit 8.4 years Deceased donor transplant Waiting list Years
40 Projected benefit from deceased donor transplant Age 70, male, white, GN, normal weight Patient survival Median benefit 6.1 years Deceased donor transplant Waiting list Years
41 Projected benefit from deceased donor transplant Age 80, male, white, GN, normal weight Patient survival Median benefit 3.9 years Deceased donor transplant Waiting list Years
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)
43 Age Current Utility-based Sex Female Male GN Analgesic Polycystic Reflux Hypertension Diabetes Other Uncertain ,000 1,500 Frequency Age (years) ,000 Frequency ,000 1,500 Frequency Race White ATSI Asian Other Primary disease Current Utility-based
44 Waiting time Current Utility-based HLA mismatch Peak PRA Waiting time (months) ,000 1,500 Frequency Frequency Peak PRA Current Utility-based Graft number Current Utility-based
45 Life expectancy Current Life expectancy by age Current vs utility-based DD allocation Utility-based Age
46 Life expectancy by age Current vs utility-based DD allocation Life expectancy Current Utility-based Waiting list age distribution Age
47 400 Living donor transplantation rate Australia Current deceased donor kidney allocation system Living donor transplants Observed Utility-based deceased donor kidney allocation system Simulated Year
48 State balance Current State Out In Balance NSW VIC QLD SA WA Utility-based State Out In Balance NSW Vic QLD SA WA
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
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