1. Ken Andreoni, MD Chair UNOS Kidney Comm The Ohio State University
UNOS Kidney Committee Allocation Concepts: Not As Different As Some Want You to Believe…
Ken Andreoni, MD
Chair UNOS Kidney Comm
The Ohio State University
Welcome to the Spring UNOS Regional Meeting Presentation by the Kidney Committee. We would like to review the first set of concepts which we hope to develop into a new kidney allocation policy. We are presenting this information to you in hope of receiving constructive feedback.

2. DD Kidney Allocation Concepts
We are ONLY talking about standard ADULT Kidney only allocation today. We are NOT talking about:
Kidney with extra-renal organ (LK, HK, KP, etc.)
Pediatric – no change (except KDPI, not age)
Prior Living Donor category
O-MM National Sharing (CPRA >20)
Geography: being thoroughly investigated by other UNOS committees; complex issue

3. DD Kidney Allocation Concepts
Though we think of allocating 10,000 deceased donor kidneys a year in the US, allocation is one kidney at a time…
This is why many theoretical allocation concepts do not work in reality!

4. DD Kidney Allocation: Recent Change
0 mm ABDR is local by category of CPRA, then regional or national for CPRA >20 only
This change has decreased the share of unsensitized 0 mm, and allowed more highly sensitized candidates to be transplanted nationally with less overall shipping of kidneys

5. DD Kidney Allocation: TODAY
Estimation of DD kidney graft potential function: ECD or SCD
If ECD: goes to those on the local ECD list (by wait time)
If not accepted, then regional, then national ECD lists
If SCD: then all candidates locally by “points”
Wait time, HLA-DR matching (2 pts max), CPRA (>80 = 4 pts)
Then regional, then national by pts

6. DD Kidney Allocation: TODAY
Most candidates at the top of list mostly by Wait Time
“If I just wait another week/month, could I get a much better kidney?”
Makes very inefficient use of very useable kidneys
Patients and Transplant Professionals need better educational tools to decide about the trade-off: time to transplant vs. quality of organ

7. DD Kidney New Allocation: Concepts (not policy)
Estimation of DD kidney graft potential function: ECD or SCD “KDPI”: < or > 20%
If KDPI is 21 to 100%, first offered to all within 15 years of donor age (30 yr span)
This large group rank ordered (WT, CPRA, HLA)
If not accepted, then to those outside of 15 yrs local, then regional, then national
If KDPI <=20%; then first to candidates with Est Post-Tx Survival longest 20%
If not accepted, then to all local, then regional, then national

8. SCD vs ECD: Overlap Too many candidates are listed for ECD
Waiting for the ‘Good ECD’
Despite this survival overlap, the current system leads to higher discard rates for potential well functioning kidneys that are labeled ECD 8

9. KDPI vs ECD KDPI ECD Donor age (c) Race/ethnicity Hypertension
Diabetes
Serum creatinine (c)
COD CVA
Height
Weight
DCD
HCV
ECD
Donor Age
>60 alone
>50 with two below:
Cr >1.5
HTN
CVA
RR of graft failure >1.7 compared to the ‘ideal’ donor (16 – 17%)
The Kidney Donor Profile Index is calculated from DONOR characteristics only. These donor characteristics are listed on the left of this slide and are already collected by OPOs. The KPDI is based on the actual outcome of kidney grafts from donors in the UNOS database over the last several years. The KPDI is an estimate of how well kidneys from donors will function if transplanted in the average recipient.
The Estimated Candidate Post-Transplant Survival is based only on Candidate characteristics if they were to receive the average transplant graft. Candidate Age, the presence of diabetes, prior transplant and time with ESRD go into this calculation. Candidates who are estimated to have the potential to live the longest after transplant are considered to be in the “Top 20%” for estimated post-transplant survival.

10. KDPI overlaps substantially for donors from most age categories
Donor Age v. KDPI
KDPI overlaps substantially for donors from most age categories
This is a box and whisker plot that shows the wide range of Kidney graft quality across donor ages. The age range of the donors are across the bottom on the X axis and the KPDI is vertically on the Y axis. The lower the KPDI, the longer the likely function of the kidney graft. As you can see, the average or 50th% kidney can come from a donor ranging from 5 years old to 54 years old. So all candidates can have access to kidney grafts of reasonable function.
Slide 10 10

11. Donor Age by Recipient Age
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SAS code in:
T:\Data Requests\Kidney-Pancreas\2009\99999\karswork\dpi by donor age.sas
Slide 11 11

12. Distribution of Relative Risks for Donor Kidneys: 2004-2007
Relative Risk for graft failure is not markedly different for top 20% of kidneys
Uses donor factors only 12

13. +- 15 years: mostly what we already do…
Median age difference is 14 years in the US
25% of DD txs <6 yrs apart
75% < 26 yrs apart
Donors <35 yo are 41% of donors
Donors <=35, mean recipient age is 49
Recip >65 more than half of donors >50yo
Segev DL. Evaluating Options… AJT 2009; 9:

14. DD Kidney Utilization
Estimation of DD kidney graft potential function:
ECD/SCD vs KDPI
Education of potential benefit to recipients (and transplant professionals)
Quality of organ vs. prolonged wait time for better organ

15. Median Expected Survival by Age for Active Kidney Candidates, 1/1/2004
Wolfe et al, SRTR
simulation models

16. Median Expected Survival by Age Active Kidney Candidates, 1/1/2004
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SAS code in
T:\Data Requests\Kidney-Pancreas\2007\4783\KARS Exploration\Avg survival by age wl pt gen pop.sas
Wolfe et al, SRTR Simulation Models 16

17. Recipient Survival by Recipient Age and Donor DPI
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SAS code in:
T:\Data Requests\Kidney-Pancreas\2009\99999\karswork\pt surv by dpi by rec age.sas
T:\Data Requests\Kidney-Pancreas\2009\99999\karswork\Donor age by DPI v2.xls
Slide 17
transplants 17

18. Candidate of X yrs old, with Y, Z co-morbidities, living in a DSA C
Hypothetical Output from an Educational Tool to help Candidates and Transplant Professionals Make More Informed Decisions on Organ Acceptance:
Candidate of X yrs old, with Y, Z co-morbidities, living in a DSA C 5 4 3 2 1

19. The easy potential increase in kidney utilization19

20. DD Kidney Utilization
Estimation of DD kidney graft potential function:
ECD/SCD vs KDPI
Education of potential benefit to recipients
Transplant Center Outcome reports

21. % Deaths by Year by DPI among candidates >50 by decade of age

22. Big Picture Slide: Most with ESRD do not live to avg
Big Picture Slide: Most with ESRD do not live to avg. pop lifetime, Transplantation is good for most candidates, young w ESRD lose more years from their expected lifetime . 22

23. Who is the Sickest? Die first?
Like MELD for liver
Then we transplant all sicker and older pts
Who loses the most years from their disease?
25yo on HD:13 yrs, w Tx:34 yrs
60yo on HD: 5 yrs, w Tx:12 yrs
25yo unlikely to reach age 60 w Tx
25yo will die at <40 yrs of age on HD
25yo gains 21 yrs of survival, 60yo gains 7 yrs of survival w Tx
25yo lives 13 yrs on HD, 60yo lives 12 yrs w Tx

24. ‘A Kidney That Looks Like You’
All candidates of all ages have access
Access for most candidates does NOT change
The average candidate will receive the SAME quality kidney
Will only prevent transplantation across many decades of age differences
All candidates may benefit…why?
Improvement of utilization of kidneys by KDPI and understanding of age ranges should increase transplantation of appropriate kidneys, especially to older candidates
Public understanding of system to increase donation

25. ‘A Kidney That Looks Like You’
Living Donation should not be influenced in the negative: No one goes to the front of the line
Whether within 30 year age group, or “top 20%” everyone within that group is then equal and put in order by variables such as: Wait time, CPRA, HLA, etc., so everyone will wait for their DD offer
NOT similar to the Pediatric Share 35 situation that occurred in some DSAs

26. Is the Data Good Enough?
80% of organs first to candidate group within 15 years (30 yr range)
Rank-ordered by variables similar to today such as Wait Time, CPRA, HLA match, etc.
Clinical common sense
Alignment of potential function of organ to post-transplant potential survival
20% DPI and EPTS
Predictive models are reasonably good to predict the longest potential functioning organs and longest surviving recipient

27. C Statistic
Measure of “goodness of fit”, or how accurately does this tool tell two people apart everywhere on the list
It gives the same weight to tell number 1 from number 10,000, as it does from telling number 5,000 from number 5,001
The first is important in allocation, the later is not!

28. Is the Data Good Enough?

29. Thank you