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Are we clear on the Concept? Empirical and normative concerns

Are we clear on the Concept? Empirical and normative concerns. Benjamin Hippen, M.D. Carolinas Medical Center Charlotte, NC. Overview. Why are we talking about this? Empirical concerns KDPI and EPTS Normative (ethical) concerns with the Concept Alternatives. Overview.

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Are we clear on the Concept? Empirical and normative concerns

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  1. Are we clear on the Concept?Empirical and normative concerns Benjamin Hippen, M.D. Carolinas Medical Center Charlotte, NC

  2. Overview • Why are we talking about this? • Empirical concerns KDPI and EPTS • Normative (ethical) concerns with the Concept • Alternatives

  3. Overview • The Concept document is only the most recent iteration of a long-standing project • KPSAM -> KARS -> LYFT -> KAS • Now: KDPI (or KDRI) and EPTS • None of these models have been prospectively validated. • The empirical limitations of the previous models persist in the current one.

  4. Risk factor versus Prognostic tool • Risk factor • Diabetes is a risk factor for renal failure • Prognostic tool • A risk factor which sharply distinguishes between a group that does and does not have an outcome • Not all risk factors are good prognostic tools. • Lots of folks with diabetes do not have renal failure • Diabetes is a poor prognostic tool for predicting renal failure

  5. Ware NEJM 355:2615

  6. A risk factor is a good prognostic tool if the risk factor(s) clearly separate the unaffected group from the affected group. A risk factor which does not do this will either have low sensitivity (shifting line To the right), or will increase sensitivity at the expense of a higher false positive rate (shifting line to the left). BMJ 1999;319:1562-5

  7. Empirical Concerns (1) • C-statistic – Measure of a Prognostic Test • Not a measure of “goodness of fit” • 0.5 = no better than chance for a binary outcome • 1.0 = Perfect prediction model • LYFT • Waitlist survival – 0.6 • Patient survival – 0.68 • Graft survival – 0.57 • EPTS “…did not provide substantially greater predictive power…” than LYFT.

  8. Empirical Concerns (2) • C-statistic for KDPI • C-statistic across all quartiles = 0.62 • C-statistic between middle quartiles – 0.5 • C-statistic between lowest/highest – 0.78 • “KDRI is more useful for distinguishing more extreme categories of graft failure risk and of less utility for distinguishing donors from the middle ranges.” (Rao Transplantation 88:235) • But, the relevant distinction is to reliably/reproducibly differentiate the top 20% from the other 80% of kidneys.

  9. Past projections, actual outcomes Meier-Kriesche AJT 4:1289

  10. Bottom line • High frequency of mistriage (30-40%) • Incorrectly identifying kidneys and candidates as conferring favorable survival or vice versa • No guarantee that mistriages will be randomly distributed • Some groups may be mistriaged more often • Frequent mistriage = a failure of the allocation system to do what it purports to do.

  11. Normative Concerns (1) • We know who is predicted to “win.” • But which groups will lose? • How many will lose? • Models of death on the waiting list • Should “life years gained” be offset by life years lost for want of a transplant? • Why not a comparative intent to treat analysis? • ITT would count additional deaths on the waiting list

  12. Add in proportion of list and new incident patients Data from OPTN.org

  13. Hippen NEJM 364:1285

  14. Normative Concerns (2) • Younger candidates disproportionately receive more kidneys from living donors. • 18-34: 53% of removals for transplant from LD • 35-49: 41% • 50-64: 33% • 65+ : 28% • Disproportionally disincentivising LD among the young may reduce total rates of living donation. • Why suppose the young are randomly distributed across DSAs? The < 20% may look quite different across DSAs and across individual centers. • Why won’t transplant centers aggressively advertise their favorable “< 20%” demographics? Why shouldn’t they?

  15. A Kidney that Looks Like You?(But Doc, I’m pretty sick!) Frei AJT 8: 50 Not a simulation

  16. More kidneys • Why would centers with conservative risk tolerance currently suddenly change their institutional minds? • 79% one year graft survival, not censored for death • More kidneys + worse outcomes versus fewer kidneys and better outcomes • Does “risk adjustment” help patients, or just help transplant centers and OPOs?

  17. Whose kidneys are they, anyway? • Not the OPO • Not the Transplant Center • Not the Transplant Surgeon/Nephrologist • These kidneys are a public resource • Individual candidates should be allowed to choose, in consultation with their physician, their own level of risk tolerance. • Additional risk and foreclosure of benefit from a public resource should not be foisted on the older and the sicker by fiat.

  18. Alternatives • Better, prospectively validated risk models for education purposes = More money from HRSA, and a novel approach from SRTR • Be a doctor • Tailor advice to individual candidates in the evaluation • Informed consent • Counsel candidates in real time when they come up for an offer • Moral obligations are sometimes inefficient • Come to terms with the fact that tinkering with allocation will never address the supply/demand disparity in a meaningful way. • More living donors, and more creative ways of procuring and distributing organs from living donors.

  19. Thank you!

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