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Evaluation of Accepting Kidneys of Varying Quality for Transplantation or Expedited Placement With Decision Trees

Kilambi, Vikram, PhD1,2,3,4; Bui, Kevin, MS1,2; Hazen, Gordon B., PhD1,2,3; Friedewald, John J., MD3; Ladner, Daniela P., MD/MPH3; Kaplan, Bruce, MD5; Mehrotra, Sanjay, PhD1,2,3

doi: 10.1097/TP.0000000000002585
Original Clinical Science—General

Background. Underutilization of marginal-quality kidneys for transplantation produced ideas of expediting kidney placement for populations with decreased opportunities of receiving transplants. Such policies can be less efficacious for specific individuals and should be scrutinized until the decision-making for accepting marginal-quality organs, which has relied on experiential judgment, is better understood at the individual level. There exist rigorous tools promoting personalized decisions with useful and objective information.

Methods. This article introduces a decision-tree methodology that analyzes a patient’s dilemma: to accept a kidney offer now or reject it. The methodology calculates the survival benefit of accepting a kidney given a certain quality now and the survival benefit of rejecting it. Survival benefit calculation accounts for patients’ and donors’ characteristics and transplant centers’ and organ procurement organizations’ performances and incorporates patients’ perceived transplant and dialysis utilities. Valuations of rejecting an offer are contingent on future opportunities and subject to uncertainty in the timing of successive kidney offers and their quality and donor characteristics.

Results. The decision tree was applied to a realistic patient profile as a demonstration. The tool was tested on 1000 deceased-donor kidney offers in 2016. Evaluating up to 1 year of future offers, the tool attains 61% accuracy, with transplant utility of 1.0 and dialysis utility of 0.5. The accuracy reveals potential bias in kidney offer acceptance/rejection at transplant centers.

Conclusions. The decision-tree tool presented could aid personalized transplant decision-making in the future by providing patients with calculated, individualized survival benefits between accepting and rejecting a kidney offer.

1 Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL.

2 Center for Engineering and Health, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL.

3 Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Northwestern University Feinberg School of Medicine, Chicago, IL.

4 RAND Corporation, Boston, MA.

5 Mayo Clinic Medical School, Phoenix, AZ.

Received 1 February 2018. Revision received 19 November 2018.

Accepted 26 November 2018.

The authors declare no conflicts of interest.

This work was supported by Agency for Healthcare Research and Quality award (grant 1R36HS024840-01) and National Institutes for Health award (grant 1R21DK108104-01).

V.K. developed research design, conducted experiments and analyses, and drafted the article. K.B. calibrated statistical models, implemented model, conducted experiments and analyses, and drafted the article. G.B.H., J.J.F., and D.P.L. developed research design and edited the article. B.K. reviewed and edited the article. S.M. developed research design, supervised experiments and analyses, and edited the article.

Supplemental digital content (SDC) is available for this article. Direct URL citations appear in the printed text, and links to the digital files are provided in the HTML text of this article on the journal’s Web site (

Correspondence: Vikram Kilambi, PhD, RAND Corporation, 20 Park Plaza Suite 920, Boston, MA 02116. (

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