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Am J Transplant. 2019 Feb;19(2):425-433. doi: 10.1111/ajt.14978. Epub 2018 Jul 14.

Who can tolerate a marginal kidney? Predicting survival after deceased donor kidney transplant by donor-recipient combination.

Author information

1
Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
2
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
3
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
4
Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Abstract

The impact of donor quality on post-kidney transplant (KT) survival may vary by candidate condition. Characterizing this variation would increase access to KT without sacrificing outcomes. We developed a tool to estimate post-KT survival for combinations of donor quality and candidate condition. We studied deceased donor KT recipients (n = 120 818) and waitlisted candidates (n = 376 272) between 2005 and 2016 by using the Scientific Registry of Transplant Recipients. Donor quality and candidate condition were measured by using the Kidney Donor Profile Index (KDPI) and the Estimated Post Transplant Survival (EPTS) score. We estimated 5-year post-KT survival based on combinations of KDPI and EPTS score using random forest algorithms and waitlist survival by EPTS score using Weibull regressions. Survival benefit was defined as absolute reduction in mortality risk with KT. For candidates with an EPTS score of 80, 5-year waitlist survival was 47.6%, and 5-year post-KT survival was 78.9% after receiving kidneys with a KDPI of 20 and was 70.7% after receiving kidneys with a KDPI of 80. The impact of KDPI on survival benefit varied greatly by EPTS score. For candidates with low EPTS scores (eg, <40), the KDPI had limited impact on survival benefit. For candidates with middle or high EPTS scores (eg, >40), survival benefit decreased with higher KDPI but was still substantial even with a KDPI of 100 (>16 percentage points). Our prediction tool (www.transplantmodels.com/kdpi-epts) can support individualized decision-making on kidney offers in clinical practice.

KEYWORDS:

clinical decision-making; clinical research/practice; epidemiology; kidney transplantation/nephrology; organ acceptance; registry/registry analysis

PMID:
29935051
PMCID:
PMC6309666
[Available on 2020-02-01]
DOI:
10.1111/ajt.14978

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