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Predicting donor, recipient and graft survival in living donor kidney transplantation to inform pretransplant counselling: the donor and recipient linked iPREDICTLIVING tool - a retrospective study.

Authors
  • Haller, Maria C1, 2
  • Wallisch, Christine1
  • Mjøen, Geir3
  • Holdaas, Hallvard3
  • Dunkler, Daniela1
  • Heinze, Georg1
  • Oberbauer, Rainer4
  • 1 Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Vienna, Austria. , (Austria)
  • 2 Nephrology, Ordensklinikum Linz, Elisabethinen, Linz, Austria. , (Austria)
  • 3 Department of Transplant Medicine, Oslo University Hospital, Oslo, Norway. , (Norway)
  • 4 Division of Nephrology and Dialysis, Department of Medicine III, Medical University of Vienna, Vienna, Austria. , (Austria)
Type
Published Article
Journal
Transplant International
Publisher
Wiley (Blackwell Publishing)
Publication Date
Jul 01, 2020
Volume
33
Issue
7
Pages
729–739
Identifiers
DOI: 10.1111/tri.13580
PMID: 31970822
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Although separate prediction models for donors and recipients were previously published, we identified a need to predict outcomes of donor/recipient simultaneously, as they are clearly not independent of each other. We used characteristics from transplantations performed at the Oslo University Hospital from 1854 live donors and from 837 recipients of a live donor kidney transplant to derive Cox models for predicting donor mortality up to 20 years, and recipient death, and graft loss up to 10 years. The models were developed using the multivariable fractional polynomials algorithm optimizing Akaike's information criterion, and optimism-corrected performance was assessed. Age, year of donation, smoking status, cholesterol and creatinine were selected to predict donor mortality (C-statistic of 0.81). Linear predictors for donor mortality served as summary of donor prognosis in recipient models. Age, sex, year of transplantation, dialysis vintage, primary renal disease, cerebrovascular disease, peripheral vascular disease and HLA mismatch were selected to predict recipient mortality (C-statistic of 0.77). Age, dialysis vintage, linear predictor of donor mortality, HLA mismatch, peripheral vascular disease and heart disease were selected to predict graft loss (C-statistic of 0.66). Our prediction models inform decision-making at the time of transplant counselling and are implemented as online calculators. © 2020 The Authors. Transplant International published by John Wiley & Sons Ltd on behalf of Steunstichting ESOT.

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