Prediction models for delayed graft function: external validation on The Dutch Prospective Renal Transplantation Registry

• Published in: Nephrology, dialysis, transplantation dialysis, transplantation, 2018-07, Vol.33 (7), p.1259-1268
• Main Authors: Kers, Jesper, Peters-Sengers, Hessel, Heemskerk, Martin B A, Berger, Stefan P, Betjes, Michiel G H, van Zuilen, Arjan D, Hilbrands, Luuk B, de Fijter, Johan W, Nurmohamed, Azam S, Christiaans, Maarten H, Homan van der Heide, Jaap J, Debray, Thomas P A, Bemelman, Fréderike J
• Format: Article
• Language: English
• ISSN: 0931-0509; 1460-2385
• DOI: 10.1093/ndt/gfy019

BackgroundDelayed graft function (DGF) is a common complication after kidney transplantation in the era of accepting an equal number of brain- and circulatory-death donor kidneys in the Netherlands. To identify those cases with an increased risk of developing DGF, various multivariable algorithms have been proposed. The objective was to validate the reproducibility of four predictive algorithms by Irish et al. (A risk prediction model for delayed graft function in the current era of deceased donor renal transplantation. Am J Transplant 2010;10:2279-2286) (USA), Jeldres et al. (Prediction of delayed graft function after renal transplantation. Can Urol Assoc J 2009;3:377-382) (Canada), Chapal et al. (A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors. Kidney Int 2014;86:1130-1139) (France) and Zaza et al. (Predictive model for delayed graft function based on easily available pre-renal transplant variables. Intern Emerg Med 2015;10:135-141) (Italy) according to a novel framework for external validation. MethodsWe conducted a prospective observational study with data from the Dutch Organ Transplantation Registry (NOTR). Renal transplant recipients from all eight Dutch academic medical centers between 2002 and 2012 who received a deceased allograft were included (N = 3333). The four prediction algorithms were reconstructed from donor, recipient and transplantation data. Their predictive value for DGF was validated by c-statistics, calibration statistics and net benefit analysis. Case-mix (un)relatedness was investigated with a membership model and mean and standard deviation of the linear predictor. ResultsThe prevalence of DGF was 37%. Despite a significantly different case-mix, the US algorithm by Irish was best reproducible, with a c-index of 0.761 (range 0.756 - 0.762), and well-calibrated over the complete range of predicted probabilities of having DGF. The US model had a net benefit of 0.242 at a threshold probability of 0.25, compared with 0.089 net benefit for the same threshold in the original study, equivalent to correctly identifying DGF in 24 cases per 100 patients (true positive results) without an increase in the number of false-positive results. ConclusionsThe US model by Irish et al. was generalizable and best transportable to Dutch recipients with a deceased donor kidney. The algorithm detects an increased risk of DGF after allocation and enables us to impro