Jesper Kers1, Hessel Peters-Sengers2, Martin B A Heemskerk3, Stefan P Berger4, Michiel G H Betjes5, Arjan D van Zuilen6, Luuk B Hilbrands7, Johan W de Fijter8, Azam S Nurmohamed9, Maarten H Christiaans10, Jaap J Homan van der Heide2, Thomas P A Debray11, Fréderike J Bemelman2. 1. Department of Pathology, Academic Medical Center (AMC), Amsterdam, The Netherlands. 2. Department of Internal Medicine, Renal Transplant Unit, Academic Medical Center (AMC), Amsterdam, The Netherlands. 3. Dutch Transplant Foundation, Leiden, The Netherlands. 4. Department of Nephrology, University Medical Center Groningen (UMCG), Groningen, The Netherlands. 5. Department of Nephrology, Erasmus University Medical Center (Erasmus MC), Rotterdam, The Netherlands. 6. Department of Nephrology, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands. 7. Department of Nephrology, Radboud University Nijmegen Medical Center (RUNMC), Nijmegen, The Netherlands. 8. Department of Nephrology, Leiden University Medical Center (LUMC), Leiden, The Netherlands. 9. Department of Nephrology, Free University Medical Center (VUMC), Amsterdam, The Netherlands. 10. Department of Nephrology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands. 11. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands.
Abstract
Background: Delayed 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. Methods: We 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. Results: The 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. Conclusions: The 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 improve individual patient management.
Background: Delayed graft function (DGF) is a common complication after kidney transplantation in the era of accepting an equal number of brain- and circulatory-deathdonor 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. Methods: We 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. Results: The 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. Conclusions: The 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 improve individual patient management.
Authors: Silvana Daher Costa; Luis Gustavo Modelli de Andrade; Francisco Victor Carvalho Barroso; Cláudia Maria Costa de Oliveira; Elizabeth De Francesco Daher; Paula Frassinetti Castelo Branco Camurça Fernandes; Ronaldo de Matos Esmeraldo; Tainá Veras de Sandes-Freitas Journal: PLoS One Date: 2020-02-06 Impact factor: 3.240
Authors: Arthur J Matas; Erika Helgeson; Ann Fieberg; Robert Leduc; Robert S Gaston; Bertram L Kasiske; David Rush; Lawrence Hunsicker; Fernando Cosio; Joseph P Grande; J Michael Cecka; John Connett; Roslyn B Mannon Journal: Transplantation Date: 2022-02-01 Impact factor: 5.385