Prediction of delayed graft function using different scoring algorithms: A single-center experience

• Published in: World journal of transplantation 2017-10, Vol.7 (5), p.260-268
• Main Authors: Michalak, Magda, Wouters, Kristien, Fransen, Erik, Hellemans, Rachel, Van Craenenbroeck, Amaryllis H, Couttenye, Marie M, Bracke, Bart, Ysebaert, Dirk K, Hartman, Vera, De Greef, Kathleen, Chapelle, Thiery, Roeyen, Geert, Van Beeumen, Gerda, Emonds, Marie-Paule, Abramowicz, Daniel, Bosmans, Jean-Louis
• Format: Article
• Language: English
• Subjects: Retrospective Cohort Study
• ISSN: 2220-3230; 2220-3230
• DOI: 10.5500/wjt.v7.i5.260

To compare the performance of 3 published delayed graft function (DGF) calculators that compute the theoretical risk of DGF for each patient. This single-center, retrospective study included 247 consecutive kidney transplants from a deceased donor. These kidney transplantations were performed at our institution between January 2003 and December 2012. We compared the occurrence of observed DGF in our cohort with the predicted DGF according to three different published calculators. The accuracy of the calculators was evaluated by means of the c-index (receiver operating characteristic curve). DGF occurred in 15.3% of the transplants under study. The c index of the Irish calculator provided an area under the curve (AUC) of 0.69 indicating an acceptable level of prediction, in contrast to the poor performance of the Jeldres nomogram (AUC = 0.54) and the Chapal nomogram (AUC = 0.51). With the Irish algorithm the predicted DGF risk and the observed DGF probabilities were close. The mean calculated DGF risk was significantly different between DGF-positive and DGF-negative subjects ( < 0.0001). However, at the level of the individual patient the calculated risk of DGF overlapped very widely with ranges from 10% to 51% for recipients with DGF and from 4% to 56% for those without DGF. The sensitivity, specificity and positive predictive value of a calculated DGF risk ≥ 30% with the Irish nomogram were 32%, 91% and 38%. Predictive models for DGF after kidney transplantation are performant in the population in which they were derived, but less so in external validations.