Validation in a Single-Center Cohort of Existing Predictive Models for Delayed Graft Function After Kidney Transplantation
Alexander Decruyenaere, Philippe Decruyenaere, Patrick Peeters, Frank Vermassen
Department of Nephrology, Ghent University Hospital, Ghent, Belgium
Ann Transplant 2015; 20:544-552
Kidney transplantation is the preferred treatment for patients with end-stage renal disease. Delayed graft function (DGF) is a common complication and is associated with short- and long-term outcomes. Several predictive models for DGF have been developed.
MATERIAL AND METHODS: 497 kidney transplantations from deceased donors at our center between 2005–2011 are included. Firstly, the predictive accuracy of the existing models proposed by Irish et al. (M1), Jeldres et al. (M2), Chapal et al. (M3), and Zaza et al. (M4) was assessed. Secondly, the existing models were aggregated into a meta-model (MM) using stacked regressions. Finally, the association between 47 risk factors and DGF was studied in our cohort-fitted model (CFM) using logistic regression. The accuracy of all models was assessed by area under the receiver operating characteristic curve (AUROC) and Hosmer-Lemeshow test.
RESULTS: M1, M2, M3, M4, MM, and CFM have AUROCs of 0.78, 0.65, 0.59, 0.67, 0.78, and 0.82, respectively. M1 (P=0.018), M2 (P<0.001), M3 (P<0.001), and M4 (P<0.001) overestimate the risk. MM (P=0.255) and CFM (P=0.836) are well calibrated. Donor subtype (P<0.001), recipient cardiac function (P<0.001), donor serum creatinine (P<0.001), donor age (P=0.006), duration of dialysis (P=0.02), recipient BMI (P=0.008), donor BMI (P=0.041), and recipient preoperative diastolic blood pressure (P=0.049) are associated with DGF in our CFM.
CONCLUSIONS: Four existing predictive models for DGF overestimate the risk in a cohort with a low incidence of DGF. We have identified 2 recipient parameters that are not included in previous models: cardiac function and preoperative diastolic blood pressure.
Keywords: Delayed Graft Function, Kidney Transplantation, Logistic Models, Multivariate Analysis, Risk Assessment, Risk Factors