Prediction of delayed graft function after renal transplantation.

Introduction: Delayed graft function (DGF), defined as the need for dialysis during the first week after renal transplantation, is an important adverse clinical outcome. A previous model relied on 16 variables to quantify the risk of DGF, thereby undermining its clinical usefulness. We explored the possibility of developing a simpler, equally accurate and more user-friendly paradigm for renal transplant recipients from deceased donors. Methods: Logistic regression analyses addressed the occurrence of DGF in 532 renal transplant recipients from deceased donors. Predictors consisted of recipient age, gender, race, weight, number of HLA-A, HLA-B and HLA-DR mismatches, maximum and last titre of panel reactive antibodies, donor age and cold ischemia time. Accuracy was quantified with the area under the curve. Two hundred bootstrap resamples were used for internal validation. Results: Delayed graft function occurred in 103 patients (19.4%). Recipient weight (p < 0.001), panel of reactive antibodies (p < 0.001), donor age (p < 0.001), cold ischemia time (p = 0.005) and HLADR mismatches (p = 0.05) represented independent predictors. The multivariable nomogram relying on 6 predictors was 74.3% accurate in predicting the probability of DGF. Conclusion: Our simple and user-friendly model requires 6 variables and is at least equally accurate (74%) to the previous nomogram (71%). We demonstrate that DGF can be accurately predicted in different populations with this new model. [ABSTRACT FROM AUTHOR]