Objective To construct a predictive model for the survival of transplant kidneys after kidney transplantation.

Methods The clinical data of 366 kidney transplant recipients and donors were retrospectively analyzed, and the recipients were divided into low-risk group (n=101), medium-risk group (n=189), and high-risk group (n=76) based on the kidney donor profile index (KDPI). Each group was further divided into Remuzzi score ≤3 group and Remuzzi score >3 group based on time-zero biopsy Remuzzi scores. Kaplan-Meier method was used to analyze the survival of transplant kidneys. Univariate and multivariate Cox regression analyses were performed to identify risk factors affecting long-term survival after kidney transplantation. A predictive model for transplant kidney survival was established and a nomogram was drawn. The predictive performance of the model was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC).

Results The median KDPI was 65%, and the median Remuzzi score was 3. The 5-year survival rate of transplant kidneys was 83.5%. Kaplan-Meier survival curves showed that in the KDPI medium-risk and KDPI high-risk groups, the subgroup with lower Remuzzi score had a higher survival rates of transplant kidneys than the subgroup with higher Remuzzi score. Univariate and multivariate Cox regression analyses showed that KDPI, Remuzzi score, and donor’s age were independent risk factors for transplant kidney loss (all P<0.05). The ROC curve showed that the AUC of the nomogram prediction model established based on independent risk factors for the 1, 3 and 5-year survival rates of transplant kidneys were 0.91, 0.93 and 0.94 for the training set, and 0.89, 0.85 and 0.88 for the validation set. Calibration curves shows good consistency between the training and validation sets of the model.

Conclusions The nomogram predictive model based on KDPI, time-zero biopsy Remuzzi score and donor’s age has good predictive value for transplant kidney survival.