Validation of 2 Prognostic Models to Predict Renal Allograft Outcome After IgA Nephropathy Recurrence

IgA nephropathy (IgAN) recurrence (IgANr) after kidney transplantation (KTx) is common and contributes to reducing graft survival. Some tools have been developed to predict the patients who are at a higher risk of poor outcomes among the native (international IgAN prediction tool [IIgAN-PT]) and graft (Bednarova's prediction tool [Bednarova-PT]) kidney. We aimed to analyze their performance in a KTx population other than the originally reported.

We performed a multicenter retrospective study including KTx with biopsy-proven IgANr. IIgAN-PT and Bednarova-PT were used to calculate the risk of death-censored graft loss (DCGL). We assessed the performance of both prediction models using discrimination and calibration metrics and Kaplan-Meier plots.

One hundred twenty KTx with IgANr were included. The time-dependent receiver operating characteristic (ROC) area under the curve (AUC) of Bednarova-PT for predicting DCGL was 83.5 (95% CI: 72.3-94.7) and the calibration slope was 0.96 (95% CI: 0.37-1.49). The time-dependent ROC AUC of IIgAN-PT for predicting DCGL was 87.3 (95% CI: 77.58-97.02) and the calibration slope was 2.49 (95% CI: 0.19-4.13). IIgAN-PT tended to underestimate the graft-loss risk in high-risk individuals. The Kaplan-Meier curve of the highest risk group, defined by using both prediction tools, was clearly separated from the other curves.

Both IIgAN-PT and Bednarova-PT performed well in predicting DCGL after IgANr and should be used to identify those KTx at the highest risk. Both models had good discriminatory ability and were well-calibrated, although the calibration slope was higher for IIgAN-PT, tending to underestimate the risk in high-risk individuals.