External Validation of the International IgA Nephropathy Prediction Tool

The International IgA Nephropathy Network recently developed and externally validated two models to predict the risk of progression of IgA nephropathy: full models without and with race. This study sought to externally validate the International IgA Nephropathy Prediction Tool in a large, independent, and contemporary cohort in China.

We included 1373 patients with biopsy-confirmed primary IgA nephropathy from The First Affiliated Hospital of Zhengzhou University from January 2012 to May 2018 and calculated predicted risks for each patient. The outcomes of interest were a 50% decline in eGFR or kidney failure. We assessed the performance of both models using discrimination (concordance statistics and Kaplan-Meier curves between subgroups), calibration (calibration plots), reclassification (net reclassification improvement and integrated discrimination improvement), and clinical utility (decision curve analysis).

The median follow-up was 29 months (interquartile range, 21-43 months; range, 1-95 months), and 186 (14%) patients reached the kidney outcomes of interest. Both models showed excellent discrimination (concordance statistics >0.85 and well separated survival curves). Overall, the full model without race generally underestimated the risk of primary outcome, whereas the full model with race was well calibrated for predicting 5-year risk. Compared with the full model without race, the full model with race had significant improvement in reclassification, as assessed by the net reclassification improvement (0.49; 95% confidence interval, 0.41 to 0.59) and integrated discrimination improvement (0.06; 95% confidence interval, 0.04 to 0.08). Decision curve analysis showed that both full models had a higher net benefit than default strategies, and the model with race performed better.

In this study, both full models demonstrated remarkable discrimination, acceptable calibration, and satisfactory clinical utility. The relatively short follow-up time may have limited the validation of these models.