The International IgA Nephropathy Network Prediction Tool Underestimates Disease Progression in Indian Patients.

ABSTRACT

Introduction: International IgA nephropathy (IgAN) network (IIgANN) prediction tool was developed to predict risk of progression in IgAN. We attempted to externally validate this tool in an Indian cohort because the original study did not include Indian patients. Methods: Adult patients with primary IgAN were stratified to low, intermediate, higher, and highest risk groups, as per the original model. Primary outcome was reduction in estimated glomerular filtration rate (eGFR) by >50% or kidney failure. Both models were evaluated using discrimination concordance statistics (C-statistics), time-dependent receiver operating characteristic (ROC) curves, R2d, Kaplan-Meier survival curves between risk groups and calibration plots. Reclassification with net reclassification improvement and integrated discrimination improvement (IDI) was used to compare the 2 models with and without race. Results: A total of 316 patients with median follow-up of 2.8 years had 87 primary outcome events. Both models with and without race showed reasonable discrimination (C-statistics 0.845 for both models, R2d 49.9% and 44.7%, respectively, and well-separated survival curves) but underestimated risk of progression across all risk groups. The calibration slopes were 1.234 (95% CI 0.973-1.494) and 1.211 (95% CI 0.954-1.468), respectively. Both models demonstrated poor calibration for predicting risk at 2.8 and 5 years. There was limited improvement in risk reclassification risk at 5 and 2.8 years when comparing model with and without race. Conclusion: IIgANN prediction tool showed reasonable discrimination of risk in Indian patients but underestimated the trajectory of disease progression across all risk groups.