A Validation Study Comparing Risk Prediction Models of IgA Nephropathy.

Area of Science:

• Nephrology
• Epidemiology
• Clinical Risk Prediction

Background:

• IgA Nephropathy (IgAN) is a leading cause of chronic kidney disease (CKD).
• Accurate risk prediction models are crucial for managing IgAN progression.
• Existing risk models require validation in diverse populations.

Purpose of the Study:

• To validate international IgAN and CKD risk models in a multicenter Chinese IgAN cohort.
• To assess model performance using discrimination, calibration, and reclassification.
• To identify optimal models for predicting IgAN progression in Chinese patients.

Main Methods:

• Validation of four risk prediction models (Clinical, Limited, Full, CKD) in 2,300 biopsy-proven IgAN patients.
• Analysis of composite outcomes (50% eGFR decline or ESRD) and End-Stage Renal Disease (ESRD).
• Subgroup analysis based on baseline estimated glomerular filtration rate (eGFR).

Main Results:

• All models demonstrated good performance for composite outcomes (C statistics ~0.82).
• Models showed superior performance for predicting ESRD (C statistics > 0.9).
• Models incorporating pathological variables outperformed clinical-only models in low-risk patients (eGFR ≥60 ml/min/1.73 m²).

Conclusions:

• Recently reported IgAN and CKD risk models are validated in a Chinese cohort.
• Adding pathological variables enhances prediction accuracy for ESRD in specific low-risk IgAN populations.
• Risk stratification in IgAN may benefit from integrating clinical and pathological data.