Development and validation of personalized risk prediction models for patients with IgA nephropathy: a nationwide multicenter cohort study.

backgroundEffective prediction of immunoglobulin A nephropathy (IgAN) progression is crucial for early intervention and management. We aimed to develop and validate distinct IgAN prediction models for clinical and research applications.

methodsWe analyzed data from the Japanese Nationwide Retrospective Cohort Study in IgAN (n = 1174) gathered over 10 years. The models were developed and tested using data from general physicians in primary care, specialists in tertiary care hospitals, and researchers at academic research institutes. Three tailored prediction models (Primary Care, Tertiary Care, and Research Institute Models) were created to address the unique needs of different clinical environments. The primary outcome was a composite renal event defined as a 1.5-fold increase in serum creatinine level or progression to kidney failure. The predictive performance was assessed using C-statistics.

resultsIn the derivation cohort, the primary care model included predictors such as estimated glomerular filtration rate < 45 mL/min/1.73 m

conclusionsThe prediction models for primary and tertiary care settings provided effective tools for forecasting renal outcomes in IgAN patients and are competitive with more complex machine learning-based models used in research. These models can help guide clinical decisions in various healthcare settings.