Interpretable machine learning model for predicting kidney failure among CAKUT children in multicenter large-scale...

Congenital anomalies of the kidney and urinary tract (CAKUT) are the leading cause of pediatric kidney failure, but predicting individual progression remains challenging. This multicenter study developed and validated POCC, a machine learning model for predicting kidney failure risk at 1, 3, and 5 years post-diagnosis in CAKUT patients. Two versions were created using data from 2249 children. The general model achieved internal AUCs of 0.93-0.99 and external AUCs of 0.89-0.98 and 0.81-0.90 in two independent validations at pediatric and general hospitals, respectively. The specialized model, integrating congenital-hereditary features, achieved internal AUCs of 0.93-0.99 and external AUCs of 0.91-0.96 in pediatric hospitals. Deployed online, POCC demonstrated 90.7% accuracy in real-world validation. As the first tool for multi-timepoint risk prediction across diverse CAKUT subphenotypes per patient, POCC has strong potential to support personalized management.