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

Authors

Tianyi Liu, Helin Wang, Jialu Liu, Xinyuan Zhao, Yilan Xia, Xiaowen Wang, Yulin Kang, Cuihua Liu, Xiaojie Gao, Xiaoyun Jiang, Jianhua Mao, Yufeng Li, Aihua Zhang, Mo Wang, Haitao Bai, Tong Shen, Xiqiang Dang, Dexuan Wang, Ruifeng Zhang, Yihan Lu, Qian Shen, Sheng Nie, Yang Chen, Hong Xu, Yihui Zhai, CCGKDD study investigators, CRDS study investigators

Published in

NPJ digital medicine. Jun 20, 2026. Epub Jun 20, 2026.

Abstract

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.

PMID:
42323445
Bibliographic data and abstract were imported from PubMed on 22 Jun 2026.

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