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Related Experiment Video

Updated: Jun 23, 2026

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
09:02

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

Published on: February 2, 2021

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

Tianyi Liu1,2, Helin Wang3, Jialu Liu1,2

  • 1Department of Nephrology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.

NPJ Digital Medicine
|June 20, 2026
PubMed
Summary

A new machine learning model, POCC, accurately predicts kidney failure risk in children with congenital anomalies of the kidney and urinary tract (CAKUT). This tool aids personalized management for pediatric kidney disease progression.

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Last Updated: Jun 23, 2026

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion
09:02

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

Published on: February 2, 2021

Area of Science:

  • Nephrology
  • Pediatric Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Congenital anomalies of the kidney and urinary tract (CAKUT) represent the primary cause of kidney failure in children.
  • Predicting individual patient outcomes and disease progression in CAKUT remains a significant clinical challenge.

Purpose of the Study:

  • To develop and validate POCC, a machine learning model designed for predicting kidney failure risk at multiple time points (1, 3, and 5 years) in pediatric patients diagnosed with CAKUT.
  • To assess the performance of both a general and a specialized version of the POCC model across different healthcare settings.

Main Methods:

  • A multicenter study utilizing data from 2249 pediatric patients with CAKUT.
  • Development of two machine learning model versions: a general model and a specialized model incorporating congenital-hereditary features.
  • External validation of the models in independent cohorts from pediatric and general hospitals, alongside real-world deployment validation.

Main Results:

  • The general POCC model demonstrated high internal AUCs (0.93-0.99) and strong external validation AUCs (0.89-0.98 and 0.81-0.90).
  • The specialized POCC model achieved excellent internal AUCs (0.93-0.99) and external AUCs (0.91-0.96) in pediatric hospital settings.
  • Online deployment of POCC showed 90.7% accuracy in real-world clinical validation.

Conclusions:

  • POCC is the first tool capable of multi-timepoint kidney failure risk prediction for diverse CAKUT subphenotypes.
  • The validated POCC model shows significant potential to enhance personalized management strategies for children with CAKUT.
  • Accurate risk stratification using POCC can support timely clinical decisions and improve patient outcomes in pediatric nephrology.