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Development of a prediction tool for kidney function decline in children with chronic kidney disease

Sangwoo Moon1, Junhyug Noh2, Hee Gyung Kang3,4,5,6

  • 1Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea.

Kidney Research and Clinical Practice
|September 4, 2025
PubMed

Insights

Researchers developed a new tool to predict short-term kidney function decline in pediatric chronic kidney disease (CKD) patients. Machine learning models identified key predictors, aiding early intervention for children with CKD.

Area of Science:

  • Nephrology
  • Pediatric Medicine
  • Biostatistics

Background:

  • Limited research exists on predictive tools for kidney function decline in pediatric chronic kidney disease (CKD).
  • Accurate prediction is crucial for timely interventions and improved outcomes in pediatric CKD patients.

Purpose of the Study:

  • To develop and internally validate a predictive tool for short-term kidney function decline in pediatric CKD patients.
  • To identify key predictors of kidney function decline using machine learning algorithms.

Main Methods:

  • Utilized data from 539 patients in the KoreaN cohort study for Outcomes in patients With Pediatric Chronic Kidney Disease (KNOW-PedCKD).
  • Assessed 48 sociodemographic, laboratory, and treatment variables using machine learning algorithms.
  • Validated predictive performance for estimated glomerular filtration rate (eGFR) decline over 1, 2, and 3 years.

Main Results:

  • Machine learning models demonstrated strong predictive performance for kidney function decline (≥20% eGFR decline).
  • Random forest and XGBoost models showed optimal performance for 1-year prediction.
  • Spot urine protein-to-creatinine ratio, baseline eGFR, serum albumin, chloride, and hemoglobin were key predictors.

Conclusions:

  • A validated tool for predicting short-term kidney function decline in pediatric CKD was developed.
  • Machine learning effectively identified critical predictors in a large Korean pediatric CKD cohort.
  • This tool can aid in early detection and management of kidney function decline in children with CKD.
Abstract

Keywords:
Artificial intelligenceChronic kidney diseasePediatricPrediction

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