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Cross-Population Validation of the Pediatric CKD Risk-Prediction Tool

Peong Gang Park1,2, Jayoun Kim3, Naye Choi4

  • 1Division of Pediatric Nephrology, Severance Children's Hospital, Seoul, South Korea.

Kidney International Reports
|March 30, 2026
PubMed

Insights

This study validated a kidney replacement therapy (KRT) risk calculator for pediatric chronic kidney disease (CKD) in East Asian children. The calculator showed strong predictive ability, especially when incorporating estimated GFR slope, aiding clinical decisions.

Area of Science:

  • Pediatric Nephrology
  • Biostatistics
  • Clinical Epidemiology

Background:

  • Chronic kidney disease (CKD) affects children globally, necessitating accurate prognostication tools.
  • Predicting kidney replacement therapy (KRT) is crucial for managing pediatric CKD.
  • External validation of existing risk calculators in diverse populations is essential.

Purpose of the Study:

  • To externally validate the performance of a KRT risk prediction calculator for pediatric CKD.
  • To assess the calculator's utility in an ethnically distinct East Asian cohort.
  • To identify potential improvements for enhanced predictive accuracy.

Main Methods:

  • Utilized the KoreaN Cohort Study on Outcomes in Pediatric CKD (KNOW-Ped CKD) cohort for validation.
  • Employed six parametric survival models from the generalized gamma family, stratified by GFR change or cross-sectional data.
  • Addressed missing data using multiple imputations and evaluated performance via goodness-of-fit, discrimination, calibration, and predictive ability.

Main Results:

  • Included 533 children with a median follow-up of 4.8 years; 32.1% initiated KRT.
  • Validated models demonstrated excellent discrimination (C-statistic: 0.911-0.972) and calibration slopes > 0.9.
  • Models incorporating estimated GFR slope showed the best alignment with observed KRT initiation risks, despite some miscalibration noted by the goodness-of-fit test.

Conclusions:

  • The KRT risk prediction calculator shows potential for improving prognostication in pediatric CKD.
  • Enhanced models, particularly those using eGFR slope, offer improved accuracy for clinical decision-making.
  • External validation in diverse populations confirms the calculator's applicability and highlights areas for refinement.
Abstract

Keywords:
chronic kidney diseasekidney replacement therapypediatric nephrologypredictionrisk stratification

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