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Development of an adaptive clinical web-based prediction tool for kidney replacement therapy in children with chronic

Derek K Ng1, Matthew B Matheson1, George J Schwartz2

  • 1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

Kidney International
|June 30, 2023
PubMed

Insights

Clinicians can now better predict when children with chronic kidney disease (CKD) will need kidney replacement therapy (KRT). A new prediction tool uses common clinical data to estimate this timeline, aiding treatment planning.

Area of Science:

  • Nephrology
  • Pediatric Medicine
  • Biostatistics
  • Medical Informatics

Background:

  • Accurate prediction of time to kidney replacement therapy (KRT) is crucial for managing pediatric chronic kidney disease (CKD).
  • Existing prediction models for pediatric CKD may lack comprehensive validation or ease of clinical application.

Purpose of the Study:

  • To develop and validate a prediction tool for estimating time to KRT in children with CKD.
  • To design a user-friendly online calculator for clinical use based on the developed prediction model.

Main Methods:

  • Utilized statistical learning, specifically random survival forest, on data from 890 children in the CKiD study.
  • Evaluated 172 potential predictors, including sociodemographics, kidney/cardiovascular health, and therapy use, with longitudinal changes.
  • Developed an elementary model and an enriched model using best subset selection, with further validation on a European pediatric CKD cohort.

Main Results:

  • An enriched prediction model was identified, incorporating diagnosis, eGFR, proteinuria, blood pressure, eGFR change, anemia, albumin, chloride, and bicarbonate.
  • Models demonstrated good performance in cross-validation and external validation of the elementary model.
  • A user-friendly online clinical prediction tool was successfully developed.

Conclusions:

  • A robust clinical prediction tool for time to KRT in pediatric CKD was developed using extensive data and advanced statistical methods.
  • The tool showed promising internal and external validation results, offering a valuable resource for clinicians.
  • Further external validation of the enriched models is recommended to confirm their generalizability.

Keywords:
kidney failurekidney replacement therapypediatric chronic kidney diseasepediatric nephrologypredictionrisk stratification

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