Development of a neural network model for early detection of creatinine change in critically Ill children

Authors

Celeste G. Dixon, Department of Pediatrics, Division of Critical Care Medicine, Children's National Hospital, George Washington University School of Medicine and Health Sciences, Washington, DC, United States.
Eduardo A. Trujillo Rivera, Department of Pediatrics, Division of Critical Care Medicine, Children's National Hospital, George Washington University School of Medicine and Health Sciences, Washington, DC, United States.
Anita K. Patel, Department of Pediatrics, Division of Critical Care Medicine, Children's National Hospital, George Washington University School of Medicine and Health Sciences, Washington, DC, United States.
Murray M. Pollack, Department of Pediatrics, Division of Critical Care Medicine, Children's National Hospital, George Washington University School of Medicine and Health Sciences, Washington, DC, United States.

Document Type

Journal Article

Publication Date

1-1-2025

Journal

Frontiers in pediatrics

Volume

13

DOI

10.3389/fped.2025.1549836

Keywords

acute kidney injury; creatinine; machine learning; neural network model; pediatric intensive care unit

Abstract

INTRODUCTION: Renal dysfunction is common in critically ill children and increases morbidity and mortality risk. Diagnosis and management of renal dysfunction relies on creatinine, a delayed marker of renal injury. We aimed to develop and validate a machine learning model using routinely collected clinical data to predict 24-hour creatinine change in critically ill children before change is observed clinically. METHODS: Retrospective cohort study of 39,932 pediatric intensive care unit encounters in a national multicenter database from 2007 to 2022. A neural network was trained to predict <50% or ≥50% creatinine change in the next 24 h. Admission demographics, routinely measured vital signs, laboratory tests, and medication use variables were used as predictors for the model. Data set was randomly split at the encounter level into model development (80%) and test (20%) sets. Performance and clinical relevance was assessed in the test set by accuracy of prediction classification and confusion matrix metrics. RESULTS: The cohort had a male predominance (53.8%), median age of 8.0 years (IQR 1.9-14.6), 21.0% incidence of acute kidney injury, and 2.3% mortality. The overall accuracy of the model for predicting change of <50% or ≥50% was 68.1% (95% CI 67.6%-68.7%). The accuracy of classification improved substantially with higher creatinine values from 29.9% (CI 28.9%-31.0%) in pairs with an admission creatinine <0.3 mg/dl to 90.0-96.3% in pairs with an admission creatinine of ≥0.6 mg/dl. The model had a negative predictive value of 97.2% and a positive predictive value of 7.1%. The number needed to evaluate to detect one true change ≥50% was 14. DISCUSSION: 24-hour creatinine change consistent with acute kidney injury can be predicted using routine clinical data in a machine learning model, indicating risk of significant renal dysfunction before it is measured clinically. Positive predictive performance is limited by clinical reliance on creatinine.

APA Citation

Dixon, Celeste G.; Trujillo Rivera, Eduardo A.; Patel, Anita K.; and Pollack, Murray M., "Development of a neural network model for early detection of creatinine change in critically Ill children" (2025). GW Authored Works. Paper 7095.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/7095

Department

Pediatrics