Development, External Validation, and Biomolecular Corroboration of Interoperable Models for Identifying Critically...

Area of Science:

• Pediatric Critical Care Medicine
• Neurocritical Care
• Machine Learning in Healthcare

Background:

• Declining mortality in pediatric critical care necessitates focus on neurodevelopmental outcomes.
• Early identification of neurologic morbidity is crucial for timely interventions and preserving neurodevelopmental trajectories.

Purpose of the Study:

• Develop and validate machine-learning models to identify acquired neurologic morbidity in critically ill children.
• Assess the correlation between model predictions and serum-based brain injury biomarkers.

Main Methods:

• Retrospective cohort study at two quaternary children's hospitals.
• Developed extreme gradient boosting (XGBoost) models using encounter data.
• Validated models externally and optimized with spline recalibration.
• Assessed correlation with glial fibrillary acidic protein (GFAP) biomarker levels.

Main Results:

• The development site model achieved an F1-score of 0.54 and AUROC of 0.82.
• The generalizable model at the validation site had an F1-score of 0.37 and AUROC of 0.81.
• Recalibrated model achieved a Brier score of 0.04.
• GFAP levels significantly correlated with model predictions (r=0.34, P=0.007).

Conclusions:

• Demonstrated well-performing machine learning models for predicting pediatric neurologic morbidity.
• Biomolecular corroboration supports the utility of these predictive models.
• Further prospective assessment of biomarker-coupled risk models in pediatric critical illness is warranted.