Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic Morbidity.

ABSTRACT:

Importance

Decreasing mortality in the field of pediatric critical care medicine has shifted practicing clinicians' attention to preserving patients' neurodevelopmental potential as a main objective. Earlier identification of critically ill children at risk for incurring neurologic morbidity would facilitate heightened surveillance that could lead to timelier clinical detection, earlier interventions, and preserved neurodevelopmental trajectory.

Objectives

To develop machine-learning models for identifying acquired neurologic morbidity in hospitalized pediatric patients with critical illness and assess correlation with contemporary serum-based, brain injury-derived biomarkers.

Design, setting, and participants

This prognostic study used data from all children admitted

SUBMITTER: Horvat CM 

PROVIDER: S-EPMC11795326 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature