Development, External Validation, and Biomolecular Corroboration of Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic Morbidity.

ABSTRACT:

Importance

Declining 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.

Objective

Develop machine-learning models for identifying acquired neurologic morbidity while hospitalized with critical illness and assess correlation with contemporary serum-based, brain injury-derived biomarkers.

Design

Retrospective cohort study.

Setting

Two large, quaternary children's hospitals.

Exposures

Cr

SUBMITTER: Horvat CM 

PROVIDER: S-EPMC11451808 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature