Development and Validation of a Bayesian Network Predicting Intubation Following Hospital Arrival Among Injured Children.

ABSTRACT:

Background

Inadequate airway management can contribute to preventable trauma deaths. Current machine learning tools for predicting intubation in trauma are limited to adult populations and include predictors not readily available at the time of patient arrival. We developed a Bayesian network to predict intubation in injured children and adolescents using observable data available upon or immediately after patient arrival.

Methods

We obtained patient demographic, injury, resuscitation, and transportation characteristics from trauma registries from four American College of Surgeons-verified level 1 pediatric trauma centers from January 2010 through December 2021. We trained and validated a Bayesian network to predict emergent intubation after pediatric injury. We evaluated mo

SUBMITTER: Sullivan TM 

PROVIDER: S-EPMC11745935 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature