Hypoxemia prediction in pediatric patients under general anesthesia using machine learning: A retrospective observational study and external validation.

ABSTRACT:

Background

Pediatric patients under general anesthesia are particularly vulnerable to hypoxemia, which can lead to rapid oxygen desaturation. This vulnerability necessitates heightened vigilance from anesthesiologists, making pediatric anesthesia management especially challenging. Continuous intraoperative monitoring of oxygenation is critical. However, traditional methods relying solely on SpO2 readings may be insufficient and prone to inaccuracies.

Methods

This study aimed to develop and externally validate various machine learning models to predict hypoxemia in pediatric patients under general anesthesia. This retrospective observational study included 800 pediatric cases from Seoul National University Hospital and 134 pediatric cases from Chungnam National University Hos

SUBMITTER: Baek S 

PROVIDER: S-EPMC12782441 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature