Performance of machine learning models in predicting difficult laryngoscopy in the emergency department: a single-centre retrospective study comparing with conventional regression method.

ABSTRACT:

Background

Emergency endotracheal intubation is a critical skill for managing airway emergencies in the emergency department (ED). Accurate prediction of difficult laryngoscopy is essential for improving first-attempt success, minimizing complications, optimizing resource utilization, and enhancing patient outcomes. Traditional methods, such as the LEMON criteria, have limited predictive accuracy. Machine learning (ML) offers advanced predictive capabilities by analyzing large datasets and identifying complex variable interactions. This study aimed to develop and validate the performance of ML models for predicting difficult laryngoscopy in the ED, comparing it with a conventional regression model.

Methods

A retrospective cohort study was conducted on 4,370 adult patients wh

SUBMITTER: Srivilaithon W 

PROVIDER: S-EPMC11846364 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature