Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study.

ABSTRACT:

Background

There is still room for improvement in the modified LEMON (look, evaluate, Mallampati, obstruction, neck mobility) criteria for difficult airway prediction and no prediction tool for first-pass success in the emergency department (ED).

Objective

We applied modern machine learning approaches to predict difficult airways and first-pass success.

Methods

In a multicenter prospective study that enrolled consecutive patients who underwent tracheal intubation in 13 EDs, we developed 7 machine learning models (eg, random forest model) using routinely collected data (eg, demographics, initial airway assessment). The outcomes were difficult airway and first-pass success. Model performance was evaluated using c-statistics, calibration slopes, and association measures

SUBMITTER: Yamanaka S 

PROVIDER: S-EPMC8826144 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature