Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height.

ABSTRACT:

Background

Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation.

Methods

Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a t

SUBMITTER: Kim JH 

PROVIDER: S-EPMC8059322 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature