%0 Journal Article
%@ 1929-073X
%I JMIR Publications
%V 11
%N 1
%P e28366
%T Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study
%A Yamanaka,Syunsuke
%A Goto,Tadahiro
%A Morikawa,Koji
%A Watase,Hiroko
%A Okamoto,Hiroshi
%A Hagiwara,Yusuke
%A Hasegawa,Kohei
%+ Department of Clinical Epidemiology & Health Economics, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan, 81 3 5841 1887, tag695@mail.harvard.edu
%K intubation
%K machine learning
%K difficult airway
%K first-pass success
%D 2022
%7 25.1.2022
%9 Original Paper
%J Interact J Med Res
%G English
%X 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 (eg, sensitivity) in the test set (randomly selected 20% of the data). Their performance was compared with the modified LEMON criteria for difficult airway success and a logistic regression model for first-pass success. Results: Of 10,741 patients who underwent intubation, 543 patients (5.1%) had a difficult airway, and 7690 patients (71.6%) had first-pass success. In predicting a difficult airway, machine learning models—except for k-point nearest neighbor and multilayer perceptron—had higher discrimination ability than the modified LEMON criteria (all, P≤.001). For example, the ensemble method had the highest c-statistic (0.74 vs 0.62 with the modified LEMON criteria; P<.001). Machine learning models—except k-point nearest neighbor and random forest models—had higher discrimination ability for first-pass success. In particular, the ensemble model had the highest c-statistic (0.81 vs 0.76 with the reference regression; P<.001). Conclusions: Machine learning models demonstrated greater ability for predicting difficult airway and first-pass success in the ED. 
%M 35076398
%R 10.2196/28366
%U https://www.i-jmr.org/2022/1/e28366
%U https://doi.org/10.2196/28366
%U http://www.ncbi.nlm.nih.gov/pubmed/35076398