[3Rin4] Interactive 1

[3Rin4-27] Machine learning-based prediction models for difficult airway and first-pass intubation success in the emergency department

〇Syunsuke Yamanaka¹, Tadahiro Goto², Koji Morikawa³, Hiroko Watase⁴, Hiroshi Okamoto⁵, Yusuke Hagiwara⁶, Kohei Hasegawa⁷ (1.Department of Emergency Medicine & General Internal Medicine, University of Fukui Hospital , 2.Department of Clinical Epidemiology & Health Economics, University of Tokyo, 3.Technology Innovation Division, Panasonic Corporation, 4.Department of Surgery, University of Washington, 5.Department of Intensive Care, St.Luke's International Hospital, 6.Department of Emergency Medicine, Tokyo Metropolitan Children's Medical Center , 7.Department of Emergency Medicine, Massachusetts General Hospital)

Keywords:Emergency medicine, Intubation, Machine learning, Difficult airway

We applied machine learning to predicting difficult tracheal intubations and successful intubation at first intubation attempt (first-pass success) in the emergency department. While conventional methods (e.g., mLEMON) have been used to predict difficult intubations, their prediction ability is suboptimal. Additionally, there has been no clinically-meaningful model that predicts first-pass success. In the current study, we used prospective data (n=10,816) to develop prediction models using machine learning and examine their performance. We used patient characteristics and vital signs for predicting difficult airway, and airway management data for predicting first-pass success. The c-statistics of machine learning models for predicting difficult airway was higher compared to that of mLEMON as the reference (e.g., ensemble method, 0.73 [95%CI 0.67-0.79] vs. mLEMON, 0.62 [95%CI 0.58-0.65], p<0.01). Similarly, the machine learning models for predicting first-pass success had higher discriminative ability compared to the reference logistic regression model (e.g., gradient boosting, 0.82 [95%CI 0.80-0.84] vs. logistic regression, 0.60 [95%CI 0.58-0.63], p<0.01).