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Machine learning-based prediction models for difficult airway and first-pass intubation success in the emergency department
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- YAMANAKA Syunsuke
- Department of Emergency Medicine & General Internal Medicine, University of Fukui Hospital
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- GOTO Tadahiro
- Department of Clinical Epidemiology & Health Economics, University of Tokyo
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- MORIKAWA Koji
- Technology Innovation Division, Panasonic Corporation
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- WATASE Hiroko
- Department of Surgery, University of Washington
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- OKAMOTO Hiroshi
- Department of Intensive Care, St.Luke's International Hospital
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- HAGIWARA Yusuke
- Department of Emergency Medicine, Tokyo Metropolitan Children's Medical Center
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- HASEGAWA Kohei
- Department of Emergency Medicine, Massachusetts General Hospital
Bibliographic Information
- Other Title
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- 救急外来における挿管困難と初回挿管成功の機械学習予測モデル
Description
<p>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).</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2020 (0), 3Rin427-3Rin427, 2020
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390848250119634560
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- NII Article ID
- 130007857108
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- ISSN
- 27587347
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed
