1. Machine learning for the prediction of preclinical airway management in injured patients: a registry-based trial
- Author
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André Luckscheiter, Wolfgang Zink, Torsten Lohs, Johanna Eisenberger, Manfred Thiel, and Tim Viergutz
- Subjects
intratracheal intubation ,machine learning ,bayes theorem ,wounds and injuries ,decision trees ,Medical emergencies. Critical care. Intensive care. First aid ,RC86-88.9 - Abstract
Objective The aim of this study was to determine the feasibility of using machine learning to establish the need for preclinical airway management for injured patients based on a standardized emergency dataset. Methods A registry-based, retrospective analysis was conducted of adult trauma patients who were treated by physician-staffed emergency medical services in southwestern Germany between 2018 and 2020. The primary outcome was to assess the feasibility of using the random forest (RF) and Naive Bayes (NB) machine learning algorithms to predict the need for preclinical airway management. The secondary outcome was to use a principal component analysis to determine the attributes that can be used and advanced for future model development. Results In total, 25,556 adults with multiple injuries were identified, including 1,451 patients (5.7%) who required airway management. Key attributes were auscultation, injury pattern, oxygen therapy, thoracic drainage, noninvasive ventilation, catecholamines, pelvic sling, colloid infusion, initial vital signs, preemergency status, and shock index. The area under the receiver operating characteristics curve was between 0.96 (RF; 95% confidence interval [CI], 0.96–0.97) and 0.93 (NB; 95% CI, 0.92–0.93; P
- Published
- 2022
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