-Original-

Development of a Machine Learning Model to Predict Cardiac Arrest during Transport of Trauma Patients

Shinnosuke Kitano^1,2, Kei Ogawa³, Yutaka Igarashi⁴, Kan Nishimura³, Shuichiro Osawa³, Kensuke Suzuki¹, Kenji Fujimoto¹, Satoshi Harada¹, Kenji Narikawa¹, Takashi Tagami⁵, Hayato Ohwada³, Shoji Yokobori⁴, Satoo Ogawa¹ and Hiroyuki Yokota¹

¹Graduate School of Medical and Health Science, Nippon Sport Science University, Kanagawa, Japan
²Department of Emergency and Critical Care Medicine, Nippon Medical School Tama Nagayama Hospital, Tokyo, Japan
³Department of Industrial Administration, Tokyo University of Science, Chiba, Japan
⁴Department of Emergency and Critical Care Medicine, Nippon Medical School, Tokyo, Japan
⁵Department of Emergency and Critical Care Medicine, Nippon Medical School Musashi Kosugi Hospital, Kanagawa, Japan

Background: Trauma is a serious medical and economic burden worldwide, and patients with traumatic injuries have a poor survival rate after cardiac arrest. The authors developed a prediction model specific to prehospital trauma care and used machine learning techniques to increase its accuracy.
Methods: This retrospective observational study analyzed data from patients with blunt trauma injuries due to traffic accidents and falls from January 1, 2018, to December 31, 2019. The data were collected from the National Emergency Medical Services Information System, which stores emergency medical service activity records nationwide in the United States. A random forest algorithm was used to develop a machine learning model.
Results: The prediction model had an area under the curve of 0.95 and a negative predictive value of 0.99. The feature importance of the predictive model was highest for the AVPU (Alert, Verbal, Pain, Unresponsive) scale, followed by oxygen saturation (SpO₂). Among patients who were progressing to cardiac arrest, the cutoff value was 89% for SpO₂ in nonalert patients.
Conclusions: The machine learning model was highly accurate in identifying patients who did not develop cardiac arrest.

J Nippon Med Sch 2023; 90: 186-193

Keywords
machine learning model, trauma, cardiac arrest, emergency medical services

Correspondence to
Yutaka Igarashi, Department of Emergency and Critical Care Medicine, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-ku, Tokyo 113-8603, Japan
igarashiy@nms.ac.jp

Received, September 26, 2022
Accepted, December 2, 2022