Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review

Jake Toy; Nichole Bosson; Shira Schlesinger; Marianne Gausche-Hill; Samuel Stratton

doi:10.1016/j.resplu.2023.100491

Artificial intelligence to support out-of-hospital cardiac arrest care: A scoping review

Resusc Plus. 2023 Nov 1:16:100491. doi: 10.1016/j.resplu.2023.100491. eCollection 2023 Dec.

Authors

Jake Toy^{1

2

3

4}, Nichole Bosson^{2

3

4}, Shira Schlesinger^{2

4}, Marianne Gausche-Hill^{2

3

4}, Samuel Stratton^{1

5}

Affiliations

¹ University of California Los Angeles, Fielding School of Public Health, 650 Charles E Young Drive South, Los Angeles, CA 90095, USA.
² Harbor-UCLA Department of Emergency Medicine & The Lundquist Research Institute, 1000 W Carson Street, Torrance, CA 90502, USA.
³ Los Angeles County EMS Agency, 10100 Pioneer Blvd, Santa Fe Springs, CA 90670, USA.
⁴ David Geffen School of Medicine at UCLA, Department of Emergency Medicine, 10833 Le Conte Ave, Los Angeles, CA 90095, USA.
⁵ Orange County California Emergency Medical Services Agency, 405 W. 5th Street, Santa Ana, CA 92705, USA.

Abstract

Background: Artificial intelligence (AI) has demonstrated significant potential in supporting emergency medical services personnel during out-of-hospital cardiac arrest (OHCA) care; however, the extent of research evaluating this topic is unknown. This scoping review examines the breadth of literature on the application of AI in early OHCA care.

Methods: We conducted a search of PubMed®, Embase, and Web of Science in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Articles focused on non-traumatic OHCA and published prior to January 18th, 2023 were included. Studies were excluded if they did not use an AI intervention (including machine learning, deep learning, or natural language processing), or did not utilize data from the prehospital phase of care.

Results: Of 173 unique articles identified, 54 (31%) were included after screening. Of these studies, 15 (28%) were from the year 2022 and with an increasing trend annually starting in 2019. The majority were carried out by multinational collaborations (20/54, 38%) with additional studies from the United States (10/54, 19%), Korea (5/54, 10%), and Spain (3/54, 6%). Studies were classified into three major categories including ECG waveform classification and outcome prediction (24/54, 44%), early dispatch-level detection and outcome prediction (7/54, 13%), return of spontaneous circulation and survival outcome prediction (15/54, 20%), and other (9/54, 16%). All but one study had a retrospective design.

Conclusions: A small but growing body of literature exists describing the use of AI to augment early OHCA care.

Keywords: Artificial intelligence; Deep learning; Emergency medical services; Machine learning; Medical dispatch; Out-of-hospital cardiac arrest; Prehospital care.

Publication types

Review