• Nikolaj BlombergStigSCopenhagen Emergency Medical Services, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark. Electronic address: nikolaj.blomberg@gmail.com., Theo W Jensen, Porsborg AndersenMikkelMDepartment of Cardiology, Nordsjællands Hospital, Denmark., Fredrik Folke, Kjær ErsbøllAnnetteACopenhagen Emergency Medical Services, Denmark; National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark., Christian Torp-Petersen, Freddy Lippert, and Helle Collatz Christensen.
• Copenhagen Emergency Medical Services, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark. Electronic address: nikolaj.blomberg@gmail.com.
• Resuscitation. 2023 Feb 1; 183: 109689109689.

BackgroundA machine-learning model trained to recognize emergency calls regarding Out-of-Hospital Cardiac Arrest (OHCA) was tested in clinical practice at Copenhagen Emergency Medical Services (EMS) from September 2018 to December 2019. We aimed to investigate emergency call characteristics where the machine-learning model failed to recognize OHCA or misinterpreted a call as being OHCA.MethodsAll emergency calls were linked to the dispatch database and verified OHCAs were identified by linkage to the Danish Cardiac Arrest Registry. Calls with either false negative or false positive predictions of OHCA were evaluated by trained auditors. Descriptive analyses were performed with absolute numbers and percentages reported.ResultsThe machine-learning model processed 169,236 calls to Copenhagen EMS and suspected 5,811 (3.4%) of the calls as OHCA, resulting in 84.5% sensitivity and 97.1% specificity. Among OHCAs not recognised by machine-learning model, a condition completely different from OHCA was presented by caller in 31% of the cases. In 28% of unrecognised calls, patient was reported breathing normally, and language barriers were identified in 23% of the cases. Among falsely suspected OHCA, the patient was reported unconscious in 28% of the cases, and in 13% of the false positive cases the machine-learning model interpreted calls regarding dead patients with irreversible signs of death as OHCA.ConclusionContinuous optimization of the language model is needed to improve the prediction of OHCA and thereby improve sensitivity and specificity of the machine-learning model on recognising OHCA in emergency telephone calls.Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

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