• James H Paxton, Kevin J Keenan, John M Wilburn, Stefanie L Wise, Howard A Klausner, BallMatthew TMTDepartment of Emergency Medicine, Wayne State University School of Medicine, Detroit, Michigan, USA., Robert B Dunne, K Derek Kreitel, Larry F Morgan, William D Fales, Debbie Madhok, Nobl Barazangi, Steven T McLean, Katherine Cross, Lisa Distenfield, Jordan Sykes, Paul Lovoi, Beth Johnson, Wade S Smith, and EPISODE‐PS‐COVID Collaborators.
• Department of Emergency Medicine, Wayne State University School of Medicine, Detroit, Michigan, USA.
• Acad Emerg Med. 2024 Sep 1; 31 (9): 848859848-859.

BackgroundLarge-vessel occlusion (LVO) stroke represents one-third of acute ischemic stroke (AIS) in the United States but causes two-thirds of poststroke dependence and >90% of poststroke mortality. Prehospital LVO stroke detection permits efficient emergency medical systems (EMS) transport to an endovascular thrombectomy (EVT)-capable center. Our primary objective was to determine the feasibility of using a cranial accelerometry (CA) headset device for prehospital LVO stroke detection. Our secondary objective was development of an algorithm capable of distinguishing LVO stroke from other conditions.MethodsWe prospectively enrolled consecutive adult patients suspected of acute stroke from 11 study hospitals in four different U.S. geographical regions over a 21-month period. Patients received device placement by prehospital EMS personnel. Headset data were matched with clinical data following informed consent. LVO stroke diagnosis was determined by medical chart review. The device was trained using device data and Los Angeles Motor Scale (LAMS) examination components. A binary threshold was selected for comparison of device performance to LAMS scores.ResultsA total of 594 subjects were enrolled, including 183 subjects who received the second-generation device. Usable data were captured in 158 patients (86.3%). Study subjects were 53% female and 56% Black/African American, with median age 69 years. Twenty-six (16.4%) patients had LVO and 132 (83.6%) were not LVO (not-LVO AIS, 33; intracerebral hemorrhage, nine; stroke mimics, 90). COVID-19 testing and positivity rates (10.6%) were not different between groups. We found a sensitivity of 38.5% and specificity of 82.7% for LAMS ≥ 4 in detecting LVO stroke versus a sensitivity of 84.6% (p < 0.0015 for superiority) and specificity of 82.6% (p = 0.81 for superiority) for the device algorithm (CA + LAMS).ConclusionsObtaining adequate recordings with a CA headset is highly feasible in the prehospital environment. Use of the device algorithm incorporating both CA and LAMS data for LVO detection resulted in significantly higher sensitivity without reduced specificity when compared to the use of LAMS alone.© 2024 The Authors. Academic Emergency Medicine published by Wiley Periodicals LLC on behalf of Society for Academic Emergency Medicine.

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