Mode of Onset Modifies the Effect of Time to Endovascular Reperfusion on Clinical Outcomes after Acute Ischemic Stroke: An Analysis of the DAWN Trial.

OBJECTIVE: We aimed to assess the impact of time to endovascular thrombectomy (EVT) on clinical outcomes in the DAWN trial, while also exploring the potential effect modification of mode of stroke onset on this relationship. METHODS: The association between every 1-h treatment delay with 90-day functional independence (modified Rankin Scale [mRS] score 0-2), symptomatic intracranial hemorrhage, and 90-day mortality was explored in the overall population and in three modes of onset subgroups (wake-up vs. witnessed vs. unwitnessed). RESULTS: Out of the 205 patients, 98 (47.8%) and 107 (52.2%) presented in the 6 to 12 hours and 12 to 24 hours time window, respectively. Considering all three modes of onset together, there was no statistically significant association between time last seen well to randomization with either functional independence or mortality at 90 days in either the endovascular thrombectomy (mRS 0-2 1-hour delay OR 1.07; 95% CI 0.93-1.24; mRS 6 OR 0.84; 95% CI 0.65-1.03) or medical management (mRS 0-2 1-hour delay OR 0.98; 95% CI 0.80-1.14; mRS 6 1-hour delay OR 0.94; 95% CI 0.79-1.09) groups. Moreover, there was no significant interaction between treatment effect and time (p = 0.439 and p = 0.421 for mRS 0-2 and 6, respectively). However, within the thrombectomy group, the models that tested the association between time last seen well to successful reperfusion (modified Treatment in Cerebral Infarction ≥2b) and 90-day functional independence showed a significant interaction with mode of presentation (p = 0.013). This appeared to be driven by a nominally positive slope for both witnessed and unwitnessed strokes versus a significantly (p = 0.018) negative slope in wake-up patients. There was no association between treatment times and symptomatic intracranial hemorrhage. INTERPRETATION: Mode of onset modifies the effect of time to reperfusion on thrombectomy outcomes, and should be considered when exploring different treatment paradigms in the extended window. ANN NEUROL 2024;96:356-364.

Headpulse measurement can reliably identify large-vessel occlusion stroke in prehospital suspected stroke patients: Results from the EPISODE-PS-COVID study.

BACKGROUND: Large-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. METHODS: We 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. RESULTS: A 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). CONCLUSIONS: Obtaining 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.