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BACKGROUND: There is substantial interest in adding endovascular stroke therapy (EST) capabilities in community hospitals. Here, we assess the effect of transitioning to an EST-performing hospital (EPH) on acute ischemic stroke (AIS) admissions in a large hospital system including academic and community hospitals. METHODS: From our prospectively collected multi-institutional registry, we collected data on AIS admissions at 10 hospitals in the greater Houston area from January 2014 to December 2022: one longstanding EPH (group A), three community hospitals that transitioned to EPHs in November 2017 (group B), and six community non-EPHs that remained non-EPH (group C). Primary outcomes were trends in total AIS admissions, large vessel occlusion (LVO) and non-LVO AIS, and tissue plasminogen activator (tPA) and EST use. RESULTS: Among 20 317 AIS admissions, median age was 67 (IQR 57-77) years, 52.4% were male, and median National Institutes of Health Stroke Scale (NIHSS) was 4 (IQR 1-10). During the first 12 months after EPH transition, AIS admissions increased by 1.9% per month for group B, with non-LVO stroke increasing by 4.2% per month (P<0.001). A significant change occurred for group A at the transition point for all outcomes with decreasing rates in admissions for AIS, non-LVO AIS and LVO AIS, and decreasing rates of EST and tPA treatments (P<0.001). CONCLUSION: Upgrading to EPH status was associated with a 2% per month increase in AIS admissions during the first year post-transition for the upgrading hospitals, but decreasing volumes and treatments at the established EPH. These findings quantify the impact on AIS admissions in hospital systems with increasing EST access in community hospitals.
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Importance: The benefit of endovascular stroke therapy (EVT) in large vessel occlusion (LVO) ischemic stroke is highly time dependent. Process improvements to accelerate in-hospital workflows are critical. Objective: To determine whether automated computed tomography (CT) angiogram interpretation coupled with secure group messaging can improve in-hospital EVT workflows. Design, Setting, and Participants: This cluster randomized stepped-wedge clinical trial took place from January 1, 2021, through February 27, 2022, at 4 comprehensive stroke centers (CSCs) in the greater Houston, Texas, area. All 443 participants with LVO stroke who presented through the emergency department were treated with EVT at the 4 CSCs. Exclusion criteria included patients presenting as transfers from an outside hospital (n = 158), in-hospital stroke (n = 39), and patients treated with EVT through randomization in a large core clinical trial (n = 3). Intervention: Artificial intelligence (AI)-enabled automated LVO detection from CT angiogram coupled with secure messaging was activated at the 4 CSCs in a random-stepped fashion. Once activated, clinicians and radiologists received real-time alerts to their mobile phones notifying them of possible LVO within minutes of CT imaging completion. Main Outcomes and Measures: Primary outcome was the effect of AI-enabled LVO detection on door-to-groin (DTG) time and was measured using a mixed-effects linear regression model, which included a random effect for cluster (CSC) and a fixed effect for exposure status (pre-AI vs post-AI). Secondary outcomes included time from hospital arrival to intravenous tissue plasminogen activator (IV tPA) bolus in eligible patients, time from initiation of CT scan to start of EVT, and hospital length of stay. In exploratory analysis, the study team evaluated the impact of AI implementation on 90-day modified Rankin Scale disability outcomes. Results: Among 243 patients who met inclusion criteria, 140 were treated during the unexposed period and 103 during the exposed period. Median age for the complete cohort was 70 (IQR, 58-79) years and 122 were female (50%). Median National Institutes of Health Stroke Scale score at presentation was 17 (IQR, 11-22) and the median DTG preexposure was 100 (IQR, 81-116) minutes. In mixed-effects linear regression, implementation of the AI algorithm was associated with a reduction in DTG time by 11.2 minutes (95% CI, -18.22 to -4.2). Time from CT scan initiation to EVT start fell by 9.8 minutes (95% CI, -16.9 to -2.6). There were no differences in IV tPA treatment times nor hospital length of stay. In multivariable logistic regression adjusted for age, National Institutes of Health Stroke scale score, and the Alberta Stroke Program Early CT Score, there was no difference in likelihood of functional independence (modified Rankin Scale score, 0-2; odds ratio, 1.3; 95% CI, 0.42-4.0). Conclusions and Relevance: Automated LVO detection coupled with secure mobile phone application-based communication improved in-hospital acute ischemic stroke workflows. Software implementation was associated with clinically meaningful reductions in EVT treatment times. Trial Registration: ClinicalTrials.gov Identifier: NCT05838456.