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BACKGROUND AND PURPOSE: Comprehensive stroke centers continually strive to narrow neurointerventional time metrics. Although process improvements have been put in place to streamline workflows, complex pathways, disparate imaging locations, and fragmented communications all highlight the need for continued improvement.

Introduction: Analyses from early- and late-window thrombectomy trials for acute large vessel occlusion (LVO) stroke have consistently demonstrated a strong time-dependent treatment effect for best outcomes. The utilization of an artificial intelligence (AI)-based care coordination platform to support LVO diagnosis and treatment has the potential to assist in reducing door to puncture times and improve outcomes. Hypothesis: Utilization of an AI-based care platform in the management of LVO patients may significantly decrease door to Neurointerventionalist (NIR) notification time. Methods: Acute stroke consultations seen by TeleSpecialists, LLC physicians in the emergency department in 166 facilities (17 states) that utilized VIZ.AI software or did not have an AI software from December 1, 2021 through March 31, 2022 were extracted from the Telecare TM database. Facilities that neurology does not initiate contact with NIR were excluded. The encounters were analyzed by age, ethnicity, last known normal, arrival time, initial telemedicine stroke code activation time (TCA), candidacy for thrombolytics, door to needle time (DTN), advanced imaging, presence of LVO, time NIR accepted, premorbid modified Rankin Score, and NIHSS score. Patients were divided into two groups (utilization and non-utilization of the AI platform) and median door to NIR notification times were compared. There was a 4 minute shorter time to TCA at AI hospitals. Results: A total of 14,116 patients (8,557 AI and 5,559 non-AI) were included. The median door to NIR notification time for AI was 50 minutes (40, 82) and non-AI was 89.5 minutes (59.3, 122), p <0.001. There was a small but significantly shorter time (3 minutes) from door to TCA at AI hospitals. Median DTN times for thrombolytics was also lower with AI, 40 minutes (30, 52) vs 44 minutes (32, 57.5) for non-AI, p=0.018. The AI group had more advanced imaging performed, a higher percentage of LVOs identified, and a higher percentage accepted by NIR. Conclusion: Hospital utilization of an AI-based care coordination platform was associated with a significant decrease of 39.5 minutes in the time to NIR contact, increase in patients taken for intervention, and lower DTN times for thrombolytics.

Introduction: Migraine is an independent risk factor for ischemic stroke. Frequency and severity increases in the perimenopausal period accompanied by marked vasomotor symptoms (VMS) such as hot flashes, flushing and night sweats. There is emerging evidence that VMS increases the risk of vascular disease including stroke. The purpose of this study was describe the demographics and co-morbidities of perimenopausal females with and without migraine that experience acute ischemic stroke (AIS). Methods: In this IRB approved study, electronic health record (EHR) data was obtained from a large, academic, comprehensive stroke center from 1/1/2015 to 1/1/2020. Inclusion criteria included female sex, age 42-65 years, and hospital diagnosis code of AIS. Hemorrhagic stroke, TIA, vasculopathy, and endocarditis associated strokes were excluded. Perimenopause was defined as age ≥42 and ≤65 years. Hormonal and menopausal status was not available in the EHR. We compared the baseline demographics and co-morbidities by ICD10 codes of subjects with and without migraine. Chi squared was used to compare categorical data and t test for continuous. Spearman rho was used to assess correlations. Results: We identified 660 subjects who met study criteria (n=83 with migraine; n=577 without migraine). Migraine positive subjects were significantly younger (mean age 58 vs 66 years, p=0.03) at time of AIS. Migraine positive subjects identified significantly more often as White (47%) compared to Black (10%), Asian (7%), Pacific Islander (1%), Native American/Alaskan (1%), Other/Mixed Race (31%), and unknown (3%), p=0.001. There was no significant difference in Hispanic ethnicity (p=0.87), hypertension (p=0.66), hyperlipidemia (p=0.12), or atrial fibrillation (p=0.84). Comorbid diabetes was significantly higher in the non-migraine group (94% vs 6%, p<0.001). Conclusion: Perimenopausal women with concomitant history of migraine present with AIS at younger ages and with lower rates of diabetes than those without a migraine history. Future research must be done to assess the correlation of menopausal symptom severity, hormone levels at time of AIS, and stroke characteristics to further understand the role of menopause in stroke risk.