Background Acute ischemic stroke (AIS) is a common complication of severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection (COVID-19), but the prognosis of these patients is poorly understood. Purpose To explore the impact of COVID-19 on neurological outcomes in AIS patients. Methods A comparative retrospective cohort study was conducted in 32 consecutive AIS patients with and 51 without COVID-19 between the 1st of March 2020 and 1st of May 2021. The evaluation was based on a detailed chart review for demographic data, medical history, stroke severity, cranial and vessel imaging results, laboratory parameters, COVID-19 severity, hospitalization time, in-hospital mortality, and functional deficits at discharge (modified Rankin Scale, mRS). Results COVID-19 AIS patients showed tendency to worse initial neurological deficit (NIHSS 9 (3–13) vs. 4 (2–10); p = 0.06), higher rate of large vessel occlusion (LVO; 13/32 vs. 14/51; p = 0.21), had prolonged hospitalization (19.4 ± 17.7 vs. 9.7 ± 7 days; p = 0.003), had lower chance of functional independence (mRS≤2) (12/32 vs. 32/51; p = 0.02) and showed higher in-hospital mortality (10/32 vs. 6/51; p = 0.02). In COVID-19 AIS patients, LVO was more common with COVID-19 pneumonia than without (55.6% vs. 23.1%; p = 0.139). Conclusion COVID-19-related AIS carries a worse prognosis. COVID-19 with pneumonia seems to be associated with a higher rate of LVO.

Purpose New guidelines recommend thrombectomy up to 24 h in selected patients; however, the workload and benefit of extending time window are not known. We conducted a prospective single-centre study to determine the caseload, imaging and interventional need of extended time window. Methods All consecutive ischemic stroke patients within 24 h from onset in an 11-month period were included. Thrombectomy eligibility in the 0–6 h time window was based on current guidelines; in the 6–24 h time window, it was based on a combination of DEFUSE 3 and DAWN study criteria using MRI to identify target mismatch. Clinical outcome in treated patients was assessed at 3 months. Results Within 24 h of onset, 437 patients were admitted. In the 0–6 h time window, 238 patients (54.5%) arrived of whom 221 (92.9%) underwent CTA or MRA, 82 (34.5%) had large vessel occlusion (LVO), 30 (12.6%) had thrombectomy and 11 (36.6%) became independent (mRS ≤ 2). In the extended 6–24 h time window, 199 patients (45.5%) arrived of whom 127 (63.8%) underwent CTA or MRA, 44 (22.1%) had LVO, 8 (4%) had thrombectomy and 4 (50%) became independent. Conclusion Extending the time window from 6 to 24 h results in a 26.7% increase in patients receiving thrombectomy and a 36.4% increase of independent clinical outcome in treated patients at the price of a significantly increased burden of clinical and imaging screening due to the similar caseload but a smaller proportion of treatment eligible patients in the extended as compared with the standard time window.

Background Patient selection for reperfusion therapies requires significant expertise in neuroimaging. Increasingly, machine learning based analysis is used for faster and standardized patient selection. However, there is little information on how such software influences real-world patient management. Aims We evaluated changes in thrombolysis and thrombectomy delivery following implementation of automated analysis at a high volume primary stroke centre. Methods We retrospectively collected data on consecutive stroke patients admitted to a large university stroke centre from two identical seven-month periods in 2017 and 2018 between which the e-Stroke Suite (Brainomix, Oxford, UK) was implemented to analyse non-contrast CT and CT angiography results. Delivery of stroke care was otherwise unchanged. Patients were transferred to a hub for thrombectomy. We collected the number of patients receiving intravenous thrombolysis and/or thrombectomy, the time to treatment; and outcome at 90 days for thrombectomy. Results 399 patients from 2017 and 398 from 2018 were included in the study. From 2017 to 2018 thrombolysis rates increased from 11.5% to 18.1% with a similar trend for thrombectomy (2.8% to 4.8%). There was a trend towards shorter door-to-needle times (44 to 42 minutes) and CT-to-groin puncture times (174 to 145 minutes). There was a non-significant trend towards improved outcomes with thrombectomy. Qualitatively, physician feedback suggested that e-Stroke Suite increased decision-making confidence and improved patient flow. Conclusions Use of artificial intelligence decision support in a hyperacute stroke pathway facilitates decision-making and can improve rate and time of reperfusion therapies in a hub-and-spoke system of care.