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.

The Author(s). Published by S. Karger AG, Basel