Improved Stroke Care in a Primary Stroke Centre Using AI-Decision Support

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 managem...

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Published in:Cerebrovascular diseases extra 2022, Vol.12 (1), p.28-32
Main Authors: Gunda, Bence, Neuhaus, Ain, Sipos, Ildikó, Stang, Rita, Böjti, Péter Pál, Takács, Tímea, Bereczki, Dániel, Kis, Balázs, Szikora, István, Harston, George
Format: Article
Language:English
Subjects:
Age
Artificial Intelligence
Automation
Computed Tomography Angiography
Conflicts of interest
Contraindications
Decision making
Humans
Imaging
Ischemia
Machine learning
Medical imaging
Patients
Retrospective Studies
Software
Stroke
Stroke - diagnostic imaging
Stroke - therapy
Thrombectomy - adverse effects
Thrombectomy - methods
Treatment Outcome
Trends
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Summary: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 7-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 (2.8–4.8%). There was a trend towards shorter door-to-needle times (44–42 min) and CT-to-groin puncture times (174–145 min). 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.
ISSN:1664-5456
1664-5456
DOI:10.1159/000522423