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 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.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Improved Stroke Care in a Primary Stroke Centre Using AI-Decision Support ; volume:12 ; number:1 ; year:2022 ; pages:28-32 ; extent:5 ; Cerebrovascular diseases / Extra. Extra ; 12, Heft 1 (2022), 28-32 (gesamt 5)

Beteiligte, Orts- und Zeitangaben

• Urheber: Gunda, Bence ; Neuhaus, Ain ; Sipos, Ildikó ; Stang, Rita ; Böjti, Péter Pál ; Takács, Tímea ; Bereczki, Dániel ; Kis, Balázs ; Szikora, István ; Harston, George

Weitere Informationen

• DOI: 10.1159/000522423
• URN: urn:nbn:de:101:1-2022051200281029126514
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:37 MESZ