Royal Berkshire Hospitals NHS Foundation Trust
Stroke Medicine
Dr Kiruba Nagaratnam is a stroke physician and geriatrician. He is the clinical lead for stroke medicine at the Royal Berkshire NHS Foundation Trust and clinical director for the Buckinghamshire, Oxfordshire and Berkshire Integrated Stroke Delivery Network. He is passionate about incorporating advancing technology into the care of stroke survivors. He has been instrumental in adopting artificial intelligence and digital technology in the acute stroke pathway in the Thames Valley region. The AF Champions programme he jointly led with the primary care physicians in West Berkshire and Oxford Academic Health Science Network has improved the detection and management of atrial fibrillation in the region. He is also involved in a research projects incorporating immersive virtual reality and artificial intelligence in stroke rehabilitation

Presenter Of 1 Presentation

ARTIFICIAL INTELLIGENCE CLINICAL DECISION AID TOOL SHORTENS TIME IN MECHANICAL THROMBECTOMY PATHWAY

Session Type
Oral Presentations
Date
27.10.2021, Wednesday
Session Time
08:00 - 08:30
Room
ORAL PRESENTATIONS 2
Lecture Time
08:10 - 08:20

Abstract

Background and Aims

Fast identification of large vessel occlusion (LVO) at primary stroke centres (PSC) and timely referral to a comprehensive centre (CSC) are critical steps to improve outcomes from mechanical thrombectomy (MT). Increasingly artificial intelligence (AI) decision aid tools are deployed to facilitate rapid identification of LVO. In our PSC we incorporated e-Stroke software (Brainomix, Oxford, UK) into the hyperacute stroke pathway. We evaluated the impact of e-Stroke on door-in-door-out time (DIDO), door-in to referral time (D2R) and 3-month modified Rankin Score (mRS) in this study.

Methods

The data was obtained from prospective thrombectomy registry between 1-Jan-2019 and 31-Mar-2021. The e-Stroke was implemented on 1-Mar-2020. The outcomes were compared between the period before (1-Jan-2019 to 28-Feb-2020) and after (1-Mar-2020 to 31-Mar-2021) implementation (Before-AI vs After-AI). No other changes to the pathway were made over this period. Welch’s t-test was used to compare time metrics and Fisher’s exact test for dichotomised mRS 0-2.

Results

Before-AI, 19 of 22 patients referred for MT were transferred. After-AI, 21 of 25 patients referred were transferred. The mean DIDO and D2R Before-AI vs After-AI were 141 vs 79 (p=0.001) and 71 vs 44 minutes (p=0.01) respectively. Dichotomized mRS 0-2 at 3 months was 16% vs 48% (p=0.04) before-AI vs after-AI. (Fig.1)

Figure1: 3-month mRS distribution before and after e-Stroke

figure1_3_month m-rs distribution.jpg

Conclusions

Incorporating e-Stroke decision aid tool into our PSC hyperacute stroke pathway led to a significant reduction in door-in-door-out and door to referral times. A significantly higher proportion of patients gained functional independence at 3 months following the implementation of e-Stroke.

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