AUTHOR=Nagaratnam Kiruba , Neuhaus Ain , Briggs James H. , Ford Gary A. , Woodhead Zoe V. J. , Maharjan Dibyaa , Harston George 

TITLE=Artificial intelligence-based decision support software to improve the efficacy of acute stroke pathway in the NHS: an observational study

JOURNAL=Frontiers in Neurology

VOLUME=Volume 14 - 2023

YEAR=2024

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1329643

DOI=10.3389/fneur.2023.1329643

ISSN=1664-2295

ABSTRACT=In the pre-e-Stroke cohort, of 19 of 22 patients referred received EVT. In the post-e-Stroke cohort, 21 of 25 patients referred were treated. The mean DIDO (door-in-door-out) and door-to-referral times in pre-e-Stroke vs post-e-Stroke cohorts were 141 vs 79 (difference 62 minutes, 95% CI 96.9-26.8 minutes, p<0.001) and 71 vs 44 minutes (difference 27 minutes, 95% CI 47.4-5.4 minutes, p=0.01) respectively. The adjusted odds ratio (age and NIHSS) for mRS ordinal shift analysis at three months was 3.14 (95% CI 0.99 -10.51, p=0.06) and the dichotomized mRS 0-2 at three months was 16% vs 48% (p=0.04) in the pre vs post-e-Stroke cohorts respectively.In this single centre study in the UK, the DIDO time significantly decreased since the introduction of e-Stroke decision support software into an ASC hyperacute stroke pathway. The reduction in door-in to referral time indicates faster image interpretation and referral for EVT. There was an indication of increased proportion of patients regaining independent function after EVT. However, this should be interpreted with caution given the small sample size. Larger, prospective studies and further systematic real-world evaluation are needed to demonstrate widespread generalisability of these findings.