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Abstract
Background Automated machine learning (ML)‐based large vessel occlusion (LVO) detection algorithms have been shown to improve in‐hospital workflow metrics including door-to-groin time (DTG). The degree to which care team engagement and interaction are required for these benefits remains incompletely characterized.
Methods This analysis was conducted as a pre‐planned post-hoc analysis of a randomized prospective clinical trial. ML‐based LVO detection software was implemented at four comprehensive stroke centers (CSCs) from January 1, 2021, to February 27, 2022. Patients were included if they underwent endovascular thrombectomy for LVO acute ischemic stroke. ML software utilization was quantified as the total number of active users and the ratio of the number of comments to the number of patients analyzed by the software by site per week. Primary outcome was the reduction in DTG relative to pre‐ML implementation by hospital utilization level. Data are expressed as median (IQR).
Results Among 101 patients who met the inclusion criteria, the median age was 71 years (IQR 59–79), with 48.5% being female. CSC 4 had the greatest number of total active users per week (32.5 (27.5–34.5)), and comment-to-patient ratio per week (5.8 (4.6–6.9)). Increased ML software utilization was associated with improvements in DTG reduction. For every 1 unit increase in the comment-to-patient ratio, DTG time decreased by 2.6 (95% CI −5.09 to –0.13) min, while accounting for site-level random effects. Number of users-to-patient was not associated with a reduction in DTG time (β=−0.22, 95% CI −1.78 to 1.33).
Conclusions In this post-hoc analysis, user engagement with software, rather than total number of users, was associated with site-specific improvements in DTG time.
- Stroke
- Thrombectomy
- Technology
- CT Angiography
Data availability statement
Data are available upon reasonable request.
Statistics from Altmetric.com
Data availability statement
Data are available upon reasonable request.
Footnotes
Contributors ECE contributed to drafting the manuscript. NML was responsible for data analysis and drafting the manuscript. JS, HA, SD and ANB contributed to drafting the manuscript, with ASI and ANB also collecting data. LG and SAS were involved in drafting the manuscript and collecting data, while EL contributed to data analysis and drafting the manuscript. SAS also conceptualized and designed the study, supervised the research, and serves as the guarantor.
Funding Foundation for the National Institutes of Health - 1R01NS138765.
Competing interests SAS reports funding from the National Institutes of Health as well as consultancy fees from Penumbra, Viz.AI and Imperative Care for unrelated topics.
Provenance and peer review Not commissioned; externally peer reviewed.
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