ArticleStroke2022
Use of Clinical Pathway Simulation and Machine Learning to Identify Key Levers for Maximizing the Benefit of Intravenous Thrombolysis in Acute Stroke.
Article in Stroke, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- A machine learning study of the effect of thrombolysis on outcome at discharge in the UK stroke registry: how does benefit compare with clinical trials?European stroke journal · 2026Article
- Review
- Scaling up thrombectomy care in transitioning health systems: a qualitative study of stroke centres in Canada.BMJ open quality · 2025Article
- Risk Factors, Treatments, and Outcomes of Adults Aged <55 Years With Acute Ischemic Stroke With Undetermined Versus Determined Pathogenesis: A Nationwide Swiss Cohort Study.Journal of the American Heart Association · 2024Article
- What would other emergency stroke teams do? Using explainable machine learning to understand variation in thrombolysis practice.European stroke journal · 2023Article
- What's the Future of Vascular Neurology?Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- Updating estimates of the number of UK stroke patients eligible for endovascular thrombectomy: incorporating recent evidence to facilitate service planning.European stroke journal · 2021Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundExpert opinion is that about 20% of emergency stroke patients should receive thrombolysis. Currently, 11% to 12% of patients in England and Wales receive thrombolysis, ranging from 2% to 24% between hospitals. The aim of this study was to assess how much variation is due to differences in local patient populations, and how much is due to differences in clinical decision-making and stroke pathway performance, while estimating a realistic target thrombolysis use.
methodsAnonymised data for 246 676 emergency stroke admissions to 132 acute hospitals in England and Wales between 2016 and 2018 was obtained from the Sentinel Stroke National Audit Programme data. We used machine learning to learn decisions on who to give thrombolysis to at each hospital. We used clinical pathway simulation to model effects of changing pathway performance. Qualitative research was used to assess clinician attitudes to these methods. Three changes were modeled: (1) arrival-to-treatment in 30 minutes, (2) proportion of patients with determined stroke onset times set to at least the national upper quartile, (3) thrombolysis decisions made based on majority vote of a benchmark set of hospitals.
resultsOf the modeled changes, any single change was predicted to increase national thrombolysis use from 11.6% to between 12.3% to 14.5% (clinical decision-making having the most effect). Combined, these changes would be expected to increase thrombolysis to 18.3%, but there would still be significant variation between hospitals depending on local patient population. Clinicians engaged well with the modeling, but those from hospitals with lower thrombolysis use were most cautious about the methods.
conclusionsMachine learning and clinical pathway simulation may be applied at scale to national stroke audit data, allowing extended use and analysis of audit data. Stroke thrombolysis rates of at least 18% look achievable in England and Wales, but each hospital should have its own target.
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