ArticleEuropean stroke journal2026
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?
Article in European stroke journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
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Authors and funding
13 authors.
Funding
Abstract
introductionThrombolysis has been shown to reduce disability after acute ischaemic stroke in randomised trials. Our study sought to compare the benefit from thrombolysis observed in a comprehensive national stroke registry with that expected from the clinical trials. PATIENTS AND
methodsData from a total of 168,347 ischaemic stroke patients who attended one of 118 emergency stroke hospitals in England and Wales from 2016 to 2021 were extracted from the Sentinel Stroke National Audit Programme. We constructed a machine learning model, designed to isolate the effect of thrombolysis, using explainable machine learning (XGBoost with SHapley Additive exPlanations [SHAP]).
resultsThrombolysis was found to be associated with a statistically significant improvement in the odds of achieving a better outcome (modified Rankin scale [mRS] threshold). Regression analysis predicted a maximum 2.5-fold improvement in odds of achieving mRS 0-1, with a decline to no treatment effect at 5 h 28 min post-onset. DISCUSSION: Our results confirm a beneficial effect of thrombolysis in a large prospective national stroke registry, and align closely with meta-analyses of clinical trials of thrombolysis both in terms of magnitude of effect and decline over time. This work also demonstrates the potential to apply explainable machine learning to observational data to assist in understanding how clinical trials are implemented in real-world settings.
conclusionsThrombolysis, in practice, has the same observed benefit as in the clinical trials.
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