ArticleEuropean stroke journal2018
Multimodal magnetic resonance imaging to identify stroke onset within 6 h in patients with large vessel occlusions.
Article in European stroke journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Predicting Clinical Outcomes in Acute Ischemic Stroke Patients Undergoing Endovascular Thrombectomy with Machine Learning : A Systematic Review and Meta-analysis.Clinical neuroradiology · 2021Pooled it
- Initial diagnosis and management of acute ischemic stroke: updates and future directions.Clinical and experimental emergency medicine · 2026Article
- Machine learning models powered by emergency medical services data enhance stroke triage in prehospital settings.Scientific reports · 2026Article
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMechanical thrombectomy within 6 h after stroke onset improves the outcome in patients with large vessel occlusions. The aim of our study was to establish a model based on diffusion weighted and perfusion weighted imaging to provide an accurate prediction for the 6 h time-window in patients with unknown time of stroke onset. PATIENTS AND
methodsA predictive model was designed based on data from the DEFUSE 2 study and validated in a subgroup of patients with large vessel occlusions from the AXIS 2 trial.
resultsWe constructed the model in 91 patients from DEFUSE 2. The following parameters were independently associated with <6 h time-window and included in the model: interquartile range and median relative diffusion weighted imaging, hypoperfusion intensity ratio, core volume and the interaction between median relative diffusion weighted imaging and hypoperfusion intensity ratio as predictors of the 6 h time-window. The area under the curve was 0.80 with a positive predictive value of 0.90 (95%CI 0.79-0.96). In the validation cohort (N = 90), the area under the curve was 0.73 ( DISCUSSION: After validation in a larger independent dataset the model can be considered to select patients for endovascular treatment in whom stroke onset is unknown.
conclusionIn patients with large vessel occlusion and unknown time of stroke onset an automated multivariate imaging model is able to select patients who are likely within the 6 h time-window.
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