ArticleStroke and vascular neurology2022
Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests.
Article in Stroke and vascular neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04487340 (Computer-based Online Database of Acute Stroke Patients for Stroke Management Quality Evaluation), which is not on this map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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.
Computer-based Online Database of Acute Stroke Patients for Stroke Management Quality Evaluation
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Systematic Review and Meta-Analysis of Prehospital Machine Learning Scores as Screening Tools for Early Detection of Large Vessel Occlusion in Patients With Suspected Stroke.Journal of the American Heart Association · 2024Pooled it
- Artificial intelligence in prehospital emergency care systems in low- and middle-income countries: cure or curiosity? Insights from a qualitative study.Frontiers in public health · 2025Article
- Utilising artificial intelligence in prehospital emergency care systems in low- and middle-income countries: a scoping review.Frontiers in public health · 2025Article
- Risk factors and operative risk of large vessel occlusion and stroke during cardiac surgery.Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2024Article
- Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study.BMC medical research methodology · 2024Article
- Safety of Antiplatelet Therapy in Noncardioembolic Ischemic Stroke With Thrombocytopenia: The CASE II Study.Journal of the American Heart Association · 2024Article
- Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis.Heliyon · 2024Article
- Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis.Scientific reports · 2023Article
- Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study.Scientific reports · 2023Article
- Prediction of Hemorrhagic Complication after Thrombolytic Therapy Based on Multimodal Data from Multiple Centers: An Approach to Machine Learning and System Implementation.Journal of personalized medicine · 2022Article
- Optimization of Large Vessel Occlusion Detection in Acute Ischemic Stroke Using Machine Learning Methods.Life (Basel, Switzerland) · 2022Article
- Comparison of prehospital stroke assessment scales for acute ischemic stroke with large vessel occlusion within six hours of onset: A single-center study in Eastern Taiwan.Tzu chi medical journalArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundsThe timely identification of large vessel occlusion (LVO) in the prehospital stage is extremely important given the disease morbidity and narrow time window for intervention. The current evaluation strategies still remain challenging. The goal of this study was to develop a machine learning (ML) model to predict LVO using prehospital accessible data.
methodsConsecutive acute ischaemic stroke patients who underwent CT or MR angiography and received reperfusion therapy within 8 hours from symptom onset in the Computer-based Online Database of Acute Stroke Patients for Stroke Management Quality Evaluation-II dataset from January 2016 to August 2021 were included. We developed eight ML models to integrate National Institutes of Health Stroke Scale (NIHSS) items with demographics, medical history and vascular risk factors to identify LVO and validate its efficiency.
resultsFinally, 15 365 patients were included in the training set and 4215 patients were included in the test set. On the test set, random forests (RF), gradient boosting machine and extreme gradient boosting presented area under the curve (AUC) of 0.831 (95% CI 0.819 to 0.843), which were higher than other models, and RF presented the highest specificity (0.827). In addition, the AUC of RF was higher than other scales, and the accuracy of the model was improved by 6.4% compared with NIHSS. We also found the top five items of identifying LVO were total NIHSS score, gaze deviation, level of consciousness (LOC), LOC commands and motor left leg.
conclusionsOur proposed model could be a useful screening tool to predict LVO based on the prehospital accessible medical data. TRIAL REGISTRATION NUMBER: NCT04487340.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.