ArticleNeurology and therapy2024
Unsupervised Machine Learning Revealed that Repeat Transcranial Magnetic Stimulation is More Suitable for Stroke Patients with Statin.
Article in Neurology and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Statins and the incidence of post-stroke depression: a systematic review and meta-analysis.Frontiers in neurology · 2024Pooled it
- Prediction models for early neurological deterioration in patients with acute ischemic stroke: a systematic review and critical appraisal.Frontiers in neurologyPooled it
- Anticoagulation management in intracerebral hemorrhage patients with deep vein thrombosis: insights from unsupervised machine learning and nomogram analysis.Frontiers in neurology · 2025Article
- Clinical mechanisms of repetitive transcranial magnetic stimulation in improving constipation in Parkinson's disease patients through the gut-brain axis.Frontiers in aging neuroscience · 2025Article
- Interpretable machine learning for predicting early neurological deterioration in symptomatic intracranial atherosclerotic stenosis.Frontiers in neurology · 2025Article
- Low-dose statins combined with repetitive transcranial magnetic stimulation reduce post-stroke depression.Frontiers in neurology · 2025Article
- Machine learning consensus clustering for inflammatory subtype analysis in stroke and its impact on mortality risk: a study based on NHANES (1999-2018).Frontiers in neurology · 2025Article
- Unsupervised machine learning revealed a correlation between low-dose statins and favorable outcomes in ICH patients.Frontiers in neurology · 2025Article
- Combination of flupentixol and melitracen tablets and rTMS improves post-stroke depression through neurovegetative symptoms changes: a retrospective analysis.Frontiers in neurologyArticle
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Authors and funding
5 authors.
Funding
Abstract
introductionRepeat transcranial magnetic stimulation (rTMS) demonstrates beneficial effects for stroke patients, though its efficacy varies due to the complexity of patient conditions and disease progression. Unsupervised machine learning could be the optimal solution for identifying target patients for transcranial magnetic stimulation treatment.
methodsWe collected data from ischaemic stroke patients treated with rTMS. Unsupervised machine learning methods, including K-means and Hierarchical Clustering, were used to explore the clinical characteristics of patients suitable for rTMS. We then utilized a prospective observational cohort to validate the effect of selected characteristics. For the validated cohort, outcomes included the presence of motor evoked potentials (MEP), favorable functional outcomes (FFO), and changes in the Fugl-Meyer Assessment (FMA) at 3 and 6 months.
resultsHierarchical clustering methods revealed that patients in the better prognosis group were more likely to take statins. The validated cohort was grouped based on statin intake. Patients taking statins exhibited a higher rate of MEP (p = 0.006), a higher rate of FFO at 3 months (p = 0.003) and 6 months (p = 0.021), and a more significant change in FMA (p < 0.001) at both 3 and 6 months. Statin intake was associated with FFO and changes in FMA at 3 and 6 months. This relationship persisted across all subgroups for FMA changes and some FFO subgroups.
conclusionStroke patients undergoing rTMS treatment taking statins exhibited greater MEP, FFO, and changes in FMA. Statin intake was associated with a better prognosis in these patients.
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