Unsupervised Machine Learning Revealed that Repeat Transcranial Magnetic Stimulation is More Suitable for Stroke Patients with Statin

Autor: Chaohua Cui, Changhong Li, Tonghua Long, Zhenxian Lao, Tianyu Xia
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Neurology and Therapy, Vol 13, Iss 3, Pp 857-868 (2024)
Druh dokumentu: article
ISSN: 2193-8253
2193-6536
DOI: 10.1007/s40120-024-00615-8
Popis: Abstract Introduction Repeat 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. Methods We 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. Results Hierarchical 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
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