ArticleAnnals of Indian Academy of Neurology
Utility of Transcranial Magnetic Stimulation and Diffusion Tensor Imaging for Prediction of Upper-Limb Motor Recovery in Acute Ischemic Stroke Patients.
Article in Annals of Indian Academy of Neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 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.
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1 citing paper in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The recovery of the upper-limb (UL) motor function after ischemic stroke (IS) remains a major scientific, clinical, and patient concern and it is hard to predict alone from the clinical symptoms. Objective: To determine the accuracy of the prediction of the recovery of UL motor function in patients with acute ischemic middle cerebral artery (MCA) stroke using individual clinical, transcranial magnetic stimulation (TMS) or diffusion tensor imaging (DTI) parameters or their combination. Methods and Material: The first-ever acute ischemic MCA stroke patients within 7 days of the stroke onset who had an obvious UL motor deficit underwent TMS for the presence of motor-evoked potential (MEP) and DTI to evaluate the integrity of corticospinal tracts. Multivariate logistic regression analysis was done to test for the accuracy of the prediction of the recovery of UL motor function. Results: Twenty-nine acute ischemic MCA stroke patients (21 males and 8 females) with a mean age of 51.45 ± 14.26 years were recruited. Model-I included clinical scales (Fugl-Meyer Assessment [FMA] + Motricity Index [MI]) + TMS (MEP) + DTI (fractional anisotropy [FA]) were found to be the most accurate predictive model, with the overall predictive ability (93.3%; 95% confidence interval [CI]: 0.87-0.99) and sensitivity: 94.9% (95% CI: 0.87-1.0) and specificity: 95.8% (95% CI: 0.89-1.0); respectively. Conclusion: The accuracy of UL motor recovery can be predicted through the clinical battery and their elements as well as TMS (MEP) and DTI (FA) parameters. Further, well-designed prospective studies are needed to confirm our findings.
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