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Our study purpose is to evaluate the predictive power of various clinical, neurophysiological, and neuroimaging biomarkers-acquired and analyzed using advanced and integrated techniques-on motor functional recovery and disability post-stroke. The goal is to construct an integrated individual biomarker algorithm with a high predictive value for outcomes.
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Ability to predict post-stroke individual recovery still represents an important challenge to develop personalized and effective neurorehabilitation strategies. The aim of this study is to verify the predictive power of different clinical, neurophysiological and neuroimaging biomarkers in combination, acquired and analysed using advanced, integrated techniques, on functional motor recovery and post-stroke disability.
Patients with ischemic or hemorrhagic stroke and hemiparesis are subsequently enrolled and undergo multimodal assessment that includes measures spanning 4 assessment categories: demographic/medical history, clinical biomarkers, neurophysiological biomarkers (Motor Evoked Potentials, High-density EEG), neuroimaging biomarkers (infarct volume, corticospinal tract injury) at T0 (from 14 to 30 days post-stroke), T1 (after 2 months of intensive rehabilitation treatment) and T2 (4-6 months after discharge). Primary endpoint is change in arm Fugl-Meyer score. Secondary endpoints are changes in lower limb Fugl Meyer motor, Nine hole peg test, National Institute of Health Stroke Scale, mRS Rankin Scale, modified Barthel Index scores.
Multivariate analyses will indicate which biomarkers at baseline have the greatest value to predict significant changes in primary and secondary endpoints at T1 and T2.
A model incorporating measures of clinical, neurophysiological and neuroimaging biomarkers could best predict individual treatment-induced behavioral gains and therefore allow to plan a "tailor-made" rehabilitation project, select rehabilitation therapy, appropriate allocation of time and human resources and stratify clinical trial enrollees.
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Domenica Le Pera, MD, PhD; Lucia Gatta, PhD
Data sourced from clinicaltrials.gov
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