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Year : 2022  |  Volume : 6  |  Issue : 2  |  Page : 76

Stroke Rehabilitation: AB No: 124: Do Kinematic variables have an added advantage over clinical variables in Predicting Upper Extremity Motor Recovery Post-Stroke?

Manipal College of Health Professions, Manipal Academy of Higher Education

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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2456-7787.361075

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Purpose: Measurement of movement quality is essential to distinguish motor recovery patterns and optimize rehabilitation strategies post-stroke. The purpose of this study was to assess the added advantage of kinematic over clinical measures for predicting post-stroke upper extremity (UE) recovery by developing a regression model comprising of bothRelevance: Meticulously formulated prognostic models could be used by rehabilitation specialists for improving prediction accuracy in stroke survivorsParticipants: This study comprises of 89 acute to early sub-acute stroke survivors (58.8 ± 11.8 years, 61 males)Methods: Baseline characteristics, demographics, grip and pinch strength were measured within 7 days and 3D kinematic analysis of a simulated drinking task was performed within 1-month post-stroke. The sensorimotor impairment through Fugl Meyer Assessment of Upper Extremity (FM-UE) was assessed at 3-months. Kinematic metrics of time, displacement, velocity, shoulder and elbow angles and reaction time were determined. Results: Clinical variables were available for 89 participants by 7 days and kinematic for 50 individuals at 1 month. A strong correlation was found between FM-UE at three months with Shoulder Abduction Finger Extension (r=0.84), Nottingham Sensory Assessment (r=0.84), Motricity index (r=0.82), National Institutes of Health Stroke Scale (r=0.75), and moderate with pinch (r=0.69) and grip strength (r=0.62) measured within 7 days post-stroke. We found a weak correlation between FM-UE at 3 months with velocity (r=0.53), time (r= -0.43) and displacement (r=0.38). However, on combining clinical and kinematic variables the linear regression model was found to have an R2 value of 0.85. Conclusion: This model would help us predict impairment at 3 months for 85% stroke survivors with similar characteristics. However, kinematic variables should be used as an adjunct to clinical variables in order to comprehensively predict UE recovery in stroke survivors. Implications: Predicting the amount of post-stroke recovery would enable us in realistic goal formation and for planning rehabilitation to improve recovery potential.

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