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

What good does doing research that only predicts failure to recover? SOLVE THE FUCKING PROBLEM OF 100% STROKE RECOVERY!

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

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 both.

Relevance: 

Meticulously formulated prognostic models could be used by rehabilitation specialists for improving prediction accuracy in stroke survivors.

Participants: 

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 (So you're trying to justify your use of the tyranny of low expectations as to why you can't get your patients recovered. I'd fire anyone using that excuse.)

 

 

 

 

 

 

) and for planning rehabilitation to improve recovery potential.

Sanjukta Sardesai, John M Solomon, Elton Nazareth, Senthil D Kumaran  Manipal College of Health Professions, Manipal Academy of Higher Education Correspondence Address: Source of Support: None, Conflict of Interest: None DOI: 10.4103/2456-7787.361075