Predicting post-stroke recovery through such models is crucial for choosing appropriate treatment options. Thus, this study aimed at determining if, by considering varied aspects of recovery, adding kinematic measurements over clinical measures would better predict upper extremity (UE) motor impairments at three months post-stroke.
Eighty-nine stroke survivors (59.9 ± 11.8 years) were recruited within 7 days post-stroke. We assessed clinical predictors between 4 and 7 days, kinematic predictors up to 1 month, and the Fugl Meyer Assessment of UE (FM-UE) and Action Research Arm Test (ARAT) at three months post-stroke. Correlation tests were performed for all predictors to explore their relationship with the outcome measures (the FM-UE and ARAT). Significant predictors (p0.05) that had a Variance Inflation Factor (VIF) 10 were selected for model development. Three models using clinical, kinematic, and a combination of the two were formulated for each outcome measure using linear regression.
Clinical data were available for 89 participants, kinematic parameters for 62, and the FM-UE and ARAT scores for 57 participants. The linear regression models for predicting the FM-UE were developed using clinical variables such as the Montreal Cognitive Assessment (MoCA), National Institutes of Health Stroke Scale (NIHSS) and Shoulder Abduction Finger Extension (SAFE criterion). Only SAFE criterion was used for predicting the ARAT. The models incorporating clinical predictors had R2 values of 0.7 and 0.59 for the FM-UE and ARAT respectively. Kinematic variables of reaction time, total time and total displacement were included for model development to predict both FM-UE and ARAT. However, the models including kinematic predictors had low R2 values of only 0.35 for the FM-UE and 0.29 for the ARAT.
Overall, the models combining clinical and kinematic predictors, which included SAFE and shoulder flexion, did not display much difference in their R2 values. Clinical measures tend to exhibit a ceiling effect that may not reflect on how much of an actual recovery is occurring over time. Thus, there is a need to incorporate objective, instrument- based measures such as kinematic metrics in order to detect minimal changes over time along with differentiating between the various types of recovery.
Through this study, we formulated a total of three models for each outcome measure for stroke recovery prediction at three months. The model combining kinematic and clinical predictors depicted that we need to carry out more thorough and comprehensive assessments, which would in turn aid in planning realistic and goal-oriented rehabilitation.
The models comprising of both clinical as well as kinematic predictors may assist in early prediction of post-stroke UE motor recovery. It would help in reducing the burden of stroke especially in low-to-middle-income countries by identifying the recovery potential and by encouraging patients to partake in early post-stroke rehabilitation thus improving the quality of life of stroke survivors as well as their caregivers.
biomechanics
recovery prediction
