Clinical Indicators that Predict Motor Recovery Post-Stroke
MetadataShow full item record
One of the leading causes of long-term disability in adults is stroke. Stroke occurs due to disruptions in the blood supply in the brain resulting from a blockage or bleed. These disruptions can lead to impaired upper extremity motor function. Typically, patients participate in rehabilitation to regain lost motor function through training. However, it remains difficult to predict the expected level of recovery for each patient post-stroke. Improving prediction accuracy has the potential to increase the efficacy of therapy, influence therapeutic practices, and encourage stroke survivors to continue training. PREP2 is one algorithm that has shown promise in predicting upper-extremity outcomes in New Zealand. However, it is unclear if PREP2 can be successfully applied in the United States. The three goals of this study were to assess the feasibility of implementing PREP2 in the United States, determine whether currently collected clinical outcome measures could be used to better predict recovery post-stroke, and investigate relationships between nonmotor domains and motor recovery. To assess feasibility, we conducted a retrospective chart review of patients admitted with stroke to Emory University Hospital. We collected measures regarding stroke characteristics, patient demographics, and progression of recovery. Some of these measures include patient age, National Institute of Stroke Scale (NIHSS) scores, and Shoulder Abduction and Finger Extension (SAFE) scores. Using the collected data, Action Research Arm Test (ARAT) scores, a measure of upper extremity motor function, were estimated. Additionally, we conducted a correlational analysis to investigate the relationship between currently taken outcome measures and recovery post-stroke as well as between motor recovery post-stroke and nonmotor domains: speech and sensory impairment. Findings suggest it is feasible to implement PREP2 as the core metrics (e.g., SAFE, NIHSS) are collected on appropriate timelines. Additionally, SAFE scores were the only measure correlated with motor recovery post-stroke. We found no significant correlations between nonmotor domains and motor recovery. These findings are significant because they help refine the algorithm to ensure it is appropriate for the healthcare system in the United States. Additionally, research into nonmotor domains can add another layer to the algorithm leading to more accurate predictions of recovery.