Abstract P082: Racial Disparities in Stroke Recovery: A Large Study Versus Meta-Analysis
Background: It is well known that Blacks have a higher stroke-related mortality compared to their White counterparts, but evidence on the influence of Black race on recovery after a stroke is not apparent. Objective: To verify our understanding on post-stroke rehabilitation trends between Blacks and Whites with the use of systematic review and meta-analysis. Methods: We performed literature search for cohort studies that investigated racial variation issues in stroke motor recovery between January 1970 and March 2016, in which outcome was measured by Functional Independence Measures (FIM) scale. We compared change scores (the difference score between discharge and admission) or endpoint scores (at the time of admission and discharge) as well as length of stay in days between Whites and Blacks by calculating standardized mean differences (Hedge’s g ) to derive a summary effect size. Random Effects model was used to account for data heterogeneity. Results: We identified 7 studies with a total 152,421 subjects, of which one influential study (Ottenbacher et al, 2008) offered a significant weight with 148,871 subjects. So, we performed meta-analysis on the remaining 6 studies (black diamond on the Figure ) and compared the results with this influential study (maroon square on the Figure ). We found that Blacks have higher FIM scores at admission and discharge, but poor change FIM scores, despite their shorter stay (about ¾ day) in rehab facility when compared to Whites. Our results contrasted findings of Ottenbacher et al. , which did not report change scores, that Whites have higher FIM scores at admission and discharge in spite of their comparable rehab facility stay. Conclusions: This meta-analysis identifies a significant evidence gap for current understanding of racial disparities in stroke recovery. At AHA SFRN WISSDOM (Wide spectrum Investigation of Stroke Outcome Disparities on Multiple Levels) center, we aim to address this gap by first-hand analyses of multiple datasets in the near future.