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Disability measures in stroke: relationship among the Barthel Index, the Functional Independence Measure, and the Modified Rankin Scale.

Kwon S, Hartzema AG, Duncan PW, Min-Lai S. Disability measures in stroke: relationship among the Barthel Index, the Functional Independence Measure, and the Modified Rankin Scale. Stroke; A Journal of Cerebral Circulation. 2004 Apr 1; 35(4):918-23.

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Abstract:

BACKGROUND AND PURPOSE: Residual disability after stroke presents a major economic and humanistic burden. To quantify disability in patients, activities of daily living (ADL; Barthel Index [BI], and motor component of Functional Independence Measure [M-FIM]) and categorical disability measures (Modified Rankin Scale [MRS]) are used. The purpose of this study is to examine the predicting ability of ADL measures to global disability scale. METHODS: Kansas City Stroke Study data were used for the present study. Correlation coefficient, Kruskal-Wallis test, and polytomous logistic regression analysis were applied to examine the relationship between the ADL measure and global disability scale. Model fit statistics were examined to verify logistic regression appropriateness. A categorization scheme, which minimized the false-positive response rate, was selected as the optimal categorizing system. RESULTS: The 3 measures were highly correlated. Both BI and M-FIM differentiated disability better in lower than higher disability. In logistic regression, BI differentiated 4 disability levels; M-FIM differentiated 3 levels in MRS. However, on the basis of results of the Kruskal-Wallis and multiple comparison tests, we suspect that M-FIM may have the potential to predict MRS categories better with a different model. CONCLUSIONS: The proposed categorization scheme can serve as a translation between measures. However, because of the ceiling effect of BI and M-FIM, the translation could not be completed for all 6 levels of MRS. No apparent variation over time in the categorization scheme was observed. Further research needs to be conducted to develop better prediction models explaining the relationship between M-FIM and MRS.





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