Kwon Sooyeon, Hartzema Abraham G, Duncan Pamela W, Min-Lai Sue
Pharmacy Health Care Administration, College of Pharmacy, University of Florida, PO Box 100496, Health Science Center, Gainesville, FL 32610-0496, USA.
Stroke. 2004 Apr;35(4):918-23. doi: 10.1161/01.STR.0000119385.56094.32. Epub 2004 Feb 19.
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.
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.
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.
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.
卒中后的残留残疾带来了重大的经济和人文负担。为了量化患者的残疾情况,使用了日常生活活动(ADL;巴氏指数[BI]以及功能独立性测量的运动部分[M-FIM])和分类残疾测量方法(改良Rankin量表[MRS])。本研究的目的是检验ADL测量方法对整体残疾量表的预测能力。
本研究使用了堪萨斯城卒中研究的数据。应用相关系数、Kruskal-Wallis检验和多分类逻辑回归分析来检验ADL测量方法与整体残疾量表之间的关系。检验模型拟合统计量以验证逻辑回归的适用性。选择一种能使假阳性反应率最小化的分类方案作为最佳分类系统。
这三种测量方法高度相关。BI和M-FIM在残疾程度较低时比在较高时能更好地区分残疾情况。在逻辑回归中,BI能区分4个残疾水平;M-FIM能区分MRS中的3个水平。然而,根据Kruskal-Wallis检验和多重比较检验的结果,我们怀疑M-FIM可能有潜力通过不同模型更好地预测MRS类别。
所提出的分类方案可作为不同测量方法之间的转换工具。然而,由于BI和M-FIM的天花板效应,无法完成MRS所有6个水平的转换。未观察到分类方案随时间有明显变化。需要进一步开展研究以开发更好的预测模型来解释M-FIM与MRS之间的关系。