Stroke outcome prediction using reciprocal number of initial activities of daily living status.
Stroke outcome prediction using reciprocal number of initial activities of daily living status.
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DOI：
10.1016/j.jstrokecerebrovasdis.2004.10.001
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发表时间：
2005-01-01
期刊：
影响因子：
--
通讯作者：
Suzuki, Miho
中科院分区：
文献类型：
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作者：
Sonoda, Shigeru;Saitoh, Eiichi;Suzuki, Miho
对87例入院康复医院的脑卒中患者进行多元回归分析，以预测出院时功能独立测量（FIM）的总运动亚分。除了入院时FIM的总认知分、年龄和中风发作至入院天数外，入院时FIM的总运动分或其倒数数被添加到自变量中。预测值与实测值的相关系数为。88（普通回归）和。验证组（44例脑卒中患者）93例（倒数回归）。倒数预测（4.57）的残差中位数（即放电时预测电机- fim与实际电机- fim相减的绝对值）显著小于普通预测（6.26）。综上所述，回归分析的倒数预测提供了更精确的预测，而无需额外的复杂计算。
Multiple regression analysis was performed in 87 stroke patients who were admitted to a rehabilitation hospital to predict the total motor subscore of the Functional Independence Measure (FIM) at discharge. In addition to the total cognitive subscore of the FIM at admission, age, and days from stroke onset to admission, the total motor subscore of the FIM at admission or its reciprocal number was added to independent variables. The correlation coefficients between the predicted and actual values were .88 (ordinary regression) and .93 (reciprocal regression) in the validation group (44 stroke patients). The median of the residuals (i.e, absolute values of subtraction of predicted motor-FIM from actual motor-FIM at discharge) of the reciprocal prediction (4.57) was significantly smaller than that of the ordinary prediction (6.26). In conclusion, the reciprocal prediction of regression analysis provided a more precise prediction without additional complex calculations.