Masiero Stefano, Avesani Renato, Armani Mario, Verena Postal, Ermani Mario
Department of Rehabilitation Medicine, Unit of Rehabilitation, University of Padova, School of Medicine, Padova, Italy.
Clin Neurol Neurosurg. 2007 Nov;109(9):763-9. doi: 10.1016/j.clineuro.2007.07.009. Epub 2007 Sep 4.
The purpose of this study was to investigate predictive factors for ambulatory recovery in stroke patients undergoing rehabilitation.
One hundred and eight-five first-stroke hemiplegics, admitted to an inpatient stroke rehabilitation program, were consecutively recruited to the study. Functional status at admission and discharge was evaluated by the Functional Independence Measure (FIM) and its motor component (motFIM), the upper and lower Motricity Index (upMI and lowMI), and the Trunk Control Test (TCT). The outcome variable was the Functional Ambulation Classification (FAC) score, assessed at discharge from rehabilitation. Multivariate analysis was used to assess the relationships between functional outcome (FAC), and the predictive variables.
Up- and lowMI, FIM and motFIM, TCT and age at admission were significantly related to ambulatory recovery at discharge. Logistic regression analysis showed that the independent variables related to FAC were age, TCT and FIM: the model correctly allocated 86 out of 100 cases in the construction set and 76% of cases in the validation set. The ROC curve with logistic function output as the risk factor afforded very good accuracy (ROC area=0.94), sensitivity=86.5% and specificity=85.4%.
Our results show that age and level of motor and functional impairment measured at baseline are significant predictors of ambulatory outcome. These findings promise to be of interest in goal optimization in the rehabilitation setting.
本研究旨在调查接受康复治疗的中风患者门诊康复的预测因素。
连续招募了185名首次中风偏瘫患者，他们均入住了中风住院康复项目。入院和出院时的功能状态通过功能独立性测量（FIM）及其运动成分（motFIM）、上下运动指数（upMI和lowMI）以及躯干控制测试（TCT）进行评估。结局变量是康复出院时评估的功能步行分类（FAC）评分。采用多变量分析来评估功能结局（FAC）与预测变量之间的关系。
upMI和lowMI、FIM和motFIM、TCT以及入院时的年龄与出院时的步行恢复显著相关。逻辑回归分析表明，与FAC相关的自变量是年龄、TCT和FIM：该模型在构建集中正确分配了100例中的86例，在验证集中正确分配了76%的病例。以逻辑函数输出作为风险因素的ROC曲线具有非常好的准确性（ROC面积=0.94），敏感性=86.5%，特异性=85.4%。
我们的结果表明，基线时测量的年龄以及运动和功能损害水平是步行结局的重要预测因素。这些发现有望在康复环境中的目标优化方面引起关注。
