步态运动学特征及MRI总负荷与脑小血管病步态障碍患者跌倒风险的相关性研究

关键词：脑小血管病  步态障碍  MRI总负荷  跌倒风险

Keywords:cerebral small vessel disease  gait disorder  total MRI burden  risk of falls

摘要：

      摘要 目的：采用Logistic回归分析和ROC曲线评估步态运动学特征联合MRI总负荷对脑小血管病（cerebral small vessel disease， CSVD）步态障碍患者跌倒风险的预测价值。 方法：选取2019年3月1日—2020年3月30日就诊于甘肃省人民医院中法神经康复科诊断为CSVD且以步态障碍为主要症状的43例患者为研究对象，统计临床数据，根据TUG测试时间将所有患者分为高跌倒风险（high risk of falling， HRF； TUG时间≥15s）组和低跌倒风险（low risk of falling， LRF； TUG时间<15s）组，采用Logistic回归分析和ROC曲线评估步态运动学特征联合MRI总负荷对CSVD步态障碍患者跌倒风险的预测价值。 结果：研究共纳入43例患者，平均年龄（71.07±8.17）岁。其中女性26例（60.4%），高血压患者30例（69.8%），Logistic回归分析显示，在校正年龄和TUG后，步长（OR 0.821，95%CI 0.702—0.959，P=0.013）为CSVD步态障碍患者跌倒风险的独立保护因素，MRI总负荷（OR 4.217，95%CI 1.444—12.317，P=0.009）为CSVD步态障碍患者跌倒风险的独立危险因素。ROC曲线分析显示步长和MRI总负荷联合对CSVD步态障碍患者的跌倒风险具有较高的预测价值（AUC=0.904），敏感性为82.6%，特异性为90%。 结论：步长联合MRI总负荷对CSVD步态障碍患者的跌倒风险有较高的预测价值。

Abstract:

      Abstract Objective：To evaluate the predictive value of gait kinematic combined with total MRI burden for the risk of falls in patients with gait disorder in cerebral small vessel disease (CSVD) using logistic regression analysis and ROC curve. Method：Forty-three patients diagnosed with CSVD and presenting primarily with gait disorder at Rehabilitation Department of Gansu Province Hospital from March 1，2019 to March 30，2020 were selected. The statistical clinical data were collected，and according to the TUG test time, all patients were divided into a group with high risk of fall （high risk of falling， HRF， TUG≥15s）and a group of low risk of falls （low risk of falling， LRF， TUG<15s）. Logistic regression analysis and ROC curve were used to assess the predictive value of gait kinematic characteristic combined with total MRI burden for the risk of falls in CSVD patients. Result：A total of 43 patients were included with average age （71.07±8.17） years. Among them，there were 26 female （60.4%） and 30 hypertension patients （69.8%）， After adjusting for age and TUG, the logistic regression analysis showed that the step length （OR 0.821，95%CI 0.702—0.959，P=0.013）was an independent protective factor against the risk of falls in CSVD patients，while the total MRI burden （OR 4.217，95%CI 1.444—12.317，P=0.009） was an independent risk factor for the falls in CSVD. The ROC curve analysis showed that the combination of step length and total MRI burden had a high predictive value for the risk of fall in CSVD patients with gait disorder（AUC=0.904）， with a sensitivity of 82.6% and a specificity of 90%. Conclusion：Step length combined with total MRI burden has a high predictive value for the risk of falls in CSVD patients with gait disorder.