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首页> 外文期刊>Archives of Physical Medicine and Rehabilitation >Using the timed up & go test in a clinical setting to predict falling in parkinson's disease
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Using the timed up & go test in a clinical setting to predict falling in parkinson's disease

机译：在临床环境中使用定时升级和去测试，以预测在帕金森病中陷入困境

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Objective: To investigate the ability of the Timed Up & Go test to identify patients with Parkinson's disease at risk for a fall. Design: Cross-sectional cohort study. Setting: Sixteen participating National Parkinson's Foundation Centers of Excellence. Participants: A query yielded a total of 2985 records (1828 men and 1157 women). From these, 884 were excluded because of a lack of crucial information (age, diagnosis, presence of deep brain stimulation, disease duration, inability of performing the Timed Up & Go test without assistance) at the time of testing, leaving 2097 patients included in the analysis. Interventions: Not applicable. Main Outcome Measures: The primary outcome measure for this study was falls. The chief independent variable was the Timed Up & Go test. Results: The initial model examined the prediction of falls from the Timed Up & Go test, adjusting for all study covariates. The estimated models in the imputed data sets represented a significant improvement above chance (χ2 range [df=17], 531.29-542.39, P.001), suggesting that 74% of participants were accurately classified as a faller or nonfaller. The secondary model in which the question of whether the effect of Timed Up & Go test was invariant across disease severity demonstrated 75% of participants were accurately classified as a faller or nonfaller. Additional analysis revealed a proposed cut score of 11.5 seconds for discrimination of those who did or did not fall. Conclusions: The findings suggest that the Timed Up & Go test may be an accurate assessment tool to identify those at risk for falls.
机译：目的：探讨超时＆amp的能力;去测试以识别帕金森病的患者，以跌倒。设计：横截面队列研究。环境：十六参加国家帕金森的卓越基础。参与者：一个查询总共产生了2985条记录（1828名男子和1157名女性）。从这些中，由于缺乏关键信息（年龄，诊断，深脑刺激，疾病持续时间，无能为准，在没有援助的情况下，在没有援助的情况下，在没有援助的情况下进行）患者，因此排除了884名在分析中。干预措施：不适用。主要观察措施：本研究的主要结果措施落下。首席独立变量是定时上升＆amp;去测试。结果：初始模型检查了从超时＆amp的跌落预测;去测试，调整所有研究协变量。估计的数据集中的估计模型表示近似的机会（χ2范围[DF = 17]，531.29-542.39，P＆ .001），表明74％的参与者被准确归类为衰落或非堕落者。次要模型，其中定时上升的问题。去试验患有疾病严重程度的不变性，75％的参与者被准确地归类为衰落或非堕落者。额外的分析显示，歧视那些或未跌倒的人的拟议削减得分为11.5秒。结论：调查结果表明，超时＆amp;去测试可能是一个准确的评估工具，以确定瀑布风险的工具。

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