《中国康复理论与实践》

《中国康复理论与实践》 ›› 2024, Vol. 30 ›› Issue (7): 811-817.doi: 10.3969/j.issn.1006-9771.2024.07.009

• 应用研究 • 上一篇    下一篇

脑卒中患者跌倒风险的相关因素研究

段林茹1, 郑洁皎1(), 陈茜2, 李燕3   

  1. 1.复旦大学附属华东医院，上海市 200040
    2.上海中医药大学，上海市 201203
    3.上海体育大学，上海市 200438
  • 收稿日期:2024-04-07 修回日期:2024-06-09 出版日期:2024-07-25 发布日期:2024-08-07
  • 通讯作者: 郑洁皎，女，主任医师，硕士、博士研究生导师，主要研究方向：老年康复，E-mail： zjjcss@163.com
  • 作者简介:段林茹(1990-)，女，汉族，河南濮阳市人，硕士，主管技师，主要研究方向：神经康复。
  • 基金资助:
    上海市科委项目(21MC1930200);上海市科委项目(22Y31900200);上海市卫健委卫生行业临床研究专项(20194Y0463)

Analysis of relevant factors for fall risk in stroke patients

DUAN Linru1, ZHENG Jiejiao1(), CHEN Xi2, LI Yan3   

  1. 1. Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China
    2. Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
    3. Shanghai University of Sport, Shanghai 200438, China
  • Received:2024-04-07 Revised:2024-06-09 Published:2024-07-25 Online:2024-08-07
  • Contact: ZHENG Jiejiao, E-mail: zjjcss@163.com
  • Supported by:
    Shanghai Municipal Commission of Science and Technology Project(21MC1930200);Shanghai Municipal Commission of Science and Technology Project(22Y31900200);Shanghai Municipal Health Commission Project of Health Industry Clinical Research(20194Y0463)

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摘要：

目的 探索影响脑卒中患者跌倒风险的相关因素，预测跌倒风险等级。
方法 回顾性分析2022年7月至2024年1月在华东医院就诊的脑卒中患者64例，记录患者就诊时的人口学资料性别、年龄、身高、体质量、卒中类型、病程，功能指标世界卫生组织残疾评定量表2.0 (WHODAS 2.0)、Fugl-Meyer评定量表(FMA)、功能性前伸测试(FRT)、多方向伸展测试(MDRT)、蒙特利尔认知评估量表(MoCA)，行走指标计时起立行走试验等。以脑卒中患者跌倒风险等级为因变量，先采用单因素分析，再采用判别分析对脑卒中患者跌倒风险因素进行观察。
结果 纳入患者平均年龄约66岁，男性多于女性，脑梗死患者多于脑出血患者，平均病程(4.50±6.02)个月，跌倒风险等级轻度、中度、重度的脑卒中患者分别为19例、26例、19例。单因素分析显示，不同跌倒风险等级脑卒中患者间WHODAS 2.0、FMA、FMA-上肢部分(FMA-UE)、FMA-下肢部分(FMA-LE)、FRT、MDRT-向前(MDRT-F)、MDRT-向右(MDRT-R)、MoCA评分有显著性差异(F > 2.277, P < 0.05)。判别分析显示，不同跌倒风险等级患者的功能参数方程不同，采用回顾法验证Fisher判别函数、Bayes判别函数正确率分别为75%、78.1%，误判率分别为25%、21.9%。
结论 活动参与能力、上下肢运动功能、向前向右方向的稳定极限及认知功能影响脑卒中患者跌倒风险等级，通过功能指标建立判别函数可对跌倒风险等级进行预测。

关键词: 脑卒中, 跌倒, 国际功能、残疾和健康分类, 世界卫生组织残疾评定量表, 活动和参与, 运动功能, 判别分析

Abstract:

Objective To explore the factors affecting the fall risks in stroke patients, and predict the level of fall risk.
Methods A retrospective analysis was conducted. A total of 64 stroke patients in Huadong Hospital from July, 2022 to January, 2024 were enrolled. The patient's demographic data, functional indicators and walking indicator, including gender, age, height, weight, stroke type, course of disease, WHO Disability Assessment Schedule 2.0 (WHODAS 2.0), simplified Fugl-Meyer Assessment (FMA), Functional Reaching Test (FRT), Multi-Directional Reach Test (MDRT), Montreal Cognitive Assessment (MoCA) and Timed Up and Go Test were recorded. Using the risk level of falls in stroke patients as the dependent variable, first, a univariate analysis was conducted, and then discriminant analysis was followed to observe the risk factors for falls in stroke patients.
Results All the included stroke patients had an average age of approximately 66 years old, more males than females, and more cerebral infarction patients than cerebral hemorrhage patients. The average course of the disease was (4.50±6.02) months. There were 19, 26 and 19 stroke patients with mild, moderate and severe fall risk levels, respectively. One-way analysis of variance showed that the scores of WHODAS 2.0, FMA, FMA-Upper Extremities (FMA-UE), FMA-Lower Extremities (FMA-LE), FRT, MDRT-Forward (MDRT-F), MDRT-Right (MDRT-R) and MoCA were significantly differenct among stroke patients with different fall risk levels (F > 2.277, P < 0.05). Discriminant analysis showed that patients with different fall risk levels had different functional parameter equations, using the retrospective method, the accuracy rates of Fisher discriminant function and Bayes discriminant function were 75% and 78.1%, and the misjudgment rates were 25% and 21.9%, respectively.
Conclusion Activity and participation ability, upper and lower extremity motor function, stability limits of the forward and right direction and cognitive function are related factors to the risk level of falls. It could predicte the risk level of falls by establishing a discriminant function through functional indicators.

Key words: stroke, fall, International Classification of Functioning, Disability and Health, World Health Organization Disability Assessment Schedule, activity and participation, motor function, discriminant analysis

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