诊断学理论与实践

诊断学理论与实践 ›› 2021, Vol. 20 ›› Issue (05): 475-479.doi: 10.16150/j.1671-2870.2021.05.011

• 论著 • 上一篇    下一篇

上海社区老年脑卒中患者跌倒风险因素分析及风险识别模型的建立

刘安平1, 凌枫1, 史超1, 孙璟2()   

  1. 1.上海市徐汇区凌云街道社区卫生服务中心,上海 200237
    2.上海交通大学医学院附属瑞金医院老年病科,上海 200025
  • 收稿日期:2021-01-18 出版日期:2021-10-25 发布日期:2022-06-28
  • 通讯作者: 孙璟 E-mail:sj10535@rjh.com.cn

Analysis of fall risk factors and establishment of risk identification model in elderly stroke patients in Shanghai community

LIU Anping1, LING Feng1, SHI Chao1, SUN Jing2()   

  1. 1. Lingyun Street Health Service Center, Xuhui District, Shanghai 200237, China
    2. Department of Geriatrics, Ruijin Hospital, Shanghai Jiao Ttong University School of Medicine, Shanghai 200025, China
  • Received:2021-01-18 Online:2021-10-25 Published:2022-06-28
  • Contact: SUN Jing E-mail:sj10535@rjh.com.cn

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摘要：

目的：分析在上海社区卫生服务中心随访的老年脑卒中患者的跌倒风险及相关危险因素。 方法：采用便利抽样方法随机选取2019年度在本中心就诊的230例老年脑卒中患者（60~90岁）为研究对象,检测其生化指标,并采用Morse跌倒风险评估量表评估其跌倒风险,根据简易营养评价精法（short-form mini-nutritional assessment,MNA-SF法）将患者分为低风险（≤45分）及高风险组（>45分）,采用多因素Logistic回归模型分析跌倒危险因素并构建多因素模型,并用受试者操作特征（receiver operator characteristic,ROC）曲线评价危险因素模型预测老年卒中患者发生跌倒风险的价值。 结果：对跌倒高风险组及跌倒低风险组进行基线分析后,筛选变量进行多因素Logistic回归分析,提示MNA-SF评分[优势比（odd ratio,OR）=0.338,95%置信区间（confidence interval,CI）为0.225~0.508,P<0.001]、血清白蛋白（OR=0.513,95%CI为0.396~0.664,P<0.001）、血红蛋白（OR=0.908,95%CI为0.858~0.961,P=0.001）、女性（OR=4.407,95%CI为1.006~19.311,P=0.049）、高龄（OR=3.464, 95%CI为1.172~10.235,P=0.025）与跌倒相关。其中,女性、高龄（≥80岁）为老年卒中患者跌到的主要危险因素,MNA-SF评分高（≥11分）、血清白蛋白升高为卒中老年患者的主要保护因素,而血红蛋白升高的保护作用较弱。根据以上 5个危险因素构建 ROC 曲线的曲线下面积为 0.925（P<0.05）,在预测老年卒中患者跌倒风险中有较高的准确率。 结论：女性、高龄（≥80岁）是老年卒中患者跌倒的危险因素,而血清白蛋白及血红蛋白水平升高、营养状况良好（MNA-SF评分≥11分）则是该人群发生跌倒的保护因素。依据相关危险因素,建立相应的ROC曲线诊断模型,有助于在老年卒中患者中合理、快速地识别跌倒高风险的患者,具有实用价值。

关键词: 老年人, 脑卒中, 跌倒风险, 营养状况, 危险因素

Abstract:

Objective: To investigate the risk for falling, and fall risk related factors by visiting elderly stroke patients in Shanghai Community Health Service Center. Methods: A total of 230 patients aged from 60 to 90 in the Service Center were randomly selected during 2019 using convenience sampling method. Biochemical indicators were detected,and Morse Fall Risk Assessment Scale was used to assess the falling risk of the subjects. According to the short-form mini-nutritional assessment (MNA-SF method), patients were divided into low-risk and high-risk groups. Multivariate Logistic regression model was used to analyze the falling risk factors, and receiver operator characteristic (ROC) curves was used to evaluate the value of risk factors in predicting falling risk in elderly stroke patients. Results: After baseline analysis, Multivariate Logistic regression analysis showed that MNA-SF (OR=0.338, 95%CI: 0.225-0.508, P<0.001), serum albumin(OR=0.513, 95%CI: 0.396-0.664, P<0.001), hemoglobin (OR=0.908, 95%CI: 0.858-0.961, P=0.001), female(OR=4.407, 95%CI: 1.006-19.311, P=0.049), older age(OR=3.464,95%CI: 1.172-10.235, P=0.025) were risk factors for falling in elderly patients. Among these factors, female,older age (≥80 years old) were the risk factors for falling in elderly stroke patients, besides high score of MNA-SF, elevated level of serum albumin were the protection factors, and elevated hemoglobin was less protective. The area under the curve of ROC curve for the model established with 5 risk factors was 0.925 (P<0.05), which has a good performance for predicting falling in elderly stroke patients. Conclusions: It indicates that female, older age (≥80 years old) are the risk factors for falling, while elevated level of serum albumin,hemogolbin, and good nutrition (MNA-SF≥11) are protective factors for falling in elderly stroke patients. Establishment of risk model will help to identify the patients with high risk of falling, which has practical value.

Key words: Elderly people, Stroke, Falling risk, Nutritional status, Risk factor

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