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护理学报 ›› 2024, Vol. 31 ›› Issue (6): 6-12.doi: 10.16460/j.issn1008-9969.2024.06.006

• 研究生园地 • 上一篇    下一篇

中老年脑卒中患者多维衰弱发展轨迹及其影响因素研究

薛荣1, 张开利1, 陈保云2a, 马荣慧2b, 张雨欣1   

  1. 1.徐州医科大学 护理学院,江苏 徐州 221000;
    2.徐州市中心医院 a.护理部;b.神经内科,江苏 徐州 221009
  • 收稿日期:2023-11-26 出版日期:2024-03-25 发布日期:2024-04-08
  • 通讯作者: 张开利（1984-）,女,山东枣庄人,博士,副教授。E-mail:xiaokai20060@xzhmu.edu.cn
  • 作者简介:薛荣（1998-）,女,山西临汾人,本科学历,硕士研究生在读。

Multidimensional frailty development trajectory and its influencing factors in middle-aged and elderly stroke patients

XUE Rong1, ZHANG Kai-li1, CHEN Bao-yun2a, MA Rong-hui2b, ZHANG Yu-xin1   

  1. 1. School of Nursing, Xuzhou Medical University, Xuzhou 221000, China;
    2a. Dept. of Nursing Administration; 2b. Dept. of Neurology, Xuzhou Central Hospital, Xuzhou 221009, China
  • Received:2023-11-26 Online:2024-03-25 Published:2024-04-08

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摘要： 目的 探讨中老年脑卒中患者多维衰弱发展轨迹及其影响因素,为临床医护人员开展衰弱干预提供理论依据。方法 选取2022年10—12月徐州市某三级甲等医院神经内科住院的中老年脑卒中患者为研究对象,采用一般资料调查表、Zung焦虑自评量表和Zung抑郁自评量表收集患者的基线资料,采用Tilburg衰弱量表调查患者入院48 h、出院后3个月和出院后6个月多维衰弱得分情况,使用潜类别增长模型探究中老年脑卒中患者多维衰弱发展轨迹类别,采用无序多分类Logistic回归及决策树模型分析识别轨迹类别的影响因素。结果 中老年脑卒中患者多维衰弱发展轨迹可分为3种类别：非衰弱稳定组（42.8%）、低水平衰弱快速加重组（36.3%）和高水平衰弱缓慢加重组（20.9%）。Logistic回归分析结果显示,年龄、婚姻状况、文化程度、卒中严重程度评分、共病评分、日常生活活动能力评分和焦虑为中老年脑卒中患者多维衰弱发展轨迹的影响因素（P<0.05）;决策树模型进一步分析结果显示,日常生活活动能力评分为最主要影响因素,其后依次为焦虑和卒中严重程度评分。结论 本研究识别出3种类别中老年脑卒中患者多维衰弱发展轨迹,验证了衰弱发展异质性的特点。未来医护人员可通过筛查识别不同类别患者多维衰弱发展轨迹,制定针对性衰弱干预和居家衰弱管理方案,延缓甚至逆转多维衰弱。

关键词: 中老年, 脑卒中, 多维衰弱, 轨迹, 潜类别增长模型

Abstract: Objective To explore the development trajectory of multidimensional frailty in middle-aged and elderly stroke patients and its influencing factors, and to provide theoretical basis for clinical staff to carry out frailty intervention. Methods Middle-aged and elderly stroke patients hospitalized in the Department of Neurology of a tertiary Grade-A hospital in Xuzhou City from October to December 2022 were selected as the study objects. The general information questionnaire, Zung Self-rating Anxiety Scale and Zung Self-rating Depression Scale were used to collect the baseline data of the patients. Tilburg Frailty Scale was used to collect the multidimensional frailty score of the patients 48 hours after admission, 3 months and 6 months after discharge, and the potential category growth model to explore the category of multidimensional frailty development trajectory of middle-aged and elderly stroke patients. Disordered multi-classification Logistic regression and decision tree model were used to analyze and identify the influencing factors of trajectory category. Results The multidimensional frailty development of middle-aged and elderly stroke patients could be divided into three categories: non-frail and stable group (42.8%), low level of frailty with rapid increase (36.3%) and high level of frailty with slow increase (20.9%). Logistic regression analysis showed that age, marital status, educational level, stroke severity score, comorbidity score, activity of daily living score and anxiety were the influencing factors of multidimensional frailty development trajectory in middle-aged and elderly stroke patients (P<0.05). Further analysis of decision tree model showed that the activity of daily living score was the most important factor, followed by anxiety and stroke severity score. Conclusion In this study, multidimensional frailty development trajectory of 3 categories of middle-aged and elderly stroke patients is identified, which verify the heterogeneity of frailty development. In the future, medical personnel can identify the development trajectory of multidimensional frailty in different categories of patients through screening, develop targeted frailty intervention and home frailty management programs, and delay or even reverse multidimensional frailty.

Key words: middle-aged and elderly people, stroke, multidimensional frailty, trajectory, latent class growth model

中图分类号: 

  • R473.74
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