中老年脑卒中患者多维衰弱发展轨迹及其影响因素研究

doi:10.16460/j.issn1008-9969.2024.06.006

摘要/Abstract

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

薛荣, 张开利, 陈保云, 马荣慧, 张雨欣. 中老年脑卒中患者多维衰弱发展轨迹及其影响因素研究[J]. 护理学报, 2024, 31(6): 6-12.

XUE Rong, ZHANG Kai-li, CHEN Bao-yun, MA Rong-hui, ZHANG Yu-xin. Multidimensional frailty development trajectory and its influencing factors in middle-aged and elderly stroke patients[J]. Journal of Nursing, 2024, 31(6): 6-12.

参考文献

[1] Wu Y, Jia M, Xiang C, et al.Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective[J].BMC Geriatr,2022,22(1):900.DOI:10.1186/s12877-022-03576-5.
[2] Álvarez-Bustos A, Carnicero-Carreño JA, Sanchez-Sanchez JL, et al.Associations between frailty trajectories and frailty status and adverse outcomes in community-dwelling older adults[J]. J Cachexia Sarcopenia Muscle,2022,13(1):230-239.DOI:10.1002/jcsm.12888.
[3] Evans NR, Wall J, To B, et al.Clinical frailty independently predicts early mortality after ischaemic stroke[J]. Age Ageing, 2020, 49(4):588-591.DOI:10.1093/ageing/afaa004.
[4] 林卫,苏俐莉,余天智,等.老年缺血性脑卒中后遗症患者衰弱状况调查及其危险因素分析[J].中华老年心脑血管病杂志,2021,23(5):499-502.DOI:10.3969/j.issn.1009-0126.2021.05.014.
[5] 张鑫宇,张磊,隋汝波.基于Logistic回归和人工神经网络构建老年脑卒中患者衰弱预测模型[J].军事护理,2023,40(2):10-14,19.DOI:10.3969/j.issn.2097-1826.2023.02.003.
[6] Zhao Y, Hu Y, Smith JP, et al.Cohort profile: the China health and retirement longitudinal study (CHARLS)[J]. Int J Epidemiol, 2014,43(1):61-68.DOI:10.1093/ije/dys203.
[7] 中华医学会神经病学分会,中华医学会神经病学分会脑血管病学组.中国各类主要脑血管病诊断要点2019[J].中华神经科杂志,2019, 52(9):710-715.DOI:10.3760/cma.j.issn.1006-7876.2019.09.003.
[8] 项李娜,万宏伟,朱毓,等.接受放疗的头颈部肿瘤病人心理弹性变化轨迹及其影响因素[J].护理研究, 2022,36(18):3202-3208. DOI:10.12102/j.issn.1009-6493.2022.18.002.
[9] Luo Z, Luo B, Wang P, et al.Predictive model of functional exercise compliance of patients with breast cancer based on decision tree[J]. Int J Womens Health, 2023,15:397-410. DOI:10.2147/IJWH.S386405.
[10] Chalos V,van der Ende NAM,Lingsma HF,et al.National Institutes of Health Stroke Scale:an alternative primary outcome measure for trials of acute treatment for ischemic stroke[J].Stroke, 2020, 51(1):282-290.DOI:10.1161/STROKEAHA.119.026791.
[11] 陈冉,李春玉,李美茜.应用Rodgers演化概念分析法界定多重用药[J].护理学报,2023,30(17):39-44. DOI:10.16460/j.issn1008-9969.2023.17.039.
[12] Charlson ME, Pompei P, Ales KL, et al.A new method of classifying prognostic comorbidity in longitudinal studies:development and validation[J]. J Chronic Dis,1987,40(5):373-383.DOI:10.1016/0021-9681(87)90171-8.
[13] Mahoney RI,Barthel DW.Functional evaluation:the Barthel Index[J].Md State Med,1965,14:61-65.
[14] Gobbens RJ,Luijkx KG,Wijnen-Sponselee MT,et al.Towards an integral conceptual model of frailty[J]. J Nutr Health Aging,2010,14(3):175-181.DOI:10.1007/s12603-010-0045-6.
[15] 奚兴,郭桂芳,孙静.中文版Tilburg衰弱评估量表的信效度研究[J].护理学报,2013,20(8B):1-4.DOI:10.3969/j.issn.1008-9969.2013.16.001.
[16] 钱军红,刘志薇,李妍,等.农村老年脑卒中患者衰弱轨迹及影响因素的纵向研究[J].中国实用神经疾病杂志,2023,26(6):748-754. DOI:10.12083/SYSJ.230529.
[17] Zung WW.A rating instrument for anxiety disorders[J].Psychosomatics,1971, 12(6):371-379.DOI:10.1016/S0033-3182(71)71479-0.
[18] 王征宇,迟玉芬.焦虑自评量表(SAS)[J].上海精神医学,1984,6(2):73-74.
[19] Dunstan DA, Scott N, Todd AK.Screening for anxiety and depression: reassessing the utility of the Zung scales[J]. BMC Psychiatry,2017,17(1):329. DOI:10.1186/s12888-017-1489-6.
[20] 王征宇,迟玉芬.抑郁自评量表(SDS)[J].上海精神医学,1984,6(2):71-72.
[21] 北京高血压防治协会,中国卒中学会高血压预防与管理分会,中国老年保健协会养老与健康专业委员会,等.基层冠心病与缺血性脑卒中共患管理专家共识2022[J].中国心血管病研究,2022,20(9):772-793.DOI:10.3969/j.issn.1009-816x.2022.04.001.
[22] 吕雨梅,郭玉成,周郁秋. 脑卒中患者康复锻炼依从性发展轨迹及其潜在类别预测因素分析[J]. 中华护理杂志,2023,58(17):2103-2111.DOI:10.3761/j.issn.0254-1769.2023.17.008.
[23] Hwang AC, Lee WJ, Huang N, et al.Longitudinal changes of frailty in 8 years: comparisons between physical frailty and frailty index[J]. BMC Geriatr,2021,21(1):726. DOI:10.1186/s12877-021-02665-1.
[24] Jang AR, Sagong H, Yoon JY.Frailty trajectory among community-dwelling middle-aged and older adults in Korea: evidence from the Korean longitudinal study of aging[J]. BMC Geriatr, 2022,22(1):524.DOI:10.1186/s12877-022-03229-7.
[25] Hu L,Liu G.Effects of early rehabilitation nursing on neurological functions and quality of life of patients with ischemic stroke hemiplegia[J]. Am J Transl Res,2021,13(4):3811-3818.
[26] Choi M, Kim H, Bae J.Does the combination of resistance training and a nutritional intervention have a synergic effect on muscle mass,strength,and physical function in older adults? A systematic review and Meta-analysis[J]. BMC Geriatr,2021,21(1):639.DOI:10.1186/s12877-021-02491-5.
[27] Pinto M, Martins S, Mesquita E,et al.Frailty in portuguese older patients from convalescence units: a cross-sectional study[J]. J Clin Med Res, 2022, 14(9):364-376.DOI:10.14740/jocmr4806.
[28] Rojas Huerta AV.Trajectories of frailty and related factors of elderly people in Mexico[J].Poblac Salud Mesoam,2022,19(2):10.DOI:10.15517/psm.v19i2.46929.

Metrics

Viewed

Full text

Abstract

Cited

Shared