中华护理杂志 ›› 2023, Vol. 58 ›› Issue (7): 829-835.DOI: 10.3761/j.issn.0254-1769.2023.07.009

• 专科实践与研究 • 上一篇    下一篇

首发脑卒中患者护理依赖轨迹的潜在类别及影响因素分析

何雯倩(), 郭园丽, 王连珂, 周乾宇, 王盼盼, 张配嘉, 赵明扬, 王荣荣, 要子慧, 胡博, 吴田田, 王昱, 孙长青()   

  1. 450001 郑州市 郑州大学护理与健康学院(何雯倩,王连珂,王盼盼,张配嘉,王荣荣,要子慧,胡博,吴田田,王昱,孙长青);郑州大学第一附属医院神经内科(郭园丽);郑州大学公共卫生学院(周乾宇,赵明扬)
  • 收稿日期:2022-09-07 出版日期:2023-04-10 发布日期:2023-04-11
  • 通讯作者: 孙长青,E-mail:suncq@zzu.edu.cn
  • 作者简介:何雯倩:女,本科(硕士在读),E-mail:1154967132@qq.com
  • 基金资助:
    国家社会科学基金项目(20BRK041);郑州大学教育教学改革研究与实践项目(2021ZZUJGLX067);绝学学科(方向)培育计划项目(XKLMJX202212)

Analysis of potential categories and influencing factors of care dependency trajectories among first stroke patients

HE Wenqian(), GUO Yuanli, WANG Lianke, ZHOU Qianyu, WANG Panpan, ZHANG Peijia, ZHAO Mingyang, WANG Rongrong, YAO Zihui, HU Bo, WU Tiantian, WANG Yu, SUN Changqing()   

  • Received:2022-09-07 Online:2023-04-10 Published:2023-04-11

摘要:

目的 探讨首发脑卒中患者护理依赖轨迹的潜在类别及影响因素。 方法 采用便利抽样法于2021年10月—2022年8月选取河南省某三级甲等医院神经内科首发脑卒中患者作为调查对象。采用护理依赖量表分别于入院第3天、出院时、出院后1个月、3个月、6个月调查患者的护理依赖水平,利用增长混合模型、单因素分析及多元Logistic回归分析数据。 结果 278例首发脑卒中患者护理依赖轨迹分为4个潜在类别,分别是低依赖-高改善型(42.1%)、中依赖-中改善型(24.8%)、中依赖-高下降型(13.3%)、高依赖-低改善型(19.8%)。年龄、Charlson共病指数、卒中类型、卒中严重程度、焦虑、抑郁及营养情况是首发脑卒中患者护理依赖轨迹潜在类别的影响因素(P<0.05)。 结论 首发脑卒中患者护理依赖轨迹存在异质性,医护人员可根据护理依赖轨迹类别的影响因素进行个体化干预。

关键词: 脑卒中, 护理依赖, 潜在类别, 影响因素分析, 增长混合模型

Abstract:

Objective To analyze the potential categories and influencing factors of care dependency trajectory among first stroke patients. Methods From October 2021 to August 2022,278 first stroke patients in the Department of Neurology of a tertiary hospital in Zhengzhou were selected as the study subjects by the convenience sampling method. The Care Dependency Scale was used to investigate the patient’s care dependency level on 3 days after admission,at discharge and 1,3,6 months after discharge,and the latent growth mixture model,univariate analysis and multiple logistic regression were used for data processing. Results The developmental trajectory of 278 first stroke patients’ care dependency was divided into 4 latent subgroups,including low dependence-high recovery(42.1%),medium dependence-medium recovery(24.8%),medium dependence-high decline(13.3%) and high dependence-low recovery(19.8%). The influencing factors included age,Charlson complication index value,stroke type,stroke severity,anxiety,depression and nutrition score(P<0.05). Conclusion There are heterogeneous trajectories of care dependency in first stroke patients. Medical staff can make individualized interventions according to the influencing factors to reduce the degree of care dependence.

Key words: Stroke, Care Dependency, Potential Categories, Root Cause Analysis, Growth Mixture Model