急性期脑卒中患者症状的潜在类别分析和网络分析

朱凤伊; 张会; 刘安诺; 许静; 葛瑶; 刘梦

doi:10.16766/j.cnki.issn.1674-4152.004508

急性期脑卒中患者症状的潜在类别分析和网络分析

doi: 10.16766/j.cnki.issn.1674-4152.004508

朱凤伊^{1, 2, 3},
张会^{1, 2, 3, 4, ,},
刘安诺^{1, 4},
许静¹,
葛瑶¹,
刘梦¹

1.
安徽医科大学护理学院，安徽合肥 230601
2.
安徽医科大学第一附属医院护理部，安徽合肥 230032
3.
安徽省公共卫生临床中心护理部，安徽合肥 230032
4.
安徽医科大学泰康康养产业研究院，安徽芜湖 241199

基金项目:

安徽省转化医学研究院护理学联合专项课题 2024zhyx-hl-B30

2025年度安徽医科大学护理学院研究生青苗培育项目 Hlqm12025094

详细信息

通讯作者:
张会，E-mail：zhanghui310@126.com

中图分类号: R743.3
计量
- 文章访问数: 12
- HTML全文浏览量: 5
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2025-08-04
- 网络出版日期: 2026-10-23

Latent profile analysis and network analysis of symptom among acute stroke patients

ZHU Fengyi^{1, 2, 3},
ZHANG Hui^{1, 2, 3, 4
, ,},
LIU Annuo^{1, 4},
XU Jing¹,
GE Yao¹,
LIU Meng¹

1.
School of Nursing, Anhui Medical University, Hefei, Anhui 230601, China

摘要

摘要: 目的针对急性期脑卒中患者症状存在群体异质性的临床特征，探索急性期脑卒中患者症状的潜在剖面类别，构建各组症状网络，并揭示各组症状网络核心症状。方法便利抽样法选取2024年7月—2025年3月在安徽医科大学第一附属医院神经内科住院的351例急性期脑卒中患者为研究对象，采用一般资料调查问卷、脑卒中症状体验量表进行调查。采用Mplus8.3软件构建潜在剖面模型, R语言构建症状网络并分析中心性指标。结果 351例患者中男性233例、女性118例；年龄为18~92（66.07±12.48）岁。急性期脑卒中患者症状可分为4个潜在类别：低症状负担组（85例，占24.2%）、中症状负担-低认知障碍组（81例，占23.1%）、中症状负担-高认知障碍组（83例，占23.6%）、高症状负担组（102例，占29.1%）。基于症状网络分析，整体样本的核心症状是肢体活动受限；低症状负担组的核心症状是对周围活动没兴趣；中症状负担-低认知障碍组的核心症状是无法保持身体平衡；中症状负担-高认知障碍组和高症状负担组的核心症状是肢体活动受限。结论急性期脑卒中患者的症状困扰明显存在群体异质性，可分为4个类别。医护人员应根据不同的症状类别中的核心症状，制订具有针对性的临床症状管理方案。
- 急性期脑卒中 /
- 潜在剖面 /
- 症状网络分析 /
- 症状管理
Abstract: Objective Aiming at the clinical feature of population heterogeneity in symptoms among patients with acute stroke, thereby exploring the latent profile categories in acute stroke patients, constructing network characteristics, and identifying core symptoms in each group. Methods From July 2024 to March 2025, 351 acute stroke patients hospitalized in the Department of Neurology of the First Affiliated Hospital of Anhui Medical University were selected by convenience sampling method. A general information questionnaire and the stroke symptom experience scale were used for data collection. Mplus 8.3 software was used to construct the latent profile model, and symptom networks were built and centrality indices were analyzed using R programming language. Results A total of 351 patients with complete data were enrolled, including 233 males and 118 females, aged 18-92 years [(66.07±12.48) years]. Symptoms in acute stroke patients could be categorized into four latent categories: low symptom burden group [85 cases (24.2%)], moderate symptom burden-low cognitive impairment group [81 cases (23.1%)], moderate symptom burden-high cognitive impairment group [83 cases (23.6%)], and high symptom burden group [102 cases (29.1%)]. Based on symptom network analysis, the core symptom in the overall sample was limitation of limb movement. The core symptom in the low-symptom burden was lack of interest in surrounding activities. The core symptom in the medium-symptom burden-low cognitive impairment group was the inability to maintain body balance. The core symptom in the medium-symptom burden-high cognitive impairment group and high-symptom burden group was limitation of limb movement. Conclusion Group heterogeneity is evident in symptom distress among acute stroke patients, which can be categorized into 4 categories. Healthcare professionals should develop targeted clinical symptom management strategies based on the core symptoms in different symptom categories.
- Acute stroke /
- Latent profile analysis /
- Network analysis /
- Symptom management

HTML全文

图 1 急性期脑卒中患者的4个潜在类别特征分布

Figure 1. Latent profile plot of four potential categories characteristics in patients with acute stroke

下载: 全尺寸图片幻灯片

图 2 整体样本和4个类别组的症状网络

注：A为整体样本；B为低症状负担组；C为中症状负担-低认知障碍组；D为中症状负担-高认知障碍组；E为高症状负担组。

Figure 2. Symptom networks of the overall sample and four categories

下载: 全尺寸图片幻灯片

图 3 整体样本和4个潜在组的强度指数

Figure 3. Strength indices of the overall sample and four latent categories

下载: 全尺寸图片幻灯片

表 1 急性期脑卒中患者潜在剖面模型拟合信息

Table 1. Latent profile model fitting indicators for acute stroke patients

类别数	AIC	BIC	aBIC	Entropy	LMR (P值)	BLRT (P值)	类别概率
1	14 866.222	14 982.045	14 886.874				1.000
2	13 909.423	14 087.019	13 941.090	0.863	0.000 5	＜0.001	0.558/0.442
3	13 622.305	13 861.674	13 664.987	0.901	0.033 4	＜0.001	0.214/0.481/0.305
4	13 287.407	13 588.548	13 341.103	0.922	0.003 7	＜0.001	0.242/0.231/0.236/0.291
5	13 156.982	13 519.895	13 221.692	0.918	0.760 0	＜0.001	0.202/0.279/0.143/0.211/0.165

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