Analysis of post-stroke fatigue development trajectory and influencing factors in patients with cerebral infarction
10.3760/cma.j.cn211501-20241113-03119
- VernacularTitle:脑梗死患者卒中后疲劳发展轨迹及影响因素分析
- Author:
Yanan WEI
1
;
Guiqin ZHAO
Author Information
1. 绍兴市第七人民医院综合科，绍兴 312000
- Publication Type:Journal Article
- Keywords:
Cerebral infarction;
Post-stroke fatigue;
Developmental trajectory;
Influencing factors
- From:
Chinese Journal of Practical Nursing
2025;41(32):2492-2500
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the developmental trajectories of post-stroke fatigue in patients with cerebral infarction and analyse its influencing factors, thereby providing a theoretical basis for clinical intervention measures.Methods:A cross-sectional survey employed convenience sampling to recruit cerebral infarction patients admitted to Shaoxing Seventh People's Hospital from January to December 2023. Data collection occurred at four time points: 1 day prior to discharge (T1), 1 month post-discharge (T2), three months post-discharge (T3), and six months post-discharge (T4). Data collection comprised general information questionnaires, the Fatigue Severity Scale, the Family Care Index Scale, the Post-Traumatic Growth Inventory, and the Disease Uncertainty Scale. Latent class growth models were employed to identify latent trajectories of post-stroke fatigue, with multifactorial logistic regression analysing influencing factors.Results:Data were collected from 260 stroke patients with complete records, comprising 136 males and 124 females aged 40 - 80 years. The incidence of post-stroke fatigue at time points T1 to T4 was 46.92% (122/260), 46.15% (120/260), 45.00% (117/260), and 43.46% (113/260), respectively. The Scores of post-stroke fatigue at time points T1-T4 were (4.56 ± 1.52), (4.25 ± 1.52), (4.18 ± 1.58), (4.09 ± 1.66) respectively, showing an overall slight downward trend in fatigue scores across time points. Results from latent class growth modelling indicated that post-stroke fatigue among cerebral infarction patients could be categorised into three groups: low fatigue (38 cases, C1 group, 14.62%), moderate fatigue decline (136 cases, C2 group, 52.31%), and persistent high fatigue (86 cases, C3 group, 33.08%). Univariate analysis revealed statistically significant differences among the three groups in age, educational attainment, carotid atherosclerosis, smoking status, family functioning, post-traumatic growth, and disease uncertainty ( χ2/ H/ F values ranging from 11.22 to 18.52, all P<0.05). Logistic regression analysis indicated that, with C3 group as reference, age ( OR = 0.478, 95% CI 0.255- 0.894), educational attainment ( OR = 3.147, 95% CI 1.558 - 6.359), carotid atherosclerosis ( OR = 0.260, 95% CI 0.094 - 0.722), smoking ( OR = 0.216, 95% CI 0.072 - 0.650), family functioning ( OR = 0.284, 95% CI 0.133 - 0.607), post-traumatic growth score ( OR = 1.115, 95% CI 1.059- 1.174), and disease uncertainty score ( OR = 0.908, 95% CI 0.873 - 0.946) were all factors influencing C1 group (all P<0.05); post-traumatic growth score ( OR = 1.039, 95% CI 1.010 - 1.068) and disease uncertainty score ( OR = 0.965, 95% CI 0.946 - 0.985) were factors influencing C2 group (both P<0.05). The regression model likelihood ratio ( χ2 = 113.73, P<0.001). Conclusions:Post-stroke fatigue in cerebral infarction patients exhibits three distinct developmental trajectories, reflecting population heterogeneity. Clinical practice should prioritise elderly patients, those with lower educational attainment, carotid atherosclerosis, and smoking habits. Targeted interventions to enhance family functioning, improve post-traumatic growth levels, and reduce disease uncertainty may lower the incidence of post-stroke fatigue.
