已发表论文

探索急性脑卒中患者的精神神经症状群:潜在类别分析

 

Authors Dong X , Yang S, Guo Y, Lv P, Liu Y

Received 26 November 2021

Accepted for publication 10 March 2022

Published 25 March 2022 Volume 2022:15 Pages 789—799

DOI https://doi.org/10.2147/JPR.S350727

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Dr Jonathan Greenberg

Purpose: To identify latent classes of acute stroke patients with distinct experiences with the symptom clusters of depression, anxiety, fatigue, sleep disturbance, and pain symptoms and assess, if the selected variables determine a symptom-cluster experience in acute stroke patients.
Participants and Methods: A sample of 690 participants were collected from July 2020 to December 2020 in a cross-sectional descriptive study. Latent class analysis was conducted to distinguish different clusters of acute stroke participants who experienced five patient-reported symptoms. Furthermore, multinomial logistic regression was selected to verify the influencing indicators of each subgroup, with selected socio-demographic variables, clinical characteristics, self-efficacy, and perceived social support as independent variables and the different latent classes as the dependent variable.
Results: Three latent classes, named “all high symptom,” “high psychological disorder,” and “all low symptom,” were identified, accounting for 9.6%, 26.3%, and 64.1% of symptom clusters, respectively. Patients in the “all high symptom” and “high psychological disorder” classes reported significantly lower quality of life (F=40.21, p < 0.05). Female gender, younger age, higher National Institutes of Health Stroke Scale scores, and lower self-efficacy and perceived social support were risk factors associated with the “high psychological disorder” class. Younger patients with lower self-efficacy and perceived social support were more likely to be in the “all high symptom” class.
Conclusion: This study identified latent classes of acute stroke patients that can be used in predicting symptom-cluster experiences following a stroke. Also, the ability to characterize subgroups of patients with distinct symptom experiences helps identify high-risk patients. Focusing on symptom clusters in clinical practice can inspire us to create effective targeted interventions for subgroups of stroke patients suffering from the same symptom cluster.
Keywords: pain, fatigue, sleep disturbance, anxiety, depression