Exploring Psychoneurological Symptom Clusters in Acute Stroke Patients: A Latent Class Analysis

Xiaofang Dong; Sen Yang; Yuanli Guo; Peihua Lv; Yanjin Liu

doi:10.2147/JPR.S350727

Exploring Psychoneurological Symptom Clusters in Acute Stroke Patients: A Latent Class Analysis

J Pain Res. 2022 Mar 25:15:789-799. doi: 10.2147/JPR.S350727. eCollection 2022.

Authors

Xiaofang Dong¹, Sen Yang², Yuanli Guo¹, Peihua Lv¹, Yanjin Liu¹

Affiliations

¹ Neurology Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, People's Republic of China.
² Orthopedics Department, The Seventh People's Hospital of Zhengzhou, Zhengzhou, Henan Province, People's Republic of China.

Abstract

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: anxiety; depression; fatigue; pain; sleep disturbance.