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Exploring Psychoneurological Symptom Clusters in Acute Stroke Patients: A Latent Class Analysis
Xiaofang Dong1, Sen Yang2, Yuanli Guo1
1Neurology Department, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, People's Republic of China.
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.
