关键词: 脑卒中；症状管理；潜在类别分析；预测指标

Abstract: Objective: To identify the internal characteristics of fatigue related symptom cluster in patients with acute stroke, and to analyze the indicators for patients with high symptoms. Methods: A cross-sectional study was used to investigate the general information, fatigue, pain, sleep status, anxiety and depression, social support, self-efficacy, and quality of life in 660 patients with acute stroke in a tertiary hospital in Zhengzhou City, and the Mplus7.4 software was used to fit the latent class of symptom clusters. Results: The fatigue related symptom cluster of patients with acute stroke were divided into three groups as "high symptom class", "high psychological disorder class" and "low symptom class" according to the symptom characteristics. Patients in the "high symptom class" were characterized by significantly younger age (OR=1.505, P=0.029), lower level of self-efficacy (OR=0.728, P=0.010) and lower level of perceived social support (OR=0.643, P=0.016). Conclusion: The quality of life in patients with high-risk symptoms needs to be improved, and how to improve social support and self-efficacy are the key intervention target to improve the management of fatigue related symptom cluster in patients with acute stroke.

Key words: stroke; symptom management; Latent Class Analysis; predictors