Symptoms among patients with breast cancer undergoing endocrine therapy: Application of latent profile analysis and network analysis.

Purpose: To identify the subgroups and symptom networks associated with endocrine therapy in patients with breast cancer.

Methods: This study was a cross-sectional design using convenience sampling to select patients from a tertiary hospital in China. A total of 406 patients were invited to complete demographic and clinical questionnaires and the Chinese Breast Cancer Prevention Trial Symptom Scale was used to assess their symptoms. Latent profile analysis was used to classify the symptom subgroups of patients with breast cancer undergoing endocrine therapy. Univariate and binary multivariate logistic regression analyses were used to analyze the influence of these factors on different subgroups. Network analysis was used to identify the core symptoms of different subgroups.

Results: The analysis revealed two distinct subgroups: low symptom distress (74%; Class 1) and high symptom distress (26%; Class 2). Patients who were older, had a lower monthly family income per capita, and had not received radiotherapy were more likely to belong to Class 2. Furthermore, the network analysis results revealed that "difficulty concentrating" was a core symptom of the entire sample. The core symptom for Class 1 was "lack of energy," and the core symptom for Class 2 was "joint pain."

Conclusion: The symptoms of patients with breast cancer undergoing endocrine therapy were heterogeneous. Different subgroups exhibited different core symptoms. Therefore, targeted interventions should be provided according to the characteristics of the different symptom subgroups and their core symptoms to achieve precise and effective symptom management.