关键词: 乳腺癌, 症状管理, 潜类别模型, 预测指标, 护理

Abstract:

Objective To identify distinct subgroups of breast cancer patients who were experiencing differential symptom burdens, and to analyze the risk indicators for patients with high symptom burdens. Methods A cross-sectional study design was used, and fatigue, anxiety, depression and sleep disturbance were reported by patients of a tertiary hospital in Shanghai from January 2016 to January 2017. Latent Class Model（LCM） was used to identify the characteristics of potential patient subgroups who were experiencing differential symptom burdens, and Multinominal logistic regression was conducted to identify risk indicators for patients with high-risk symptom burdens. Results 3 latent classes of symptoms were identified, which were named as “high symptom burden class”, “high psychological-related symptom burden class” and “low symptom burden class” according to the symptom characteristics of patients in different classes, accounting for 25. 0%, 19. 7% and 55. 3%, respectively. Compared to patients in the low symptom burden class, patients in the high symptom burden class reported significantly lower quality of life scores（F=55. 499, P<0. 001）. Patients in the “high symptom burden class” were characterized by significantly lower level of exercise self-efficacy（OR=0. 949, P=0. 019）, while patients in the “high psychological-related symptom burden class” were characterized by personality traits of intermediate type（OR=6. 189, P=0. 007）, no breast cancer resection（OR=4. 718, P=0. 020）, and low exercise self-efficacy level（OR=0. 926, P=0. 002）. Conclusion Distinct subgroups of patients with different symptom characters were identified, and quality of life for patients with high-risk symptom burdens were significantly poorer, while exercise self-efficacy can be used as one of the important risk indicators for recognition of high-risk patient groups. For symptom management of breast cancer patients in the future, patients should be assigned into different class memberships, so that subgroups with high symptom burden can be located and person-centered intervention tailored to an individual’s symptom cluster can be offered.

Key words: Breast Cancer, Symptom Management, Latent Class Model, Predictors, Nursing Care