乳腺癌
潜在类模型
肿瘤科
医学
星团(航天器)
内科学
化疗
癌症
统计
计算机科学
数学
程序设计语言
作者
Gee-Chen Chang,Xiaoyuan Lin,Meijiao Qin,Lixia Wang,San-Jun Cai
出处
期刊:Asia-Pacific Journal of Oncology Nursing
[Medknow Publications]
日期:2024-05-01
卷期号:: 100499-100499
标识
DOI:10.1016/j.apjon.2024.100499
摘要
PurposeThis study aims to explore the subgroups and networks of symptom clusters in breast cancer patients undergoing chemotherapy, and to provide effective interventions for the core symptoms.Patients and methodsA cross-sectional survey was conducted at four comprehensive hospitals in Foshan City, China, from August to November 2023. A total of 292 participants completed the Social Determinants of Health Questionnaire, the Numerical Rating Scale (NRS), the Pittsburgh Sleep Quality Index (PSQI), the Chinese version of the Cancer Fatigue Scale (CFS), and the Hospital Anxiety and Depression Scale (HADS). Latent class analysis (LCA) was utilized to distinguish subgroups, and network analysis was utilized to identify core symptoms among different subgroups.ResultsBreast cancer patients undergoing chemotherapy exhibit symptoms were divided into two subgroups: the High Burden Group of Symptoms (72.3%, Class 1) and the Low Burden Group of Symptoms (27.7%, Class 2). Education attainment, work status, family monthly income per capita, and daily sleep duration (hours) were associated with subgroup membership. "Panic feelings" (# HADS-A11) were the core symptom in both the full sample and Class 2, while "tension or pain" (# HADS-A1) was the core symptom in Class 1.ConclusionThe core symptoms of fear, enjoyment, nervousness, and pain varied across subgroups of patients and could inform the current strategies for symptom management in breast cancer chemotherapy patients.
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