Arm symptom pattern among breast cancer survivors with and without lymphedema: a contemporaneous network analysis

淋巴水肿 医学 乳腺癌 癌症 物理疗法 外科 肿瘤科 内科学
作者
Aomei Shen,Zhongning Zhang,Jingming Ye,Yinghong Wang,Hongmeng Zhao,Xin Li,Peipei Wu,Wanmin Qiang,Qian Lü
出处
期刊:Oncologist [Wiley]
标识
DOI:10.1093/oncolo/oyae217
摘要

Abstract Background Arm symptoms commonly endure in post-breast cancer period and persist into long-term survivorship. However, a knowledge gap existed regarding the interactions among these symptoms. This study aimed to construct symptom networks and visualize the interrelationships among arm symptoms in breast cancer survivors (BCS) both with and without lymphedema (LE). Patients and Methods We conducted a secondary analysis of 3 cross-sectional studies. All participants underwent arm circumference measurements and symptom assessment. We analyzed 17 symptoms with a prevalence >15%, identifying clusters and covariates through exploratory factor and linear regression analysis. Contemporaneous networks were constructed with centrality indices calculated. Network comparison tests were performed. Results 1116 cases without missing data were analyzed, revealing a 29.84% prevalence of LE. Axillary lymph node dissection [ALND] (vs sentinel lymph node biopsy [SLNB]), longer post-surgery duration, and radiotherapy significantly impacted overall symptom severity (P < .001). “Lymphatic Stasis,” “Nerve Injury,” and “Movement Limitation” symptom clusters were identified. Core symptoms varied: tightness for total sample network, firmness for non-LE network, and tightness for LE network. LE survivors reported more prevalent and severe arm symptoms with stronger network connections than non-LE group (P = .010). No significant differences were observed among different subgroups of covariates (P > .05). Network structures were significantly different between ALND and SLNB groups. Conclusion Our study revealed arm symptoms pattern and interrelationships in BCS. Targeting core symptoms in assessment and intervention might be efficient for arm symptoms management. Future research is warranted to construct dynamic symptom networks in longitudinal data and investigate causal relationships among symptoms.
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