Stable Symptom Clusters and Evolving Symptom Networks in Relation to Chemotherapy Cycles

医学 关系(数据库) 数据挖掘 计算机科学
作者
Sun Young Rha,Jiyeon Lee
出处
期刊:Journal of Pain and Symptom Management [Elsevier BV]
卷期号:61 (3): 544-554 被引量:81
标识
DOI:10.1016/j.jpainsymman.2020.08.008
摘要

Context The existence of stable symptom clusters with variations or changes in cluster membership and the merging of symptom clusters over time urge us to investigate how symptom relationships change over time. Objectives To identify stable symptom clusters and understand networks among symptoms using longitudinal data. Methods Secondary data analysis was conducted using data from a nonblinded randomized clinical trial, which evaluated the effect and feasibility of the developed cancer symptom management system. For the present study, data from all participants of the original trial were analyzed (N = 249). The severity of 20 symptoms was measured before the start of chemotherapy (CTx) and during the initial four cycles of CTx. Symptom clusters were identified using principal component and hierarchical cluster analyses, and network analysis was used to explore the relationships among symptoms. Results Three common symptom clusters were identified. The first cluster consisted of anxiety, depression, sleep disturbance, pain, and dyspnea. Fatigue, difficulty concentrating, and drowsiness formed a second stable cluster throughout the CTx cycles. The third cluster comprised loss of appetite, taste change, nausea, and vomiting. In terms of the symptom networks, close relationships were recognized, irrespective of symptom severity level, between anxiety and depression, fatigue and drowsiness, and loss of appetite and taste change. Fatigue was the most central symptom with the highest strength. Edge thickening after starting CTx demonstrated evolving symptom networks in relation to CTx cycles. Conclusion Stable symptom clusters and evolving networks were identified. The most central symptom was fatigue; however, the paucity of studies that investigated symptom networks and central symptoms calls for further investigations on these phenomena. Identification of central symptoms and underlying mechanisms will guide efficient symptom management. Future studies will need to focus on developing comprehensive interventions for managing symptom clusters or targeting central symptoms.
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