Network analysis of multidimensional symptom experience among postoperative esophageal cancer survivors

BACKGROUND: Postoperative symptom burden is considerable and markedly undermines the quality of life of esophageal cancer (EC) survivors. This study aimed to examine symptom clusters and the interrelationships among symptoms in postoperative EC survivors, with the goal of identifying core symptoms. METHODS: A cross-sectional study was conducted using the European Cancer Life Questionnaire and the EC-Specific Supplementary Questionnaire. EC survivors were recruited in Shandong between February 2023 and February 2024. Principal component analysis (PCA) was utilized to identify symptom clusters, while Gaussian graphical network models were used to estimate the network structure. RESULTS: A total of 460 EC survivors were included in the study, revealing three distinct symptom clusters: the reflux-dysphagia cluster, the respiratory-related symptom cluster, and the recovery-fatigue cluster. The final network model demonstrated interconnections among these symptoms. "Fatigue" (FA) exhibited the highest strength centrality, identifying it as the most prominent core symptom in the network. "Emotional functioning" (EF), "Fatigue" (FA), and "cognitive functioning" (CF) ranked highest in terms of bridge strengths. Additionally, the model showed excellent network stability. CONCLUSIONS: EC survivors experienced significant postoperative symptom burden, with symptom network analysis revealing the complex interrelations among postoperative symptoms. This approach also identified core symptoms that play a crucial role in the network. Fatigue emerged as the most influential core symptom, highlighting the significance of targeted interventions to mitigate negative symptom interactions and improve quality of life.