Network evolution of core symptoms after lung cancer thoracoscopic surgery:A dynamic network analysis.

To investigate relationships between various symptoms occurring 1–2 and 5–6 days following days after thoracoscopic surgery, to identify core symptoms, and to monitor changes in core symptoms over time following lung cancer thoracoscopic surgery. We evaluated symptoms using the Anderson Symptom Scale (Chinese version) and the Lung Cancer-Specific Symptoms Template in 214 lung cancer patients hospitalized in the Department of Thoracic Surgery of a provincial hospital in Jiangsu Province from March 2023 to September 2023. Data was collected at 1–2 days and 5–6 days postoperatively. Symptom networks were constructed for each time point, and centrality indicators were analyzed to identify core symptoms while controlling for influencing factors. According to the network analysis, fatigue (rs = 26.00、rc = 0.05、rb = 1.02) had the highest strength, closeness, and betweenness in the symptom network 1–2 days after lung cancer surgery. At 5–6 days after surgery, shortness of breath (rs = 27.00) emerged as the symptom with the highest strength, fatigue (rc = 0.04) had the highest closeness, and cough (rb = 1.08) ranked highest in betweenness within the symptom network. Fatigue stands out as the most core symptom in the network 1–2 days after lung cancer surgery. Shortness of breath, fatigue and cough are the most core symptoms in the symptom network 5–6 days after surgery. Therefore, clinical staff can improve the postoperative symptom experience of lung cancer patients by developing symptom management programmes tailored to these core symptoms. • This study used R network analysis to construct symptom networks for different periods after lung cancer surgery. • This study visualises the complex relationship between multiple symptoms after lung cancer surgery and identifies the most central and influential symptoms. • This study explores the dynamic evolution of core symptoms after lung cancer surgery. • The results of this study will provide a research base for accurate and efficient clinical symptom management and the development of symptom networks in oncology. [ABSTRACT FROM AUTHOR]