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Temporal dynamics of symptom networks during combination therapy in patients with lung cancer: A longitudinal study
Biqin Ye1, Chenxing Zhang2, Yiyang Lin2
1Day Medical Center, Affiliated Hospital of Putian University, Putian, Fujian, China; School of Nursing, Fujian Medical University, Fuzhou, Fujian, China.
Purpose:
Patients with lung cancer receiving combination therapy experience co-occurring symptoms that change across treatment intervals. This study aimed to identify core symptom systems and central symptoms using a hierarchical longitudinal network approach.
Methods:
A prospective longitudinal study was conducted among patients with lung cancer receiving combination therapy at two tertiary hospitals in China. Symptoms were assessed on days 1, 3, 7, 14, and 20 within each treatment interval across four consecutive treatment cycles (20 assessments). Gaussian graphical models were first used to identify symptom systems with consistently high centrality, followed by multilevel vector autoregressive models to examine temporal, contemporaneous, and between-subject symptom associations within these systems.
Results:
Among 243 patients included in the final network analyses, digestive, neuromuscular, and psychological systems showed consistently high centrality and were identified as core symptom systems. Nausea showed the highest contemporaneous centrality (Strength = 1.326) and between-subject expected influence (Expected Influence = 0.922) within the digestive system. In the neuromuscular system, muscle soreness had the highest contemporaneous and between-subject centrality (Strength = 0.523 and 0.658), while numbness showed the strongest outgoing temporal association (OutStrength = 0.199). In the psychological system, hopelessness showed the highest integrated centrality, while helplessness had the highest between-subject expected influence (Expected Influence = 0.847).
Conclusions:
Digestive, neuromuscular, and psychological systems emerged as potentially important symptom domains. The identified central symptoms and temporal associations may inform focused longitudinal symptom assessment and future network-informed supportive care strategies.
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