Symptom Network Evolution and Longitudinal Interrelationships in Early-Stage Lung Cancer Patients Postoperatively.

Objective: To analyze and construct symptom networks at four postoperative time-points in early-stage lung cancer patients using cross-lagged panel networks (CLPN), so as to examine their dynamic evolution and longitudinal predictive relationships. Methods: This prospective study captured the incidence and severity of symptoms in patients by employing the Postoperative Symptom Scale for Lung Cancer on postoperative day 1 (T1), day 3 (T2), day 30 (T3) and day 90 (T4). Partial-correlation networks and CLPN were built in R Language, key nodes were identified using longitudinal data to explore the predictive/reciprocal effects among symptoms. Results: Partial-correlation networks showed that fatigue (D4) and dizziness (D5) had the highest strength centrality at T1, pain (D2) and fatigue (D4) were most central at T2, insomnia (D8) and cough (D1) dominated at T3, and insomnia (D8) alone was most central at T4. Furthermore, CLPN revealed that fatigue (D4) exerted the greatest out-expected influence during T1→T2, with the strongest predictive path from T1-fatigue to T2-insomnia. While pain (D2) became the most influential sender during T2→T3, with the strongest predictive path from T2-pain to T3-shortness of breath. There is a time-dynamic postoperative symptom network in early-stage lung cancer patients, with core symptoms shifting across the recovery trajectory. Conclusion: Findings in our study highlight the adoption of time-specific symptom management, targeting the dominant symptoms at each phase to achieve precision care and optimize patients' postoperative recovery course.