Liu Jiarui, Xia Li, Zhe Wang, Zhang Yan, Chang Huixia, Yan Hong, Yan Shurong, Liu Mengzhen
OBJECTIVE: To explore the dynamic evolution of symptom clusters in patients with gynecologic malignancies during the early postoperative period and identify key transition points and influencing factors, providing evidence for precision symptom management in clinical nursing. METHODS: A longitudinal study was conducted among 324 patients using the MDASI-PeriOp-GYN on postoperative days 1 (T1), 5 (T2), and 7 (T3). Exploratory factor analysis identified symptom clusters at each time point, and growth mixture modeling (GMM) was applied to examine trajectory patterns. Latent profile analysis (LPA) and network analysis were performed at T2 to identify patient subgroups, influencing factors, and core symptoms. RESULTS: Five symptom clusters were identified: disease behavior, gastrointestinal, endocrine, neurological, and emotional. The emotional cluster, independent at T1 and T3, merged with the disease behavior cluster at T2. GMM indicated that all clusters declined from T1 to T2, followed by divergence after T2. LPA identified high- and low-symptom subgroups. Patients with ovarian cancer and those with KPS_1 were more likely to belong to the high-symptom group. Network analysis revealed "poor appetite" as the most central symptom at T2. CONCLUSION: Postoperative day 5 (T2) represents a critical transition point in symptom evolution. Ovarian and KPS_1 are at higher risk for severe symptoms, and "poor appetite" plays a key driving role. Targeted assessment and intervention at T2 may reduce symptom burden and improve recovery outcomes in patients with gynecologic malignancies.