Abstract
OBJECTIVE: This study analyzed factors associated with complete cytoreductive surgery and postoperative frailty in advanced ovarian cancer using key neutrophil extracellular trap (NETosis) markers—neutrophil elastase (NE), myeloperoxidase (MPO), and citrullinated histone H3 (Cit-H3). A risk prediction model was developed and validated. METHODS: In this prospective cohort study, 189 advanced ovarian cancer patients (2020–2023) were classified into frail (n=41) and non-frail (n=148) groups based on postoperative status, and all patients were followed up for 2 years. Clinical data were collected, and risk factors for postoperative frailty in advanced ovarian cancer were identified using a machine learning method (LASSO - XGBoost). A nomogram−based prediction model was constructed. Internal validation and decision curve analysis confirmed favorable predictive efficacy and clinical net benefit of the model. RESULTS: Significant differences were found between groups in age, education, marital status, daily activity, nutrition score, State-Trait Anxiety Inventory (STAI), Pittsburgh Sleep Quality Index (PSQI), NE, MPO, and Cit-H3 (P<0. 05). Kaplan-Meier survival curves showed that postoperative frailty was associated with worse prognosis (P<0. 05). Eight common risk factors were identified through overlapping screening by two machine learning methods, LASSO regression and XGBoost. Multivariate Logistic regression confirmed age, STAI, MPO, NE, and Cit-H3 as independent risk factors (all OR>1, P<0. 05), while nutrition score was protective (OR<1, P<0. 05). The constructed nomogram model exhibited good discriminative ability (AUC = 0. 882) and calibration (C-index=0. 856, calibration slope=0. 92). The Hosmer-Lemeshow test indicated good model fit (P = 0. 893), and decision curve analysis demonstrated high net clinical benefit. CONCLUSION: Postoperative frailty in advanced ovarian cancer is associated with a multifactorial profile, primarily driven by age, nutritional status, STAI scores, MPO, NE, and Cit-H3 levels. The nomogram model was constructed based on these factors initially demonstrated favorable predictive efficacy, and it is expected to serve as an auxiliary tool for the early clinical identification of high-risk populations. However, this study is an exploratory single-center, small-sample research, and its conclusions still need to be further verified through external validation studies with multicenter and large-sample designs.