Objective  To investigate the influencins factors for asymptomatic venous thromboembolism (VTE) in patients with ovarian clear cell carcinoma (OCCC) after primary surgery and to construct a predictive model for postoperative asymptomatic VTE.

Methods  The patients who underwent primary surgical treatment at Zhongnan Hospital of Wuhan University from 2013 to 2023 were enrolled as the research subjects and the clinical data were retrospectively collected. Univariate analysis and multivariate Logistic regression analysis were employed to identify independent influencins factors for postoperative VTE and to construct a nomogram model. The model's discrimination, calibration, and clinical utility were evaluated using, receiver operating characteristic (ROC) curves with area under the curve (AUC), calibration curves and decision curve analysis (DCA). Additionally, multivariable Cox regression was used to assess the impact of VTE on patients' overall survival (OS).

Results  A total of 172 OCCC patients were included. The incidence of postoperative asymptomatic VTE was 26.16%. Multivariable Logistic regression indicated that elevated preoperative D-dimer levels [OR=1.002, 95%CI (1.001, 1.004)] served as an independent risk factor for postoperative asymptomatic VTE. Conversely, higher preoperative hemoglobin levels [OR=0.849, 95%CI (0.767, 0.912)] and prolonged prothrombin time [OR=0.096, 95%CI (0.013, 0.329)] were identified as independent protective factors. The predictive model yielded an AUC of 0.989 [95%CI (0.969, 1.000)]. The calibration curve demonstrated high consistency between the predicted probability and actual risk, while the decision curve showed significant net benefit. Multivariable Cox regression revealed that postoperative asymptomatic VTE was an independent risk factor for OS [adjusted HR=3.770, 95%CI (1.112, 12.821)].

Conclusion  Postoperative asymptomatic VTE significantly impairs the long-term survival of OCCC patients. The predictive model based on preoperative hematological parameters enables individualized risk stratification, facilitating the identification of high-risk populations and guiding early screening and intervention.

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