A nomogram model to predict the venous thromboembolism risk after surgery in patients with gynecological tumors

Venous thromboembolism (VTE) is a common post-surgical complication of gynecological malignant tumors that has serious implications on the prognosis and quality-of-life of patients. However, there exists only a few recognized specific evaluation models for the occurrence of VTE after gynecological malignant tumor surgery. We aimed to establish a nomogram model that could predict the probability of post-surgical VTE in patients with gynecological malignancies. We collected the clinical information of 673 patients who underwent surgery for gynecological malignant tumor in our hospital between January 2014 and May 2020. To reduce bias from confounding factors between groups, a 1:1 ratio propensity score matching (PSM) method was performed; meanwhile, univariate and multivariate analyses were applied to analyze the risk factors of VTE after surgeries. A nomogram prediction model was accordingly established and internally validated. The predictors contained in the nomogram model included age, D-dimer value, body mass index (BMI), and surgical approach. The C-index of the model was 0.721 (95% confidence interval: 0.644–0.797), with good discrimination and calibration effect. The internally verified C-index value was 0.916. Decision curve analysis confirmed that the nomogram model was clinically useful when the incidence of thrombosis in patients was 10–75%. Considering the risk factors of VTE after surgery for gynecological malignant tumor, a high-performance nomogram model was established and then validated to provide individual risk assessment and guide treatment decisions. •Using propensity score matching method can better eliminate the interference factors of thrombosis after gynecological tumor surgery.•Age, BMI, D-dimer level and surgical approach are independent risk factors for post-surgical VTE of gynecological malignant tumors.•The establishment of VTE nomogram for gynecological malignant tumors provides clinicians with a more personalized tool for VTE assessment.•The nomogram model has high prediction accuracy by internal validation.