To identify predictive factors and develop a nomogram to predict the probability of venous thromboembolism for epithelial ovarian cancer patients. Methods: Our study cohort was composed of 208 EOC patients who had received initial treatment in Sun Yat-sen Memorial Hospital from January 2016 to March 2020. Clinicopathological variables predictive of VTE were identified using univariate logistic analysis. A multivariate logistic regression model was used to select the predictive factors used for nomogram. The accuracy of nomogram was evaluated by the Concordance index (C-index), the area under the receiver-operator characteristic (ROC) curve, area under concentration-time curve (AUC) and the calibration curve. Results: Advancing age (hazard ratio [HR], 1.042; 95% confidence interval [CI], 1.000-1.085; P = .048), higher D-dimer level (HR, 1.144; 95%CI, 1.020-1.283; P = .022), lower PR immunohistochemical positive rate (HR, 0.186; 95%CI, 0.034-1.065; P = .059) and higher Ki67 immunohistochemical positive rate (HR, 4.502; 95%CI, 1.637-12.380; P = .004) were found to be independent risk factors for VTE, and were used to construct the nomogram. The C-index for VTE prediction of the nomogram was 0.75. Conclusions: We constructed and validated a nomogram able to quantify the risk of VTE for EOC patients, which can be applied in recognizing EOC patients with high risk of VTE.
Keywords: epithelial ovarian cancer; nomogram; predictive factor; predictive model; venous thromboembolism.