Construction of a nomogram prediction model for deep vein thrombosis in epithelial ovarian cancer.

Objective: To develop and validate a nomogram prediction model for deep vein thrombosis (DVT) in epithelial ovarian cancer (EOC).

Methods: Between May 2021 and May 2024, 429 EOC patients admitted to our hospital were retrospectively identified. The patients were randomly divided into a modeling group and a validation group. Based on whether DVT occurred, the modeling group was classified into a DVT group and a non-DVT group. The influencing factors associated with DVT in EOC were analyzed using multivariable logistic regression. R software was used to construct the nomogram model. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the nomogram. Moreover, the decision curve analysis (DCA) was used to evaluate the clinical utility of the model.

Results: Of 429 patients, 116 developed DVT, with an incidence rate of 27.04%. In the modeling group of 300 patients, 81 developed DVT, with an incidence rate of 27.00%. Multivariate logistic regression showed that age, BMI, hypertriglyceridemia, tumor staging, tumor grade, CA125 level, platelet count (PLT), and fibrinogen level (FIB) were independent risk factors for developing DVT in EOC ( P <0.05). The area under the ROC curve (AUC) for the modeling group was 0.893, and the AUC of the validation group was 0.973. The Hosmer-Lemeshow (H-L) test of the modeling group showed χ 2 =7.324 ( P= 0.722), and the H-L test of the validation group showed χ 2 =7.043 ( P= 0.711), suggesting good calibration. DCA curve showed that the threshold probability was between 0.08 and 0.97, the clinical value of the DVT nomogram model provided a net clinical benefit.

Conclusion: Age, BMI, hypertriglyceridemia, tumor stage, tumor grade, CA125 level, platelet count (PLT), and fibrinogen level (FIB) are significant independent risk factors for EOC patients developing DVT. The nomogram constructed with these factors demonstrates good predictive performance and clinical utility in predicting the risk of DVT in EOC patients.