Development and validation of a nomogram for predicting venous thromboembolism risk in post-surgery patients with cervical cancer
Postoperative venous thromboembolism (VTE) is a potentially life-threatening complication. This study aimed to develop a predictive model to identify independent risk factors and estimate the likelihood of VTE in patients undergoing surgery for cervical cancer. We conducted a retrospective cohort study involving 1,174 patients who underwent surgery for cervical carcinoma between 2019 and 2022. The cohort was randomly divided into training and validation sets at 7:3. Univariate and multivariate logistic regression analyses were used to determine the independent factors associated with VTE. The results of the multivariate logistic regression were used to construct a nomogram. The nomogram's performance was assessed via the concordance index (C-index) and calibration curve. Additionally, its clinical utility was assessed through decision curve analysis (DCA). The predictive nomogram model included factors such as age, pathology type, FIGO stage, history of chemotherapy, the neutrophil-lymphocyte ratio (NLR), fibrinogen degradation products (FDP), and D-dimer levels. The model demonstrated robust discriminative power, achieving a C-index of 0.854 (95% CI: 0.799-0.909) in the training cohort and 0.757 (95% CI: 0.657-0.857) in the validation cohort. Furthermore, the nomogram showed excellent calibration and clinical utility, as evidenced by the calibration curve and decision curve analysis (DCA) results. We developed a high-performance nomogram that accurately predicts the risk of VTE in cervical cancer patients undergoing surgery, providing valuable guidance for thromboprophylaxis decision-making.