Nomogram model combined thrombelastography for venous thromboembolism risk in patients undergoing lung cancer surgery.

The aim of this study was to develop a nomogram model in combination with thromboelastography (TEG) to predict the development of venous thromboembolism (VTE) after lung cancer surgery. The data of 502 patients who underwent surgical treatment for lung cancer from December 2020 to December 2022 were retrospectively analyzed. Patients were then randomized into training and validation groups. Univariate and multivariate logistic regression analyses were carried out in the training group and independent risk factors were included in the nomogram to construct risk prediction models. The predictive capability of the model was assessed by the consistency index (C-index), receiver operating characteristic curves (ROC), the calibration plot and decision curve analysis (DCA). The nomogram risk prediction model comprised of the following five independent risk factors: age, operation time, forced expiratory volume in one second and postoperative TEG parameters k value(K) and reaction time(R). The nomogram model demonstrated better predictive power than the modified Caprini model, with the C-index being greater. The calibration curve verified the consistency of nomogram between the two groups. Furthermore, DCA demonstrated the clinical value and potential for practical application of the nomogram. This study is the first to combine TEG and clinical risk factors to construct a nomogram to predict the occurrence of VTE in patients after lung cancer surgery. This model provides a simple and user-friendly method to assess the probability of VTE in postoperative lung cancer patients, enabling clinicians to develop individualized preventive anticoagulation strategies to reduce the incidence of such complications.