Nomogram prediction for the risk of venous thromboembolism in patients with lung cancer

OBJECTIVE: The aim of this study was to establish a nomogram graph model to accurately predict the venous thromboembolism (VTE) risk probability in the general population with lung cancer. METHODS: Based on data from patients with lung cancer in Chongqing University Cancer Hospital of China, the independent risk factors of VTE were identified by the logistic univariable and multivariable analysis and were integrated to construct a nomogram, which was validated internally. The predictive effectiveness of the nomogram was evaluated by the receiver operating characteristic curve (ROC) and calibration curve. RESULTS: A total of 3398 lung cancer patients were included for analysis. The nomogram incorporated eleven independent VTE risk factors including karnofsky performance scale (KPS), stage of cancer, varicosity, chronic obstructive pulmonary disease (COPD), central venous catheter (CVC), albumin, prothrombin time (PT), leukocyte counts, epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), dexamethasone, and bevacizumab. The C-index of the nomogram model was 0.843 and 0.791 in the training and validation cohort, respectively, demonstrating good discriminative power. The calibration plots of the nomogram revealed excellent agreement between the predicted and actual probabilities. CONCLUSIONS: We established and validated a novel nomogram for predicting the risk of VTE in patients with lung cancer. The nomogram model could precisely estimate the VTE risk of individual lung cancer patients and identify high-risk patients who are in need of a specific anticoagulation treatment strategy.