Development and validation of a nomogram to assess postoperative venous thromboembolism risk in patients with stage IA non‐small cell lung cancer

BACKGROUND: Venous thromboembolism (VTE) is a common postoperative complication in patients with lung cancer that seriously affects prognosis and quality of life. At present, the detection rate of patients with early‐stage lung cancer is increasing, but there are few studies on the risk factors for postoperative venous thromboembolism (VTE) in patients with stage IA non‐small cell lung cancer (NSCLC). This study aimed to establish a nomogram for predicting the probability of postoperative VTE risk in patients with stage IA NSCLC. METHODS: The clinical data of 452 patients with stage IA NSCLC from January 2017 to January 2022 in our center were retrospectively analyzed and randomly divided into a training set and a validation set at a ratio of 7:3. Independent risk factors were identified by univariate and multivariate logistic regression analyses, and a nomogram was established based on the results and internally validated. The predictive power of the nomogram was evaluated by receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). RESULTS: The nomogram prediction model included three risk factors: age, preoperative D‐dimer, and intermuscular vein dilatation. The areas under the ROC curve of this predictive model were 0.832 (95% CI: 0.732–0.924) and 0.791 (95% CI: 0.668–0.930) in the training and validation sets, respectively, showing good discriminative power. In addition, the probability of postoperative VTE occurrence predicted by the nomogram was consistent with the actual occurrence probability. In the decision curve, the nomogram model had a better net clinical benefit at a threshold probability of 5%–90%. CONCLUSION: This study is the first to develop a nomogram for predicting the risk of postoperative VTE in patients with stage IA NSCLC; this nomogram can accurately and intuitively evaluate the probability of VTE in these patients and help clinicians make decisions on prevention and treatment.