Development and Validation of a Nomogram for Predicting Postoperative Deep Vein Thrombosis Risk in Patients After Laparoscopic Partial Nephrectomy

Objective: To construct a nomogram model for individualized prediction of risk of deep vein thrombosis (DVT) in renal cell carcinoma (RCC) patients after laparoscopic partial nephrectomy (PN). Methods: A retrospective study was conducted on 556 RCC patients admitted to our hospital from January 2015 to January 2025. Patients were randomly divided into a modeling group (n=389) and a validation group (n=167) at a ratio of 7:3. The modeling group was further subdivided into DVT and non-DVT groups based on postoperative DVT occurrence. Clinical data were collected. Logistic regression was used to analyze the influencing factors. R software was used to construct the nomogram. ROC curves were generated to evaluate the discrimination ability of the nomogram. Decision curve analysis (DCA) was performed to assess its clinical utility. Results: Of 556 patients, 100 (17.98%) developed DVT, including 70 (17.99%) in the modeling group. Logistic regression identified age, operation time, time in bed, pneumoperitoneum pressure, duration of indwelling drains, total cholesterol (TC), and D-dimer as significant risk factors (P < 0.05). The area under the curve (AUC) was 0.889 for the modeling group and 0.866 for the validation group. Hosmer-Lemeshow test indicated good calibration (modeling group: χ²=7.120, P=0.714; validation group: χ²=7.058, P=0.724). DCA showed clinical utility for predicted probabilities between 0.08 and 0.96. Conclusion: Age, operation time, time in bed, pneumoperitoneum pressure, duration of indwelling drains, TC, and D-dimer are risk factors for DVT in patients after laparoscopic PN. The constructed nomogram demonstrates good predictive performance and can be used for individualized risk assessment of postoperative DVT in RCC patients.