Developing a nomogram to predict the risk of deep vein thrombosis progression following lower limb fracture surgery

Venous thromboembolism (VTE) is a growing public health threat whose prevalence imposes a significant burden on patients and the economy. It mostly affects the lower limbs and is more prevalent in patients who have fractures or who are bedridden for long periods of time. Deep vein thrombosis (DVT) is more common and advances more quickly in cases of traumatizing lower extremity fractures. This study aims to develop a nomogram to predict the risk of deep vein thrombosis (DVT) progression following lower limb fracture surgery, providing a theoretical foundation for preoperative prevention. This retrospective study analyzed 500 patients who underwent lower limb fracture surgery at the Third Hospital of Hebei Medical University. The mean age was 54 years, and 65.8% were male. Thrombotic progression occurred in 30.2% of patients. Data collected included demographics, comorbidities, surgical details, and laboratory parameters. Independent risk factors for postoperative DVT progression were identified using logistic regression. A nomogram prediction model was developed and evaluated using the area under the ROC curve (AUC), calibration curves, and decision curve analysis (DCA). Results showed 5 significant (P < .05) independent risk factors for thrombosis progression: age (45-59 years), blood transfusion, short time from fracture to surgery, elevated d-dimer levels, and preoperative thrombosis location (intermuscular vein thrombosis). Using these 5 independent criteria, a risk prediction model for DVT progression in patients following lower extremity fracture surgery exhibited good performance with an AUC value of 0.691 (95% CI: 0.642-0.740). Internal validation of the model revealed that the calibration curves were near the ideal curves. The developed and validated prediction model demonstrated good accuracy in identifying high-risk patients and provided key insights into DVT pathogenesis. This reliable tool informs clinical strategies for targeted interventions, ultimately improving patient outcomes.