A clinical risk prediction model for perioperative lower extremity DVT in patients undergoing spinal fracture surgery.

Objective: To develop a perioperative lower-extremity deep vein thrombosis (DVT) risk prediction model for spinal fracture surgery patients using logistic regression, supporting clinical prevention strategies. Methods: Clinical data from 249 patients undergoing spinal fracture surgery (July 2019-October 2024) were retrospectively analyzed. Participants were divided into a model group (n = 166) and a validation group (n = 83) in a 2:1 ratio. Univariate and multivariate logistic regression identified independent risk factors for perioperative DVT, and a predictive model was established. Model fit was evaluated using the Hosmer-Lemeshow test, and predictive performance was assessed via receiver operating characteristic (ROC) curve analysis. Results: Independent risk factors included perioperative blood transfusion, elevated C-reactive protein, D-dimer >500 μg/L, hypertension, age ≥60 years, and prolonged bed rest. The model [P = 1/(1 + e^-Z)] demonstrated a good fit (Hosmer-Lemeshow χ 2 = 12.139, P = 0.807). ROC analysis showed AUC values of 0.75 (95% CI: 0.80-0.92) for the model group and 0.81 (95% CI: 0.64-0.98) for the validation group, indicating robust predictive performance. Conclusion: The identified risk factors are critical predictors of perioperative DVT in spinal fracture patients. The proposed model exhibits strong clinical utility for early risk stratification and intervention guidance.