Prediction of risk factors for preoperative deep vein thrombosis in patients with pelvic fracture.

OBJECTIVE

This study aims to develop a preoperative risk assessment tool for deep vein thrombosis (DVT) in pelvic fracture patients, offering evidence-based guidance for surgeons.

METHODS

A cohort of 400 pelvic fracture patients was analyzed. Ten candidate predictors were initially identified via LASSO regression from 25 clinical variables. Four independent risk factors-emergency abdominal surgery, Injury Severity Score (ISS), serum creatinine levels, and aspartate aminotransferase (AST)-were subsequently incorporated into a multivariate logistic regression model. A nomogram was developed using R software, with calibration accuracy assessed via the rms package and clinical utility evaluated through decision curve analysis (DCA) using the ggDCA package.

RESULTS

The final model demonstrated excellent discriminative ability, with area under the curve (AUC) values of 0.88 (95% CI: 0.81-0.93) in the training cohort and 0.88 (95% CI: 0.80-0.95) in the validation cohort. Calibration curves confirmed strong alignment between predicted and observed DVT probabilities, while DCA highlighted the nomogram's clinical applicability across a wide risk threshold range.

CONCLUSION

The validated nomogram provides a reliable preoperative tool for stratifying DVT risk in pelvic fracture patients. By enabling early identification of high-risk individuals, this model supports targeted prophylactic interventions, ultimately enhancing perioperative safety and patient outcomes.