Integration of Surgical and Coagulation Risk Factors for Predicting Postoperative Pulmonary Embolism in Thoracic...

Postoperative pulmonary embolism (PE) is a severe and potentially fatal complication following thoracic surgery. Existing prediction methods often lack accuracy and timeliness. This study aimed to develop an early and reliable multifactorial prediction model for PE using multicenter data to identify high-risk patients.

We retrospectively analyzed data from 977 patients who underwent pulmonary surgery at three medical centers. Independent risk factors for PE were identified, and a logistic regression model was constructed and validated both internally and externally.

Significant predictors included older age, upper lobe lesions, open thoracic surgery, longer surgical duration, greater intraoperative blood loss, and elevated D-dimer and fibrinogen levels. The model demonstrated excellent discrimination, with AUC values of 0.97, 0.95, and 0.94 in the training, internal validation, and external validation sets, respectively. Calibration curves showed strong consistency between predicted and observed outcomes (p > 0.05). In the external validation cohort, risk stratification based on the 85th percentile of estimated risk effectively distinguished between high-risk and low-risk groups.

This predictive model, integrating surgical and coagulation related factors, shows strong potential for early PE detection and clinical utility. Further prospective studies are warranted to confirm its effectiveness in improving patient outcomes.