What is the clinical evidence from this study?

Study design: Cohort. Population: Postoperative pulmonary embolism (n=977). Intervention: Multifactorial prediction model (nomogram). Primary outcome: Prediction of postoperative pulmonary embolism (Training set) (AUC 0.97, 95% CI 0.95-0.99).

November 1, 2025Open Access

Integration of Surgical and Coagulation Risk Factors for Predicting Postoperative Pulmonary Embolism in Thoracic Surgery: A Multi-Center Retrospective Study

JLJianfeng LiShanghai Jiao Tong University XWXintian WangArmy Medical University LMLongyong MeiArmy Medical University

Key Result

A multifactorial prediction model integrating surgical and coagulation risk factors accurately predicted postoperative pulmonary embolism in thoracic surgery patients, achieving an AUC of 0.97 in the training set and 0.94 in external validation.

Study Design

Type

Cohort (n=977)

Multicenter

Yes

Structured PICO

Population

977 patients aged 20-75 years who underwent pulmonary surgery (for lung cancer or benign tumors) at three medical centers in China. Key inclusion: postoperative histopathological diagnosis of lung cancer or benign tumors, at least one postoperative contrast-enhanced chest CT or pulmonary angiography within 30 days after surgery. Key exclusion: poor cardiopulmonary function, preoperative PE or DVT, long-term use of anticoagulants/antiplatelets, coexisting coagulation disorders.

Outcome

Postoperative pulmonary embolism (PE) evaluated by contrast-enhanced chest CT or pulmonary angiography within 30 days after surgeryhard clinical

A multifactorial prediction model integrating surgical characteristics and coagulation biomarkers (D-dimer and fibrinogen) accurately predicts the risk of postoperative pulmonary embolism in patients undergoing thoracic surgery.

Main Result

Effect estimate: AUC 0.97 (95% CI 0.95-0.99)

Limitations

Retrospective design may introduce selection bias
Prospective clinical trials are needed to confirm the model's effectiveness in routine practice
Did not incorporate other potential contributors to PE risk, such as genetic predisposition, systemic inflammation, or tumor microenvironment changes
Relies on postoperative day 3 values of coagulation markers for risk assessment, which may delay timely interventions

Abstract

Introduction: 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. Methods: 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. Results: 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. Conclusion: 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.

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