Risk prediction models for venous thromboembolism in lung cancer patients after surgery: a systematic review and meta-analysis
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By
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Tenglu Sun
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Yuanyuan Chen
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Xuli Shang
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Haifang Lin
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Yongxia Wang
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He Wei
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Fei Yang
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May 20, 2026
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Clinical Scorecard: Systematic Review and Meta-Analysis of Venous Thromboembolism Risk Prediction Models in Surgical Lung Cancer Patients
At a Glance
| Category | Detail |
|---|
| Condition | Venous Thromboembolism (VTE) in lung cancer patients post-surgery |
| Key Mechanisms | Surgical trauma, immobility, cancer-related hypercoagulability |
| Target Population | Patients with lung cancer undergoing surgical resection |
| Care Setting | Postoperative care in surgical settings |
Key Highlights
- High risk of bias in all included studies according to PROBAST
- Pooled AUC for validated models was 0.85 (95% CI: 0.78–0.93)
- Significant variability in model discrimination (AUC range: 0.66 to 0.99)
- Common predictors included D-dimer and age
- Existing models not recommended for routine clinical use
Guideline-Based Recommendations
Diagnosis
- Utilize multiple predictors including demographics and laboratory parameters
Management
- Implement timely thromboprophylaxis based on risk assessment
Monitoring & Follow-up
- Personalized monitoring for VTE risk in postoperative patients
Risks
- Postoperative VTE can lead to significant morbidity and mortality
Patient & Prescribing Data
Lung cancer patients undergoing surgery
Current models lack sufficient methodological rigor and clinical applicability
Clinical Best Practices
- Adopt rigorous methodological frameworks in future studies
- Ensure adequate sample sizes and standardized predictor handling
- Conduct multicenter external validation for prediction models
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