ArticleJournal of thoracic disease2025
Construction and validation of a risk prediction model for venous thromboembolism post-VATS in simultaneous multicentric primary lung cancers.
Article in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for venous thromboembolism in lung cancer patients after surgery: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- The Caprini score for venous thromboembolism risk assessment: A scoping review of applications, validation, and future directions.Journal of vascular surgery. Venous and lymphatic disorders · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Synchronous multiple primary lung cancers (sMPLCs) represent 0.8% to 20% of new lung cancer diagnoses. Currently, there is a lack of risk prediction models for venous thromboembolism (VTE) after video-assisted thoracoscopic surgery (VATS) in sMPLC patients. This study seeks to create and validate a VTE risk prediction model tailored for sMPLC patients undergoing VATS. Methods: A retrospective cohort analysis was conducted on patients who underwent lung cancer resection from November 2017 to December 2024 using Hospital Information System (HIS), telephone follow-up, and the Questionnaire Star electronic questionnaire. Categorical variables were analyzed using χ Results: The occurrence of VTE post-VATS in patients with sMPLC was associated with age, smoking history, coronary artery disease, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), atherosclerotic plaques in the extremities, surgical method, intraoperative transfusion, Postoperative Caprini score, and the number of primary lesions (P<0.05). The area under the ROC curve was 0.917 [95% confidence interval (CI): 0.894-0.941], with a sensitivity of 0.885 and a specificity of 0.818. The calibration curve demonstrated a good fit between the observed and predicted curves, with a mean absolute error of 0.008. The clinical decision curve analysis indicated that the model offered superior clinical benefits compared to the Caprini score. Conclusions: The prediction model constructed in this study exhibits robust predictive performance, providing a theoretical basis for clinical medical staff to identify high-risk groups of patients with sMPLC who may develop VTE after VATS at an early stage and to facilitate timely interventions.
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