ArticleJournal of thoracic disease2024
Risk factor analysis and predictive model development for air leakage after thoracoscopic pulmonary wedge resection.
Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for prolonged air leak after pulmonary surgery: a systematic review and critical appraisal.Frontiers in oncology · 2026Pooled it
- Analysis of risk factors for prolonged postoperative chest tube drainage after uniportal video-assisted thoracoscopic surgery pulmonary resection.Frontiers in surgery · 2026Article
- Bovine pericardial patch for preventing air leak after thoracoscopic-assisted pulmonary wedge resection: a retrospective cohort study with predictive modeling.Frontiers in oncology · 2026Article
- Research advances of tubeless thoracic surgery for pulmonary nodules: current status and future challenges.Frontiers in surgery · 2026Review
- Significance of staple height on air leak after wedge resection for pulmonary malignant tumor.Journal of thoracic disease · 2025Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The rate of postoperative complications in wedge resection is low because it does not involve major structures. However, postoperative air leakage (AL) is common. This research sought to determine the risk factors associated with AL following thoracoscopic pulmonary wedge resection and to create a predictive model for identifying patients suitable for tubeless procedures. Methods: This study included individuals who underwent thoracoscopic pulmonary wedge resection at Fujian Medical University Union Hospital from January 2015 to December 2020. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors and construct relevant models. Concurrent data from two other centers were collected as validation sets for external validation. Results: A total of 2,503 patients meeting the inclusion criteria were included in the study, with an overall incidence of AL at 11.35% (284/2,503). The development dataset included 2,006 cases, and columnar plots were drawn based on the outcomes of the multivariate logistic regression analysis. The final model included age >70, forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) ratio (FEV1%) <80%, nodule size, benignity/malignancy, and pleural adhesions (none, focal, diffuse). In the development dataset, the C-index was 0.829. The external validation set included 497 cases, with a C-index of 0.833. Conclusions: The AL prediction model performed well and may be clinically useful for assessing AL and identifying patients who can benefit from tubeless strategies.
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