ArticleSurgery today2022
A risk model for prolonged air leak after lobectomy using the National Clinical Database in Japan.
Article in Surgery today, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 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
- Postoperative pleurodesis using OK-432 for pulmonary air leak in lung cancer patients with idiopathic interstitial pneumonia: A retrospective study.Surgery today · 2026Article
- Predictors of intraoperative and postoperative air leakage in anatomical pulmonary resection.Surgery today · 2025Article
- Evaluating air leakage from staple line reinforcements in anatomical pulmonary resection (AIRSTOP): a prospective randomized controlled trial protocol.General thoracic and cardiovascular surgery · 2025Article
- Re-appraisal of bronchus-first right upper lobectomy as an alternative routine procedure.Interdisciplinary cardiovascular and thoracic surgery · 2024Article
- Effectiveness of fibrin sealant after pulmonary resection: a propensity score matching analysis.Kardiochirurgia i torakochirurgia polska = Polish journal of cardio-thoracic surgery · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe objective of our study was to develop a clinical prediction model for prolonged air leak (PAL) after lobectomy for lung cancer using preoperative variables in a large patient dataset from the National Clinical Database (NCD) in Japan.
methodsThe preoperative characteristics of 57,532 and 30,967 patients who had undergone standard lobectomy for lung cancer were derived from the 2014 to 2015 and 2016 NCD datasets, respectively. PAL was defined as air leak persisting ≥ 7 days postoperatively or requiring postoperative interventional treatment, such as pleurodesis or reoperation. Risk models were developed from the 2014 to 2015 dataset and validated using the 2016 dataset. When performing model derivation, the least absolute shrinkage and selection operator (LASSO) method were applied for parameter selection.
resultsThe rate of PAL was 4.5% in 2014-2015 and 5.3% in 2016. The age, sex, body mass index, comorbid interstitial pneumonia, smoking habits, forced expiratory volume in 1 s, tumor histology, multiple lung cancer, and tumor location were selected as important variables for PAL. Our risk model for predicting PAL was fair with a concordance index of 0.6895.
conclusionThe LASSO-based risk model for PAL after lobectomy provided important preoperative variables for PAL and risk weighting for each variable.
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