ArticleJournal of thoracic disease2026
Application of a predictive model for postoperative pulmonary complications in patients with non-small cell lung cancer based on cardiopulmonary exercise testing.
Article in Journal of thoracic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Association of postoperative orthostatic intolerance with early recovery quality and clinical outcomes following thoracoscopic pulmonary resection: a propensity-matched study.Journal of thoracic disease · 2026Article
- Perioperative ΔNLR and ΔPNI independently predict postoperative pulmonary complications in non-small cell lung cancer: a difference analysis-based nomogram.American journal of cancer research · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
Background: In patients with non-small cell lung cancer (NSCLC), postoperative pulmonary complications (PPCs) significantly increase morbidity and healthcare costs. To improve upon models based solely on static variables, this study aimed to develop a preoperative nomogram integrating cardiopulmonary exercise testing (CPET) parameters for predicting PPCs. The primary objective of this study was to develop a nomogram for predicting PPCs in NSCLC patients using preoperative CPET parameters combined with clinical variables, and to validate its discriminatory power and predictive. Methods: Data, including clinical and CPET results, were collected from patients who underwent CPET before video-assisted thoracic surgery (VATS) at the Department of Thoracic Surgery, Xuzhou Central Hospital between August 2019 and November 2023. Independent risk factors for PPCs were identified through univariate and multivariate stepwise logistic regressions, and a nomogram prediction model was constructed. The model's discriminatory power and accuracy were assessed using the concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and area under the curve (AUC) in the validation cohort. Results: Data from 607 patients were used to construct the nomogram, which included age, intraoperative blood loss, chronic obstructive pulmonary disease (COPD), peak oxygen uptake (VO Conclusions: The predictive model for PPCs in patients with NSCLC exhibits strong discrimination and accuracy. It offers valuable support for clinicians in making informed treatment decisions.
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