ArticleJournal of multidisciplinary healthcare2021
A Prediction Model for Postoperative Pulmonary Complication in Pulmonary Function-Impaired Patients Following Lung Resection.
Article in Journal of multidisciplinary healthcare, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 11 citations in OpenAlex.
- A transfer learning-based hybrid deep- and machine-learning regression approach for predicting the postoperative pulmonary function.European radiology experimental · 2026Article
- Effects of prehabilitation on outcomes among patients undergoing surgery for lung cancer: a systematic review and meta-analysis of randomized controlled trials.Journal of thoracic disease · 2026Article
- Association of intraoperative end-tidal CO₂ levels with postoperative pulmonary complications in thoracoscopic lobectomy: a retrospective cohort study.BMC anesthesiology · 2026Observational
- Article
- Evaluating the Role of Advanced Age and Risk Factors in Postoperative Outcomes Following Major Lung Cancer Resection.Thoracic cancer · 2026Article
- Pulmonary Risk Stratification in Open Thoracoabdominal Aortic Aneurysm Repair.Journal of clinical medicine · 2026Article
- Development and validation of an explainable machine learning model for predicting postoperative pulmonary complications after lung cancer surgery: a machine learning study.EClinicalMedicine · 2025Article
- Risk factors for postoperative pulmonary complications in non-adenocarcinoma non-small cell lung cancer patients undergoing surgery after neoadjuvant therapy.Translational lung cancer research · 2025Article
- Prediction models for postoperative pulmonary complications in intensive care unit patients after noncardiac thoracic surgery.BMC pulmonary medicine · 2024Article
- Incidence and risk factors of pulmonary complications after lung cancer surgery: A systematic review and meta-analysis.Heliyon · 2024Article
- Preoperative Physical Dysfunction Characteristics and Influence Factors Among Elderly Patients with Early Lung Cancer: A Latent Class Analysis.Journal of multidisciplinary healthcare · 2024Article
- Prediction of postoperative cardiopulmonary complications after lung resection in a Chinese population: A machine learning-based study.Frontiers in oncology · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMost patients with lung cancer have impaired pulmonary function. Single pulmonary function parameters have been suggested as good indices for predicting postoperative pulmonary complications (PPC). The purpose of this retrospective study was to construct a prediction model, including more than one pulmonary function parameter, for better prediction of PPC in patients with lung cancer and impaired pulmonary function. PATIENTS AND
methodsOur database of patients who underwent lung resection for non-small cell lung cancer was reviewed and those with impaired pulmonary function were enrolled. Clinical data, including PPC, were recorded. Univariate and logistic regression analyses were applied to explore potential predictors and a prediction model constructed based on the results of logistic regression.
resultsPatients with impaired pulmonary function (n = 124) were enrolled. Most patients were male, current smokers, >60 years old, and had adenocarcinoma and mild ventilatory dysfunction or diffusion dysfunction. In univariate analysis, we identified six pulmonary function parameters that differed significantly between the PPC and non-PPC groups. Receiver operating characteristic curves were used to determine the best cutoff values. In logistic regression, only forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC%), peak expiratory flow (PEF%), and post predictive operation (ppo)-FEV1% remained significant. Based on these results, we constructed a prediction model for PPC including FEV1/FVC%, PEF%, and ppo-FEV1%, which had an good diagnostic performance of, with 76.7% sensitivity and 67.6% specificity.
conclusionOur prediction model, including the pulmonary function parameters, FEV1/FVC%, PEF%, and ppo-FEV1%, shows excellent performance for predicting PPC in patients with lung cancer and impaired pulmonary function following resection, and has potential for wide application in clinical practice.
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