ArticleCancers2022
ARDS after Pneumonectomy: How to Prevent It? Development of a Nomogram to Predict the Risk of ARDS after Pneumonectomy for Lung Cancer.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- Neoadjuvant immunochemotherapy and postoperative acute hypoxemic respiratory failure in thoracic surgery: a multicentre cohort study.Annals of medicine · 2026Article
- Will We Need a Novel Heuristic in Resectable Lung Cancer?: A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- The Survival for Pneumonectomy After Neoadjuvant Therapy in Non-small Cell Lung Cancer: A Retrospective Study from the National Cancer Center in China.Annals of surgical oncology · 2026Article
- The Short-Term Impact of Neoadjuvant Chemotherapy on the Outcome of Patients Undergoing Pneumonectomy for Lung Cancer: Is It Acceptable Nowadays?Journal of clinical medicine · 2025Article
- The Actual Role of CPET in Predicting Postoperative Morbidity and Mortality of Patients Undergoing Pneumonectomy.Journal of personalized medicine · 2025Article
- Article
- How General and Inflammatory Status Impacts on the Prognosis of Patients Affected by Lung Cancer: State of the Art.Biomedicines · 2024Review
- Pneumonectomy for Primary Lung Tumors and Pulmonary Metastases: A Comprehensive Study of Postoperative Morbidity, Early Mortality, and Preoperative Clinical Prognostic Factors.Current oncology (Toronto, Ont.) · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: The cause of ARDS after pneumonectomy is still unclear, and the study of risk factors is a subject of debate. (2) Methods: We reviewed a large panel of pre-, peri- and postoperative data of 211 patients who underwent pneumonectomy during the period 2014−2021. Univariable and multivariable logistic regression was used to quantify the association between preoperative parameters and the risk of developing ARDS, in addition to odds ratios and their respective 95% confidence intervals. A backward stepwise selection approach was used to limit the number of variables in the final multivariable model to significant independent predictors of ARDS. A nomogram was constructed based on the results of the final multivariable model, making it possible to estimate the probability of developing ARDS. Statistical significance was defined by a two-tailed p-value < 0.05. (3) Results: Out of 211 patients (13.3%), 28 developed ARDS. In the univariate analysis, increasing age, Charlson Comorbidity Index and ASA scores, DLCO < 75% predicted, preoperative C-reactive protein (CRP), lung perfusion and duration of surgery were associated with ARDS; a significant increase in ARDS was also observed with decreasing VO2max level. Multivariable analysis confirmed the role of ASA score, DLCO < 75% predicted, preoperative C-reactive protein and lung perfusion. Using the nomogram, we classified patients into four classes with rates of ARDS ranking from 2.0% to 34.0%. (4) Conclusions: Classification in four classes of growing risk allows a correct preoperative stratification of these patients in order to quantify the postoperative risk of ARDS and facilitate their global management.
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