Jiajia Ma, Bei Xue, Zhengmin Zhang, Liping Yao, Xiaoxin Liu
{"title":"Incidence, risk factors, and predictive modeling of pulmonary infection after high-risk surgery for lung cancer: a retrospective case-control study.","authors":"Jiajia Ma, Bei Xue, Zhengmin Zhang, Liping Yao, Xiaoxin Liu","doi":"10.21037/jtd-2024-2276","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The hierarchical operation management system is one of the core medical systems. Graded management based on the degree of surgical risk, difficulty, resource consumption, and ethical risks can help ensure the quality and safety of the surgery. With the progress of medical technology and the continuous development of medical standards, the proportion of lung cancer patients who underwent high-risk surgery was increasing rapidly. The purpose of this study is to explore the incidence, risk factors, and prediction models of pulmonary infection after high-risk surgery for lung cancer based on machine learning algorithms.</p><p><strong>Methods: </strong>This study included individuals who underwent lung cancer high-risk surgery at Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine from January 2021 to December 2023. Five machine learning algorithms including least absolute shrinkage and selection operator (LASSO)-assisted logistic regression (LR), artificial neural network (ANN), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGB) were adopted to explore risk factors and prediction models of pulmonary infection after high-risk surgery for lung cancer.</p><p><strong>Results: </strong>A cohort of 2,650 patients were eligible for the study after application of the exclusion criteria, with an overall incidence of postoperative pulmonary infection at 9.66% (256/2,650). LASSO regression screened out eight characteristic variables including daily smoking, history of diabetes, diffusing capacity of the lung for carbon monoxide percentage of predicted (DLCO%Pred), airway resistance percentage of predicted (Raw%Pred), maximum tumor diameter, perioperative oral nutritional supplements (ONS) supplement, postoperative urinary catheter, and pleural adhesion degree. The risk prediction model of postoperative pulmonary infection was constructed using these eight clinical features. The area under the curve (AUC) range of the five models was 0.893-0.936. The XGB model outperformed the others, with an AUC of 0.936 [95% confidence interval (CI): 0.923-0.949]. The LR model had an AUC of 0.927 (95% CI: 0.921-0.939), second only to the XGB model, which was converted into a nomogram for model visualization.</p><p><strong>Conclusions: </strong>The establishment of a risk prediction model based on machine learning can help clinical nursing staff identify high-risk patients for pulmonary infection after lung cancer high-risk surgery. The nomogram is expected to be an effective tool for nursing staff to manage the risk of pulmonary infection after lung cancer high-risk surgery.</p>","PeriodicalId":17542,"journal":{"name":"Journal of thoracic disease","volume":"17 6","pages":"3702-3715"},"PeriodicalIF":2.1000,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268473/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of thoracic disease","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/jtd-2024-2276","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/6/10 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RESPIRATORY SYSTEM","Score":null,"Total":0}
引用次数: 0
Abstract
Background: The hierarchical operation management system is one of the core medical systems. Graded management based on the degree of surgical risk, difficulty, resource consumption, and ethical risks can help ensure the quality and safety of the surgery. With the progress of medical technology and the continuous development of medical standards, the proportion of lung cancer patients who underwent high-risk surgery was increasing rapidly. The purpose of this study is to explore the incidence, risk factors, and prediction models of pulmonary infection after high-risk surgery for lung cancer based on machine learning algorithms.
Methods: This study included individuals who underwent lung cancer high-risk surgery at Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine from January 2021 to December 2023. Five machine learning algorithms including least absolute shrinkage and selection operator (LASSO)-assisted logistic regression (LR), artificial neural network (ANN), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGB) were adopted to explore risk factors and prediction models of pulmonary infection after high-risk surgery for lung cancer.
Results: A cohort of 2,650 patients were eligible for the study after application of the exclusion criteria, with an overall incidence of postoperative pulmonary infection at 9.66% (256/2,650). LASSO regression screened out eight characteristic variables including daily smoking, history of diabetes, diffusing capacity of the lung for carbon monoxide percentage of predicted (DLCO%Pred), airway resistance percentage of predicted (Raw%Pred), maximum tumor diameter, perioperative oral nutritional supplements (ONS) supplement, postoperative urinary catheter, and pleural adhesion degree. The risk prediction model of postoperative pulmonary infection was constructed using these eight clinical features. The area under the curve (AUC) range of the five models was 0.893-0.936. The XGB model outperformed the others, with an AUC of 0.936 [95% confidence interval (CI): 0.923-0.949]. The LR model had an AUC of 0.927 (95% CI: 0.921-0.939), second only to the XGB model, which was converted into a nomogram for model visualization.
Conclusions: The establishment of a risk prediction model based on machine learning can help clinical nursing staff identify high-risk patients for pulmonary infection after lung cancer high-risk surgery. The nomogram is expected to be an effective tool for nursing staff to manage the risk of pulmonary infection after lung cancer high-risk surgery.
期刊介绍:
The Journal of Thoracic Disease (JTD, J Thorac Dis, pISSN: 2072-1439; eISSN: 2077-6624) was founded in Dec 2009, and indexed in PubMed in Dec 2011 and Science Citation Index SCI in Feb 2013. It is published quarterly (Dec 2009- Dec 2011), bimonthly (Jan 2012 - Dec 2013), monthly (Jan. 2014-) and openly distributed worldwide. JTD received its impact factor of 2.365 for the year 2016. JTD publishes manuscripts that describe new findings and provide current, practical information on the diagnosis and treatment of conditions related to thoracic disease. All the submission and reviewing are conducted electronically so that rapid review is assured.