ArticleAnnals of medicine2025
Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study.
Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Impact of surgical approaches on prognosis in elderly patients with T4N0M0 non-small cell lung cancer: a competing risk analysis based on the SEER database.Journal of thoracic disease · 2026Article
- Machine Learning for Predicting Postoperative Complications After Hypospadias Surgery: A 10-Year Single-Center Retrospective Cohort Study.Children (Basel, Switzerland) · 2026Article
- Association of sleep duration and quality with age at natural menopause: results from NHANES 2005-2018.BMC women's health · 2026Observational
- Artificial intelligence-assisted risk prediction of postoperative pulmonary complications in non-small cell lung cancer surgery.Journal of cardiothoracic surgery · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Survival impact of pN2a and pN2b nodal status in stage IIB-IIIB non-small cell lung cancer under the 9Turk gogus kalp damar cerrahisi dergisi · 2026Article
- Association of multi-site microbial features with malignancy risk in pulmonary ground-glass nodules and identification of predictive biomarkers: a prospective multicenter cohort study.Journal of translational medicine · 2025Article
- Development and validation of an interpretable shap-based machine learning model for predicting postoperative complications in laryngeal cancer.BMC surgery · 2025Article
- A machine learning-based predictive model for complication risks in vacuum-assisted breast biopsy.Frontiers in surgery · 2025Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundReducing postoperative cardiovascular and neurological complications (PCNC) during thoracic surgery is the key to improving postoperative survival.
objectiveWe aimed to investigate independent predictors of PCNC, develop machine learning models, and construct a predictive nomogram for PCNC in patients undergoing thoracic surgery for lung cancer.
methodsThis study used data from a previous retrospective study of 16,368 patients with lung cancer (training set: 11,458; validation set: 4,910) with American Standards Association physical statuses I-IV who underwent surgery. Postoperative information was collected from electronic medical records to help build models based on cause-and-effect and statistical data, potentially revealing hidden dependencies between factors and diseases in a big data environment. The optimal model was analyzed and filtered using multiple machine-learning models (Logistic regression, eXtreme Gradient Boosting, Random forest, Light Gradient Boosting Machine and Naïve Bayes). A predictive nomogram was built and receiver operating characteristics were used to assess the validity of the model. The discriminative power and clinical validity were assessed using calibration and decision-making curve analyses.
resultsMultivariate logistic regression analysis revealed that age, surgery duration, intraoperative intercostal nerve block, postoperative patient-controlled analgesia, bronchial blocker use and sufentanil use were independent predictors of PCNC. Random forest was identified as the optimal model with an area under the curve of 0.898 in the training set and 0.752 in the validation set, confirming the excellent prediction accuracy of the nomogram. All the net benefits of the five machine-learning models in the training and validation sets demonstrated excellent clinical applicability, and the calibration curves showed good agreement between the predicted and observed risks.
conclusionThe combination of machine-learning models and nomograms may contribute to the early prediction and reduction in the incidence of PCNC.
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