ArticleBMC pulmonary medicine2025
Predicting postoperative pulmonary infection in elderly patients undergoing major surgery: a study based on logistic regression and machine learning models.
Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06491459 (Predicting Postoperative Pulmonary Infection in Elderly Patients Undergoing Major Surgery), which is not on this map. Cited by 3 papers.
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Predicting Postoperative Pulmonary Infection in Elderly Patients Undergoing Major Surgery: a Study Based on Logistic Regression and Machine Learning Models
Who cites it
3 citing papers in PubMed.
- Risk stratification for postoperative complications after CRS and HIPEC in recurrent ovarian cancer patients: a comparative analysis of logistic regression and machine learning models.Scientific reports · 2025Article
- Incidence, risk factors, and predictive modeling of pulmonary infection after high-risk surgery for lung cancer: a retrospective case-control study.Journal of thoracic disease · 2025Article
- Predicting the Risk of Opioid-induced Respiratory Depression Using ChatGPT-4o and Machine Learning Techniques.Archives of academic emergency medicine · 2025Article
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12 authors.
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Abstract
backgroundPostoperative pulmonary infection (POI) is strongly associated with a poor prognosis and has a high incidence in elderly patients undergoing major surgery. Machine learning (ML) algorithms are increasingly being used in medicine, but the predictive role of logistic regression (LR) and ML algorithms for POI in high-risk populations remains unclear.
methodsWe conducted a retrospective cohort study of older adults undergoing major surgery over a period of six years. The included patients were randomly divided into training and validation sets at a ratio of 7:3. The features selected by the least absolute shrinkage and selection operator regression algorithm were used as the input variables of the ML and LR models. The random forest of multiple interpretable methods was used to interpret the ML models.
resultsOf the 9481 older adults in our study, 951 developed POI. Among the different algorithms, LR performed the best with an AUC of 0.80, whereas the decision tree performed the worst with an AUC of 0.75. Furthermore, the LR model outperformed the other ML models in terms of accuracy (88.22%), specificity (90.29%), precision (44.42%), and F1 score (54.25%). Despite employing four interpretable methods for RF analysis, there existed a certain degree of inconsistency in the results. Finally, to facilitate clinical application, we established a web-friendly version of the nomogram based on the LR algorithm; In addition, patients were divided into three significantly distinct risk intervals in predicting POI.
conclusionsCompared with popular ML algorithms, LR was more effective at predicting POI in older patients undergoing major surgery. The constructed nomogram could identify high-risk elderly patients and facilitate perioperative management planning.
trial registrationThe study was retrospectively registered (NCT06491459).
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