Abstract
BACKGROUND: Postoperative 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. METHODS: We 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. RESULTS: Of 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. CONCLUSIONS: Compared 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 REGISTRATION: The study was retrospectively registered (NCT06491459).