Machine learning prediction of long-term postoperative pneumonia risk: a retrospective cohort study

Postoperative pneumonia is a significant complication, highlighting a patient's ongoing vulnerability. While traditional tools focus on short-term outcomes, the perioperative period offers a unique "stress test" window to identify high-risk patients. This study developed and validated a machine-learning-based prognostic framework to predict pneumonia risk up to one year after surgery.

This retrospective study examined 11,655 surgical encounters at a tertiary hospital. Multiple machine learning algorithms, including random forest (RF), extreme gradient boosting, support vector machine, multilayer perceptron, and penalized logistic regression, were compared using 5-fold cross-validation. Class imbalance was handled using random oversampling (ROS) and undersampling. Models were tested on a separate set, and Shapley additive explanation (SHAP) analysis identified key predictors to improve clinical understanding.

Postoperative pneumonia occurred in 238 encounters (2.04%) within 365 days, peaking in the second postoperative month. The RF model with ROS (1:4 ratio) achieved the highest performance with an area under the receiver operating characteristic curve of 0.886, sensitivity of 85.4%, specificity of 77.4%, positive predictive value of 7.4%, and negative predictive value of 99.6%. SHAP analysis identified preoperative hemoglobin, European Society of Cardiology surgical risk, age, American Society of Anesthesiologists Physical Status class, and estimated glomerular filtration rate as key predictors of long-term vulnerability.

Machine learning facilitates prognostic stratification of patients at high risk of long-term vulnerability. By functioning as a high-sensitivity secondary screening tool, this model allows clinicians to safely "rule out" low-risk individuals and concentrate intensive surveillance and resources on the high-risk cohort, thereby improving long-term outcomes.