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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月30日

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  • Cheng-An Lin,
  • Kuan-Lin Sung,
  • Chun Lee,
  • Sheng-Feng Sung

摘要

Background

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.

Methods

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.

Results

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.

Conclusions

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.