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Machine learning-based prediction models for postoperative pulmonary complications in elderly patients undergoing
Qiang Zhong1, Guiming Huang1, Wen Zhou1
1Ganzhou Hospital-Nanfang Hospital, Southern Medical University (Ganzhou People's Hospital), Ganzhou, Jiangxi, China.
Background:
Postoperative pulmonary complications (PPCs) are common adverse events after abdominal surgery in older adults, but existing risk scores may have limited transportability and clinical interpretability in elderly surgical populations.
Methods:
This retrospective cohort study included 2,456 patients aged >=65 years who underwent abdominal surgery in the development/internal cohort and 542 patients in an independent external-validation cohort. Six algorithms were compared, including logistic regression, random forest, support vector machine, neural network, XGBoost, and LightGBM. Model performance was evaluated using discrimination, calibration, decision curve analysis, and external validation. SHAP analysis was used to support interpretability. Additional revision analyses examined pulmonary-function-test missingness, minor versus major PPCs, PPC co-occurrence patterns, temporal stability, COVID-era effects, and comparator-score performance.
Results:
PPCs occurred in 425 of 2,456 patients (17.3%) in the development/internal cohort and 105 of 542 patients (19.4%) in the external-validation cohort. XGBoost showed the best overall performance, with AUCs of 0.856 (95% CI, 0.811-0.900) in the independent test set and 0.821 (95% CI, 0.781-0.861) in the external-validation cohort. At a 20% risk threshold, the independent-test sensitivity, specificity, PPV, and NPV were 87.5%, 60.9%, 32.0%, and 95.9%, respectively. SHAP analysis identified ASA physical status, COPD, upper abdominal surgery, age, emergency surgery, albumin, and surgical duration as leading contributors. The model separated patients into low-, moderate-, and high-risk groups with observed PPC rates of 6.9%, 24.4%, and 37.6%. Sensitivity analyses supported robustness to pulmonary-function missingness and temporal variation.
Conclusion:
An interpretable gradient-boosting model may support risk-stratified perioperative assessment for elderly patients undergoing abdominal surgery. Prospective multicenter validation is required before routine clinical implementation.