Machine Learning Prediction of Prolonged Length of Stay in Older Patients with Lung Cancer: A Multicenter Study Using XGBoost with SHAP Interpretation
Abstract
Background Prolonged length of stay (PLOS) is common among older patients with lung cancer and is associated with increased risks of adverse outcomes, greater healthcare resource utilization, and reduced treatment efficiency. However, reliable risk stratification tools specifically designed for this population, particularly in the Chinese healthcare context, remain lacking. Methods This multicenter retrospective study included patients aged 65 years or older with primary lung cancer from two Chinese hospitals. PLOS was defined as a length of stay exceeding the 75th percentile of the overall cohort. After least absolute shrinkage and selection operator‑based feature selection from 34 candidate variables, six machine learning models were developed in the training set and validated using both internal and external validation sets. Model performance was assessed using discrimination, calibration, and clinical utility metrics. The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Results A total of 5,836 patients were included. Among the six models, XGBoost achieved the highest discriminative performance, with area under the receiver operating characteristic curve values of 0.821 (95% CI: 0.806–0.837) in the training set, 0.773 in internal validation, and 0.727 in external validation. Calibration curves and decision curve analysis demonstrated satisfactory agreement and net clinical benefit. SHAP analysis identified pneumonia, hypoproteinemia, C‑reactive protein (CRP), uric acid (UA), and admission pathway as the top five predictors. Pneumonia, hypoproteinemia, CRP, admission pathway, and systolic blood pressure were positively associated with PLOS, whereas UA and total protein showed negative associations. Conclusion The XGBoost model provided robust and generalizable predictions of PLOS in older Chinese patients with lung cancer. Its interpretability may facilitate early identification of high‑risk individuals and support targeted perioperative interventions in geriatric oncology care.