Development and Validation of a Machine Learning-Based Risk Assessment Tool for In-Hospital Mortality in Elderly Patients with Postoperative Hypoxemia Following Non-Cardiac Surgery

Background/Objectives: Postoperative hypoxemia is a frequent complication after non-cardiac surgery and is correlated with elevated mortality rates in elderly patients. However, a dedicated predictive tool for mortality in this specific patient subgroup remains unavailable. To construct and validate a machine learning (ML) model for predicting in-hospital mortality among elderly adults who develop hypoxemia after non-cardiac surgery. Methods: Data for this retrospective cohort study were obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The study encompassed patients aged 65 years or older who exhibited hypoxemia, defined as a PaO2/FiO2 ratio below 300 mmHg, within the initial 48 h of intensive care unit (ICU) stay. LASSO (Least Absolute Shrinkage and Selection Operator) regression was applied for feature selection, after which six distinct machine learning models and five conventional scoring systems were constructed and evaluated. SHapley Additive exPlanations (SHAP) was employed to improve model interpretability. Results: Out of 6051 eligible patients, 1838 (30.4%) succumbed during hospitalization. The XGBoost algorithm demonstrated superior predictive capability, achieving an area under the curve (AUC) of 0.794, along with a specificity of 0.917, accuracy of 0.769, and positive predictive value of 0.693. Critical predictors identified included administration of vasopressors, advanced age, and the PaO2/FiO2 ratio. Conclusions: The Extreme Gradient Boosting (XGBoost)-driven ML model provides accurate prediction of in-hospital mortality in elderly patients with postoperative hypoxemia after non-cardiac surgery, presenting a valuable instrument for early risk evaluation and potential intervention.