Association between the Cholesterol, High-Density Lipoprotein, and Glucose Index and All-Cause Mortality in Critically Ill Patients with Atherosclerotic Cardiovascular Disease: A machine learning-based retrospective cohort study from the MIMIC-IV database

Background: The cholesterol, high-density lipoprotein, and glucose (CHG) index, as a novel comprehensive marker of lipid and glucose metabolism, has yet to be fully elucidated regarding its prognostic value for all-cause mortality in critically ill ASCVD patients. The present study aims to ascertain the correlation between CHG index and mortality in this population and to identify key predictors using machine learning techniques. Methods: Patients diagnosed with ASCVD were enrolled from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients were divided into five groups based on CHG index values. The association between the CHG index and mortality was evaluated using Kaplan-Meier curves, Cox proportional hazards models, restricted cubic splines (RCS), and subgroup analyses. Ten machine learning models were applied to predict mortality risk, with the SHapley Additive exPlanations (SHAP) method used to identify key predictors. Results: 1,959 patients were involved (median age 71.99 years; 52.8% male). Following multivariate adjustment, a one-unit increase in the CHG index was found to be significantly associated with an elevated mortality risk (30-day HR: 1.58, 95% CI: 1.22–2.05; 90-day HR: 1.61, 95% CI: 1.28–2.03). In comparison with patients in the first quintile, those in the fifth quintile demonstrated the highest mortality risk (30-day HR: 2.10, 95% CI: 1.34–3.29; 90-day HR: 2.07, 95% CI: 1.39–3.07). RCS analysis demonstrated a linear positive association between CHG index and mortality. Among machine learning models, the Stacking Classifier demonstrated the most optimal predictive performance for 30-day mortality, with an Area Under the Curve (AUC) of 0.882 in the training set and 0.833 in the test set. The SHAP analysis identified the CHG index as a key predictor. Conclusions: The elevated CHG index demonstrated a direct correlation with an augmented mortality risk in critically ill ASCVD patients. The CHG index has the potential to be a valuable predictor for mortality risk assessment in this population. Clinical trial number: not applicable.