Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases across different glucose metabolic states: an observational cohort study with machine learning.

Abstract

Background: Stress hyperglycemia ratio (SHR) and glycemic variability (GV) reflect acute glucose elevation and fluctuations, which correlate with adverse outcomes in patients with atherosclerotic cardiovascular disease (ASCVD). However, the prognostic significance of combined SHR-GV evaluation for ASCVD mortality remains unclear. This study examines associations of SHR, GV, and their synergistic effects with mortality in patients with ASCVD across different glucose metabolic states, incorporating machine learning (ML) to identify critical risk factors influencing mortality.

Methods: Patients with ASCVD were screened in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and stratified into normal glucose regulation (NGR), pre-diabetes mellitus (Pre-DM), and diabetes mellitus (DM) groups based on glucose metabolic status. The primary endpoint was 28-day mortality, with 90-day mortality as the secondary outcome. SHR and GV levels were categorized into tertiles. Associations with mortality were analyzed using Kaplan-Meier(KM) curves, Cox proportional hazards models, restricted cubic splines (RCS), receiver operating characteristic (ROC) curves, landmark analyses, and subgroup analyses. Five ML algorithms were employed for mortality risk prediction, with SHapley Additive exPlanations (SHAP) applied to identify critical predictors.

Results: A total of 2807 patients were included, with a median age of 71 years, and 58.78% were male. Overall, 483 (23.14%) and 608 (29.13%) patients died within 28 and 90 days of ICU admission, respectively. In NGR and Pre-DM subgroups, combined SHR-GV assessment demonstrated superior predictive performance for 28-day mortality versus SHR alone [NGR: AUC 0.688 (0.636-0.739) vs. 0.623 (0.568-0.679), P = 0.028; Pre-DM: 0.712 (0.659-0.764) vs. 0.639 (0.582-0.696), P = 0.102] and GV alone [NGR: 0.688 vs. 0.578 (0.524-0.633), P < 0.001; Pre-DM: 0.712 vs. 0.593 (0.524-0.652), P < 0.001]. Consistent findings were observed for 90-day mortality prediction. However, in the DM subgroup, combined assessment improved prediction only for 90-day mortality vs. SHR alone [AUC 0.578 (0.541-0.616) vs. 0.560 (0.520-0.599), P = 0.027], without significant advantages in other comparisons.

Conclusions: Combined SHR and GV assessment serves as a critical prognostic tool for ASCVD mortality, providing enhanced predictive accuracy compared to individual metrics, particularly in NGR and Pre-DM patients. This integrated approach could inform personalized glycemic management strategies, potentially improving clinical outcomes.