Simultaneous assessment of stress hyperglycemia ratio and glycemic variability to predict mortality in patients with coronary artery disease: a retrospective cohort study from the MIMIC-IV database

Abstract

Background: Stress hyperglycemia and glycemic variability (GV) can reflect dramatic increases and acute fluctuations in blood glucose, which are associated with adverse cardiovascular events. This study aimed to explore whether the combined assessment of the stress hyperglycemia ratio (SHR) and GV provides additional information for prognostic prediction in patients with coronary artery disease (CAD) hospitalized in the intensive care unit (ICU).

Methods: Patients diagnosed with CAD from the Medical Information Mart for Intensive Care-IV database (version 2.2) between 2008 and 2019 were retrospectively included in the analysis. The primary endpoint was 1-year mortality, and the secondary endpoint was in-hospital mortality. Levels of SHR and GV were stratified into tertiles, with the highest tertile classified as high and the lower two tertiles classified as low. The associations of SHR, GV, and their combination with mortality were determined by logistic and Cox regression analyses.

Results: A total of 2789 patients were included, with a mean age of 69.6 years, and 30.1% were female. Overall, 138 (4.9%) patients died in the hospital, and 404 (14.5%) patients died at 1 year. The combination of SHR and GV was superior to SHR (in-hospital mortality: 0.710 vs. 0.689, p = 0.012; 1-year mortality: 0.644 vs. 0.615, p = 0.007) and GV (in-hospital mortality: 0.710 vs. 0.632, p = 0.004; 1-year mortality: 0.644 vs. 0.603, p < 0.001) alone for predicting mortality in the receiver operating characteristic analysis. In addition, nondiabetic patients with high SHR levels and high GV were associated with the greatest risk of both in-hospital mortality (odds ratio [OR] = 10.831, 95% confidence interval [CI] 4.494-26.105) and 1-year mortality (hazard ratio [HR] = 5.830, 95% CI 3.175-10.702). However, in the diabetic population, the highest risk of in-hospital mortality (OR = 4.221, 95% CI 1.542-11.558) and 1-year mortality (HR = 2.013, 95% CI 1.224-3.311) was observed in patients with high SHR levels but low GV.

Conclusions: The simultaneous evaluation of SHR and GV provides more information for risk stratification and prognostic prediction than SHR and GV alone, contributing to developing individualized strategies for glucose management in patients with CAD admitted to the ICU.

Keywords: Coronary artery disease; Glycemic variability; MIMIC-IV database; Mortality; Stress hyperglycemia ratio.

Fig. 1

Flowchart of patient inclusion and exclusion from the MIMIC-IV database. Abbreviations: HbA1c, glycated hemoglobin A1c; ICU, intensive care unit; MIMIC-IV, Medical Information Mart for Intensive Care-IV

Fig. 2

Kaplan-Meier curves of SHR, GV, and their combination for 1-year mortality. (A-C) Overall population; (D-F) patients without diabetes; (G-I) patients with diabetes. Abbreviations: GV, glycemic variability; SHR, stress hyperglycemia ratio

Fig. 3

Multivariable-adjusted restricted cubic spline analyses of SHR and GV for 1-year mortality. Adjusted for covariates as in Table 2. (A and D) Overall population; (B and E) patients without diabetes; (C and F) patients with diabetes. Abbreviations: CI, confidence interval; GV, glycemic variability; HR, hazard ratio; SHR, stress hyperglycemia ratio

Fig. 4

Forest plots for subgroup analyses of (A) SHR and (B) GV with 1-year mortality. Abbreviations: AMI, acute myocardial infarction; CI, confidence interval; HR, hazard ratio; MI, myocardial infarction

Fig. 5

Receiver operating characteristic curves of SHR, GV, and their combination in predicting in-hospital and 1-year mortality. (A) In-hospital mortality; (B) 1-year mortality. Abbreviations: GV, glycemic variability; SHR, stress hyperglycemia ratio