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.

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.

[Effects of socioeconomic status on the distribution of cardiovascular risk factors and clinical treatments of patients with acute myocardial infarction in Beijing].

OBJECTIVE: To evaluate the effects of socioeconomic status on the distribution of cardiovascular risk factors and clinical treatments of patients with acute myocardial infarction in Beijing. METHODS: In Beijing, a prospective, multi-center, registration study was carried out which including 800 patients who were consecutively hospitalized for ST-segment elevation acute myocardial infarction within 24 hours after event attack in 19 different hospitals in Beijing between November, 2005 and December, 2006. Indicators of socioeconomic status included self-reported personal income (< 500, 500-2000, > 2000 RMB/ month), educational attainment (< or = 12 and > 12 years) and status of medical insurance (yes/no). According to categories of education, patients were categorized into two groups of lower socioeconomic status and higher socioeconomic status. Differences of cardiovascular risk factors and clinical treatments were compared across the two groups respectively. RESULTS: Proportion of diabetes and hyperlipidemia in patients with higher socioeconomic status was much higher than that of patients with lower socioeconomic status (P < 0.05, P < 0.01 respectively). Patients with lower socioeconomic status were more likely to be smokers (P < 0.05). The rates of receiving coronary angiography and PTCA were much lower in patients with lower socioeconomic status. Medical insurance and income were the most important two socioeconomic factors determining the use of PTCA. CONCLUSION: Compared to patients with lower socioeconomic status, patients with higher socioeconomic status had higher rates of hyperlipidemia and diabetes but lower smoking rate among cardiovascular risk factors. The rates of receiving interventional therapies were much lower in patients with lower socioeconomic status.