In-hospital glycemic variability and all-cause mortality among patients hospitalized for acute heart failure

Pre-hospital glycemia as a biomarker for in-hospital all-cause mortality in diabetic patients - a pilot study

BACKGROUND

Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with considerable economic ramifications. While blood glucose management and long-term metabolic target setting for home care and outpatient treatment follow established procedures, the approach for short-term targets during hospitalization varies due to a lack of clinical consensus. Our study aims to elucidate the impact of pre-hospitalization and intra-hospitalization glycemic indexes on in-hospital survival rates in individuals with T2DM, addressing this notable gap in the current literature.

METHODS

In this pilot study involving 120 hospitalized diabetic patients, we used advanced machine learning and classical statistical methods to identify variables for predicting hospitalization outcomes. We first developed a 30-day mortality risk classifier leveraging AdaBoost-FAS, a state-of-the-art ensemble machine learning method for tabular data. We then analyzed the feature relevance to identify the key predictive variables among the glycemic and routine clinical variables the model bases its predictions on. Next, we conducted detailed statistical analyses to shed light on the relationship between such variables and mortality risk. Finally, based on such analyses, we introduced a novel index, the ratio of intra-hospital glycemic variability to pre-hospitalization glycemic mean, to better characterize and stratify the diabetic population.

RESULTS

Our findings underscore the importance of personalized approaches to glycemic management during hospitalization. The introduced index, alongside advanced predictive modeling, provides valuable insights for optimizing patient care. In particular, together with in-hospital glycemic variability, it is able to discriminate between patients with higher and lower mortality rates, highlighting the importance of tightly controlling not only pre-hospital but also in-hospital glycemic levels.

CONCLUSIONS

Despite the pilot nature and modest sample size, this study marks the beginning of exploration into personalized glycemic control for hospitalized patients with T2DM. Pre-hospital blood glucose levels and related variables derived from it can serve as biomarkers for all-cause mortality during hospitalization.

Associations of variability in blood glucose and systolic blood pressure with mortality in patients with coronary artery disease: A retrospective cohort study from the MIMIC-IV database

AIMS

Variability of metabolic parameters, such as glycemic variability (GV) and systolic blood pressure variability (SBPV), are associated with adverse cardiovascular outcomes. However, whether these parameters have additive effects on mortality in patients with coronary artery disease (CAD) hospitalized in the intensive care unit (ICU) remains unclear.

METHODS

We retrospectively enrolled patients with CAD from the Medical Information Mart for Intensive Care-IV database. The highest tertile of variability was defined as high variability. A variability scoring system was established, which assigned 0 points to tertile 1, 1 point to tertile 2, and 2 points to tertile 3 for GV and SBPV.

RESULTS

Among 4237 patients with CAD, 400 patients died in hospital, and 967 patients died during 1-year follow-up. High GV and high SBPV were associated with an increased risk of mortality. The effects of GV and SBPV on in-hospital mortality were partially mediated by ventricular arrhythmias (18.0 % and 6.6 %, respectively). The risk of mortality gradually increased with the number of high-variability parameters and increasing variability scores.

CONCLUSIONS

GV and SBPV have additive effects on the risk of mortality in patients with CAD hospitalized in the ICU. Ventricular arrhythmias partially mediate the effects of GV and SBPV on in-hospital mortality.

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.

Relationship Between Fasting Blood Glucose and Readmission Within 1 Year in Elderly Patients with Heart Failure

INTRODUCTION

Elevated blood glucose has been linked to unfavorable outcomes among individuals with heart failure (HF). Nevertheless, evidence is scarce regarding the association between fasting blood glucose (FBG) levels and the likelihood of readmission within one year for elderly patients. To address this gap, a retrospective cohort study was conducted, integrating electronic health records of restricted health data from PhysioNet.

METHODS

The study focused on HF patients aged 60 years and older, utilizing baseline data, comorbidities, and laboratory test results as covariates. A total of 374 patients were included in the study. The relationship between 1-year readmission rates and various glucose levels was assessed using Kaplan-Meier plots. The analysis employed three multivariate Cox regression models to examine patients with varying glucose levels.

RESULTS

Following adjustments for relevant factors, an association was observed between FBG levels and the rate of readmission in elderly patients with HF (HR=1.0264 [95% CI 0.9994-1.0541]). The diabetes group faced a higher risk of readmission compared to the normal group. However, this difference in outcome events was not statistically significant, with hazard ratios and their corresponding 95% confidence intervals of 1.2134 (0.9811~1.5007), 1.2393 (0.9993~1.5371), and 1.1905 (0.9570~1.4809), respectively. The robustness of the model was further demonstrated through risk models with subgroup analysis, revealing that FBG levels consistently exerted a stable effect on outcome events, unaffected by covariates such as age, gender, body mass index, glomerular filtration rate, and brain natriuretic peptide.

CONCLUSION

These findings suggest a notable association between elevated FBG at the time of initial hospitalization and the likelihood of readmission within one year among elderly patients with HF.

Metformin treatment is associated with improved survival in diabetic patients hospitalized with acute heart failure: A prospective observational study using the Korean acute heart failure registry data

AIMS

Although the hypothesis that metformin is beneficial for patients with diabetes and heart failure (HF) has been steadily raised, there is limited data on metformin use in patients with acute HF. We analyzed the association of metformin on all-cause mortality in hospitalized patients with type 2 diabetes and acute HF.

METHODS

The Korean Acute Heart Failure registry prospectively enrolled patients hospitalized for acute HF from 2011 to 2014. Among this cohort, we analyzed patients with diabetes with baseline estimated glomerular filtration rate (eGFR) of 30 ml/min/1.73 m2 or more. We analyzed the all-cause mortality and re-hospitalization for HF within 1 year after discharge. Inverse probability treatment weighting method was used to adjust baseline differences on metformin treatment.

RESULTS

The study analyzed data from 1,309 patients with HF and diabetes (mean age 69 years, 56 % male). Among them, 613 (47 %) patients were on metformin at admission. During the median follow-up period of 11 months, 132 (19 %) and 74 (12 %) patients not receiving and receiving metformin treatment died, respectively. The mortality rate was lower in metformin users than in non-users (hazard ratio 0.616 [0.464-0.819] P<0.001). After adjustment, metformin was significantly associated with a lower risk for the mortality (hazard ratio 0.677 [0.495-0.928] P=0.015). In subgroup analyses, this association remains significant irrespective of baseline kidney function (eGFR <60 or ≥60 ml/min/1.73 m2, P-for-interaction=0.176) or left ventricular ejection fraction (<40 %, 40-49 %, or ≥50 %, P-for-interaction=0.224).

CONCLUSIONS

Metformin treatment at the time of admission was associated with a lower risk for 1-year mortality in patients with diabetes, hospitalized for acute HF.

Association of glycemic variability with death and severe consciousness disturbance among critically ill patients with cerebrovascular disease: analysis of the MIMIC-IV database

BACKGROUND

The association of glycemic variability with severe consciousness disturbance and in-hospital all-cause mortality in critically ill patients with cerebrovascular disease (CVD) remains unclear, This study aimed to investigate the association of glycemic variability with cognitive impairment and in-hospital death.

METHOD

We extracted all blood glucose measurements of patients diagnosed with CVD from the Medical Information Mart for Intensive Care IV (MIMIC-IV). Glycemic variability was defined as the coefficient of variation (CV), which was determined using the ratio of standard deviation and the mean blood glucose levels. Cox hazard regression models were applied to analyze the link between glycemic variability and outcomes. We also analyzed non-linear relationship between outcome indicators and glycemic variability using restricted cubic spline curves.

RESULTS

The present study included 2967 patients diagnosed with cerebral infarction and 1842 patients diagnosed with non-traumatic cerebral hemorrhage. Log-transformed CV was significantly related to cognitive impairment and in-hospital mortality, as determined by Cox regression. Increasing log-transformed CV was approximately linearly with the risk of cognitive impairment and in-hospital mortality.

CONCLUSION

High glycemic variability was found to be an independent risk factor for severe cognitive decline and in-hospital mortality in critically ill patients with CVD. Our study indicated that enhancing stability of glycemic variability may reduced adverse outcomes in patients with severe CVD.

Impact of early postoperative blood glucose variability on serum endocan level in cardiac surgery patients: a sub study of the ENDOLUNG observational study

BACKGROUND

Early postoperative glycemic variability is associated with worse outcome after cardiac surgery, but the underlying mechanisms remain unknown. This study aimed to describe the relationship between postoperative glycemic variability and endothelial function, as assessed by serum endocan level in cardiac surgery patients.

METHODS

We performed a post hoc analysis of patients included in the single-center observational ENDOLUNG study. Adult patients who underwent planned isolated coronary artery bypass graft surgery were eligible. Postoperative glycemic variability was assessed by calculating the coefficient of variability (CV) of blood glucose measured within 24 (CV24) and 48 (CV48) hours after surgery. Serum endocan level was measured at 24 (Endocan24) and 48 (Endocan48) hours after surgery. Pearson's correlation coefficient with 95% confidence interval (95% CI) was calculated between CV24 and Endocan24, and between CV48 and Endocan48.

RESULTS

Data from 177 patients were analyzed. Median CV24 and CV48 were 18% (range 7 to 39%) and 20% (range 7 to 35%) respectively. Neither CV48 nor CV24 were significantly correlated to Endocan48 and Endocan24 respectively (r (95% CI) = 0.150 (0.001 to 0.290; and r (95% CI) = 0.080 (-0.070 to 0.220), respectively).

CONCLUSIONS

Early postoperative glycemic variability within 48 h after planned cardiac surgery does not appear to be correlated with postoperative serum endocan level.

CLINICAL TRIAL REGISTRATION NUMBER

NCT02542423.

Glycemic variability and in-hospital death of critically ill patients and the role of ventricular arrhythmias

BACKGROUND

Abnormal glycemic variability is common in the intensive care unit (ICU) and is associated with increased in-hospital mortality and major adverse cardiovascular events, but little is known about whether adverse outcomes are partly mediated by ventricular arrhythmias (VA). We aimed to explore the association between glycemic variability and VA in the ICU and whether VA related to glycemic variability mediate the increased risk of in-hospital death.

METHODS

We extracted all measurements of blood glucose during the ICU stay from The Medical Information Mart for Intensive Care IV (MIMIC-IV) database version 2.0. Glycemic variability was expressed by the coefficient of variation (CV), which was calculated by the ratio of standard deviation (SD) and average blood glucose values. The outcomes included the incidence of VA and in-hospital death. The KHB (Karlson, KB & Holm, A) is a method to analyze the mediation effect for nonlinear models, which was used to decompose the total effect of glycemic variability on in-hospital death into a direct and VA-mediated indirect effect.

RESULTS

Finally, 17,756 ICU patients with a median age of 64 years were enrolled; 47.2% of them were male, 64.0% were white, and 17.8% were admitted to the cardiac ICU. The total incidence of VA and in-hospital death were 10.6% and 12.8%, respectively. In the adjusted logistic model, each unit increase in log-transformed CV was associated with a 21% increased risk of VA (OR 1.21, 95% CI: 1.11-1.31) and a 30% increased risk (OR 1.30, 95% CI: 1.20-1.41) of in-hospital death. A total of 3.85% of the effect of glycemic variability on in-hospital death was related to the increased risk of VA.

CONCLUSION

High glycemic variability was an independent risk factor for in-hospital death in ICU patients, and the effect was caused in part by an increased risk of VA.