An Elevated Glycemic Gap is Associated with Adverse Outcomes in Diabetic Patients with Acute Myocardial Infarction

The Interaction and Implication of Stress-Induced Hyperglycemia and Cytokine Release Following Traumatic Injury: A Structured Scoping Review

INTRODUCTION

This is a structured scoping review to assess whether there is a relationship between stress-induced hyperglycemia (SIH), cytokine interactions, and mortality in trauma patients in comparison to non-diabetic normoglycemia [NDN], diabetic normoglycemia [DN], and diabetic hyperglycemia [DH].

METHODS

We conducted a literature search of MEDLINE (PubMed) databases from 2000 to 2022 using a search strategy to identify observational studies. Initially, 2879 articles were retrieved. Of these, 2869 were excluded due to insufficient variables, and non-trauma focuses.

RESULTS

Nine studies on the interaction between SIH and proinflammatory cytokines were analyzed. SIH was associated with the highest mortality rate (21.3%), followed by DH (5.4%), DN (2.8%), and NDN (2.3%) (p < 0.001). Furthermore, SIH patients exhibited an 11.28-fold higher likelihood of mortality compared to NDN patients (95% CI [9.13-13.93]; p < 0.001) and a 4.72-fold higher likelihood compared to DH patients (OR 4.72; 95% CI [3.55-6.27]; p < 0.001).

CONCLUSIONS

SIH patients had elevated IL-6 concentrations relative to NDN, DN, and DH patients. SIH is linked to higher mortality in trauma, with greater odds than NDN. However, the robustness of this association is still being determined due to statistical and clinical variability. Uncertainties about injury severity and IL-6 level similarities between SIH and DH patients require further investigation.

Evaluating HbA1c-to-average glucose conversion with patient-specific kinetic models for diverse populations

The discrepancy between estimated glycemia from HbA1c values and actual average glucose (AG) levels has significant implications for treatment decisions and patient understanding. Factors contributing to the gap include red blood cell (RBC) lifespan and glucose uptake into the RBC. Personalized models have been proposed to enhance AG prediction accuracy by considering interpersonal variation. This study contributes to our understanding of personalized models for estimating AG from HbA1c. Utilizing data from seven studies (340 participants), including Hispanic/Latino populations with or at risk of non-insulin-treated type 2 diabetes (T2D), we examined kinetic features across cohorts. Additionally, the study simulated scenarios to understand data requirements for improving accuracy. Personalized approaches improved agreement between AG estimations and CGM-AG, particularly with four or more weeks of training CGM data. A multiple linear regression model using kinetic parameters and added clinical features was shown to improve the accuracy of personalized models further. As CGM usage extends beyond type 1 diabetes, there is growing interest in leveraging CGM data for clinical decision-making. Patient-specific models offer a valuable tool for managing glycemic status in patients with discordant HbA1c and AG values.

Association of acute glycemic parameters at admission with cardiovascular mortality in the oldest old with acute myocardial infarction

OBJECTIVES

Stress-related glycemic indicators, including admission blood glucose (ABG), stress-hyperglycemia ratio (SHR), and glycemic gap (GG), have been associated with worse outcomes after acute myocardial infarction (AMI). However, data regarding their prognostic value in the oldest old with AMI are unavailable. Therefore, this study aimed to investigate the association of stress-related glycemic indicators with short- and long-term cardiovascular mortality (CVM) in the oldest old (≥ 80 years) with AMI.

METHODS

In this prospective study, a total of 933 consecutive old patients with AMI admitted to FuWai hospital (Beijing, China) were enrolled. On admission, ABG, SHR, and GG were assessed and all participants were classified according to their quartiles. Kaplan-Meier, restricted cubic splines (RCS), and multivariate Cox regression analyses were performed to evaluate the association between these glycemic indicators and CVM within 30 days and long-term follow-up.

RESULTS

During an average of 1954 patient-years of follow-up, a total of 250 cardiovascular deaths were recorded. Kaplan-Meier analyses showed the lowest CVM in quartile 1 of ABG and in quartile 2 of SHR and GG. After adjusting for potential covariates, patients in quartile 4 of ABG, SHR, and GG had a respective 1.67-fold (95% CI: 1.03-2.69; P = 0.036), 1.80-fold (95% CI: 1.16-2.79; P = 0.009), and 1.78-fold (95% CI: 1.14-2.79; P = 0.011) higher risk of long-term CVM risk compared to those in the reference groups (quartile 1 of ABG and quartile 2 of SHR and GG). Furthermore, RCS suggested a J-shaped relationship of ABG and a U-shaped association of SHR and GG with long-term CVM. Additionally, we observed similar associations of these acute glycemic parameters with 30-day CVM.

CONCLUSIONS

Our data first indicated that SHR and GG consistently had a U-shaped association with both 30-day and long-term CVM among the oldest old with AMI, suggesting that they may be useful for risk stratification in this special population.

Stress hyperglycemia and risk of adverse outcomes in patients with acute ischemic stroke: a systematic review and dose-response meta-analysis of cohort studies

Background

Stroke represents a prominent global health issue, exhibiting the third highest incidence of disability and a significant burden on both healthcare and the economy. Stress hyperglycemia, an acute reaction of the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system, leading to adverse outcomes and mortality. Several previous studies have indicated that stress hyperglycemia, as evaluated by the stress hyperglycemia ratio (SHR), significantly increases the risk of adverse outcomes and mortality in stroke patients. However, there is a lack of further investigation into the influence of dynamic changes in stress hyperglycemia on the clinical outcomes of acute ischemic stroke (AIS) patients. Consequently, we performed a meticulous analysis, considering dose-response relationships from existing studies, to ascertain the correlation between dynamic changes in stress hyperglycemia and the susceptibility to adverse outcomes in patients with AIS.

Methods

This investigation was prospectively registered in PROSPERO and adhered to the PRISMA guidelines. A comprehensive search was performed across English and Chinese databases. A two-sided random-effects model was employed to consolidate the odds ratios (ORs) of the highest vs. lowest categories of SHR. Restricted cubic spline (RCS) models were employed to estimate potential non-linear trends between SHR and the risk of adverse outcomes in AIS patients. Egger's test was utilized to assess publication bias. Heterogeneity was evaluated using Cochran's Q-test. The Newcastle-Ottawa Scale (NOS) tool was employed to evaluate the risk of bias of the included studies.

Results

The final analysis incorporated a total of thirteen studies, which were published between 2019 and 2023, encompassing a participant cohort of 184,179 individuals. The SHR exhibited a significant association with the risk of various adverse outcomes. Specifically, a higher SHR was correlated with a 2.64-fold increased risk of 3-month poor functional outcomes (OR: 2.64, 95% CI 2.05-3.41, I2 = 52.3%, P < 0.001), a 3.11-fold increased risk of 3-month mortality (OR: 3.11, 95% CI 2.10-4.59, I2 = 38.6%, P < 0.001), a 2.80-fold increased risk of 1-year mortality (OR: 2.80, 95% CI 1.81-4.31, I2 = 88%, P < 0.001), a 3.90-fold increased risk of intracerebral hemorrhage (ICH) and 4.57-fold increased risk of symptomatic ICH (sICH) (ICH-OR: 3.90, 95% CI 1.52-10.02, I2 = 84.3%, P = 0.005; sICH-OR: 4.57, 95% CI 2.05-10.10, I2 = 47.3%, P < 0.001), a 1.73-fold increased risk of neurological deficits (OR: 1.73, 95 CI 1.44-2.08, I2 = 0%, P < 0.001), and a 2.84-fold increased risk of stroke recurrence (OR: 2.84, 95 CI 1.48-5.45, I2 = 50.3%, P = 0.002). It is noteworthy that, except for hemorrhagic transformation (HT) and stroke recurrence, the remaining adverse outcomes exhibited a "J-shaped" non-linear dose-response relationship.

Conclusion

In summary, our findings collectively suggest that increased exposure to elevated SHR is robustly linked to a heightened risk of adverse outcomes and mortality in individuals with AIS, exhibiting a non-linear dose-response relationship. These results underscore the significance of SHR as a predictive factor for stroke prognosis. Therefore, further investigations are warranted to explore the role of SHR in relation to adverse outcomes in stroke patients from diverse ethnic populations. Furthermore, there is a need to explore the potential benefits of stress hyperglycemia control in alleviating the physical health burdens associated with AIS. Maintaining a lower SHR level may potentially reduce the risk of adverse stroke outcomes.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier: CRD42023424852.

Association between diabetes and stress-induced hyperglycemia with skeletal muscle gene expression of INSR in critically ill patients: A prospective cohort study

This study aimed to investigate the association between diabetes and stress-induced hyperglycemia with skeletal muscle gene expression of INSR of critically ill patients. Skeletal muscle biopsies were prospectively taken from the vastus muscle, and the expression level of INSR was analyzed using RT-qPCR. Fifty patients were included from April 2018 to September 2018. No significant differences in skeletal muscle gene expression were found between patients with or without diabetes. Similarly, there were no differences in gene expression between groups according to the presence of hypoglycemia 〈 70 mg/dl or hyperglycemia 〉 140 mg/dl. Patients with glycemic variability ≥ 40 mg/dl exhibited a downregulation of INSR compared to those with glycemic variability < 40 mg/dl (1.3 [0.01-5] vs. 2.1 [0.7 - 3.4] fold-changes, P = 0.045). The same pattern was observed when glycemic gap threshold of 80 mg/dl was used (1.4 [0.25-5] vs 1 [0.01 - 2.3] fold-changes in patients with glycemic gap < 80 mg/dl and glycemic gap ≥ 80 mg/dl respectively, P = 0.015). In conclusion, INSR was downregulated in the skeletal muscle of critically ill patients with stress-induced hyperglycemia.

Predictive value of glycemic gap and stress glycemia ratio among critically ill patients with acute kidney injury: a retrospective analysis of the MIMIC-III database

BACKGROUND AND AIMS

Acute hyperglycemia has been identified as a risk factor for acute kidney injury occurrence and mortality in various diseases. The aim of the current study was to investigate the relationship between stress-induced hyperglycemia and adverse outcomes in critically ill patients with AKI.

METHODS

We extracted clinical data from Multiparameter Intelligent Monitoring in Intensive Care III version 1.4. Blood glucose and glycosylated hemoglobin during the first 24 h of ICU admission were used to calculate glycemic gap and stress hyperglycemia ratio (SHR). The outcomes included ICU mortality and need for renal replacement therapy. The association of the glycemic gap and SHR with outcomes were determined via logistic regression model and receiver-operating curves. The subgroup analysis of patients with and without diabetes was performed separately.

RESULTS

Higher glycemic gap and SHR were observed in patients who had increased need of RRT, higher mortality rates and longer ICU stay. Multivariate analysis demonstrated that higher glycemic gap (OR 1.01, 95%CI 1.00-1.02, P = 0.015), as well as SHR (OR 1.32; 95%CI 1.07-1.64, P = 0.009), were independently associated with ICU mortality after adjusting for potential covariates. In subgroup analysis, the association of glycemic gap and SHR were only significant in the non-diabetic population as for the outcome of ICU mortality (OR 2.25, 95%CI 1.64-3.08, P < 0.001 and OR 1.99; 95%CI 1.46-2.72, P < 0.001, respectively).

CONCLUSIONS

The glycemic gap and SHR might serve as a potential prognostic indicator of ICU mortality in critically ill patients with AKI, especially in the non-diabetic population.

Diabetes associates with mortality in critically ill patients with SARS-CoV-2 pneumonia: No diabetes paradox in COVID-19

Background

Diabetes mellitus (DM) is not associated with increased mortality in critically ill patients, a phenomenon known as the "diabetes paradox". However, DM is a risk factor for increased mortality in patients with COVID-19. This study aims to investigate the association of DM and stress-induced hyperglycemia at intensive care unit (ICU) with mortality in this population.

Methods

This is a retrospective study. Electronic medical records from patients admitted from March 2020 to September 2020 were reviewed. Primary outcome was mortality. Secondary outcomes were ICU and hospital mortality and stay, and need for mechanical ventilation and renal replacement therapy.

Results

187 patients were included. Overall mortality was 43.2%, higher in patients with DM (55.7% vs. 34%; p = 0.007), even after adjustment for age, hypertension, and disease severity. When patients were separated into groups, named normoglycemia (without DM and glycemia ≤140 mg/dL), stress-induced hyperglycemia (without DM and glycemia >140 mg/dL), and DM (previous diagnosis or HbA1c ≥ 6.5%), the mortality rate was 25.8%, 37.3%, and 55.7%, respectively (p = 0.021). Mortality was higher in patients with higher glycemic variability. No statistical difference related to secondary outcomes was observed.

Conclusions

DM, hyperglycemia, and glycemic variability associated with increased mortality in critically ill patients with severe COVID-19, but did not increase the rates of other clinical outcomes. More than stress-induced hyperglycemia, DM was associated with mortality.

Inflammation and vascular dysfunction: The negative synergistic combination of diabetes and COVID-19

AIMS

Several reports indicate that diabetes determines an increased mortality risk in patients with coronavirus disease 19 (COVID-19) and a good glycaemic control appears to be associated with more favourable outcomes. Evidence also supports that COVID-19 pneumonia only accounts for a part of COVID-19 related deaths. This disease is indeed characterised by abnormal inflammatory response and vascular dysfunction, leading to the involvement and failure of different systems, including severe acute respiratory distress syndrome, coagulopathy, myocardial damage and renal failure. Inflammation and vascular dysfunction are also well-known features of hyperglycemia and diabetes, making up the ground for a detrimental synergistic combination that could explain the increased mortality observed in hyperglycaemic patients.

MATERIALS AND METHODS

In this work, we conduct a narrative review on this intriguing connection. Together with this, we also present the clinical characteristics, outcomes, laboratory and histopathological findings related to this topic of a cohort of nearly 1000 subjects with COVID-19 admitted to a third-level Hospital in Milan.

RESULTS

We found an increased mortality in subjects with COVID-19 and diabetes, together with an altered inflammatory profile.

CONCLUSIONS

This may support the hypothesis that diabetes and COVID-19 meet at the crossroads of inflammation and vascular dysfunction. (ClinicalTrials.gov NCT04463849 and NCT04382794).

Factors Associated With Discordant A1C-Estimated and Measured Average Glucose Among Hospitalized Patients With Diabetes

In this retrospective analysis, we explored the correlation between measured average glucose (mAG) and A1C-estimated average glucose (eAG) in hospitalized patients with diabetes and identified factors associated with discordant mAG and eAG at the transition from home to hospital. Having mAG lower than eAG was associated with Black race, other race, increasing length of stay, community hospital setting, surgery, fever, metformin use, certain inpatient diets, home antihyperglycemic treatment, and coded type 1 or type 2 diabetes. Having mAG higher than eAG was associated with certain discharge services (e.g., intensive care unit), higher BMI, hypertension, tachycardia, higher albumin, higher potassium, anemia, inpatient glucocorticoid use, and treatment with home insulin, secretagogues, and glucocorticoids. These factors should be considered when using patients' A1C as an indicator of outpatient glycemic control to determine the inpatient antihyperglycemic regimens.

The relationship between glycated hemoglobin A1c levels and exacerbation status in the patients with chronic obstructive pulmonary disease

OBJECTIVE

This study was performed in Razi Hospital, Rasht, Iran, between March 2016 and August 2018 on a population of chronic obstructive pulmonary disease (COPD) patients (56 as COPD exacerbation group and 56 as COPD stable group). Study variables include age, sex, occupation, body mass index (BMI), cigarette consumption, duration of COPD, annual hospitalization, dyspnea, glycated hemoglobin (HbA1c), FEV1, and FEV1/FVC indices.

RESULT

The mean age of the participants was 63.92 ± 10.75 years. There was a significant difference in the hospitalization between the patients with both exacerbation and normal state of COPD (P ≤ 0.001). HbA1c in the patients with exacerbation of COPD was significantly higher than stable status (P = 0.001). Logistic regression showed that HbA1c levels and hospitalization were predictors of exacerbation of COPD. HbA1c levels were statistically significant in terms of hospitalization in patients with COPD exacerbation. There was a significant difference between the HbA1c levels and MMRC in patients with COPD. The percentage of HbA1c was associated with exacerbation of COPD and HbA1c is a good predictor of disease severity in patients with COPD. It also shows that patients with COPD exacerbation and severe COPD are at the higher risk of hyperglycemia.

Stress hyperglycemia ratio, rather than admission blood glucose, predicts in-hospital mortality and adverse outcomes in moderate-to severe COVID-19 patients, irrespective of pre-existing glycemic status

AIM

To compare admission-blood-glucose (ABG) or stress-hyperglycemia-ratio (SHR) performs better in predicting mortality and worse outcomes in COVID-19 patients with (DM) and without known Type 2 Diabetes Mellitus (UDM).

METHODS

ABG and SHR were tested for 451 patients with moderate-severe COVID-19 infection [DM = 216,47.9%; pre-diabetes = 48,10.6%, UDM = 187,41.4%],who were followed-up to look for in-hospital-mortality (primary outcome) and secondary outcomes (ICU stay or mechanical ventilation, hospital-acquired-sepsis and multiple organ dysfunction syndrome [MODS]). Those with and without SHR ≥ 1.14 were compared; logistic regression was done to identify predictors of outcomes, with subgroup analysis based on pre-existing DM status and COVID-19 severity.

RESULTS

Those who died (n = 131) or developed ≥ 1 secondary outcomes (n = 218) had higher prevalence of SHR ≥ 1.14, ABG ≥ 180 mg/dl and higher median SHR (pall < 0.01). Those with SHR ≥ 1.14 had higher mortality (53.7%), higher incidence of ≥ 1 secondary outcomes (71.3%) irrespective of pre-existing diabetes status. SHR ≥ 1.14, but not ABG ≥ 180 was an independent predictor of mortality in the whole group (OR: 7.81,4.07-14.98), as also the DM (OR:10.51,4.34-25.45) and UDM (5.40 (1.57-18.55) subgroups. SHR ≥ 1.14 [OR: 4.41 (2.49-7.84)] but not ABG ≥ 180 could independently predict secondary outcomes AUROC of SHR in predicting mortality was significantly higher than ABG in all subgroups.

CONCLUSION

SHR better predicts mortality and adverse outcomes than ABG in patients with COVID-19, irrespective of pre-existing chronic glycemic status.

Type 2 Diabetes Mellitus with Tight Glucose Control and Poor Pre-Injury Stair Climbing Capacity May Predict Postoperative Delirium: A Secondary Analysis

(1) Background: Previous evidence demonstrates that tight glycemic control and good physical function could reduce the risk of delirium. This study aimed to investigate whether the occurrence of postoperative delirium (POD) in older hip fracture surgery patients is associated with preoperative glycemic control factors or pre-injury physical performance. (2) Methods: Three-hundred and nine individuals aged over 65 years and scheduled for hip fracture surgery were included at a single center. Glycemic control factors and pre-injury physical performance were assessed preoperatively. The presence of delirium was assessed using the Confusion Assessment Method on postoperative hospitalization days. Univariate and multivariable logistic regression models and a risk prediction model of POD were established. (3) Results: Among the 309 patients, 52 (16.83%) experienced POD during the hospital stay. The numbers of pre-injury physical performance and type 2 diabetes mellitus (T2DM) patients were significantly different in the POD and non-POD groups. The multivariable model showed that development of delirium was significantly explained by preoperative fasting blood glucose (FBG) (OR 0.804, p = 0.004), stair climbing (OR 0.709, p = 0.003), T2DM (odds ratio (OR) 3.654, p = 0.001), and age-adjusted Charlson comorbidity index (ACCI) (OR 1.270, p = 0.038). The area under the receiver operating characteristic curve (AUROC) of the risk prediction model including those covariates was 0.770. (4) Conclusions: More older T2DM patients develop POD after hip fracture surgery than patients without T2DM. A simple assessment of preoperative FBG and pre-injury stair climbing capacity may identify those at high risk for the development of POD. Higher preoperative FBG and good pre-injury stair climbing capacity are protective factors for POD.

Prognostic value of glycemic gap in patients with spontaneous intracerebral hemorrhage

BACKGROUND

Glycemic gap (GG), as a novel biomarker showing the acute glycemic change after the onset of acute illness, has been found to be associated with adverse outcomes in many diseases. This study aimed to explore the prognostic value of GG on long-term outcomes of spontaneous intracerebral hemorrhage (sICH).

METHODS

The current study included 528 patients from a multi-center, prospective, consecutive, observational cohort study. Poor clinical outcome was defined as the modified Rankin Scale ≥ 3. GG was calculated using admission blood glucose minus hemoglobin A1c-derived average blood glucose. Logistic regression analyses were performed to determine the association between GG and poor clinical outcomes at 30-day, 90-day and 1-year.

RESULTS

GG was significantly associated with poor clinical outcomes at 30-day, 90-day, and 1-year (P < 0.05 for all models), where patients with higher GG were more likely to have poor clinical outcome. Restricted cubic splines revealed a positive association between GG and poor clinical outcome. In addition, patients with higher GG were more likely to have a higher 1-year mortality rate. The addition of GG to the intracerebral hemorrhage score improved the discrimination and calibration properties for the prediction of poor clinical outcome.

CONCLUSIONS

GG was independently associated with poor outcomes and may be a valuable prognostic factor in patients with sICH.

Quantitative Analysis of Stress-Induced Hyperglycemia and Intracranial Blood Volumes for Predicting Mortality After Intracerebral Hemorrhage

Stress-induced hyperglycemia (SIH) is a neuroendocrine response to acute illness. Although SIH has an adverse association with intracerebral hemorrhage (ICH), quantitative measures and determinants of SIH are not well delineated. In the present study, we objectively evaluated SIH using glycemic gap (GG) and identified its radiological and clinical determinants, with a 5-year retrospective review of charts of ICH patients. We calculated GG using the regression equation (GG = AG -28.7 × HbA1c + 46.7) and evaluated whether GG is an independent predictor of mortality using a multivariate regression model. Radiological volumes of different intracranial compartments were determined using image segmentation software. We correlated GG with different clinical and radiological parameters using Pearson correlation coefficient (PCC), Spearman's rank correlation (SRC), and Wilcoxon rank sum test. Then, we calculated the value of GG associated with mortality. Out of 328 patients, 238 (73%) survived hospitalization and 90 (27%) expired. GG was found to be an independent predictor of mortality (r=0.008, p=0.04). Additionally, GG was positively correlated with intraparenchymal hemorrhage (IPH) volume (PCC=0.185, p<0.01) and intraventricular hemorrhage (IVH) volume (PCC=0.233, p<0.01) and negatively correlated with cerebrospinal fluid (CSF) volume (PCC=-0.151, p<0.01) and brain tissue volume (PCC=-0.099, p=0.08). GG was positively correlated with patients' ICH score (SRC=0.377, p<0.01), Glasgow Coma Scale (GCS) (PCC=-0.356, p<0.01), hydrocephalus (p<0.01), and IVH in the third ventricle (p<0.01). The univariate logistic regression model identified 30.0 mg/dl as the value of GG (AUC=0.655, p<0.01) that predicted mortality with 52.2% sensitivity and 75.2% specificity and defined SIH. In conclusion, GG independently predicts mortality in ICH patients and positively correlates with IPH and IVH volumes. However, causality between the two is not established and would require specifically designed studies.

Hyperglycemia on Admission Predicts Acute Kidney Failure and Renal Functional Recovery among Inpatients

BACKGROUND

Hyperglycemia is associated with adverse outcomes in hospitalized patients. We aimed to assess the impact of glucose levels upon admission on the subsequent deterioration or improvement of kidney function in inpatients with a focus on diabetes or reduced baseline kidney function as possible modifiers of this effect.

METHODS

Running a retrospective cohort analysis, we compared patients with normal vs. high glucose levels upon admission. We applied multivariable logistic regression models to study the association between baseline glucose levels with subsequent renal and clinical outcomes. Interaction terms were used to study a possible modifier effect of diabetes.

RESULTS

Among 95,556 inpatients (52% males, mean age 61 years), 15,675 (16.5%) had plasma glucose higher than 180 mg/dL, and 72% of them were diabetics. Patients with higher glucose at presentation were older, with a higher proportion of co-morbid conditions. Rates of acute kidney injury (AKI), acute kidney functional recovery (AKR), and mortality were proportional to reduced renal function. AKI, AKR, and mortality were almost doubled in patients with high baseline glucose upon admission. Multivariable analysis with interaction terms demonstrated an increasing adjusted probability of all events as glucose increased, yet this association was observed principally in non-diabetic patients.

CONCLUSIONS

Hyperglycemia is associated with AKI, AKR, and mortality in non-diabetic inpatients in proportion to the severity of their acute illness. This association diminishes in diabetic patients, suggesting a possible impact of treatable and easily reversible renal derangement in this population.

Association Between Glycemic Gap and In-hospital Outcomes in Aneurysmal Subarachnoid Hemorrhage

Introduction: Glycemic gap (GG), as determined by the difference between glucose and the hemoglobin A1c (HbA1c)-derived estimated average glucose (eAG), is associated with poor outcomes in various clinical settings. There is a paucity of data describing GG and outcomes after aneurysmal subarachnoid hemorrhage (aSAH). Our main objectives were to evaluate the association of admission glycemic gap (aGG) with in-hospital mortality and with poor composite outcome and to compare aGG's predictive value to admission serum glucose. Secondary outcomes were the associations between aGG and neurologic complications including vasospasm and delayed cerebral ischemia following aSAH.

Methods: We retrospectively reviewed 119 adult patients with aSAH admitted to a single tertiary care neuroscience ICU. Spearman method was used for correlation for non-normality of data. Area under the curve (AUC) for Receiver Operating Characteristic (ROC) curve was used to estimate prediction accuracy of aGG and admission glucose on outcome measures. Multivariable analyses were conducted to assess the value of aGG in predicting in-hospital poor composite outcome and death.

Results: Elevated aGG at or above 30 mg/dL was identified in 79 (66.4%) of patients. Vasospasm was not associated with the elevated aGG. Admission GG correlated with admission serum glucose (r = 0.94, p < 0.01), lactate (r = 0.41, p < 0.01), procalcitonin (r = 0.38, p < 0.01), and Hunt and Hess score (r = 0.51, p < 0.01), but not with HbA1c (r = 0.02, p = 0.82). Compared to admission glucose, aGG had a statistically significantly improved accuracy in predicting inpatient mortality (AUC mean ± SEM: 0.77 ± 0.05 vs. 0.72 ± 0.06, p = 0.03) and trended toward statistically improved accuracy in predicting poor composite outcome (AUC: 0.69 ± 0.05 vs. 0.66 ± 0.05, p = 0.07). When controlling for aSAH severity, aGG was not independently associated with delayed cerebral ischemia, poor composite outcome, and in-hospital mortality.

Conclusion: Admission GG was not independently associated with in-hospital mortality or poor outcome in a population of aSAH. An aGG ≥30 mg/dL was common in our population, and further study is needed to fully understand the clinical importance of this biomarker.

Optimal glucose, HbA1c, glucose-HbA1c ratio and stress-hyperglycaemia ratio cut-off values for predicting 1-year mortality in diabetic and non-diabetic acute myocardial infarction patients

Background

Stress-induced hyperglycaemia at time of hospital admission has been linked to worse prognosis following acute myocardial infarction (AMI). In addition to glucose, other glucose-related indices, such as HbA1c, glucose-HbA1c ratio (GHR), and stress-hyperglycaemia ratio (SHR) are potential predictors of clinical outcomes following AMI. However, the optimal blood glucose, HbA1c, GHR, and SHR cut-off values for predicting adverse outcomes post-AMI are unknown. As such, we determined the optimal blood glucose, HbA1c, GHR, and SHR cut-off values for predicting 1-year all cause mortality in diabetic and non-diabetic ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) patients.

Methods

We undertook a national, registry-based study of patients with AMI from January 2008 to December 2015. We determined the optimal blood glucose, HbA1c, GHR, and SHR cut-off values using the Youden’s formula for 1-year all-cause mortality. We subsequently analyzed the sensitivity, specificity, positive and negative predictive values of the cut-off values in the diabetic and non-diabetic subgroups, stratified by the type of AMI.

Results

There were 5841 STEMI and 4105 NSTEMI in the study. In STEMI patients, glucose, GHR, and SHR were independent predictors of 1-year all-cause mortality [glucose: OR 2.19 (95% CI 1.74–2.76); GHR: OR 2.28 (95% CI 1.80–2.89); SHR: OR 2.20 (95% CI 1.73–2.79)]. However, in NSTEMI patients, glucose and HbA1c were independently associated with 1-year all-cause mortality [glucose: OR 1.38 (95% CI 1.01–1.90); HbA1c: OR 2.11 (95% CI 1.15–3.88)]. In diabetic STEMI patients, SHR performed the best in terms of area-under-the-curve (AUC) analysis (glucose: AUC 63.3%, 95% CI 59.5–67.2; GHR 68.8% 95% CI 64.8–72.8; SHR: AUC 69.3%, 95% CI 65.4–73.2). However, in non-diabetic STEMI patients, glucose, GHR, and SHR performed equally well (glucose: AUC 72.0%, 95% CI 67.7–76.3; GHR 71.9% 95% CI 67.7–76.2; SHR: AUC 71.7%, 95% CI 67.4–76.0). In NSTEMI patients, glucose performed better than HbA1c for both diabetic and non-diabetic patients in AUC analysis (For diabetic, glucose: AUC 52.8%, 95% CI 48.1–57.6; HbA1c: AUC 42.5%, 95% CI 37.6–47. For non-diabetic, glucose: AUC 62.0%, 95% CI 54.1–70.0; HbA1c: AUC 51.1%, 95% CI 43.3–58.9). The optimal cut-off values for glucose, GHR, and SHR in STEMI patients were 15.0 mmol/L, 2.11, and 1.68 for diabetic and 10.6 mmol/L, 1.72, and 1.51 for non-diabetic patients respectively. For NSTEMI patients, the optimal glucose values were 10.7 mmol/L for diabetic and 8.1 mmol/L for non-diabetic patients.

Conclusions

SHR was the most consistent independent predictor of 1-year all-cause mortality in both diabetic and non-diabetic STEMI, whereas glucose was the best predictor in NSTEMI patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-021-01395-3.

Glycemic Gap as a Useful Surrogate Marker for Glucose Variability and Progression of Diabetic Retinopathy

(1) Background: Recent studies have reported that the glucose variability (GV), irrespective of glycosylated hemoglobin (HbA1c), could be an additional risk factor for the development of diabetic retinopathy (DR). However, measurements for GV, such as continuous glucose monitoring (CGM) and fasting plasma glucose (FPG) variability, are expensive and time consuming. (2) Methods: This present study aims to explore the correlation between the glycemic gap as a measurement of GV, and DR. In total, 2565 patients were included in this study. We evaluated the effect of the different types of glycemic gaps on DR progression. (3) Results: We found that the area under the curve (AUC) values of both the glycemic gap and negative glycemic gap showed an association with DR progression. (4) Conclusions: On eliminating the possible influences of chronic blood glucose controls, the results show that GV has deleterious effects that are associated with the progression of DR. The glycemic gap is a simple measurement of GV, and the predictive value of the negative glycemic gap in DR progression shows that GV and treatment-related hypoglycemia may cause the development of DR. Individual treatment goals with a reasonable HbA1c and minimal glucose fluctuations may help in preventing DR.

The paradox of the glycemic gap: Does relative hypoglycemia exist in critically ill patients?

BACKGROUND & AIMS

Elevated glycemic gap, as the differences between measured glucose and hemoglobin A1c (HbA1c)-derived average glucose (ADAG) levels, is a marker of stress-induced hyperglycemia and is a predictor of mortality in critically ill patients. Whether low glycemic gaps are associated with outcomes in critically ill patients remains unclear. We investigated the association of different glycemic gaps on mortality in critically ill patients.

METHODS

Totally 935 patients admitted to intensive care units (ICUs) were enrolled retrospectively after the exclusion of patients with absolute hypoglycemia, extreme hyperglycemia, and incomplete glycemic records. Patients were divided into 3 groups according to their glycemic gaps (<-29.7, -29.7-40, ≧40 mg/dL) at the time of ICU admission. The patients were followed for 1 year or until death.

RESULTS

Patients with low glycemic gap (glycemic gap < -29.7 mg/dL), which implied relative hypoglycemia, had lower serum glucose levels, higher HbA1c levels, and greater disease severity. Compared with medium group (glycemic gap -29.7-40 mg/dL), both the low and the high glycemic gap (glycemic gap ≧40 mg/dL) groups had significantly greater 30-day (log-rank p = 0.0464) and 1-year mortality (log-rank p = 0.0016). However, only the low glycemic gap group was independently associated with greater in-hospital mortality after adjusting for comorbidities (adjusted OR 1.78, 95% CI 1.00-3.16, p = 0.048).

CONCLUSION

This study revealed the presence of a U-shaped relationship between the glycemic gap and mortality in critically ill patients. Low glycemic gaps suggested relative hypoglycemia at the time of ICU admission, and were associated independently with greater in-hospital mortality.

The Interaction of Acute and Chronic Glycemia on the Relationship of Hyperglycemia, Hypoglycemia, and Glucose Variability to Mortality in the Critically Ill

OBJECTIVES

To determine the relationship between preadmission glycemia, reflected by hemoglobin A1c level, glucose metrics, and mortality in critically ill patients.

DESIGN

Retrospective cohort investigation.

SETTING

University affiliated adult medical-surgical ICU.

PATIENTS

The investigation included 5,567 critically ill patients with four or more blood glucose tests and hemoglobin A1c level admitted between October 11, 2011 and November 30, 2019. The target blood glucose level was 90-120 mg/dL for patients admitted before September 14, 2014 (n = 1,614) and 80-140 mg/dL or 110-160 mg/dL for patients with hemoglobin A1c less than 7% or greater than or equal to 7% (n = 3,953), respectively, subsequently.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Patients were stratified by hemoglobin A1c: less than 6.5.(n = 4,406), 6.5-7.9% (n = 711), and greater than or equal to 8.0% (n = 450). Increasing hemoglobin A1c levels were associated with significant increases in mean glycemia, glucose variability, as measured by coefficient of variation, and hypoglycemia (p for trend < 0.0001, < 0.0001, and 0.0010, respectively). Among patients with hemoglobin A1c less than 6.5%, mortality increased as mean glycemia increased; however, among patients with hemoglobin A1c greater than or equal to 8.0%, the opposite relationship was observed (p for trend < 0.0001 and 0.0027, respectively). Increasing glucose variability was independently associated with increasing mortality only among patients with hemoglobin A1c less than 6.5%. Hypoglycemia was independently associated with higher mortality among patients with hemoglobin A1c less than 6.5% and 6.5-7.9% but not among those with hemoglobin A1c greater than or equal to 8.0%. Mean blood glucose 140-180 and greater than or equal to 180 mg/dL were independently associated with higher mortality among patients with hemoglobin A1c less than 6.5% (p < 0.0001 for each). Among patients with hemoglobin A1c greater than or equal to 8.0% treated in the second era, mean blood glucose greater than or equal to 180 mg/dL was independently associated with decreased risk of mortality (p = 0.0358).

CONCLUSIONS

Preadmission glycemia, reflected by hemoglobin A1c obtained at the onset of ICU admission, has a significant effect on the relationship of ICU glycemia to mortality. The different responses to increasing mean glycemia support a personalized approach to glucose control practices in the ICU.

Stress Induced Hyperglycemia in the Context of Acute Coronary Syndrome: Definitions, Interventions, and Underlying Mechanisms

Elevation of glucose level in response to acute coronary syndrome (ACS) has been recognized as stress induced hyperglycemia (SIH). Plenty of clinical studies have documented that SIH occurs very common in patients hospitalized with ACS, even in those without previously known diabetes mellitus. The association between elevated blood glucose levels with adverse outcome in the ACS setting is well-established. Yet, the precise definition of SIH in the context of ACS remains controversial, bringing confusions about clinical management strategy. Several randomized trials aimed to evaluate the effect of insulin-based therapy on outcomes of ACS patients failed to demonstrate a consistent benefit of intensive glucose control. Mechanisms underlying detrimental effects of SIH on patients with ACS are undetermined, oxidative stress might play an important role in the upstream pathways leading to subsequent harmful effects on cardiovascular system. This review aims to discuss various definitions of SIH and their values in predicting adverse outcome in the context of ACS, as well as the effect of intensive glucose control on clinical outcome. Finally, a glimpse of the underlying mechanisms is briefly discussed.

Effects of Glycemic Gap on Post-Stroke Cognitive Impairment in Acute Ischemic Stroke Patients

Background: Post-stroke hyperglycemia is a frequent finding in acute ischemic stroke patients and is associated with poor functional and cognitive outcomes. However, it is unclear as to whether the glycemic gap between the admission glucose and HbA1c-derived estimated average glucose (eAG) is associated with post-stroke cognitive impairment (PSCI). Methods: We enrolled acute ischemic stroke patients whose cognitive functions were evaluated three months after a stroke using the Korean version of the vascular cognitive impairment harmonization standards neuropsychological protocol (K-VCIHS-NP). The development of PSCI was defined as having z-scores of less than −2 standard deviations in at least one cognitive domain. The participants were categorized into three groups according to the glycemic gap status: non-elevated (initial glucose − eAG ≤ 0 mg/dL), mildly elevated (0 mg/dL < initial glucose − eAG < 50 mg/dL), and severely elevated (50 mg/dL ≤ initial glucose − eAG). Results: A total of 301 patients were enrolled. The mean age was 63.1 years, and the median National Institute of Health Stroke Scale (NIHSS) score was two (IQR: 1–4). In total, 65 patients (21.6%) developed PSCI. In multiple logistic regression analyses, the severely elevated glycemic gap was a significant predictor for PSCI after adjusting for age, sex, education level, initial stroke severity, Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification, and left hemispheric lesion (aOR: 3.65, p-value = 0.001). Patients in the severely elevated glycemic gap group showed significantly worse performance in the frontal and memory domains. Conclusions: In conclusion, our study demonstrated that an elevated glycemic gap was significantly associated with PSCI three months after a stroke, with preferential involvement of frontal and memory domain dysfunctions.

Glycated Hemoglobin (HbA1c) as a Predictor of Outcomes during Acute Exacerbations of Chronic Obstructive Pulmonary Disease

Systemic inflammation may be the common denominator between COPD and type 2 diabetes and may explain the correlation in both diseases' development and progress. The aim of this prospective observational study is to examine the prognostic value of glycated hemoglobin levels (HbA1c) and HbA1c-adjusted glycemic variables (glycemic gap, stress hyperglycemia ratio και modified stress hyperglycemia ratio) in an acute exacerbation of COPD (AECOPD) as well as in COPD disease's morbidity and mortality during the following year. We evaluated patients hospitalized only for COPD exacerbations. Levels of HbA1c and HbA1c-adjusted glycemic variables were recorded upon admission. The study outcomes included duration of hospital stay, need for mechanical ventilation and exacerbation outcome. All subjects were followed up for one year. A total of 156 patients were included in the study (74.4% men, age [mean ± SD] 72 ± 7 years). Patients (21.8%) had type 2 diabetes and 67.9% of patients were receiving ICS treatment. The median value of HbA1c was 5.9 (IQR: 5.4, 6.5). Necessity for mechanical ventilation was significantly higher for patients with lower values of HbA1c [median: 5.3 (IQR 5.02, 6.3) vs. 5.9 (IQR 5.5, 6.5), p = .038]. However, duration of hospitalization, death during hospitalization as well as the number of new exacerbation events, time to next exacerbation and mortality during the following year did not differ significantly. Moreover, none of the HbA1c-adjusted glycemic variables examined, demonstrated any statistical significance. In conclusion neither the preceding nor the present glycemic state exhibit a predictive value regarding short- or long-term outcomes of an AECOPD.

Glycemia, Beta-Cell Function and Sensitivity to Insulin in Mildly to Critically Ill Covid-19 Patients

Background and objectives: Critically and non-critically ill patients with SARS-CoV-2 infection (Covid-19) may present with higher-than-expected glycemia, even in the absence of diabetes. With this study we aimed to assess glucose, glycemic gap (GlyG) and insulin secretion/sensitivity measures in patients with Covid-19. Materials and Methods: We studied, upon admission, 157 patients with Covid-19 (84: in wards and 73: in intensive care units; ICU); 135 had no history of diabetes. We measured blood glucose upon admission as well as glycated hemoglobin (A1c), plasma insulin and C-peptide. We calculated the GlyG and the Homeostasis Model Assessment 2 (HOMA2) estimates of steady state beta cell function (HOMA2%B) and insulin sensitivity (HOMA2%S). Statistical assessment was done with analysis or the Kruskal-Wallis test. Results: Compared to patients in the wards without diabetes, patients with diabetes in the wards, as well as patients in the ICU (without or with diabetes) had higher admission glycemia. The GlyG was significantly higher in patients without diabetes in the ICU compared to patients without diabetes in the wards, while HOMA2%B based on glucose and insulin was significantly higher in the ICU patients compared to patients in the wards. Of all the parameters, HOMA2%S based on C-peptide/glucose was higher in survivors (n = 133). Conclusions: In our series of patients with Covid-19, a substantial number of patients with and without diabetes had admission hyperglycemia and those who were critically ill may have had compromised insulin secretion and lowered sensitivity to insulin. These findings lend credence to reports of association between Covid-19 and hyperglycemia/secondary diabetes.

Relative Hyperglycemia Is an Independent Determinant of In-Hospital Mortality in Patients With Critical Illness

OBJECTIVES

To determine whether relative hyperglycemia was associated with in-hospital mortality in critically ill patients independent of other prognostic variables and whether this association is affected by background glycemia.

DESIGN

Prospective observational study.

SETTING

Mixed medical-surgical ICU in a metropolitan teaching hospital.

PATIENTS

From 2,617 admissions to ICU between January 27, 2016, and March 30, 2017, 1,262 consecutive patients who met inclusion and exclusion criteria were studied.

INTERVENTIONS

Glycosylated hemoglobin was used to estimate average glucose concentration over the prior 3 months. Glucose concentration on ICU admission was divided by estimated average glucose concentration to calculate the stress hyperglycemia ratio, an index of relative glycemia. Risk of death score was calculated using data submitted to the Australia and New Zealand Intensive Care Society.

MEASUREMENTS AND MAIN RESULTS

In this study, there were 186 deaths (14.7%). Admission glucose was significantly associated with mortality in univariate analysis (odds ratio = 1.08 per mmol/L glucose increment; p < 0.001) but not after adjustment for risk of death score (odds ratio = 1.01; p = 0.338). In contrast, stress hyperglycemia ratio was significantly associated with mortality both in univariate analysis (odds ratio = 1.09 per 0.1 stress hyperglycemia ratio increment; p < 0.001) and after adjustment for risk of death score (odds ratio = 1.03; p = 0.014). Unlike admission glucose concentration, stress hyperglycemia ratio was significantly associated with mortality in patients with glycosylated hemoglobin less than 6.5% (odds ratio = 1.08 per 0.1 stress hyperglycemia ratio increment; p < 0.001) and glycosylated hemoglobin greater than or equal to 6.5% (48 mmol/mol) (odds ratio = 1.08 per 0.1 stress hyperglycemia ratio increment; p = 0.005).

CONCLUSIONS

Unlike absolute hyperglycemia, relative hyperglycemia, as assessed by the stress hyperglycemia ratio, independently predicts in-hospital mortality in critically ill patients across the glycemic spectrum. Future studies should investigate whether using measures of relative hyperglycemia to determine individualized glycemic treatment targets improves outcomes in ICU.

Augmented glycaemic gap is a marker for an increased risk of post-infarct left ventricular systolic dysfunction

BACKGROUND

Left ventricular systolic dysfunction (LVSD) occurs frequently after acute ST-segment elevation myocardial infarction (STEMI). The predisposing factors and underlying mechanism of post-infarct LVSD are not fully understood. The present study mainly investigated the correlation between glycaemic gap, a novel index of stress-induced hyperglycaemia (SIH), and post-infarct LVSD.

METHODS

A total of 274 first STEMI patients were enrolled in this cross-sectional study. Transthoracic echocardiography was performed within 48 h after admission and at 6 months after discharge to obtain left ventricular ejection fraction (LVEF). The change in LVEF was calculated as LVEF at 6 months after discharge minus baseline LVEF. Additionally, post-infarct LVSD was defined as LVEF ≤ 50%. Most importantly, glycaemic gap was calculated as admission blood glucose (ABG) minus the estimated average glucose over the previous 3 months.

RESULTS

In patients without diabetes mellitus (DM), multivariate linear regression analysis revealed that both glycaemic gap (Beta = - 1.214, 95% CI - 1.886 to - 0.541, p < 0.001) and ABG (Beta = - 1.124, 95% CI - 1.795 to - 0.453, p = 0.001) were associated with change in LVEF. In DM patients, only glycaemic gap was still associated with change in LVEF, although this association was not observed in univariate linear regression analysis. Regarding the association between SIH and post-infarct LVSD, multivariate logistic regression analysis revealed that both glycaemic gap (OR = 1.490, 95% CI 1.043 to 2.129, p = 0.028) and ABG (OR = 1.600, 95% CI 1.148 to 2.229, p = 0.005) were associated with an increased risk of having post-infarct LVSD in non-DM patients. However, after multivariate adjustment in DM patients, only glycaemic gap (OR = 1.399, 95% CI 1.021 to 1.919, p = 0.037) remained associated with an increased risk of having post-infarct LVSD. Furthermore, the predictive value of glycaemic gap for post-infarct LVSD was not inferior to ABG in non-DM patients (p = 0.499), and only glycaemic gap, instead of ABG, could significantly predict post-infarct LVSD in DM patients (AUC = 0.688, 95% CI 0.591 to 0.774, p = 0.002).

CONCLUSIONS

Glycaemic gap was strongly associated with a change in LVEF and an increased risk of having post-infarct LVSD in patients following STEMI. In STEMI patients with DM, glycaemic gap could provide more valuable information than ABG in identifying patients at high risk of developing post-infarct LVSD.

Is it time to abandon glucose control in critically ill adult patients?

PURPOSE OF REVIEW

To summarize the advances in literature that support the best current practices regarding glucose control in the critically ill.

RECENT FINDINGS

There are differences between patients with and without diabetes regarding the relationship of glucose metrics during acute illness to mortality. Among patients with diabetes, an assessment of preadmission glycemia, using measurement of Hemoglobin A1c (HgbA1c) informs the choice of glucose targets. For patients without diabetes and for patients with low HgbA1c levels, increasing mean glycemia during critical illness is independently associated with increasing risk of mortality. For patients with poor preadmission glucose control the appropriate blood glucose target has not yet been established. New metrics, including stress hyperglycemia ratio and glycemic gap, have been developed to describe the relationship between acute and chronic glycemia.

SUMMARY

A 'personalized' approach to glycemic control in the critically ill, with recognition of preadmission glycemia, is supported by an emerging literature and is suitable for testing in future interventional trials.

Association of multiple glycemic parameters at intensive care unit admission with mortality and clinical outcomes in critically ill patients

The aim of the present study was to investigate the association of multiple glycemic parameters at intensive care unit (ICU) admission with outcomes in critically ill patients. Critically ill adults admitted to ICU were included prospectively in the study and followed for 180 days until hospital discharge or death. Patients were assessed for glycemic gap, hypoglycemia, hyperglycemia, glycemic variability, and stress hyperglycemia ratio (SHR). A total of 542 patients were enrolled (30% with preexisting diabetes). Patients with glycemic gap >80 mg/dL had increased need for renal replacement therapy (RRT; 37.7% vs. 23.7%, p = 0.025) and shock incidence (54.7% vs. 37.4%, p = 0.014). Hypoglycemia was associated with increased mortality (54.8% vs. 35.8%, p = 0.004), need for RRT (45.1% vs. 22.3%, p < 0.001), mechanical ventilation (MV; 72.6% vs. 57.5%, p = 0.024), and shock incidence (62.9% vs. 35.8%, p < 0.001). Hyperglycemia increased mortality (44.3% vs. 34.9%, p = 0.031). Glycemic variability >40 mg/dL was associated with increased need for RRT (28.3% vs. 14.4%, p = 0.002) and shock incidence (41.4% vs.31.2%, p = 0.039). In this mixed sample of critically ill subjects, including patients with and without preexisting diabetes, glycemic gap, glycemic variability, and SHR were associated with worse outcomes, but not with mortality. Hypoglycemia and hyperglycemia were independently associated with increased mortality.

An elevated glycemic gap predicts adverse outcomes in diabetic patients with necrotizing fasciitis

Background

Diabetes is the most common comorbidity of necrotizing fasciitis (NF), but the effect of stress-induced hyperglycemia (SIH) on diabetic patients with NF has never been investigated. The aim of this study was to assess whether SIH, as determined by the glycemic gap between admission glucose levels and A_1C-derived average glucose levels, predicts adverse outcomes in diabetic patients hospitalized with NF.

Methods

We retrospectively reviewed the glycemic gap and clinical outcomes in 252 diabetic patients hospitalized due to NF from 2011 to 2018 in a single medical center in Taiwan. A receiver operating characteristic (ROC) curve was used to analyze the optimal cutoff values for predicting adverse outcomes. Univariate and multivariate logistic regression analyses were employed to identify significant predictors of adverse outcomes.

Results

In total, 194 diabetic NF patients were enrolled. Compared with patients without adverse outcomes, patients with adverse outcomes had significantly higher glycemic gaps, Acute Physiology and Chronic Health Evaluation (APACHE) II scores and C-reactive protein (CRP) levels; lower albumin and hemoglobin levels; greater incidence of limb loss; and longer hospital and intensive care unit stays. The glycemic gap positively correlates with the laboratory risk indicator for NF scores, APACHE II scores and CRP levels. A glycemic gap of 146 mg/dL was the optimal cutoff value for predicting adverse outcomes using the ROC curve. Compared with patients with glycemic gaps ≤146 mg/dL, those with glycemic gaps >146 mg/dL had higher APACHE II scores and incidence rates of adverse outcomes, especially bacteremia and acute kidney injury. Multivariate analysis revealed that a glycemic gap >146 mg/dL and APACHE II score >15 were independent predictors of adverse outcomes, while the presence of hyperglycemia at admission was not.

Conclusions

An elevated glycemic gap was significantly independently associated with adverse outcomes in diabetic NF patients. Further prospective studies are warranted to validate the role of the glycemic gap in NF patients with diabetes.

Glycated hemoglobin, admission blood glucose delta, and associated mortality in patients with acute ST-segment elevation myocardial infarction

The importance of glucose metabolism in patients with acute coronary syndrome has been increasingly recognized. We conducted a retrospective study of the associations between prior glycated hemoglobin (HbA1c) levels, admission glucose values, HbA1c-derived estimated average glucose levels (eAG), admission glucose delta (admission glucose - eAG), and mortality in patients presenting with ST-segment elevation myocardial infarction. A total of 676 and 566 patients were included in the in-hospital and 12-month mortality models, respectively. Mean admission blood glucose values were higher in the in-hospital and 12-month mortality groups in patients with no prior diagnosis of diabetes (P < 0.001). Pre, post, and admission HbA1c levels and eAG levels were not associated with in-hospital or 12-month all-cause mortality. The admission glucose delta was higher in patients with in-hospital mortality (87.3 ± 122.7 mg/dL) than in patients who survived (35.88 ± 81.23 mg/dL; P = 0.040). The in-hospital death rate was significantly higher (17% vs 4%, P = 0.017) for patients with admission glucose delta ≥140 mg/dL compared to lower values. In conclusion, HbA1c-derived admission glucose delta is associated with in-hospital mortality in patients with ST-segment elevation myocardial infarction. The mechanisms for the association of acute hyperglycemia with increased acute coronary syndrome mortality are unclear.

Elevated Glycemic Gap Predicts Acute Respiratory Failure and In-hospital Mortality in Acute Heart Failure Patients with Diabetes

Diabetes is a common comorbidity in patients hospitalized for acute heart failure (AHF), but the relationship between admission glucose level, glycemic gap, and in-hospital mortality in patients with both conditions has not been investigated thoroughly. Clinical data for admission glucose, glycemic gap and in-hospital death in 425 diabetic patients hospitalized because of AHF were collected retrospectively. Glycemic gap was calculated as the A1c-derived average glucose subtracted from the admission plasma glucose level. Receiver operating characteristic (ROC) curves were used to determine the optimal cutoff value for glycemic gap to predict all-cause mortality. Patients with glycemic gap levels >43 mg/dL had higher rates of all-cause death (adjusted hazard ratio, 7.225, 95% confidence interval, 1.355-38.520) than those with glycemic gap levels ≤43 mg/dL. The B-type natriuretic peptide levels incorporated with glycemic gap could increase the predictive capacity for in-hospital mortality and increase the area under the ROC from 0.764 to 0.805 (net reclassification improvement = 9.9%, p < 0.05). In conclusion, glycemic gap may be considered a useful parameter for predicting the disease severity and prognosis of patients with diabetes hospitalized for AHF.

Admission Glucose Number (AGN): A Point of Admission Score Associated With Inpatient Glucose Variability, Hypoglycemia, and Mortality

AIMS

We investigated a point of admission metric of glycemia, the Admission Glucose Number (AGN), and its relationship with both high risk inpatient glucose patterns and mortality in hospital inpatients with type 2 diabetes (T2DM).

METHODS

Inpatient capillary blood glucose (CBG) data for patients with T2DM in our health board were identified for a 5-year period and associated with most recent preadmission HbA1c. AGN was calculated as first CBG measured during admission (mmol/L), subtracted from most recent preadmission HbA1c (converted to estimated median glucose mmol/l) within 15 months preadmission. The association between AGN and CBG variability (interquartile range), hypoglycemia free survival (HR) and both inpatient and 100-day mortality (HR) were investigated.

RESULTS

A total of 21 045 first admissions with available HbA1c data were identified. A positive correlation between AGN and glycemic variability was described (partial correlation coefficient 0.25, P < .001), which was stronger than the correlation of either of AGNs' individual components: adjusted CBG1 = 0.07 ( P < .001), eAG = 0.08 ( P < .001). The hazard ratio for time to first recorded CBG < 3 mmol/L for high AGN versus low AGN was 1.74 (95% CI 1.55-1.96), P < .001. A high AGN was associated with increased 100-day mortality (HR 1.26, P = .005), however not with in-hospital mortality (HR = 1.31, P = .08).

CONCLUSION

AGN is a simple metric that combines 2 readily available measures associated with adverse outcome in T2DM. AGN may be a useful tool to stratify patients for risk of hypoglycemia and postdischarge death.

The association between long-term glycaemic control, glycaemic gap and neurological outcome of in-hospital cardiac arrest in diabetics: A retrospective cohort study

AIM

Resuscitation guidelines do not recommend a target blood glucose (BG) level specifically tailored for diabetics experiencing an in-hospital cardiac arrest (IHCA). The glycosylated haemoglobin (HbA1c) level may be associated with neurological prognosis and used to identify the optimal BG level for diabetic IHCA patients.

METHODS

This study was a retrospective study in a single medical centre. Patients with an IHCA between 2006 and 2015 were screened. The estimated average glucose (eAG) level was converted from the HbA1c level measured within three months prior to the IHCA. The minimum glycaemic gap was calculated from the post-resuscitation minimum BG level minus the eAG level.

RESULTS

A total of 141 patients were included in this study. The mean HbA1c was 7.2% (corresponding eAG: 160.2 mg/dL [8.9 mmol/L]). Multivariable logistic regression analysis indicated an eAG level of less than 196 mg/dL (10.9 mmol/L; corresponding HbA1c: 8.5%) was positively associated with a favourable neurological outcome at hospital discharge (odds ratio [OR]: 5.12, 95% confidence interval [CI]: 1.11-23.70; p-value = 0.04). An absolute minimum glycaemic gap of less than 70 mg/dL (3.9 mmol/L) was also positively associated with a favourable neurological outcome (OR: 5.41, 95% CI: 1.41-20.78; p-value = 0.01).

CONCLUSION

For diabetic patients, poor long-term glycaemic control correlated with worse neurological recovery following an IHCA. The HbA1c-derived average BG level could be used as a reference point for glycaemic management during the early stage of post-cardiac arrest syndrome. The glycaemic gap could be used to identify the optimal glycaemic range around the reference point.

Dysglycemia in the critically ill patient: current evidence and future perspectives

Dysglycemia in critically ill patients (hyperglycemia, hypoglycemia, glycemic variability and time in range) is a biomarker of disease severity and is associated with higher mortality. However, this impact appears to be weakened in patients with previous diabetes mellitus, particularly in those with poor premorbid glycemic control; this phenomenon has been called "diabetes paradox". This phenomenon determines that glycated hemoglobin (HbA1c) values should be considered in choosing glycemic control protocols on admission to an intensive care unit and that patients' target blood glucose ranges should be adjusted according to their HbA1c values. Therefore, HbA1c emerges as a simple tool that allows information that has therapeutic utility and prognostic value to be obtained in the intensive care unit.

Hydration prevents chronic hyperglycaemic patients from neurological deterioration post-ischaemic stroke

OBJECTIVES

To determine whether chronic hyperglycaemia predisposes patients to dehydration, which may promote neurological deterioration, and to investigate whether dehydration control improves functional outcome.

PATIENTS AND METHODS

This study included 355 patients hospitalized with acute ischaemic stroke and diabetes mellitus who fulfilled the glycaemic gap ≤0. We used the following cut-offs: (i) no chronic hyperglycaemia (glycated haemoglobin A1c [HbA1c] < 7%) and (ii) chronic hyperglycaemia (HbA1c ≥ 7%). The chronic hyperglycaemic patients were randomly divided into the control group and the hydration group. Hydration therapy was only initiated in the hydration group. The blood urea nitrogen (BUN)/creatinine (Cr) ratio was used as an indicator of dehydration. Stroke severity on admission and discharge was assessed by means of National Institutes of Health Stroke Scale (NIHSS).

RESULTS

The mean baseline BUN/Cr ratios were higher in the control group and hydration group than in the no chronic hyperglycaemia group. The mean BUN/Cr ratio decreased from 91.22 ± 29.95 on the first day to 77.03 ± 18.23 on the third day (P < .001) in the hydration group. On the third day after admission, there was no significant difference in the BUN/Cr ratio between the hydration group and the no chronic hyperglycaemia group (P = .831). Moreover, neurological deterioration was highest in the control group (33.6%, 36/107), followed by the hydration group (10.5%, 11/105) and the no chronic hyperglycaemia group (5.6%, 8/143).

CONCLUSIONS

Chronic hyperglycaemia was associated with the admission NIHSS score and neurological deterioration after excluding the effect of stress hyperglycaemia. Furthermore, hydration therapy may help prevent neurological deterioration.

Association between Glycemic Gap and Adverse Outcomes in Critically Ill Patients with Diabetes

OBJECTIVES

Glycemic excursions are commonly seen in patients admitted to the Intensive Care Unit (ICU) and are related to adverse outcomes. Glycemic gap is a marker of this excursion in patients with diabetes. It can be used to predict adverse outcomes in patients with diabetes admitted to the ICU. It is calculated by subtracting A1C-derived average glucose (ADAG) = ([28.7 × HbA1c]-46.7) from plasma glucose at admission. Objective of this study was to correlate glycemic gap and adverse outcomes in patients with type 2 diabetes mellitus (DM) admitted to the ICU.

MATERIALS AND METHODS

We conducted an ambispective study to include patients with type 2 DM admitted to the ICUs from July 2015 to June 2016. The following adverse outcomes were recorded: Multiorgan dysfunction syndrome (MODS), acute respiratory distress syndrome (ARDS), shock, upper gastrointestinal (UGI) bleed, acute kidney injury (AKI), and acute respiratory failure (ARF).

RESULTS

A total of 200 patients were enrolled, with a mean age ± standard deviation of 62 ± 11.24 years, and 64.5% were males. The median (interquartile range) duration of hospital stay and ICU stay were 8 (6-12) days and 4 (3-7) days, respectively. The most common primary diagnosis was cardiovascular (39.5%) followed by neurological (16.5%), infection at diagnosis (16.5%), respiratory (14%), gastrointestinal (7.5%), and others (6%). A higher glycemic gap was associated with occurrence of MODS (P < 0.01), ARDS (P = 0.026), shock (P = 0.043), UGI bleed (P = 0.013), AKI (P = 0.01), and ARF (P < 0.01). Glycemic gap cutoffs of 43.31, 45.26, and 39.12 were found to be discriminatory for predicting ICU mortality (area under the receiver operating characteristic [AUROC]=0.631, P = 0.05), MODS (AUROC = 0.725, P < 0.001), and ARF (AUROC = 0.714, P < 0.001).

CONCLUSION

This study showed that higher glycemic gap levels were associated with an increased risk of MODS, ARDS, shock, UGI bleed, AKI, and ARF. Glycemic gap is a tool that can be used to determine prognosis in patients with diabetes admitted to the ICU.

Glycine Protects H9C2 Cardiomyocytes from High Glucose- and Hypoxia/Reoxygenation-Induced Injury via Inhibiting PKCβ2 Activation and Improving Mitochondrial Quality

BACKGROUND

Patients with diabetes are more vulnerable to myocardial ischemia reperfusion injury (IRI), which is involved in PKCβ2 activation and mitochondrial dysfunction. Glycine has been documented as a cytoprotective agent to attenuate diabetes-related abnormalities and reduce myocardial IRI, but the underlying mechanisms are still unclear. We determined whether glycine could attenuate high glucose- (HG-) and hypoxia/reoxygenation- (H/R-) induced injury by inhibiting PKCβ2 activation and improving mitochondrial quality in cultured H9C2 cells.

METHODS

H9C2 cells were either exposed to low glucose (LG) or HG conditions with or without treatment of glycine or CGP53353 (a selective inhibitor of PKCβ2) for 48 h, then subjected to 4 h of hypoxia followed by 2 h of reoxygenation (H/R). Cell viability, lactate dehydrogenase (LDH) release, mitochondrial membrane potential (MMP), superoxide dismutase (SOD) activity, and malondialdehyde (MDA) concentration were detected using corresponding commercial kits. Mitochondrial quality control-related proteins (LC-3II, Mfn-2, and Cyt-C) and PKCβ2 activation were detected by Western blot.

RESULTS

HG stimulation significantly decreased cell viability and SOD activity and increased LDH release, MDA production, and PKCβ2 activation as compared to LG group, all of which changes were further increased by H/R insult. Glycine or CGP53353 treatment significantly reduced the increase of LDH release, MDA production, PKCβ2 activation, and Cyt-C expression and the decrease of cell viability, SOD activity, MMP, Mfn-2 expression, and LC-3II/LC-3I ratio induced by HG and H/R stimulation.

CONCLUSIONS

Supplementary glycine protects H9C2 cells from HG- and H/R-induced cellular injury by suppressing PKCβ2 activation and improving mitochondria quality.

Relative hyperglycemia is associated with complications following an acute myocardial infarction: a post-hoc analysis of HI-5 data

Background

Hyperglycemia is associated with increased morbidity and mortality in patients with an acute myocardial infarction (AMI). We evaluated whether complications after AMI are associated with absolute or relative glycemia.

Methods

A total of 192 patients with AMI were randomized to intensive or conventional insulin therapy. Absolute glycemia was defined as mean blood glucose level (BGL) during the first 24 h following randomization. Relative glycemia was defined by the stress hyperglycaemia ratio (SHR), calculated as mean BGL divided by average glucose concentration over the prior 3 months estimated from glycosylated haemoglobin. The primary endpoint was a “complicated AMI”, defined as an AMI complicated by death, congestive cardiac failure, arrhythmia, cardiac arrest, reinfarction, cardiogenic shock, inotrope use or emergency revascularization.

Results

There was not a significant association between mean BGL and complicated AMI (odds ratio (OR) 1.05 per mmol/L glucose increment, 95% confidence intervals (CI) 0.93–1.19). In contrast, SHR was positively associated with a complicated myocardial infarction (OR 1.22 per 0.1 SHR increment, 95% CI 1.06–1.42), and individual complications of death (OR 1.55, 95% CI 1.14–2.11), congestive cardiac failure (OR 1.27, 95% CI 1.05–1.54), arrhythmia (OR 1.31, 95% CI 1.12–1.54) and cardiogenic shock (OR 1.42, 95% CI 1.03–1.97). The relationship between SHR and a complicated AMI was independent of diabetic status, intensive insulin therapy, sex and hypoglycemia.

Conclusions

Relative, but not absolute, glycemia during insulin treatment is independently associated with complications after an AMI. Future studies should investigate whether basing therapeutic glycaemic targets on relative glycemia improves patient outcomes.

Stress-Induced Hyperglycemia in Diabetes: A Cross-Sectional Analysis to Explore the Definition Based on the Trauma Registry Data

Background: The diagnosis of diabetic hyperglycemia (DH) does not preclude a diabetes patient from having a stress-induced hyperglycemic response. This study aimed to define the optimal level of elevated glucose concentration for determining the occurrence of stress-induced hyperglycemia (SIH) in patients with diabetes. Methods: This retrospective study reviewed the data of all hospitalized trauma patients, in a Level I trauma center, from 1 January 2009 to 31 December 2016. Only adult patients aged ≥20 years, with available data on serum glucose and glycated hemoglobin A1c (HbA1c) levels upon admission, were included in the study. Long-term average glucose levels, as A1c-derived average glucose (ADAG), using the equation, ADAG = ((28.7 × HbA1c) − 46.7), were calculated. Patients with high glucose levels were divided into three SIH groups with diabetes mellitus (DM), based on the following definitions: (1) same glycemic gap from ADAG; (2) same percentage of elevated glucose of ADAG, from which percentage could also be reflected by the stress hyperglycemia ratio (SHR), calculated as the admission glucose level divided by ADAG; or (3) same percentage of elevated glucose as patients with a defined SIH level, in trauma patients with and without diabetes. Patients with incomplete registered data were excluded. The primary hypothesis of this study was that SIH in patients with diabetes would present worse mortality outcomes than in those without. Detailed data of SIH in patients with diabetes were retrieved from the Trauma Registry System. Results: Among the 546 patients with DH, 332 (32.0%), 188 (18.1%), and 106 (10.2%) were assigned as diabetes patients with SIH, based on defined glucose levels, set at 250 mg/dL, 300 mg/dL, and 350 mg/dL, respectively. In patients with defined cut-off glucose levels of 250 mg/dL and 300 mg/dL, SIH was associated with a 3.5-fold (95% confidence interval (CI) 1.61–7.46; p = 0.001) and 3-fold (95% CI 1.11–8.03; p = 0.030) higher odds of mortality, adjusted by sex, age, pre-existing comorbidities, and injury severity score, than the 491 patients with diabetic normoglycemia (DN). However, in patients with a defined cut-off glucose level of 350 mg/dL, adjusted mortality in SIH in DM was insignificantly different than that in DM. According to the receiver operating characteristic (ROC) curve analysis, a blood sugar of 233 mg/dL, a glycemic gap of 79 (i.e., blood sugar of 251 mg/dL), and a SHR of 1.45 (i.e., blood sugar of 250 mg/dL) were identified as cut-offs for mortality outcomes, with AUCs of 0.622, 0.653, and 0.658, respectively. Conclusions: In this study, a cut-off glucose level of 250 mg/dL was selected to provide a better definition of SIH in DM than glucose levels of 300 mg/dL or 350 mg/dL.

Glycated hemoglobin A1c-based adjusted glycemic variables in patients with diabetes presenting with acute exacerbation of chronic obstructive pulmonary disease

Acute hyperglycemia is a common finding in patients presenting to emergency departments (EDs) with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Several studies have argued against the association between hyperglycemia at admission and adverse outcomes in patients with diabetes and an acute illness. Recent studies have shown that glucose-related variables (eg, glycemic gaps and stress hyperglycemia ratios) that are adjusted for glycated hemoglobin levels can indicate the severity of a variety of diseases. The objective of this study was to assess whether these hemoglobin A1c (HbA1c)-based adjusted average glycemic variables were associated with unfavorable outcomes in patients admitted to a hospital with AECOPD. We found that 1) pulmonary infection is a major risk factor for AECOPD; 2) a higher glycemic gap and modified stress hyperglycemia ratio were associated with the development of acute respiratory failure (ARF) in patients with diabetes admitted to an ED because of AECOPD; and 3) the glycemic gap and modified stress hyperglycemia ratio had superior discriminative power over acute hyperglycemia and HbA1c for predicting the development of ARF, although the HbA1c-adjusted glycemic variables alone were not independent risk factors for ARF.