Glycaemic variability has been associated with poor outcomes in critically ill patients. We aimed to study the association between glycaemic variability and functional outcome in patients with acute ischaemic stroke using continuous glucose monitoring to ensure all episodes of hyper- and hypoglycaemia were captured.

Participants with acute ischaemic stroke were enrolled and started blinded continuous glucose monitoring (Dexcom G6) between November 2020 and December 2022. Glucose data from the first 72 hours after admission were analysed. Patients were classified into 3 groups based on change in functional status (Modified Rankin Scale) between admission and discharge. These included (i) remained independent (RI); (ii) deteriorated to dependent (DD); and (iii) remained dependent (RD).

Data of 67 patients (mean±SD age 72.1 ± 14 years) were analysed; 19 participants had diabetes. The median (IQR) National Institutes of Health Stroke Scale (NIHSS) was 8 (3,14), and 34.3% received reperfusion therapy. The percentage of patients with RI, DD, and RD was 25.4, 55.2, 19.4%. Patients with DD had older age, higher rate of atrial fibrillation, systolic blood pressure, rate of in-hospital infection, NIHSS at admission and at 24 hours after reperfusion therapy compared to those RI. Continuous glucose monitoring was started at 38.4 (29.5,51) hours after stroke onset. Those with DD had higher mean glucose, %time above 180 mg/dL, and glucose standard deviation than the RI group at discharge. Multivariate analysis showed only an association between NIHSS at admission and deterioration in functional status.

In this pilot study, an association between glycaemic variability and functional deterioration after acute ischaemic stroke was not observed. Clinical Trial Registration numberNCT04521634.

Glucose control is an important aspect of acute ischemic stroke management. Although absolute glucose concentration remains the focus in clinical stroke care, glucose variability is increasingly recognized as a viable treatment target. To assess the relationship between acute post-stroke glycemic control parameters and patient outcomes, we reanalyzed the data from the first 8 h of treatment for patients in the Stroke Hyperglycemia Insulin Network Effort (SHINE) clinical trial, when glycemic variability is highest.

In this secondary analysis of the SHINE dataset, the rate of glucose change during the first 8 h was evaluated for its association with patient outcomes, dichotomized as modified Rankin scale (mRS) 0-2 versus 3-6, using logistic regression and a linear mixed-effects model.

Unadjusted analysis of the glucose correction period during the first 8 h suggested that patients with mRS 3-6 had a faster glucose correction compared to those with mRS 0-2 (-8.9 and -6.7 mg/dL/h, p < 0.001). This finding remained statistically significant in both the intensive intervention group and the poorly controlled diabetic sub-group (glycosylated hemoglobin [HbA1c] ≥ 6.4). Mixed-effects models also indicated a significant difference in the rate of glucose change (1.9 mg/dL/h, p < 0.001) between outcome groups (mRS 0-2 versus 3-6) across both treatment and HbA1c sub-groups.

Analysis of the first 8 h of the SHINE data suggests that early, rapid correction of glucose is associated with poor outcomes, particularly in the sub-group of patients with HbA1c ≥ 6.4. Further research is warranted to assess early glycemic correction as a possible personalized glucose management goal.

Dysglycemia has deleterious outcomes on critically ill patients with diabetes mellitus (DM). Insulin degludec, an ultralong-acting insulin, is associated with lower rates of hypoglycemia and blood glucose (BG) variability in non-critically ill patients. The experience with insulin degludec in the intensive care units is lacking. This study aimed to assess the effect of insulin degludec on glycemic control in critically ill patients with type 2 DM.

A prospective, interventional study enrolled critically ill patients with type 2 DM. Subjects were started on insulin degludec plus insulin regular correctional doses. BG levels were assessed every 6 hours. The primary outcome was the percentage of BG levels within a target of 140 to 180 mg/dL. The secondary outcomes included the median BG levels, severe hypoglycemia rate, and BG variability.

In total, 155 patients were enrolled. The percentage of BG levels within the target was 28.5%. The first day that the median of BG levels within target was on day 2 of insulin degludec therapy, which continued to be within the target for 1 week. Severe hypoglycemia developed in 5 patients (3.2%). The BG variability in the study was 26% using the coefficient of variation.

In critically ill patients with type 2 DM, one-fourth of BG levels were within the glycemic target (140-180 mg/dL) with insulin degludec plus insulin regular correctional doses. The median BG levels were in target starting the second day of insulin degludec therapy. The favorable BG variability using insulin degludec merits further investigation for effect on clinical outcomes.

Acute pancreatitis (AP) is an inflammatory reaction of the pancreas. When the disease is severe, it is often accompanied by destruction of the pancreatic islets, resulting in dysfunction of the endocrine system of the pancreas. Stress hyperglycemia is a transient increase in glucose during a critical illness, and its possible mechanism is related to abnormal glucose metabolism and insulin resistance due to the increased release of counterregulatory hormones and cytokines, such as glucagon, cortisol, and catecholamines. Numerous studies have shown that stress hyperglycemia is strongly associated with morbidity, mortality, and increased risk of post-acute pancreatitis diabetes in AP patients. Therefore, stress hyperglycemia may be a significant independent risk factor for poor clinical outcomes and prognosis in patients with AP. This article reviews the clinical features, risk factors, and mechanisms of action of stress hyperglycemia in AP and its influence on adverse clinical outcomes and the prognosis of inpatients with AP. For AP patients with stress hyperglycemia, it is necessary to comprehensively consider their blood glucose levels, daily habits, and complications to develop an appropriate treatment plan for hyperglycemia. Limited evidence indicates that in the case of acute hyperglycemia in critically ill patients, especially during the first 3 days of hospitalization, insulin therapy should not be undertaken if the blood glucose level does not exceed 10 mmol/L. However, some important questions related to clinical practice remain to be answered. More clinical trials and studies are needed in the future to provide a sufficient basis for clinical practice.

This study aimed to evaluate the relationship between time in targeted blood glucose range (TIR) and 28-day mortality in critically ill patients.

A retrospective cohort analysis was conducted using data from the MIMIC-IV database. Patients (n = 18,905) were stratified into four quartiles based on TIR values. The association between TIR and mortality was assessed using multivariable logistic regression models with adjustments for potential confounders.

In the fully adjusted model, each percentage point increase in TIR was associated with a 1 % reduction in 28-day mortality risk (OR = 0.99, 95 % CI: 0.98-0.99, P < 0.001). Patients in the highest TIR quartile showed a 60 % lower mortality risk compared to those in the lowest quartile (OR = 0.40, 95 % CI: 0.22-0.74, P = 0.003). The predictive model demonstrated good discriminative ability (AUC = 0.7543).

Time in targeted blood glucose range is independently associated with 28-day mortality in ICU patients, suggesting its potential value as a metric for risk stratification and glycemic management optimization.

High glycemic variability (GV) often indicates a poor prognosis. Our aim is to investigate the relationship between GV and short and long-term mortality in critically ill heart failure (HF) patients.

We extracted data from the Medical Information Mart for Intensive Care IV database. The risks of in-hospital and 1-year mortality were calculated using Logistic and COX regression. In addition, mediation analysis was used to investigate the indirect effect of ventricular arrhythmias (VA) on in-hospital mortality.

Among 8,980 critically ill HF patients, the multifactorial regression analysis showed that high GV was associated with an increased risk of in-hospital and 1-year mortality (OR 1.69, 95 % CI 1.47-1.93; HR 1.12, 95 % CI 1.02-1.22). The Kaplan-Meier survival curve and restricted cubic spline plot also emphasized this association. Furthermore, the impact of GV on in-hospital mortality was partially mediated by VA (4.98%). And the increased risk of 1-year mortality associated with high GV was more significant in person with diabetes (p for interaction =0.018).

Our Study indicates that high GV may be an independent risk factor for short and long-term mortality in critically ill HF patients. Maintaining the stability of blood glucose can reduce adverse outcomes in critically ill HF patients.

Various blood glucose (BG) variability-related indexes have been widely used to assess glycemic control and predict glycemic risks, but the association between BG variations and prognosis in non-diabetic patients with sepsis remains unclear.

The single-center retrospective cohort study included 7,049 non-diabetic adults with sepsis who had at least 3 records of bedside capillary point of care BG testing during the first day after ICU admission from MIMIC-IV database (2008 to 2019). Coefficient of variation and standard deviation of glucose (i.e., GluCV and GluSD), M-value, J-index, high blood glucose index (HBGI), and low blood glucose index (LBGI) were used to describe glucose variability, quality of glycemic control, and glycemic risk of patients with sepsis. The dose-response relationship between BG variability-related indexes and mortality was explored using multivariate logistic regression with restricted cubic spline (RCS) function. If the dose-response curve presented a J-shape with a specific threshold value, a linear threshold function instead of RCS would be employed.

There is a J-shaped relationship between hospital mortality risk and glucose variability-related indexes in ICU patients with sepsis. The mortality risk remained relatively stable below the threshold of these indexes. However, over the threshold, the 28-day mortality risk increased by 2.82% (95% CI: 1.80-3.85%), 1.13% (95% CI: 0.66-1.60%), 1.96% (95% CI: 0.98-2.95%), 1.37% (95% CI: 0.57-2.16%), 11.19% (95% CI: 6.56-15.98%) and 39.04% (95% CI: 29.86-48.81%) for each unit increases in GluCV, GluSD, M-value, J-index, LBGI and HBGI, respectively. The effects of LBGI and HBGI on 7-day and 14-day mortality were more pronounced.

High levels of GluCV, GluSD, M-value, J-index, HBGI, and LBGI on the first day of ICU admission were important risk markers of hospital mortality among non-diabetic patients with sepsis.

The glycemic response to critical COVID-19 remains uncertain. We aimed to assess the association between COVID-19, insulin requirements, glycemic control, and mortality in intensive care unit (ICU) patients.

We conducted a retrospective observational study of 350 COVID-19 patients and 1067 non-COVID-19 patients admitted to the ICU. Insulin requirement was defined as the total units of exogenous insulin required to cover one gram of administered carbohydrates (insulin-to-carbohydrate ratio, ICR). We used multivariable generalized linear mixed-model (GLMM) analysis to assess the association of the interaction between COVID-19 and ICU-day with daily ICR, adjusted for fixed and time-dependent covariates. Glycemic control was assessed after stratification on diabetes and COVID-19. We used multivariable logistic regression analysis to assess the association between ICR and 90-day mortality.

The mean (95% CI) of the mean daily ICR among patients without diabetes was 0.09 (0.08-0.11) U/g and 0.15 (0.11-0.18) U/g in the non-COVID-19 group and COVID-19 group (p = .01), respectively. In diabetes patients, the corresponding ICRs were 0.52 (0.43-0.62) U/g and 0.59 (0.50-0.68) U/g (p = .32). In multivariable GLMM analysis, the interaction between COVID-19 and ICU-day was independently associated with ICR (risk estimate 1.22, 95% CI 1.15-1.31, p < .001). COVID-19 was associated with higher hypoglycemia prevalence irrespective of diabetes status, higher average glucose levels, more pronounced glucose variability, and a lower proportion of glucose values within target range among patients without diabetes. On multivariable logistic regression analysis, the adjusted odds ratio for 90-day mortality was 1.77 (95% CI 0.94-3.34, p = .076) per one unit increase in mean ICR.

In our cohort of ICU patients, COVID-19 was associated with higher daily insulin requirements per gram of administered carbohydrates, and worse glycemic control. We found no robust association between ICR and increased odds of death at 90 days.

Bakground: The mortality rate from community-acquired pneumonia (CAP) or coronavirus disease 19 (COVID-19) is high, especially in hospitalized patients. This study aimed to assess the disturbances of glucose and lipid metabolism with in-hospital complications and short-term outcomes for patients with pneumonia with different etiologies. Methods: This observational study comprised 398 patients divided as follows: 155 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia, 129 participants with viral CAP, and 114 with bacterial pneumonia. Results: Fasting plasma glucose (FPG) at admission and glycemic variation during hospitalization was linked with acute kidney injury (AKI) in bacterial CAP. Compared with a value <110 mg/dL for FPG at admission, levels between 110 and 126 mg/dL are associated with mortality in both COVID-19 (OR = 3.462, 95% CI: 1.275-9.398, p = 0.015) and bacterial CAP participants (OR = 0.254; 95% CI: 0.069-0.935, p = 0.039), while a value ≥126 mg/dL was linked with mortality only in patients with SARS-CoV-2 (OR = 3.577, 95% CI: 1.166-10.976, p = 0.026). No relation between lipid biomarkers and complications or in-hospital outcomes was observed in all three participant groups. Conclusions: Patients with bacterial CAP are more prone to developing AKI due to increased FBG at admission and glycemic variations during hospitalization, while elevated FBG values at admission are associated with mortality in both COVID-19 and bacterial CAP.

This study evaluated the clinical utility of continuous glucose monitoring system (CGMS) in critically ill patients.

In this randomized controlled trial, we randomly assigned critically ill participants with diabetes or stress-induced hyperglycemia to the CGMS group (n = 48) or to the conventional point-of-care monitoring (POCM) group (n = 48). The glucose values and clinical outcome were compared between the two group. The primary endpoint was 28-day mortality after intensive care unit admission.

The 28-day mortality was not significantly different between the CGMS and POCM group (20.8% vs 31.3%, P = 0.25). The mean glucose, time-weighted average glucose, glucose standard deviation and time in range (3.9-10.0) were significantly improved in the CGMS group (all P < 0.05).

Compared with conventional POCM, CGMS did not decrease the 28-day mortality in critically ill participants with diabetes or stress-induced hyperglycemia. But CGMS may improve the glycemic control and may be increasingly used in critically ill patients.

The burden of atrial fibrillation (AF) in the intensive care unit (ICU) remains heavy. Glycaemic control is important in the AF management. Glycaemic variability (GV), an emerging marker of glycaemic control, is associated with unfavourable prognosis, and abnormal GV is prevalent in ICUs. However, the impact of GV on the prognosis of AF patients in the ICU remains uncertain. This study aimed to evaluate the relationship between GV and all-cause mortality after ICU admission at short-, medium-, and long-term intervals in AF patients.

Data was obtained from the Medical Information Mart for Intensive Care IV 3.0 database, with admissions (2008-2019) as primary analysis cohort and admissions (2020-2022) as external validation cohort. Multivariate Cox proportional hazards models, and restricted cubic spline analyses were used to assess the associations between GV and mortality outcomes. Subsequently, GV and other clinical features were used to construct machine learning (ML) prediction models for 30-day all-cause mortality after ICU admission.

The primary analysis cohort included 8989 AF patients (age 76.5 [67.7-84.3] years; 57.8% male), while the external validation cohort included 837 AF patients (age 72.9 [65.3-80.2] years; 67.4% male). Multivariate Cox proportional hazards models revealed that higher GV quartiles were associated with higher risk of 30-day (Q3: HR 1.19, 95%CI 1.04-1.37; Q4: HR 1.33, 95%CI 1.16-1.52), 90-day (Q3: HR 1.25, 95%CI 1.11-1.40; Q4: HR 1.34, 95%CI 1.29-1.50), and 360-day (Q3: HR 1.21, 95%CI 1.09-1.33; Q4: HR 1.33, 95%CI 1.20-1.47) all-cause mortality, compared with lowest GV quartile. Moreover, our data suggests that GV needs to be contained within 20.0%. Among all ML models, light gradient boosting machine had the best performance (internal validation: AUC [0.780], G-mean [0.551], F1-score [0.533]; external validation: AUC [0.788], G-mean [0.578], F1-score [0.568]).

GV is a significant predictor of ICU short-term, mid-term, and long-term all-cause mortality in patients with AF (the potential risk stratification threshold is 20.0%). ML models incorporating GV demonstrated high efficiency in predicting short-term mortality and GV was ranked anterior in importance. These findings underscore the potential of GV as a valuable biomarker in guiding clinical decisions and improving patient outcomes in this high-risk population.

It's recognized that stress hyperglycemia ratio (SHR) is considered a significant indicator of poor prognosis in many diseases. However, its role in critically ill patients with acute heart failure (acute HF) remains underexplored.

We conducted a retrospective cohort study on patients with acute HF included in the Medical Information Mart for Intensive Care IV (MIMIC-IV) version 2.2 database. A restricted cubic spline (RCS) regression analysis was used to explore the relationship between SHR and the risk of all-cause mortality in these patients. Subsequently, a Cox regression model was used to evaluate the relationship between SHR and mortality in acute HF patients.

A total of 1,644 acute HF patients were included in the study and divided into two groups: the low SHR group (SHR < 1.06, N = 823) and the high SHR group (SHR ≥ 1.06, N = 821). In our study, the 30-day, 90-day, 180-day, and 365-day mortality rates for acute HF were 7.0%, 12%, 15%, and 19%, respectively, with higher mortality rates observed in the high SHR group compared to the low SHR group. SHR levels showed a linear relationship with all-cause mortality. Furthermore, SHR as a continuous variable shows a significant positive correlation with 30-day (HR = 2.31, 95% CI: 1.58-3.39), 90-day (HR = 1.81, 95% CI: 1.31-2.52), 180-day (HR = 1.57, 95% CI: 1.16-2.12), and 365-day (HR = 1.41, 95% CI: 1.07-1.85) all-cause mortality. After categorization, high SHR remains associated with increased 30-day (HR = 2.4, 95% CI: 1.59-3.61), 90-day (HR = 1.76, 95% CI: 1.31-2.36), 180-day (HR = 1.51, 95% CI: 1.16-1.95), and 365-day (HR = 1.38, 95% CI: 1.09-1.73) all-cause mortality.

Our findings indicate that high SHR is an independent predictor of poor short- and long-term prognosis in acute HF patients. Understanding the impact of SHR on mortality in acute HF is crucial as it can assist clinicians in identifying high-risk patients and adjusting treatment strategies accordingly.

Background: Glycemic variability (GV) is a prevalent and significant condition observed in critically ill patients. This study aimed to investigate the relationship between early glycemic variability parameters and 28-day mortality in critically ill patients. Methods: A multicenter, prospective, and observational study was performed at five tertiary intensive care units (ICUs) in Turkey. All patients who had more than six blood glucose level (BGL) measures per 24 h were included. The parameters of GV including the SD, MGL, MGD (the difference between the maximal and minimal glucose level), and the CV (the percentage of SD to the MGL) in the first 24 h were recorded. Results: A total of 578 eligible patients were enrolled in the study, of whom 43.6% were women. The mean age of the patients was 68.09 ± 16.62 years. Overall mortality was 31.5% (n = 182). The glycemic parameters of the CV, SD, and MGD were significantly higher in the non-survivor group than in the survivor group (p = 0.040, 0.006, and 0.002, respectively). The multivariate logistic regression analysis revealed that the CV (OR 1.023; 95% CI 1.004-1.042; p = 0.017) was an independent factor that increased mortality. Spearman's rho correlation analysis revealed a strong (r:0.871) and statistically significant correlation (p < 0.001) between the CV and MGD. Conclusions: The CV calculated within the first 24 h of ICU admission is independently associated with 28-day mortality. The MGD is correlated with the CV and is maybe a practical tool to predict increased risk of mortality at the bedside. However, further studies are needed to establish the independent association of the MGD with mortality.

Elevated glycemic variability (GV) often occurs in intensive care unit (ICU) patients and is associated with patient prognosis. However, the association between GV and prognosis in ICU patients with traumatic brain injury (TBI) remains unclear.

Clinical data of ICU patients with TBI were obtained from the Medical Information Mart for Intensive Care (MIMIC) -IV database. The coefficient of variation (CV) was utilized to quantify GV, while the Glasgow Coma Scale (GCS) was employed to evaluate the consciousness status of TBI patients. Pearson linear correlation analysis, linear regression, COX regression and restricted cubic spline (RCS) were used to investigate the relationship between CV and consciousness impairment, as well as the risk of in-hospital mortality.

A total of 1641 ICU patients with TBI were included in the study from the MIMIC-IV database. Pearson linear correlation and restricted cubic spline (RCS) analysis results showed a negative linear relationship between CV and the last GCS (P = 0.002) with no evidence of nonlinearity (P for nonlinear = 0.733). Multivariable linear regression suggested a higher CV was associated with a lower discharge GCS [β (95 %CI) = -1.86 (-3.08 ∼ -0.65), P = 0.003]. Furthermore, multivariable COX regression indicated that CV ≥ 0.3 was a risk factor for in-hospital death in TBI patients [HR (95 %CI) = 1.74 (1.15-2.62), P = 0.003], and this result was also consistent across sensitivity and subgroup analyses.

Higher GV is related to poorer consciousness outcomes and increased risk of in-hospital death in ICU patients with TBI. Additional research is needed to understand the logical relationship between GV and TBI progression.

Background and Aims: Guidelines now recommend inpatient continuous glucose monitor (CGM) use with confirmatory blood glucose measurements. However, the Food and Drug Administration has not yet officially approved CGM for inpatient use in large part because its accuracy has not been established in this setting. We tested the accuracy of the Dexcom G6 (G6) in 28 adults on an insulin infusion in a medical-surgical intensive care unit with 1064 matched CGM and arterial point-of-care pairs. Methods: The participants were on average 57.29 (SD 2.39) years, of whom 13 had a prior diagnosis of diabetes and 14 were admitted for a surgical diagnosis. The first 19 participants received the G6 without calibration and had a mean absolute relative difference (MARD) of 13.19% (IQR 5.11, 19.03) across 659 matched pairs, which just meets the critical care expert recommendation of MARD <14%. We then aimed to improve accuracy for the subsequent 9 participants using a calibration protocol. Results: The MARD for calibrated participants was 9.65% (3.03, 13.33), significantly lower than for uncalibrated participants (P < 0.001). Calibration also demonstrated excellent safety with 100% of values within the Clarke Error Grid zones A and B compared with 99.07% without calibration. Our protocol achieved the lowest MARD and safest CEG profile in the critical care setting and well exceeds the critical care expert recommendations. Our large sample of heterogenous critically ill patients also reached comparable accuracy to the MARD of 9% for G6 in outpatients. We believe our calibration protocol will allow G6 to be used with sufficient accuracy in inpatients.

Introduction: Glycemic management in the intensive care unit is an evolving practice area. This evolution has included the refinement of blood glucose targets, matching glycemic management to premorbid status, and investigations into the impact of glycemic variability and relative hypoglycemia on ICU outcomes. The interplay between these phenomena and absolute hypoglycemia has yet to be investigated in hyperglycemic emergencies. Objectives: To examine the incidence of and risk factors for relative hypoglycemia and absolute hypoglycemia in patients admitted to an intensive care unit for the management of hyperglycemic emergencies. Methods: This was a retrospective, single-center, exploratory analysis of adults admitted to the medical intensive care unit for diabetic ketoacidosis or hyperosmolar hyperglycemic syndrome. The primary outcome was the incidence of relative hypoglycemia, defined as a blood glucose level 30% lower than baseline. The baseline was determined by the estimated average blood glucose calculated from hemoglobin A1c within 3 months of index admission. Secondary outcomes were ICU length of stay, glycemic variability, and incidence of absolute hypoglycemia.Results: Relative hypoglycemia was observed in 60% of patients in the cohort. Longer insulin infusion duration and higher hemoglobin A1c levels were found to statistically increase the risk of developing relative hypoglycemia. Higher glycemic variability and longer ICU length of stay were associated with the risk of developing absolute hypoglycemia. Conclusions: Relative hypoglycemia is a frequent occurrence in this patient population. Hemoglobin A1c and duration of the insulin infusion statistically influenced the risk of developing relative hypoglycemia. Higher glycemic variability and longer ICU stay were significantly associated with developing absolute hypoglycemia. While relative hypoglycemia is common in hyperglycemic emergencies, the clinical impact remains uncertain and warrants additional investigation.

Background/Objectives: Elevated glycemic variability (GV) has been associated with postoperative morbidity. Traditional preoperative fasting guidelines may contribute to high GV by driving the body into catabolism. Enhanced recovery after surgery (ERAS) protocols that include a preoperative carbohydrate load (PCL) reduce hospital length of stay and healthcare costs; however, it remains unclear whether PCL improves GV in surgical patients. The aim of this retrospective study was to determine the effect of a PCL on postoperative GV in diabetic and non-diabetic patients having gynecological surgery. Methods: Retrospective data were collected on patients who had gynecological surgery before and after the rollout of an institutional ERAS protocol that included PCL ingestion. The intervention group included patients who underwent surgery in 2019 and were enrolled in the ERAS protocol and, therefore, received a PCL. The control group included patients who underwent surgery in 2016 and, thus, were not enrolled in the protocol. The primary endpoint was GV, calculated by the coefficient of variance (CV) and glycemic lability index (GLI). Results: A total of 63 patients in the intervention group and 45 in the control were analyzed. GV was not statistically significant between the groups for CV (19.3% vs. 18.6%, p = 0.65) or GLI (0.58 vs. 0.54, p = 0.86). Postoperative pain scores (4.5 vs. 5.2 p = 0.23) and incentive spirometry measurements (1262 vs. 1245 p = 0.87) were not significantly different. A subgroup analysis of patients with and without type 2 diabetes mellitus revealed no significant differences in GV for any of the subgroups. Conclusions: This retrospective review highlights the need for additional GV research, including consensus agreement on a gold standard GV measurement. Large-scale prospective studies are needed to test the effectiveness of the PCL in reducing GV.

Cardiac surgery with cardiopulmonary bypass results in global myocardial ischemia-reperfusion injury, leading to significant postoperative morbidity and mortality. Although cardioplegia is the cornerstone of intraoperative cardioprotection, a number of additional strategies have been identified. The concept of preconditioning and postconditioning, despite its limited direct clinical application, provided an essential contribution to the understanding of myocardial injury and organ protection. Therefore, physicians can use different tools to limit perioperative myocardial injury. These include the choice of anesthetic agents, remote ischemic preconditioning, tight glycemic control, optimization of respiratory parameters during the aortic unclamping phase to limit reperfusion injury, appropriate choice of monitoring to optimize hemodynamic parameters and limit perioperative use of catecholamines, and early reintroduction of cardioprotective agents in the postoperative period. Appropriate management before, during, and after cardiopulmonary bypass will help to decrease myocardial damage. This review aimed to highlight the current advancements in cardioprotection and their potential applications during cardiac surgery.

There is limited evidence evaluating the impact of insulin treatment strategies on glucose variability in critically ill patients without preexisting diabetes.

Compare basal plus insulin (BPI) and sliding scale insulin (SSI) impact on glycemic control outcomes in critically ill patients without preexisting diabetes experiencing hyperglycemia.

This multicenter, retrospective review analyzed critically ill patients with hyperglycemia who received either BPI or SSI. Patients with a hemoglobin A1C >6.5% during the admission of interest or in the previous 3 months, or a diagnosis of diabetes at the time of discharge were excluded. The primary outcome was glucose variability during the intensive care unit (ICU) admission. Secondary outcomes included hypoglycemia frequency, frequency of goal glucose levels, mortality, and length of stay.

The analysis included 228 patients (39 in BPI, 189 in SSI). Average glucose variability was higher in the BPI group compared with the SSI group (85.8 mg/dL ± 33.1 vs 70.2 mg/dL ± 30.7; P = 0.009), which remained when controlling for baseline confounding (-12.1 [5.6], 95% CI -23.2 to -0.99; P = 0.033). Hypoglycemia incidence was similar between groups. BPI patients had a lower incidence of glucose values within goal range than SSI patients (P = 0.046). There was no difference in length of stay or hospital mortality.

The use of SSI compared with a BPI regimen may result in reduced glycemic variability in critically ill patients without preexisting diabetes. Future prospective studies, with a larger sample size, are warranted to confirm our exploratory findings and characterize clinically significant benefits.

Type 2 diabetes mellitus (T2DM) is a complex metabolic disease that occurs as a result of insulin resistance and low insulin production. T2DM involves many organ systems that include macro-vascular and micro-vascular complications. Several genome-wide association studies (GWAS) and candidate gene studies have suggested a large number of single nucleotide polymorphisms (SNPs) on several genes such as HHEX that were associated with T2DM susceptibility. The current study aims to look at the relationship between the risk of T2DM and the HHEX gene variant rs7923837.

In this case-control study genotyping of rs7923837 of the HHEX gene was performed using the PCR-RFLP and Sanger sequencing method.

Frequencies of GG genotype of rs7923837 polymorphism of HHEX among subjects with and without diabetes mellitus were 33.77% and 25.47% respectively. Corresponding prevalence for the AG genotype was 51.08% and 64.15% among subjects with and without diabetes mellitus respectively. The differences were not statistically significant (p = 0.08).

Our study revealed that polymorphisms rs7923837 of HHEX were not associated with T2DM.

The online version contains supplementary material available at 10.1007/s40200-024-01407-5.

Background Acute decompensated heart failure (ADHF) significantly contributes to global morbidity. Stress hyperglycemia (SHGL), although commonly observed in non-diabetic ADHF patients, remains underexplored. This study investigates the predictive value of SHGL for major adverse cardiac events (MACEs) and its impact on coronary intervention outcomes. Methods In this prospective observational study at a tertiary care center, 650 non-diabetic ADHF patients admitted for coronary intervention between April 2021 and April 2022 were assessed. SHGL was defined by random blood sugar levels >140 mg/dl. We monitored the incidence of MACEs, including cardiac death, non-fatal myocardial infarction, and heart failure rehospitalization, alongside the success rates of coronary revascularizations over 12 months. Results SHGL was present in 54% of patients (n=352) and was significantly associated with increased MACEs (p<0.001), higher rehospitalization rates (p<0.01), and lower success in revascularization (p<0.05). Using logistic regression, SHGL, age >65, and prior heart failure hospitalization were identified as independent predictors of MACEs. Statistical analyses were performed using two-tailed Mann-Whitney U tests, with significance levels set at p<0.05 for noteworthy findings and p<0.01 or p<0.001 for highly significant findings. Conclusions SHGL significantly impacts coronary intervention outcomes and the future prognosis of heart failure in non-diabetic ADHF patients, identifying it as a critical, modifiable risk factor. These findings advocate integrating SHGL management into ADHF care, emphasizing the need for further research to develop standardized treatment protocols. Proper management of SHGL could potentially improve patient outcomes, highlighting the importance of metabolic control in heart failure management.

Two high-intensity interval training (HIIT) regimens are often used in research and clinical settings. Yet, there has been no direct comparison to determine if one can improve glucose control and variability to a greater extent in individuals living with type 2 diabetes (T2D). Fourteen older females with T2D participated in a semi-randomized control trial where HIIT10 (10 × 1-min intervals at 90% heart rate max; HRmax) and HIIT4 (4 × 4-min intervals at 90% of HRmax) were compared to a control condition (CON; no exercise). Continuous glucose monitoring was used to assess glucose control and variability over 24 h after each condition. Both HIIT10 (-2.1 ± 1.1 mmol/L) and HIIT4 (-2.1 ± 1.3 mmol/L) acutely lowered glucose compared to CON (-0.7 ± 0.8 mmol/L; p = 0.001), with no difference between exercise conditions. This glucose-lowering effect did not persist over the 24-h post-exercise period, as both mean glucose (p = 0.751) and glucose variability (p = 0.168) were not significantly different among conditions. However, exploratory analyses focusing on individuals with less optimal glucose control (above median 24-h mean glucose in the CON condition; n = 7) revealed that 24-h mean glucose (7.4 [7.14-8.92] vs. 8.4 [7.5-9.9] mmol/L; p = 0.048), glucose variability (p = 0.010), and peak glucose (p = 0.048) were lower following HIIT10 compared to CON, while HIIT4 reduced time spent in moderate hyperglycemia compared to CON (p = 0.023). Both HIIT10 and HIIT4 acutely lower glycemia, but the effect does not persist over 24 h. However, in individuals with worse glucose control, HIIT10 may improve mean 24-h glucose and glycemic variability, while HIIT4 may reduce time spent in moderate hyperglycemia.

Glycemic control is essential for improving the prognosis of cardiac surgery, although precise recommendations have not yet been established. Under a constant blood glucose level, the insulin infusion rate correlates with insulin resistance during glycemic control using an artificial pancreas (AP). We conducted this retrospective study to elucidate changes in intraoperative insulin sensitivity as a first step to creating glycemic control guidelines.

Fifty-five cardiac surgery patients at our hospital who underwent intraoperative glycemic control using an AP were enrolled. Twenty-three patients undergoing surgical procedures requiring cardiac arrest under hypothermic cardiopulmonary bypass (CPB) with minimum rectal temperatures lower than 32°C, 13 patients undergoing surgical procedures requiring cardiac arrest under hypothermic CPB with minimum rectal temperatures of 32°C, eight patients undergoing on-pump beating coronary artery bypass grafting and 11 patients undergoing off-pump coronary artery bypass were assigned to groups A, B, C and D, respectively. We analyzed the time course of changes in the data derived from glycemic control using the AP.

Significant time course changes were observed in groups A and B, but not in groups C and D. Insulin resistance was induced after the start of hypothermic CPB in groups A and B, and the induced change was not resolved by the rewarming procedure, remaining sustained until the end of surgery.

Hypothermia is the predominant factor of the induced insulin resistance during cardiac surgery. Thus, careful glycemic management during hypothermic CPB is important. Prospective clinical studies are required to confirm the findings of this study.

Hyperglycemia is considered an adaptive metabolic manifestation of stress and is associated with poor outcomes. Herein, we analyzed the association between glycemic variability (GV) and hospital mortality in patients with coronavirus disease 2019 (COVID-19) admitted to the intensive care unit (ICU), and the association between GV and mechanical ventilation (MV), ICU stay, length of hospital stays, renal replacement therapy (RRT), hypoglycemia, nosocomial infections, insulin use, and corticosteroid class.

In this retrospective observational study, we collected information on blood glucose levels during the first 10 days of hospitalization in a cohort of ICU patients with COVID-19 and its association with outcomes.

In 239 patients, an association was observed between GV and hospital mortality between the first and last quartiles among patients without diabetes [odds ratio (OR), 3.78; confidence interval, 1.24-11.5]. A higher GV was associated with a greater need for RRT (p = 0.002), regular insulin (p < 0.001), and episodes of hypoglycemia (p < 0.001). Nosocomial infections were associated with intermediate GV quartiles (p = 0.02). The corticosteroid class had no association with GV (p = 0.21).

Glycemic variability was associated with high mortality in patients with COVID-19 and observed in the subgroup of patients without diabetes.

Glycemic control in critically ill patients remains controversial and hyperglycemia is associated with worse outcomes. Diabetes mellitus (DM) is one of the most prevalent comorbidities in patients with COVID-19. In addition, they require corticosteroids due to pulmonary involvement, representing a challenge and an opportunity to better understand how glycemic changes can influence the outcome of these patients.

Boschi E, Friedman G, Moraes RB. Effects of Glycemic Variability in Critically Ill Patients with Coronavirus Disease 2019: A Retrospective Observational Study. Indian J Crit Care Med 2024;28(4):381-386.

To perform a systematic review and meta-analysis to evaluate the association of various measures of glycemic variability, including time-domain and complexity-domain, with short-term mortality in patients with critical illness.

We searched Embase Classic +, MEDLINE, and the Cochrane Database of Systematic Reviews from inception to November 3, 2023.

We included English language studies that assessed metrics of glycemic variation or complexity and short-term mortality in patients admitted to the ICU.

Two authors performed independent data abstraction and risk-of-bias assessments. We used a random-effects model to pool binary and continuous data and summarized estimates of effect using odds ratios and mean difference. We used the Quality in Prognosis Studies tool to assess risk of bias and the Grading of Recommendations, Assessment, Development and Evaluations to assess certainty of pooled estimates.

We included 41 studies (n = 162,259). We demonstrate that increased sd, coefficient of variance, glycemic lability index, and decreased time in range are probably associated with increased mortality in critically ill patients (moderate certainty) and that increased mean absolute glucose, mean amplitude of glycemic excursion, and detrended fluctuation analysis may be associated with increased mortality (low certainty).

We found a consistent association between increased measures of glycemic variability and higher short-term mortality in patient with critical illness. Further research should focus on standardized measurements of glycemic variation and complexity, along with their utility as therapeutic targets and prognostic markers.

Data on hyperglycemia and glucose variability in relation to diabetes mellitus, either known or unknown in ICU-setting in COVID-19, are scarce. We prospectively studied daily glucose variables and mortality in strata of diabetes mellitus and glycosylated hemoglobin among mechanically ventilated COVID-19 patients.

We used linear-mixed effect models in mechanically ventilated COVID-19 patients to investigate mean and maximum difference in glucose concentration per day over time. We compared ICU survivors and non-survivors and tested for effect-modification by pandemic wave 1 and 2, diabetes mellitus, and admission HbA1c.

Among 232 mechanically ventilated COVID-19 patients, 21.1% had known diabetes mellitus, whereas 16.9% in wave 2 had unknown diabetes mellitus. Non-survivors had higher mean glucose concentrations (ß 0.62 mmol/l; 95%CI 0.20-1.06; ß 11.2 mg/dl; 95% CI 3.6-19.1; P = 0.004) and higher maximum differences in glucose concentrations per day (ß 0.85 mmol/l; 95%CI 0.37-1.33; ß 15.3; 95%CI 6.7-23.9; P = 0.001). Effect modification by wave, history of diabetes mellitus and admission HbA1c in associations between glucose and survival was not present. Effect of higher mean glucose concentrations was modified by pandemic wave (wave 1 (ß 0.74; 95% CI 0.24-1.23 mmol/l) ; (ß 13.3; 95%CI 4.3-22.1 mg/dl)) vs. (wave 2 (ß 0.37 (95%CI 0.25-0.98) mmol/l) (ß 6.7 (95% ci 4.5-17.6) mg/dl)).

Hyperglycemia and glucose variability are associated with mortality in mechanically ventilated COVID-19 patients irrespective of the presence of diabetes mellitus.

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.

Diabetes Technology Society organized an expert consensus panel to develop metrics for research in the use of continuous glucose monitors (CGMs) in a hospital setting. The experts met virtually in small groups both before and after an April 13, 2023 virtual meeting of the entire panel. The goal of the panel was to develop consensus definitions in anticipation of greater use of CGMs in hospital settings in the future. Establishment of consensus definitions of inpatient analytical metrics will be easier to compare outcomes between studies. Panelists defined terms related to 10 dimensions of measurements related to the use of CGMs including (1) hospital hypoglycemia, (2) hospital hyperglycemia, (3) hospital time in range, (4) hospital glycemic variability, (5) hospital glycemia risk index, (6) accuracy of CGM devices and reference methods for CGMs in the hospital, (7) meaningful time blocks for hospital glycemic goals, (8) hospital CGM data sufficiency, (9) using CGM data for insulin dosing, and (10) miscellaneous factors. The panelists voted on 51 proposed recommendations. Based on the panel vote, 51 recommendations were classified as either strong (43) or mild (8). Additional research is needed on CGM performance in the hospital. This consensus report is intended to support that type of research intended to improve outcomes for hospitalized people with diabetes.

Following the new ESPEN Standard Operating Procedures, the previous 2019 guideline to provide best medical nutritional therapy to critically ill patients has been shortened and partially revised. Following this update, we propose this publication as a practical guideline based on the published scientific guideline, but shortened and illustrated by flow charts. The main goal of this practical guideline is to increase understanding and allow the practitioner to implement the Nutrition in the ICU guidelines. All the items discussed in the previous guidelines are included as well as special conditions.

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.

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.

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.

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.

It is not well understood whether glucose control in the early stage of acute pancreatitis(AP) is related to outcome. This study aimed to investigate the association between blood glucose time in range (TIR) of 70-180 mg/dL in the first 72 h(h) on admission and the progression of AP.

Individuals admitted with AP to the Gastroenterology Department of the Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University between January 2017 and December 2021 were included and retrospectively evaluated. The percentage of TIR between 70 and 180 mg/dL in the first 72 h was calculated. According to the progress of AP at discharge, patients were divided into mild pancreatitis(MAP), and moderately severe acute pancreatitis (MSAP), or severe acute pancreatitis (SAP) groups. We examined the association between TIR or TIR ≥ 70% and AP severity using logistic regression models stratified by a glycosylated hemoglobin (HbA1c) level of 6.5%. Receiver operating characteristic (ROC) curves were generated to assess the ability of the TIR to predict MSAP or SAP.

A total of 298 individuals were included, of whom 35 developed MSAP or SAP. Logistic regression analyses indicated that TIR was independently associated with the incidence of more serious AP (odds ratio [OR] = 0.962, 95% CI = 0.941-0.983, p = 0.001). This association remained significant in individuals with HbA1c levels ≤ 6.5% (OR = 0.928, 95% CI = 0.888-0.969, p = 0.001). A TIR ≥ 70% was independently associated with reduced severity only in people with well-antecedent controls (OR = 0.238; 95% CI = 0.071-0.802; p = 0.020). TIR was not powerful enough to predict the severity of AP in both patients with poor antecedent glucose control (AUC = 0.641) or with HbA1c < 6.5% (AUC = 0.668).

TIR was independently associated with severity in patients with AP, particularly those with good antecedent glucose control.

The detrimental effects of both diabetes mellitus (DM) and hyperglycemia in the perioperative period are well established and have driven extensive efforts to control blood glucose concentration (BGC) in a variety of clinical settings. It is now appreciated that acute BGC spikes, hypoglycemia, and high glycemic variability (GV) lead to more endothelial dysfunction and oxidative stress than uncomplicated, chronically elevated BGC. In the perioperative setting, fasting is the primary approach to reducing the risk for pulmonary aspiration; however, prolonged fasting drives the body into a catabolic state and therefore may increase GV. Elevated GV in the perioperative period is associated with an increased risk for postoperative complications, including morbidity and mortality. These challenges pose a conundrum for the management of patients typically instructed to fast for at least 8 h before surgery. Preliminary evidence suggests that the administration of an oral preoperative carbohydrate load (PCL) to stimulate endogenous insulin production and reduce GV in the perioperative period may attenuate BGC spikes and ultimately decrease postoperative morbidity, without significantly increasing the risk of pulmonary aspiration. The aim of this scoping review is to summarize the available evidence on the impact of PCL on perioperative GV and surgical outcomes, with an emphasis on evidence pertaining to patients with DM. The clinical relevance of GV will be summarized, the relationship between GV and postoperative course will be explored, and the impact of PCL on GV and surgical outcomes will be presented. A total of 13 articles, presented in three sections, were chosen for inclusion. This scoping review concludes that the benefits of a PCL outweigh the risks in most patients, even in those with well controlled type 2 DM. The administration of a PCL might effectively minimize metabolic derangements such as GV and ultimately result in reduced postoperative morbidity and mortality, but this remains to be proven. Future efforts to standardize the content and timing of a PCL are needed. Ultimately, a rigorous data-driven consensus opinion regarding PCL administration that identifies optimal carbohydrate content, volume, and timing of ingestion should be established.

Evaluation of glucometrics in the first week of ICU stay and its association with outcomes.

Prospective observational study.

Mixed ICU of teaching hospital.

Adults initiated on insulin infusion for 2 consecutive blood glucose (BG) readings ≥180mg/dL.

Glucometrics calculated from the BG of first week of admission: hyperglycemia (BG>180mg/dL) and hypoglycemia (BG<70mg/dL) episodes; median, standard deviation (SD) and coefficient of variation (CV) of BG, glycemic lability index (GLI), time in target BG range (TIR). Factors influencing glucometrics and the association of glucometrics to patient outcomes analyzed.

A total of 5762 BG measurements in 100 patients of median age 55 years included. Glucometrics: hyperglycemia: 2253 (39%), hypoglycemia: 28 (0.48%), median BG: 169mg/dL (162-178.75), SD 31mg/dL (26-38.75), CV 18.6% (17.1-22.5), GLI: 718.5 [(mg/dL)²/h]/week (540.5-1131.5) and TIR 57% (50-67). Diabetes and higher APACHE II score were associated with higher SD and CV, and lower TIR. On multivariate regression, diabetes (p=0.009) and APACHE II score (p=0.016) were independently associated with higher SD. Higher SD and CV were associated with less vasopressor-free days; lower TIR with more blood-stream infections (BSI). Patients with higher SD, CV and GLI had a higher 28-day mortality. On multivariate analysis, GLI alone was associated with a higher mortality (OR 2.99, p=0.04).

Glycemic lability in the first week in ICU patients receiving insulin infusion is associated with higher mortality. Lower TIR is associated with more blood stream infections.

Associations between degrees of postoperative hyperglycemia and morbidity has previously been established. There may be an association between the glycemic profile and patient-reported recovery, and this may be a target for perioperative quality improvements. We aimed to investigate the association between metrics of the 30-day glycemic profile and patient-reported recovery in nondiabetic patients after major abdominal surgery. In a prospective, explorative cohort study, nondiabetic adult patients undergoing acute, major abdominal surgery were included within 24 h after surgery. Interstitial fluid glucose concentration was measured for 30 consecutive days with a continuous glucose measurement device. The validated questionnaire 'Quality of Recovery-15' was used to assess patient-reported quality of recovery on postoperative days 10, 20, and 30. Follow-up time was divided into five-day postoperative intervals using days 26-30 as a reference. Linear mixed models were applied to investigate temporal changes in mean p-glucose, coefficient of variation, time within 70-140 mg/dl, and time above 200 mg/dl in relation to patient-reported recovery. Twenty-seven patients completed the study per protocol. A hyperglycemic event (>200 mg/dl) occurred in 18 of 27 patients (67%) within the first three postoperative days. Compared to the reference period, the coefficient of variation was significantly increased during all time intervals, indicating prolonged postoperative insulin resistance. During 30 days of follow-up, patient-reported recovery was associated with the coefficient of variation measured for 3 and 5 days before the corresponding recovery score assessment (recovery score estimate -1.52 [p < .001] and -0.92 [p = .006], respectively). We did not find an association between the remaining metrics and patient-reported recovery. Alterations in the glycemic profile are frequent and prolonged during the first postoperative month after major surgery probably due to peripheral insulin resistance. Our findings indicate that high-glycemic variation is associated with poorer patient-reported recovery and might represent a proxy for care improvements in the postoperative period.

Whether subcutaneous continuous glucose monitoring (CGM) can safely replace intermittent arterial blood gas glucose analyses in intensive care unit (ICU) patients remains uncertain. We aimed to compare CGM to blood gas glucose values and assess whether CGM use reduces blood gas sampling frequency and glucose variability in ICU patients with type 2 diabetes managed with liberal glucose control.

We used the FreeStyle Libre CGM in 15 ICU patients and compared their blood glucose metrics with a pre-CGM control population of 105 ICU patients with type 2 diabetes. Both groups received insulin to target glucose range of 10-14 mmol/L. We used linear regression analysis adjusted for illness severity to assess the association of CGM use with blood gas sampling frequency and glucose variability. We used mean absolute relative difference (MARD) and Clarke error grid analysis to assess accuracy of matched CGM-blood glucose values overall, across glucose stata (<10, 10-14, >14 mmol/L), and over time (≤48, 48-96, >96 h).

We analyzed 483 matched glucose values. Overall MARD was 11.5 (95% CI, 10.7-12.3)% with 99% of readings in Clarke zones A and B. MARD was 15.5% for glucose values <10 mmol/L, 11.1% at 10-14 mmol/L, and 11.4% >14 mmol/L. MARD was 13.8% in the first 48 h, 10.9% at 48-96 h, and 8.9% beyond 96 h. CGM use was associated with 30% reduction in blood gas sampling frequency. CGM use was not associated with glucose variability as determined by glycemic lability index or standard deviation of blood glucose.

In our cohort of ICU patients with type 2 diabetes receiving liberal glycemic control, CGM showed acceptable accuracy and was associated with a reduction in blood gas sampling frequency without compromising glucose control. Lowest accuracy was observed at glucose values below 10 mmol/L and during the first 48 h of CGM use.

High glycemic variability (GV) is a poor prognostic marker in cardiovascular diseases. We aimed to investigate the association of GV with all-cause mortality in patients with acute heart failure (HF).

The Korean Acute Heart Failure registry enrolled patients hospitalized for acute HF from 2011 to 2014. Blood glucose levels were measured at the time of admission, during hospitalization, and at discharge. We included those who had 3 or more blood glucose measurements in this study. Patients were divided into two groups based on the coefficient of variation (CoV) as an indicator of GV. Among survivors of the index hospitalization, we investigated all-cause mortality at 1 year after discharge.

The study analyzed 2,617 patients (median age, 72 years; median left-ventricular ejection fraction, 36%; 53% male). During the median follow-up period of 11 months, 583 patients died. Kaplan-Meier curve analysis revealed that high GV (CoV > 21%) was associated with lower cumulative survival (log-rank P < 0.001). Multivariate Cox proportional analysis showed that high GV was associated with an increased risk of 1-year (HR 1.56, 95% CI 1.26-1.92) mortality. High GV significantly increased the risk of 1-year mortality in non-diabetic patients (HR 1.93, 95% CI 1.47-2.54) but not in diabetic patients (HR 1.19, 95% CI 0.86-1.65, P for interaction = 0.021).

High in-hospital GV before discharge was associated with all-cause mortality within 1 year, especially in non-diabetic patients with acute HF.

This study aimed to evaluate the effect of early glycaemic variability (GV) on 28-day mortality in critically ill patients with pneumonia.

This single-centre retrospective study included patients admitted to the intensive care unit (ICU) due to pneumonia between 2018 and 2019. A total of 282 patients (mean age, 68.6 years) with blood sugar test (BST) results measured more than three times within 48 h after hospitalization and haemoglobin A1c (HbA1c) levels recorded within 2 months were enrolled. Coefficient of variation (CV) was calculated using the BST values. The effects of GV on 28-day mortality and prolonged ICU stay (>14 days) were also assessed.

The mean age was 60.6 years (male to female ratio, 2.5:1). The 28-day mortality rate was 31.6% (n = 89) and was not different according to the presence of diabetes (DM vs. non-DM) or HbA1c levels (≥7.5 vs. <7.5%; both p > .05). However, the mortality rate was significantly higher in patients with high GV (CV ≥ 36%) than in those with low GV (CV < 36%; 37.5 vs. 25.4%, p = .028). The risk of mortality in patients with high GV was prominent in the subgroups with DM or low HbA1c levels. Among the surviving patients (n = 193), 44 remained in the ICU for more than 14 days. Compared to low GV, high GV was associated with a higher rate of prolonged ICU stay, although not statistically significant (27.8 vs. 18.5%, p = .171). After adjusting for the severity of illness and treatment strategy, CV was an independent risk factor for 28-day mortality (hazard ratio [HR], 1.01, p = .04) and prolonged ICU stay (odds ratio, 1.02; p = .04).

High GV within 48 h of ICU admission was associated with an increased 28-day mortality risk and prolonged ICU stay. Early phase GV should be carefully managed in critically ill patients with pneumonia.KEY MESSAGESThe presence of diabetes or HbA1c alone is insufficient to predict 28-day mortality and prolonged ICU stay in critically ill patients with pneumonia.High glycaemic variability (GV) within 48 h of ICU admission increases 28-day mortality and prolongs ICU stay, which is consistent after adjusting for severity of illness and treatment strategy.Patients with high GV, especially those with DM or low HbA1c levels (<7.5%) should be more carefully treated to reduce mortality.

Stress hyperglycaemia is common following cardiac surgery. Its optimal management is uncertain and emerging literature suggests that flexible glycaemic control in diabetic patients may be preferable. This study aims to assess the relationship between maximal postoperative in-hospital blood glucose levels (BSL) and the morbidity and mortality outcomes of diabetic and non-diabetic cardiac surgery patients.

A retrospective cohort analysis of all patients undergoing cardiac surgery at a tertiary single centre institution from 2015 to 2019 was undertaken. Early management and outcomes of hyperglycaemia following cardiac surgery were assessed via multivariable regression modelling. Follow-up was assessed to 1 year postoperatively.

Consecutive non-diabetic patients (n=1,050) and diabetic patients (n=689) post cardiac surgery were included. Diabetics with peak BSL ≤13.9 mmol/L did not have an increased risk of morbidity or mortality compared to non-diabetics with peak BSL ≤10.0 mmol/L. In non-diabetics, stress hyperglycaemia with peak BSL >10.0 mmol/L was associated with overall wound complications (5.7% vs 8.8%, OR 1.64 [1.00-2.69], p=0.049) and postoperative pneumonia (2.7% vs 7.3%, OR 2.35 [1.26-4.38], p=0.007). Diabetic patients with postoperative peak BSL >13.9 mmol/L were at an increased risk of overall wound complication (7.4% vs 14.8%, OR 2.47 [1.46-4.16], p<0.001), graft harvest site infection (3.7% vs 11.8%, OR 3.75 [1.92-7.30], p<0.001), and wound-related readmission (3.1% vs 8.8%, OR 3.11 [1.49-6.47], p=0.002) when compared to diabetics with peak BSL ≤13.9 mmol/L.

In non-diabetics, stress hyperglycaemia with peak BSL >10.0 mmol/L is associated with morbidity. In diabetic patients, hyperglycaemia with peak BSL ≤13.9 mmol/L was not associated with an increased risk of morbidity or mortality compared to non-diabetics with peak BSL ≤10.0 mmol/L. Further investigation of flexible glycaemic targets (target BSL ≤13.9 mmol/L) in diabetic patients is warranted.