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

Stress-induced hyperglycemia and expression of glucose cell transport genes in skeletal muscle of critically ill patients: a cross-sectional study

OBJECTIVE

To explore the association between diabetes and stress-induced hyperglycemia with skeletal muscle expression of key genes related to glucose transport.

METHODS

This is a cross-sectional study. Skeletal muscle biopsies were taken from the left vastus muscle of critically ill adult patients within 24 hours of intensive care unit admission, and the expression of the genes of interest, namely insulin receptor substrate 1 (IRS1), insulin receptor substrate 2 (IRS2), solute carrier family 2 member 1 (SLC2A1), and solute carrier family 2 member 4 (SLC2A4), was analyzed using quantitative polymerase chain reaction. The primary analysis was planned to compare the gene expression pattern between patients with and without diabetes mellitus. The secondary analyses compared the gene expression in subgroups of patients with different levels of glycemia, glycemic variability, and glycemic gap.

RESULTS

A total of 50 consecutive patients (15 with diabetes mellitus and 35 without diabetes mellitus) were included from April 2018 to September 2018. No differences in gene expression were found between patients with or without diabetes mellitus. Individuals with hyperglycemia > 200 mg/dL at intensive care unit admission exhibited a downregulation of IRS1 compared to those without (0.4 [0.1-0.8] versus 1.1 [0.3-2.2], p = 0.04). Similarly, patients with a glycemic gap ≥ 80 mg/dL exhibited a downregulation of IRS1 compared to those with a glycemic gap < 80 mg/dL (0.3 [0.1-0.7] versus 1 [0.4-2] p=0.04). There was no difference in gene expression between patients with glycemic variability higher or lower than 40 mg/dL.

CONCLUSION

No significant changes were found in skeletal muscle expression of IRS1, IRS2, SLC2A1, and SLC2A4 in critically ill patients with or without diabetes mellitus. However, IRS1 was downregulated in patients with stress-induced hyperglycemia.

Insulin Degludec in Critically Ill Patients With Type 2 Diabetes Mellitus: A Prospective Interventional Study

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

The effect of percentage of time spent above different glucose levels on 90 days mortality of critically ill patients - A retrospective cohort study

INTRODUCTION

Glycemic control is a major concern during critical illness. Several prospective studies have yielded conflicting results regarding its mortality effect. Current recommendations are to initiate insulin therapy for all patients when glucose levels are higher than 180 mg/dL. Some suggest decreasing this threshold for non-diabetic patients to 140 mg/dL. These thresholds haven't been compared to each other or to other glucose thresholds. This study aimed to find out whether different glucose levels are associated with 90-d mortality.

METHODS

A retrospective cohort study. Critically ill patients who were admitted from 2019 to 2022 to a mixed medical-surgical intensive care unit for more than 48 h were included. Collected data included baseline characteristics, and all glucose levels recorded (time-indexed to the admission time). Glucose levels were considered constant until the following glucose level. The percentage of time above several chosen glucose cutoff levels was calculated and analyzed for mortality adjusted to other baseline covariates.

RESULTS

45,512 glucose measurements of 1429 patients were included in the study; 21.76 % of the patients had diabetes. Mean glucose level and glucose variability were higher in diabetic patients (165.86 mg/dL vs 135.47 mg/dL, p < 0.0001, and 30.81 % vs 20.86 %, p < 0.0001, respectively), along with a higher incidence of hypoglycemia (40.84 % vs 24.89 %, p < 0.001). 90-d mortality was higher in diabetic patietns (42.12 % vs 32.41 %, p = 0.0014) and was found associated with age, acute physiology and chronic health evaluation 2 score, medical or surgical admission reasons. Percentage of time above cutoffs ≥150 mg/dL was associated with 90-d mortality only in non-diabetic patients.

CONCLUSIONS

In non-diabetic patients, hyperglycemia greater than 150 mg/dL, was associated with increased 90-day mortality.

Association and predictive ability between significant perioperative cardiovascular adverse events and stress glucose rise in patients undergoing non-cardiac surgery

BACKGROUND

The predictive importance of the stress hyperglycemia ratio (SHR), which is composed of admission blood glucose (ABG) and glycated hemoglobin (HbA1c), has not been fully established in noncardiac surgery. This study aims to evaluate the association and predictive capability the SHR for major perioperative adverse cardiovascular events (MACEs) in noncardiac surgery patients.

METHODS

Individuals who underwent noncardiac surgical procedures between 2011 and 2020, including both diabetic and non-diabetic patients, were identified in the perioperative medicine database (INSPIRE 1.1) and classified into tertiles based on their SHR. The connection between the SHR and the risk of MACEs was studied using Cox proportional hazards regression analysis, then restricted cubic spline (RCS) was employed to assess the association's form. Additionally, the SHR's incremental predictive utility for MACEs was assessed by the C-statistic, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI), thereby quantifying the enhancement in predictive accuracy brought by incorporating the SHR into existing risk models. Feature importance and predictive models were generated utilizing the Boruta algorithm and machine learning approaches.

RESULTS

A total of 5609 patients were enrolled. With an upwards shift in SHR vertices, the rate of perioperative MACEs and cardiac death event steadily rose. The RCS analysis for perioperative MACEs and cardiac death event both indicated J-shaped associations. Inflection points occurred at SHR = 0.81 for MACEs and SHR = 0.97 for cardiac death. The model's fit improved significantly, with a continuous NRI of 0.067 (95% CI: 0.025-0.137, P < 0.001) and an IDI of 0.305 (95% CI: 0.155-0.430, P < 0.001). When SHR was added as a categorical variable (> 0.81), the C-statistic increased to 0.785 (95% CI: 0.756-0.814) with a ΔC-statistic of 0.035 (P = 0.009), a continuous NRI of 0.007 (95% CI: 0.000-0.021, P = 0.016), and an IDI of 0.076 (95% CI -0.024-0.142, P = 0.092). In the Boruta algorithm, variables identified as important features in the green area were incorporated into the machine learning models development.

CONCLUSIONS

The SHR was related with an increased risk of perioperative MACEs in patients following noncardiac surgery, highlighting its potential as a useful and reliable predictive tool for assessing the risk of perioperative MACEs.

Accuracy of a Continuous Glucose Monitor in the Intensive Care Unit: A Proposed Accuracy Standard and Calibration Protocol for Inpatient Use

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.

Association of Relative Dysglycemia With Hospital Mortality in Critically Ill Patients: A Retrospective Study

OBJECTIVES

Relative dysglycemia has been proposed as a clinical entity among critically ill patients in the ICU, but is not well studied. This study aimed to clarify associations of relative hyperglycemia and hypoglycemia during the first 24 hours after ICU admission with in-hospital mortality and the respective thresholds.

DESIGN

A single-center retrospective study.

SETTING

An urban tertiary hospital ICU.

PATIENTS

Adult critically ill patients admitted urgently between January 2016 and March 2022.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Maximum and minimum glycemic ratio (GR) was defined as maximum and minimum blood glucose values during the first 24 hours after ICU admission divided by hemoglobin A1c-derived average glucose, respectively. Of 1700 patients included, in-hospital mortality was 16.9%. Nonsurvivors had a higher maximum GR, with no significant difference in minimum GR. Maximum GR during the first 24 hours after ICU admission showed a J-shaped association with in-hospital mortality, and a mortality trough at a maximum GR of approximately 1.12; threshold for increased adjusted odds ratio for mortality was 1.25. Minimum GR during the first 24 hours after ICU admission showed a U-shaped relationship with in-hospital mortality and a mortality trough at a minimum GR of approximately 0.81 with a lower threshold for increased adjusted odds ratio for mortality at 0.69.

CONCLUSIONS

Mortality significantly increased when GR during the first 24 hours after ICU admission deviated from between 0.69 and 1.25. Further evaluation will necessarily validate the superiority of personalized glycemic management over conventional management.

Nutritional support in hospitalised patients with diabetes and risk for malnutrition: a secondary analysis of an investigator-initiated, Swiss, randomised controlled multicentre trial

OBJECTIVES

The main objective of this study was to investigate the effects of nutritional support on mortality in hospitalised patients with diabetes and nutritional risk participating in the Effect of early nutritional support on Frailty, Functional Outcomes, and Recovery of malnourished medical inpatients Trial (EFFORT) trial.

DESIGN

Secondary analysis of a Swiss-wide multicentre, randomised controlled trial.

PARTICIPANTS

Patients with diabetes and risk for malnutrition.

INTERVENTIONS

Individualised nutritional support versus usual care.

PRIMARY OUTCOME MEASURE

30-day all-cause mortality.

RESULTS

Of the 2028 patients included in the original trial, 445 patients were diagnosed with diabetes and included in this analysis. In terms of efficacy of nutritional therapy, there was a 25% lower risk for mortality in patients with diabetes receiving nutritional support compared with controls (7% vs 10%, adjusted HR 0.75 (95% CI 0.39 to 1.43)), a finding that was not statistically significant but similar to the overall trial effects with no evidence of interaction (p=0.92). Regarding safety of nutritional therapy, there was no increase in diabetes-specific complications associated with nutritional support, particularly there was no increase in risk for hyperglycaemia (adjusted OR 0.97, 95% CI 0.56 to 1.67 p=0.90).

CONCLUSION

Patients with diabetes and malnutrition in the hospital setting have a particularly high risk for adverse outcomes and mortality. Individualised nutritional support reduced mortality in this secondary analysis of a randomized trial, but this effect was not significant calling for further large-scale trials in this vhighly ulnerable patient population.

TRIAL REGISTRATION NUMBER

NCT02517476.

BMI and mortality in critically ill patients with COVID-19: another brick in the wall of the obesity paradox

OBJECTIVE

The objective of this study was to assess the existence of the obesity paradox in patients with COVID-19 admitted to the intensive care unit.

METHODS

This was a multicentric retrospective cohort study including individuals aged 18 years or older admitted to the intensive care unit with SARS-CoV-2. Data were obtained from electronic medical records. The primary outcome was in-hospital mortality. Multiple logistic regression and restricted cubic splines analyses were conducted to assess the association between BMI and mortality.

RESULTS

From March 2020 to December 2021, 977 patients met the inclusion criteria, and 868 were included in the analysis. Obesity was identified in 382 patients (44%). Patients with obesity more often underwent prone positioning (42% vs. 28%; p < 0.001), although they used less vasoactive medications (57% vs. 68%; p < 0.001). The overall in-hospital mortality was 48%, with 44% observed in the subgroup of individuals with obesity and 50% in those without obesity (p = 0.06). Patients with BMI < 25 kg/m2 had the highest mortality.

CONCLUSIONS

Obesity was not associated with higher mortality rates in critically ill patients with COVID-19. Moreover, patients with BMI < 25 kg/m2 had a higher mortality rate compared with those in higher BMI categories.

Between blood glucose and mortality in critically ill patients: Retrospective analysis of the MIMIC-IV database

AIMS/INTRODUCTION

Mean blood glucose (MBG) level is associated with mortality among critically ill patients. We undertook a cohort study to investigate the relationship between MBG and mortality in critically ill patients.

MATERIALS AND METHODS

Critically ill patients were enrolled from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. MBG was calculated to represent the overall glycemic status during intensive care unit (ICU) hospitalization, and a multivariate logistic regression determined the relationship between MBG and ICU mortality in different subgroups of critically ill patients.

RESULTS

A total of 8,973 patients were included in the study, 1,244 of whom died within 28 days, including 5,402 men and 3,571 women. Multivariate adjusted restricted cubic spline analyses suggested that the relationship between MBG and ICU mortality was a "J" shape. Logistic regression showed 28 day mortality in group 3 (glucose ≥10 mmol/L): the adjusted odds ratio was 2.06 (95% confidence interval 1.65-2.57). The results of subgroup analysis showed that hyperglycemia had a more significant impact on ICU mortality in patients without diabetes, hypoglycemia and liver disease, and the ICU mortality risk of non-diabetes patients was always higher than that of diabetes patients with the same hyperglycemia level.

CONCLUSIONS

Current evidence suggested a J-shaped relationship between MBG and mortality in critically ill patients.

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.

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.

Glycemic variability and mortality in oncologic intensive care units

OBJECTIVE

This study aimed to investigate the association between glycemic variability and mortality in patients admitted to oncologic intensive care units.

METHODS

A retrospective cohort study was conducted using a convenience sample of 30 medical records of patients over 18 years of age of both sexes. Glycemic variability was measured using the standard deviation and general amplitude. Statistical analysis was performed using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The significance level (α) was set at 5% with a confidence interval (CI) of 95%.

RESULTS

The study included 14 male patients (46.67%) with a mean age of 60±15 years. A total of 1503 blood glucose samples were collected. The AUC analysis for the standard deviation did not show a statistically significant result (p=.966; 95% CI=[0.283, 0.726]). In contrast, the general amplitude had a statistically significant association with mortality (p=.049; 95% CI=[0.514, 0.916]).

CONCLUSIONS

This study found that glycemic variability measured by the general amplitude was significantly associated with patient mortality in oncologic intensive care units. These findings suggest that monitoring glycemic variability may be an important factor in the management of critically ill patients in oncologic intensive care units.

Use of Peak Glucose Level and Peak Glycemic Gap in Mortality Risk Stratification in Critically Ill Patients with Sepsis and Prior Diabetes Mellitus of Different Body Mass Indexes

Sepsis remains a critical concern in healthcare, and its management is complicated when patients have pre-existing diabetes and varying body mass indexes (BMIs). This retrospective multicenter observational study, encompassing data from 15,884 sepsis patients admitted between 2012 and 2017, investigates the relationship between peak glucose levels and peak glycemic gap in the first 3 days of ICU admission, and their impact on mortality. The study reveals that maintaining peak glucose levels between 141-220 mg/dL is associated with improved survival rates in sepsis patients with diabetes. Conversely, peak glycemic gaps exceeding 146 mg/dL are linked to poorer survival outcomes. Patients with peak glycemic gaps below -73 mg/dL also experience inferior survival rates. In terms of predicting mortality, modified Sequential Organ Failure Assessment-Peak Glycemic Gap (mSOFA-pgg) scores outperform traditional SOFA scores by 6.8% for 90-day mortality in overweight patients. Similarly, the modified SOFA-Peak Glucose (mSOFA-pg) score demonstrates a 17.2% improvement over the SOFA score for predicting 28-day mortality in underweight patients. Importantly, both mSOFA-pg and mSOFA-pgg scores exhibit superior predictive power compared to traditional SOFA scores for patients at high nutritional risk. These findings underscore the importance of glycemic control in sepsis management and highlight the potential utility of the mSOFA-pg and mSOFA-pgg scores in predicting mortality risk, especially in patients with diabetes and varying nutritional statuses.

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.

Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study

BACKGROUND

Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person's home. CGM systems inspired analytics methods to measure glycemic variability (GV), but existing GV analytics methods disregard glucose trends and patterns; hence, they fail to capture entire temporal patterns and do not provide granular insights about glucose fluctuations.

OBJECTIVE

This study aimed to propose a machine learning-based framework for blood glucose fluctuation pattern recognition, which enables a more comprehensive representation of GV profiles that could present detailed fluctuation information, be easily understood by clinicians, and provide insights about patient groups based on time in blood fluctuation patterns.

METHODS

Overall, 1.5 million measurements from 126 patients in the United Kingdom with type 1 diabetes mellitus (T1DM) were collected, and prevalent blood fluctuation patterns were extracted using dynamic time warping. The patterns were further validated in 225 patients in the United States with T1DM. Hierarchical clustering was then applied on time in patterns to form 4 clusters of patients. Patient groups were compared using statistical analysis.

RESULTS

In total, 6 patterns depicting distinctive glucose levels and trends were identified and validated, based on which 4 GV profiles of patients with T1DM were found. They were significantly different in terms of glycemic statuses such as diabetes duration (P=.04), glycated hemoglobin level (P<.001), and time in range (P<.001) and thus had different management needs.

CONCLUSIONS

The proposed method can analytically extract existing blood fluctuation patterns from CGM data. Thus, time in patterns can capture a rich view of patients' GV profile. Its conceptual resemblance with time in range, along with rich blood fluctuation details, makes it more scalable, accessible, and informative to clinicians.

Inter-relationships between gastric emptying and glycaemia: Implications for clinical practice

Gastric emptying (GE) exhibits a wide inter-individual variation and is a major determinant of postprandial glycaemia in health and diabetes; the rise in blood glucose following oral carbohydrate is greater when GE is relatively more rapid and more sustained when glucose tolerance is impaired. Conversely, GE is influenced by the acute glycaemic environment acute hyperglycaemia slows, while acute hypoglycaemia accelerates it. Delayed GE (gastroparesis) occurs frequently in diabetes and critical illness. In diabetes, this poses challenges for management, particularly in hospitalised individuals and/or those using insulin. In critical illness it compromises the delivery of nutrition and increases the risk of regurgitation and aspiration with consequent lung dysfunction and ventilator dependence. Substantial advances in knowledge relating to GE, which is now recognised as a major determinant of the magnitude of the rise in blood glucose after a meal in both health and diabetes and, the impact of acute glycaemic environment on the rate of GE have been made and the use of gut-based therapies such as glucagon-like peptide-1 receptor agonists, which may profoundly impact GE, in the management of type 2 diabetes, has become commonplace. This necessitates an increased understanding of the complex inter-relationships of GE with glycaemia, its implications in hospitalised patients and the relevance of dysglycaemia and its management, particularly in critical illness. Current approaches to management of gastroparesis to achieve more personalised diabetes care, relevant to clinical practice, is detailed. Further studies focusing on the interactions of medications affecting GE and the glycaemic environment in hospitalised patients, are required.

Positive association between stress hyperglycemia ratio and pulmonary infection in patients with ST-segment elevation myocardial infarction undergoing percutaneous coronary intervention

BACKGROUND

Previous studies have shown that the stress hyperglycemia ratio (SHR), a parameter of relative stress-induced hyperglycemia, is an excellent predictive factor for all-cause mortality and major adverse cardiovascular events (MACEs) among patients with ST-segment elevation myocardial infarction (STEMI). However, its association with pulmonary infection in patients with STEMI during hospitalization remains unclear.

METHODS

Patients with STEMI undergoing percutaneous coronary intervention (PCI) were consecutively enrolled from 2010 to 2020. The primary endpoint was the occurrence of pulmonary infection during hospitalization, and the secondary endpoint was in-hospital MACEs, composed of all-cause mortality, stroke, target vessel revascularization, or recurrent myocardial infarction.

RESULTS

A total of 2,841 patients were finally included, with 323 (11.4%) developing pulmonary infection and 165 (5.8%) developing in-hospital MACEs. The patients were divided into three groups according to SHR tertiles. A higher SHR was associated with a higher rate of pulmonary infection during hospitalization (8.1%, 9.9%, and 18.0%, P < 0.001) and in-hospital MACEs (3.7%, 5.1%, and 8.6%, P < 0.001). Multivariate logistic regression analysis demonstrated that SHR was significantly associated with the risk of pulmonary infection during hospitalization (odds ratio [OR] = 1.46, 95% confidence interval [CI] 1.06-2.02, P = 0.021) and in-hospital MACEs (OR = 1.67, 95% CI 1.17-2.39, P = 0.005) after adjusting for potential confounding factors. The cubic spline models demonstrated no significant non-linear relationship between SHR and pulmonary infection (P = 0.210) and MACEs (P = 0.743). In receiver operating characteristic curve, the best cutoff value of SHR for pulmonary infection was 1.073.

CONCLUSIONS

The SHR is independently associated with the risk of pulmonary infection during hospitalization and in-hospital MACEs for patients with STEMI undergoing PCI.

Glycemic Gap Predicts Mortality in a Large Multicenter Cohort Hospitalized With COVID-19

CONTEXT

Diabetes or hyperglycemia at admission are established risk factors for adverse outcomes during hospitalization for COVID-19, but the impact of prior glycemic control is not clear.

OBJECTIVE

We aimed to examine the associations between admission variables, including glycemic gap, and adverse clinical outcomes in patients hospitalized with COVID-19 infection.

METHODS

We examined the relationship between clinical predictors, including acute and chronic glycemia, and clinical outcomes, including intensive care unit (ICU) admission, mechanical ventilation (MV), and mortality among 1786 individuals with diabetes or hyperglycemia (glucose > 10 mmol/L twice in 24 hours) who were admitted from March 2020 through February 2021 with COVID-19 infection at 5 university hospitals in the eastern United States.

RESULTS

The cohort was 51.3% male, 53.3% White, 18.8% Black, 29.0% Hispanic, with age = 65.6 ± 14.4 years, BMI = 31.5 ± 7.9 kg/m2, glucose = 12.0 ± 7.5 mmol/L [216 ± 135 mg/dL], and HbA1c = 8.07% ± 2.25%. During hospitalization, 38.9% were admitted to the ICU, 22.9% received MV, and 10.6% died. Age (P < 0.001) and admission glucose (P = 0.014) but not HbA1c were associated with increased risk of mortality. Glycemic gap, defined as admission glucose minus estimated average glucose based on HbA1c, was a stronger predictor of mortality than either admission glucose or HbA1c alone (OR = 1.040 [95% CI: 1.019, 1.061] per mmol/L, P < 0.001). In an adjusted multivariable model, glycemic gap, age, BMI, and diabetic ketoacidosis on admission were associated with increased mortality, while higher estimated glomerular filtration rate (eGFR) and use of any diabetes medication were associated with lower mortality (P < 0.001).

CONCLUSION

Relative hyperglycemia, as measured by the admission glycemic gap, is an important marker of mortality risk in COVID-19.

Association between the stress hyperglycemia ratio and severity of coronary artery disease under different glucose metabolic states

BACKGROUND

Stress hyperglycemia ratio (SHR) is significantly related to adverse cardiovascular clinical outcomes and increased in-hospital mortality. However, the relationship between SHR and coronary artery disease (CAD) severity has hitherto not been reported. This study sought to clarify the relationship between the SHR and CAD severity of individuals with different glucose metabolic statuses.

METHODS

A retrospective analysis was performed on 987 patients who underwent coronary angiography (CAG) from October 2020 to May 2022. Based on CAG results, patients were divided into single-vessel CAD and multi-vessel CAD groups. All subjects were stratified into three groups according to the tertiles of the SHR (T1 group: SHR < 0.930; T2 group: 0.930 ≤ SHR < 1.154; T3 group: 1.154 ≤ SHR). Moreover, according to glucose metabolism status, study subjects were divided into normal glucose regulation (NGR), pre-diabetes mellitus (pre-DM) and diabetes mellitus (DM) groups. Finally, the correlation between SHR and CAD severity was analyzed by logistic regression analysis and receiver operating characteristic (ROC) curve.

RESULTS

The results showed significantly higher SHR in the multi-vessel CAD group than in the single-vessel group. Logistic regression analysis showed that SHR was an independent risk factor for multi-vessel CAD when used as a continuous variable (OR, 4.047; 95% CI 2.137-7.663; P < 0.001). After adjusting for risk factors, the risk of multi-vessel CAD in the T2 and T3 groups was 1.939-fold (95% CI 1.341-2.804; P < 0.001) and 1.860-fold (95% CI 1.272-2.719; P = 0.001) higher than in the T1 group, respectively. The area under the curve (AUC) of ROC plots was 0.613 for SHR. In addition, SHR was significantly correlated with an increased risk of multi-vessel CAD in the pre-DM and DM groups.

CONCLUSIONS

Our study indicated that SHR was significantly correlated with the risk of multi-vessel CAD and predicted CAD severity, especially in pre-DM and DM patients.

Stress-induced Hyperglycemia Ratio as an Independent Risk Factor of In-hospital Mortality in Nonresuscitation Intensive Care Units: A Retrospective Study

PURPOSE

To determine whether the stress-induced hyperglycemia ratio (SHR) is independently associated with in-hospital mortality in critically ill patients in nonresuscitation ICUs.

METHODS

In this retrospective cohort study, clinical- and laboratory-related data from patients first admitted to nonresuscitation ICUs were extracted from an open-access database of >50,000 ICU admissions. Patients were assigned to one of two groups according to an SHR threshold of 1.1. The primary end point of this study was the in-hospital mortality rate. The associations between SHR and length of stay in the ICU and hospital, duration of mechanical ventilation use, and vasopressor use were secondary end points. Logistic regression models were established in the analysis of in-hospital mortality risk, and areas under the receiver operating characteristic curve (AUC) were analyzed to investigate the association between the primary end point and SHR used alone or together with the Simplified Acute Physiology Scale (SAPS) II score. The Youden index, specificity, and sensitivity of SHR and SAPS-II were also assessed.

FINDINGS

In this study, 1859 patients were included, 187 of whom (10.06%) died during hospitalization. The group with an SHR of ≥1.1 had a greater in-hospital mortality rate (13.7% vs 7.4%; P < 0.001), longer length of stay both in the ICU and in the hospital, a longer duration of mechanical ventilation use, and a greater rate of vasopressor use. On adjustment for multivariate risk, a 0.1-point increment in SHR was significantly associated with in-hospital mortality (OR = 1.08; 95% CI, 1.00-1.16; P = 0.036). The AUC of the association between risk and the SAPS-II score was significantly greater than that with SHR (0.797 [95% CI, 0.576-0.664] vs 0.620 [95% CI, 0.764-0.830]; P < 0.001). The AUC with SAPS-II + SHR was significantly greater than that with SAPS-II used alone (0.802 [95% CI, 0.770-0.835] vs 0.797 [95% CI, 0.764-0.830]; P = 0.023). The Youden index, specificity, and sensitivity of SAPS-II + SHR were 0.473, 0.703, and 0.770, respectively.

IMPLICATIONS

Stress-induced hyperglycemia, as evaluated using the SHR, was associated with increased in-hospital mortality and worse clinical outcomes in these critically ill patients in nonresuscitation ICUs. SHR was an independent risk factor for in-hospital mortality, and when used together with the SAPS-II, added to the capacity to predict mortality in these patients in nonresuscitation ICUs. Prospective data are needed to validate the capacity of SHR in predicting in-hospital mortality in patients in the nonresuscitation ICU.

Clinical challenges of glycemic control in the intensive care unit: A narrative review

Glucose control in patient admitted to the intensive care unit has been a topic of much debate over the past 20 years. The harmful effects of uncontrolled hyperglycemia and hypoglycemia in critically ill patients is well established. Although a large clinical trial in 2001 demonstrated significant mortality and morbidity benefits with tight glucose control in this patient population, the results could not be replicated by other investigators. The “Normoglycemia in Intensive Care Evaluation-Survival Using Glucose Algorithm Regulation” trial in 2009 established that tight glucose control was not only of no benefit, but in fact harmful due to the significant risk of hypoglycemia. The current guidelines suggest a moderate approach with the initiation of intravenous insulin therapy in critically ill patients when the blood glucose level is above 180 mg/dL. The most important factor that underpins glycemic management in intensive care unit patients is the consequent prevention of hypoglycemia. Robust glucose monitoring strategies and insulin protocols need to be implemented in order to achieve this goal.

In-Hospital Hyperglycemia Is Associated With Worse Outcomes in Patients Admitted With COVID-19

OBJECTIVE

Diabetes and the outpatient diabetes treatment regimen have been identified as risk factors for poor outcomes in patients with sepsis. However, little is known about the effect of tight inpatient glycemic control in the setting of coronavirus disease 2019 (COVID-19). Therefore, we examined the effect of hyperglycemia in patients with diabetes hospitalized because of COVID-19.

RESEARCH DESIGN AND METHODS

We analyzed data from 1,938 COVID-19 patients with diabetes hospitalized for COVID-19 from March to May 2020 at a large academic medical center in New York City. Patients were divided into two groups based on their inpatient glycemic values, and a Cox proportional hazards regression model was used to assess the independent association of inpatient glucose levels with mortality (primary outcome) and the risk of requiring mechanical ventilation (MV) (secondary outcome).

RESULTS

In our analysis, 32% of the patients were normoglycemic and 68% hyperglycemic. Moreover, 31% of the study subjects died during hospitalization, and 14% required MV, with inpatient hyperglycemia being significantly associated with both mortality and the requirement for MV. Additionally, in the Cox regression analysis, after adjustment for potential confounders, including age, sex, race, BMI, HbA1c, comorbidities, inflammatory markers, and corticosteroid therapy, patients with uncontrolled hyperglycemia had a higher risk of dying (hazard ratio [HR] 1.54, 95% CI 1.00-2.36, P = 0.049) and of requiring MV (HR 4.41, 95% CI 1.52-2.81, P = 0.006) than those with normoglycemia.

CONCLUSIONS

A tight control of inpatient hyperglycemia may be an effective method for improving outcomes in patients with diabetes hospitalized for COVID-19.

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.

Challenges in hyperglycemia management in critically ill patients with COVID-19

Hyperglycemia is commonly associated with adverse outcomes especially in patients requiring intensive care unit stay. Data from the corona virus disease 2019 (COVID-19) pandemic indicates that individuals with diabetes appear to be at similar risk for COVID-19 infection to those without diabetes but are more likely to experience increased morbidity and mortality. The proposed hypothesis for hyperglycemia in COVID-19 include insulin resistance, critical illness hyperglycemia (stress- induced hyperglycemia) secondary to high levels of hormones like cortisol and catecholamines that counteract insulin action, acute cytokine storm and pancreatic cell dysfunction. Diabetic patients are more likely to have severe hyperglycemic complications including diabetic ketoacidosis and hyperosmolar hyperglycemic state. Management of hyperglycemia in COVID-19 is often complicated by use of steroids, prolonged total parenteral or enteral nutrition, frequent acute hyperglycemic events, and restrictions with fluid management due to acute respiratory distress syndrome. While managing hyperglycemia special attention should be paid to mode of insulin delivery, frequency of glucose monitoring based on patient and caregiver safety thereby minimizing exposure and conserving personal protective equipment. In this article we describe the pathophysiology of hyperglycemia, challenges encountered in managing hyperglycemia, and review some potential solutions to address them.

Association between dysglycemia and mortality by diabetes status and risk factors of dysglycemia in critically ill patients: a retrospective study

AIMS

Dysglycemia, including the three domains hyperglycemia, hypoglycemia, and increased glycemic variability (GV), is associated with high mortality among critically ill patients. However, this association differs by diabetes status, and reports in this regard are limited. This study aimed to evaluate the associations between the three dysglycemia domains and mortality in critically ill patients by diabetes status and determined the contributing factors for dysglycemia.

METHODS

This retrospective study included 958 critically ill patients (admitted to the ICU) with or without DM. Dysglycemia was defined as abnormality of any of the three dimensions. We evaluated the effects of the three domains of glucose control on mortality using binary logistic regression and then adjusted for confounders. The associations between dysglycemia and other variables were investigated using cumulative logistic regression analysis.

RESULT

GV independently and similarly affected mortality in both groups after adjustment for confounders (DM: odds ratio [OR], 1.05; 95% confidence interval [CI]: 1.03-1.08; p <0.001; non-DM: OR, 1.07; 95% CI, 1.03-1.11; p = 0.002). Hypoglycemia was strongly associated with ICU mortality among patients without DM (3.12; 1.76-5.53; p <0.001) and less so among those with DM (1.18; 0.49-2.83; p = 0.72). Hyperglycemia was non-significantly associated with mortality in both groups. However, the effects of dysglycemia seemed cumulative. The factors contributing to dysglycemia included disease severity, insulin treatment, glucocorticoid use, serum albumin level, total parenteral nutrition, duration of diabetes, elevated procalcitonin level, and need for mechanical ventilation and renal replacement therapy.

CONCLUSION

The association between the three dimensions of dysglycemia and mortality varied by diabetes status. Dysglycemia in critical patients is associated with excess mortality; however, glucose management in patients should be specific to the patient's need considering the diabetes status and broader dimensions. The identified factors for dysglycemia could be used for risk assessment in glucose management requirement in critically ill patients, which may improve clinical outcomes.

Novel appearance of hyperglycemia/diabetes, associated with COVID-19

In a recent meta-analysis the prevalence of coronavirus disease 2019 (COVID-19)-associated hyperglycemia was 25%, and that of COVID-19-associated new-onset diabetes was 19%. An association between hyperglycemia or new-onset diabetes and COVID-19 has been suggested. In a recent relevant study of critically and non-critically ill patients with COVID-19, we found that indeed beta-cell function was compromised in critically ill patients with COVID-19 and that these patients showed a high glycemic gap. Nevertheless, one quarter of critically ill patients with no history of diabetes have stress hyperglycemia, a finding which could obscure the prevalence of hyperglycemia or new-onset diabetes that could be attributed to COVID-19 per se.

How much underfeeding can the critically ill adult patient tolerate?

Critical illness leads to significant metabolic alterations that should be considered when providing nutritional support. Findings from key randomized controlled trials (RCTs) indicate that underfeeding (<70% of energy expenditure [EE]) during the acute phase of critical illness (first 7 days of intensive care unit [ICU] admission) may not be harmful and could instead promote autophagy and prevent overfeeding in light of endogenous energy production. However, the optimal energy target during this period is unclear and full starvation is unlikely to be beneficial. There are limited data regarding the effects of prolonged underfeeding on clinical outcomes in critically ill patients, but recent studies show that oral food intake is suboptimal both in the ICU and following discharge to the acute care setting. It is hypothesized that provision of full nutrition (70-100% of EE) may be important in the recovery phase of critical illness (>7 days of ICU admission) for promoting recovery and rehabilitation; however, studies on nutritional intervention delivered from ICU admission through hospital discharge are needed. The aim of this review is to provide a narrative synthesis of the existing literature on metabolic alterations experienced during critical illness and the impact of underfeeding on clinical outcomes in the critically ill adult patient.

Hyperglycaemia increases mortality risk in non-diabetic patients with COVID-19 even more than in diabetic patients

Aim

Diabetes has been identified as a risk factor for poor outcomes in patients with COVID‐19. We examined the association of hyperglycaemia, both in the presence and absence of pre‐existing diabetes, with severity and outcomes in COVID‐19 patients.

Methods

Data from 74,148 COVID‐19‐positive inpatients with at least one recorded glucose measurement during their inpatient episode were analysed for presence of pre‐existing diabetes diagnosis and any glucose values in the hyperglycaemic range (>180 mg/dl).

Results

Among patients with and without a pre‐existing diabetes diagnosis on admission, mortality was substantially higher in the presence of high glucose measurements versus all measurements in the normal range (70–180 mg/dl) in both groups (non‐diabetics: 21.7% vs. 3.3%; diabetics 14.4% vs. 4.3%). When adjusting for patient age, BMI, severity on admission and oxygen saturation on admission, this increased risk of mortality persisted and varied by diabetes diagnosis. Among patients with a pre‐existing diabetes diagnosis, any hyperglycaemic value during the episode was associated with a substantial increase in the odds of mortality (OR: 1.77, 95% CI: 1.52–2.07); among patients without a pre‐existing diabetes diagnosis, this risk nearly doubled (OR: 3.07, 95% CI: 2.79–3.37).

Conclusion

This retrospective analysis identified hyperglycaemia in COVID‐19 patients as an independent risk factor for mortality after adjusting for the presence of diabetes and other known risk factors. This indicates that the extent of glucose control could serve as a mechanism for modifying the risk of COVID‐19 morality in the inpatient environment.

Admission glycemic gap in the assessment of patients with intracerebral hemorrhage

BACKGROUND

Intracerebral hemorrhage (ICH) is the most common type of hemorrhagic stroke. Glycemic gap, determined by the difference between glucose and the HbA1c-derived average glucose, predicts poor outcomes in various clinical settings. Our main objective was to evaluate association of some admission factors and outcomes in relation to admission glycemic gap (AGG) in patients with ICH.

METHODS

We retrospectively analyzed 506 adult patients with ICH between 2014 and 2019. AGG was defined as A1c-derived average glucose (28.7×HbA1c-46.7) subtracted from admission glucose. Admission factors and hospital outcomes indicative of poor outcome (i.e. death, gastrostomy tube, tracheostomy, and discharge status) were compared between patients with elevated (greater than 80 mg/dL) vs. non-elevated (less than or equal-to 80 mg/dL) AGG. Pearson chi-square test was used for independence, and multivariate analysis was used for association. SPSS and excel were used for all data analysis.

RESULTS

We found that 67 of 506 (13%) ICH patients had elevated AGG with a mean of 137.3 mg/dL compared to 439 (87%) non-elevated AGG with a mean of 12.6 mg/dL. While mean and standard deviation values for age, weight,and body mass index were comparable between groups, the elevated AGG group had significantly higher admission glucose (286.1 ± 84.3 vs. 140.1 ± 42.5, p < 0.001), higher lactic acid (3.26 ± 2.04 mmol/L vs. 1.99 ± 1.33 mmol/L, p < 0.001), lower Glasgow Coma Scale (GCS) scores (7.70 ± 4.28 vs. 11.24 ± 4.14, p < 0.001), and higher ICH score (median 3, IQR 2-4 vs. median 1, IQR 0-3, p < 0.001). Higher AGG was associated with an increased likelihood of mechanical ventilation, and in-hospital mortality (74.6% vs. 38.3% and 47.8% vs. 15.0% respectively, p < 0.001). Placements of tracheostomy and gastrostomy were similar between the two groups (13.4% vs. 11.8%, p = 0.69% and 1.5% and 4.6%, p = 0.34 respectively). The higher AGG group had a more common poor discharge outcome to either long-term acute care, skilled nursing facility, and/or hospice (65.7% vs. 42.6%, p < 0.001). Hospital cost and length of hospitalization did not differ significantly. Although AGG was not an independent predictor of poor outcome, multivariate analysis showed it was significantly associated with poor outcome while admission glucose was not (p < 0.001 vs. p = 0.167).

CONCLUSION

Elevated AGG was associated with worse GCS and ICH scores on admission, as well as need for mechanical ventilation, in hospital mortality and poor discharge status. Elevated AGG has value in prediction of outcome, but existing understanding is limited.

Pharmacological management of COVID-19 in type 2 diabetes

Evidence suggests that diabetes is one the most relevant comorbidity in affecting the prognosis of COVID-19. Albeit there are no specific trials nor subgroup analysis showing the effect of COVID-19 therapies in patients with diabetes, selected features of this disease and the side effects associated with certain drugs require a proper knowledge to optimize the pharmacological therapy of patients with diabetes and COVID-19. While chronic anti-hypertensive and glucose-lowering therapies should not be discontinued nor preferred for preventive purposes, the low-grade pro-inflammatory, the thrombosis-prone status of diabetes, the role of acute hyperglycaemia in promoting adverse outcomes in patients admitted to ICU, and the observed increased mortality in patients with poor long-term glycaemic control delineate a delicate balance in case of severe forms of COVID-19. Here, we briefly summarized some of the key pharmacological issues linked to the management of patients with diabetes and COVID-19, in order to provide indications to minimize the deleterious effects of the concomitant presentation of these diseases and to use the existing pharmacological options in an appropriate manner.

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.

Association of specialized enteral nutrition with glycemic control and clinical outcomes in critically ill patients: A meta-analysis of randomized controlled trials

OBJECTIVE

To evaluate the association of glycemic-control formulae (GCF) with measurements of glycemic control and clinical outcomes compared to standard enteral formulae (SF) in critically ill patients.

DATA SOURCES

MEDLINE, EMBASE, Scopus and the Cochrane Central Register of Controlled Trials were searched from inception up to January, 2021.

STUDY SELECTION

RCTs that assessed the effects of GCF relative to SF in adult critically ill patients.

DATA EXTRACTION

Measurements of glycemic control were the primary outcomes. Secondary outcomes included insulin requirements, mechanical ventilation (MV), length of intensive care unit (ICU) stay and mortality. Two authors independently extracted data and assessed risk of bias using the Cochrane's RoB 2 tool and the GRADE approach was used to assess the quality of evidence.

DATA SYNTHESIS

Ten studies (12 reports, 685 patients) were included. The use of GCFs was associated with lower blood glucose (WMD, -16.06 mg/dL; 95% CI -23.48 to -8.63; I² = 47%) and lower daily administered insulin (WMD, -7.20 IU; 95% CI -13.92 to -0.48; I² = 53%). Glycemic variability, measured by the coefficient of variation, was also associated with the use of GCFs (WMD, -6.84%; 95% CI, -13.57 to -0.11; I² = 95%). In contrast, analyses for length of ICU stay (WMD, -0.12, 95% CI -1.77 to 1.52; I² = 0%), duration of MV (WMD, -0.34 days; 95% CI, -1.72 to 1.04; I² = 0%) and mortality (RR, 1.13; 95% CI 0.82 to 1.56; I² = 0%) were not statistically significant. Quality of evidence ranged from low to very low, and only one study was judged as at low risk of bias.

CONCLUSIONS

In this meta-analysis, GCFs were significantly associated with lower insulin requirements and improved glycemic control. Although results for clinical outcomes were not statistically significant, there is insufficient evidence to confirm or exclude important differences due to serious imprecision in the effect estimates and overall low quality of evidence. The effects of GCFs on clinical outcomes require confirmation in larger randomized trials.

Nutritional Support in the Intensive Care Unit: Implications for Nursing Care From Evidence-Based Guidelines and Supporting Literature

Several nursing practices are related to oral, enteral, or parenteral feeding on the intensive care unit. Nurses are the group of health care professionals who provide 24-hour care for patients. Therefore, they play a key role in not only identifying nutritional problems but also in ensuring the success of nutritional therapy by implementing evidence-based feeding protocols and ongoing care for (par)enteral access devices. Having an up-to-date evidence-based knowledge about nutritional support can increase safety and quality of care and can contribute to better outcomes. In this narrative review, the most recent European intensive care unit nutrition guidelines and related research are highlighted, and where appropriate, specific applications for nursing practice are described.

Copeptin and stress-induced hyperglycemia in critically ill patients: A prospective study

Objectives

Copeptin, an equimolar indicator of serum antidiuretic hormone levels, has been associated with higher mortality in critically ill patients and with the development of diabetes in the general population. The aim of the present study was to investigate the association of copeptin levels with glycemic parameters in critically ill patients and to compare the time-course of copeptin in survivors and non-survivors.

Design

Prospective cohort study.

Patients

From June to October 2019, critically ill patients were prospectively enrolled and followed for 90 days.

Measurements

Plasma copeptin levels were determined at intensive care unit (ICU) admission (copeptin T1), 24 h (copeptin T2), and 48 h (copeptin T3) after study entry. Blood glucose and glycated hemoglobin levels were measured. ICU, in-hospital, and 90-day mortality, and length of stay in the ICU and hospital were evaluated.

Results

104 patients were included. No significant correlation was detected between copeptin levels and blood glucose (r = -0.17, p = 0.09), HbA1c (r = 0.01, p = 0.9), glycemic gap (r = -0.16, p = 0.11), and stress hyperglycemia ratio (r = -0.14, p = 0.16). Copeptin T3 levels were significantly higher in survivors than in non-survivors at hospital discharge (561 [370–856] vs 300 [231–693] pg/mL, p = 0.015) and at 90 days (571 [380–884] vs 300 [232–698] pg/mL, p = 0.03).

Conclusions

No significant correlations were found between copeptin levels and glycemic parameters, suggesting that copeptin is not a relevant factor in the induction of hyperglycemia during critical illness. Copeptin levels at ICU day 3 were higher in survivors than in non-survivors.

Association of Multiple Glycemic Parameters at Hospital Admission with Mortality and Short-Term Outcomes in Acutely Poisoned Patients

The prognostic value of multiple glycemic parameters in poisoned patients was never assessed. We aim to explore the effects of glucose variability on short-term outcomes in nondiabetic and diabetic patients acutely poisoned with undifferentiated xenobiotics. We performed a prospective observational study in a tertiary center for toxicology in northeastern Romania. Over the course of 3 years, we included 1076 adults, older than 18 years, admitted for acute poisoning with a xenobiotic. The mortality rate was 4.1%. The admission blood glucose level (BGL) predicted mortality (OR 1.015, 95% CI 1.011–1.019, p < 0.001) and complications (OR 1.005, 95% CI 1.001–1.009, p 0.02). The mean glucose level (MGL) after admission (OR 1.007, 95% CI 1.000–1.013, p 0.034) and coefficient of glucose variability (CV) were predictive for complications (OR 40.58, 95% CI 1.35–1220.52, p 0.033), using the same multivariable model. The receiver operating characteristic curve (ROC) analysis revealed that BGL had good predictive value for in-hospital mortality (area under the curve (AUC) = 0.744, 95% CI = 0.648–0.841, p < 0.001), and complications (AUC = 0.618, 95% CI = 0.584–0.653, p < 0.001). In patients acutely poisoned with xenobiotics, the BGL, MGL and CV can be useful as mortality and short-outcome predictors.

Insulin Use, Diabetes Control, and Outcomes in Patients with COVID-19

Background: The novel coronavirus (SARS CoV-2) has caused significant morbidity and mortality in patients with diabetes. However, the effects of diabetes control including insulin use remain uncertain in terms of clinical outcomes of patients with COVID-19.Methods: In this single-center, retrospective observational study, all adult patients admitted to Einstein Medical Center, Philadelphia, from March 1 through April 24, 2020 with a diagnosis of COVID-19 and diabetes were included. Demographic, clinical and laboratory data, insulin dose at home and at the hospital, other anti-hyperglycemic agents use, and outcomes were obtained. Multivariate logistic regression was used to evaluate the factors associated with diabetes control and mortality.Results: Patients who used insulin at home had higher mortality compared to those who did not (35% vs 18% p = .015), this was true even after adjustment for demographics, comorbidities and a1c OR 2.65 95% CI (1.23-5.71) p = .013. However, the mean a1c and the median home requirements of insulin did not significantly differ among patients who died compared to the ones that survived. Patients who died had significantly higher inpatient insulin requirements (highest day insulin requirement recorded in units during hospitalization) 36 (11-86) vs 21 (8-52) p = .043 despite similar baseline a1c and steroid doses received. After adjusting for demographics, comorbidities and a1c, peak insulin requirements remained significantly associated with inpatient mortality OR 1.022 95% CI (1.00-1.04) p = .044.Conclusion: Among diabetic patients infected with COVID-19, insulin therapy at home was significantly independently associated with increased mortality. Peak daily inpatient insulin requirements was also independently associated with increased inpatient mortality.

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.

Maximal Glycemic Difference, the Possible Strongest Glycemic Variability Parameter to Predict Mortality in ICU Patients

Background

This retrospective study aimed to determine the correlation of blood glucose and glycemic variability with mortality and to identify the strongest glycemic variability parameter for predicting mortality in critically ill patients.

Methods

A total of 528 patients admitted to the medical intensive care unit were included in this study. Blood glucose levels during the first 24 hours of admission were recorded and calculated to determine the glycemic variability. Significant glycemic variability parameters, including the standard deviation, coefficient of variation, maximal blood glucose difference, and J-index, were subsequently compared between intensive care unit survivors and nonsurvivors. A binary logistic regression was performed to identify independent factors associated with mortality. To determine the strongest glycemic variability parameter to predict mortality, the area under the receiver operating characteristic of each glycemic variability parameter was determined, and a pairwise comparison was performed.

Results

Among the 528 patients, 17.8% (96/528) were nonsurvivors. Both survivor and nonsurvivor groups were clinically comparable. However, nonsurvivors had significantly higher median APACHE-II scores (23 [21, 27] vs. 18 [14, 22]; p < 0.01) and a higher mechanical ventilator support rate (97.4% vs. 74.9%; p < 0.01). The mean blood glucose level and significant glycemic variability parameters were higher in nonsurvivors than in survivors. The maximal blood glucose difference yielded a similar power to the coefficient of variation (p = 0.21) but was significantly stronger than the standard deviation (p = 0.005) and J-index (p = 0.006).

Conclusions

Glycemic variability was independently associated with intensive care unit mortality. Higher glycemic variability was identified in the nonsurvivor group regardless of preexisting diabetes mellitus. The maximal blood glucose difference and coefficient of variation of the blood glucose were the two strongest parameters for predicting intensive care unit mortality in this study.

Mortality Predictors and Associated Factors in Patients in the Intensive Care Unit: A Cross-Sectional Study

BACKGROUND

Mortality in the intensive care unit (ICU) has been associated to an array of risk factors. Identification of risk factors potentially contribute to predict and reduce mortality rates in the ICU. The objectives of the study were to determine the prevalence and the factors associated with the mortality and to analyze the survival.

METHOD

A cross-sectional study conducted in two clinical and surgical ICU in the state of Sergipe, northeastern Brazil. We enrolled 316 patients with at least 48 h of hospitalization, minimum age of 18 years old, sedated or weaned, with RASS ≥ -3, between July 2017 and April 2018. We categorized data in (1) age and gender, (2) clinical condition, and (3) prevalence of delirium. Data from enrolled patients were collected from enrollment until death or ICU discharge. Patients' outcomes were categorized in (1) death and (2) nondeath (discharge).

RESULTS

Twenty-one percent of participants died. Age (53 ± 17 years vs. 45 ± 18 years, p < 0.01), electrolyte disturbance (30.3% vs 18.1%, p=0.029), glycemic index (33.3% vs 18.2%, p=0.008), tube feeding (83.3% vs 67.1%, p=0.01), mechanical ventilation (50% vs 35.7%, p=0.035), sedation with fentanyl (24.2 vs 13.6, p=0.035), use of insulin (33.8% vs 21.7%, p=0.042), and higher Charlson score (2.61 vs 2.17, p=0.041) were significantly associated with death on the adjusted model. However, the regression model indicated that patients admitted from the emergency (HR = 0.40, p=0.006) and glycemic index alterations (HR = 1.68, p=0.047) were associated with mortality. There was no statistically significant difference (p=0.540) in survival between patients with and without delirium, based on the survival analysis and length of hospitalization.

CONCLUSION

The prevalence of death was 21%, and age, electrolyte disturbance, glycemic index, tube feeding, mechanical ventilation, sedation with fentanyl, use of insulin, and higher Charlson score were associated with mortality.

Issues for the management of people with diabetes and COVID-19 in ICU

In the pandemic “Corona Virus Disease 2019” (COVID-19) people with diabetes have a high risk to require ICU admission. The management of diabetes in Intensive Care Unit is always challenging, however, when diabetes is present in COVID-19 the situation seems even more complicated. An optimal glycemic control, avoiding acute hyperglycemia, hypoglycemia and glycemic variability may significantly improve the outcome. In this case, intravenous insulin infusion with continuous glucose monitoring should be the choice. No evidence suggests stopping angiotensin-converting-enzyme inhibitors, angiotensin-renin-blockers or statins, even it has been suggested that they may increase the expression of Angiotensin-Converting-Enzyme-2 (ACE2) receptor, which is used by “Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to penetrate into the cells. A real issue is the usefulness of several biomarkers, which have been suggested to be measured during the COVID-19. N-Terminal-pro-Brain Natriuretic-Peptide, D-dimer and hs-Troponin are often increased in diabetes. Their meaning in the case of diabetes and COVID-19 should be therefore very carefully evaluated. Even though we understand that in such a critical situation some of these requests are not so easy to implement, we believe that the best possible action to prevent a worse outcome is essential in any medical act.