Glucose variability and mortality in patients with sepsis

Continuous glucose monitoring versus conventional glucose monitoring in the ICU: A randomized controlled trial

PURPOSE

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

METHODS

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.

RESULTS

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).

CONCLUSION

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.

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 between glycemic variability and short-term mortality in patients with acute kidney injury: a retrospective cohort study of the MIMIC-IV database

Acute kidney injury (AKI) represents a significant challenge to global public health problem and is associated with poor outcomes. There is still considerable debate about the effect of mean blood glucose (MBG) and coefficient of variation (CV) of blood glucose on the short-term mortality of AKI patients. This retrospective cohort study aimed to explore the association between glycemic variability and short-term mortality in patients with AKI. Data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed, including 6,777 adult AKI patients. MBG and CV on the first day of ICU admission were calculated to represent the overall glycemic status and variability during the ICU stay in AKI patients. The primary outcome indicator was ICU 30-day mortality of AKI patients. Multivariate Cox regression analysis and smoothed curve fitting were used to assess the relationship between blood glucose levels and mortality. Eventually, the ICU 30-day mortality rate of AKI patients was 23.5%. The increased MBG and CV were significantly correlated with ICU 30-day mortality (hazards ratio (HR) = 1.20, 95% confidence interval (CI) 1.14-1.27; HR = 1.08, 95% CI 1.03-1.13). The smoothed curve fitting showed a U-shaped relationship between MBG on the first day of ICU admission and ICU 30-day mortality (inflection point = 111.3 mg/dl), while CV had a linear relationship with 30-day ICU mortality. Thus, we conclude that MBG and CV were significantly associated with short-term mortality in intensive care patients with AKI. Tighter glycemic control may be an effective measure to improve the prognosis of patients with AKI.

Prevalence and outcomes of chronic comorbid conditions in patients with sepsis in Korea: a nationwide cohort study from 2011 to 2016

BACKGROUND

Chronic comorbid conditions are common in patients with sepsis and may affect the outcomes. This study aimed to evaluate the prevalence and outcomes of common comorbidities in patients with sepsis.

METHODS

We conducted a nationwide retrospective cohort study. Using data from the National Health Insurance Service of Korea. Adult patients (age ≥ 18 years) who were hospitalized in tertiary or general hospitals with a diagnosis of sepsis between 2011 and 2016 were analyzed. After screening of all International Classification of Diseases 10th revision codes for comorbidities, we identified hypertension, diabetes mellitus (DM), liver cirrhosis (LC), chronic kidney disease (CKD), and malignancy as prevalent comorbidities.

RESULTS

Overall, 373,539 patients diagnosed with sepsis were hospitalized in Korea between 2011 and 2016. Among them, 46.7% had hypertension, 23.6% had DM, 7.4% had LC, 13.7% had CKD, and 30.7% had malignancy. In-hospital mortality rates for patients with hypertension, DM, LC, CKD, and malignancy were 25.5%, 25.2%, 34.5%, 28.0%, and 33.3%, respectively, showing a decreasing trend over time (P < 0.001). After adjusting for baseline characteristics, male sex, older age, use of mechanical ventilation, and continuous renal replacement therapy, LC, CKD, and malignancy were significantly associated with in-hospital mortality.

CONCLUSIONS

Hypertension is the most prevalent comorbidity in patients with sepsis, and it is associated with an increased survival rate. Additionally, liver cirrhosis, chronic kidney disease, and malignancy result in higher mortality rates than hypertension and DM, and are significant risk factors for in-hospital mortality in patients with sepsis.

Association Between Hypoglycemia and the Occurrence of Early Onset Sepsis in Premature Infants

BACKGROUND

We examined the association between hypoglycemia and the occurrence of early onset sepsis (EOS) in premature infants admitted to the neonatal intensive care unit (NICU).

METHODS

We included infants discharged from 358 NICUs between 1997 and 2020 with gestational age <34 weeks, ≥1 culture collected in the first 3 days of life, and ≥1 serum glucose value recorded on the day of or day prior to culture collection. We used multivariable logistic regression and inverse probability weighting (IPW) and constructed models for three definitions of hypoglycemia: American Academy of Pediatrics (AAP), Pediatric Endocrine Society, and a definition based on neurodevelopmental studies. We performed subgroup analysis in EOS episodes caused by Gram-negative and Gram-positive organisms.

RESULTS

Of the 62,178 infants and 64,559 cultures that met study inclusion criteria, 739 (1%) cultures were positive. The median (25th, 75th percentile) glucose value was 75 mg/dL (50, 106) on the day of or day prior to a positive culture versus 70 mg/dL (50, 95) on the day of or day prior to a negative culture. We found that hypoglycemia was not associated with the occurrence of EOS for all organisms and Gram-positive organisms, whereas there was a small but significant association between the lower AAP glucose cutoff value and EOS due to Gram-negative organisms (logistic regression: risk difference [RD] 0.24% [95% CI, 0.01-0.47]; IPW: RD 0.22% [95% CI, 0.00-0.43]).

CONCLUSIONS

Hypoglycemia may be an early marker of EOS, particularly in episodes caused by Gram-negative organisms and when using a stricter definition of hypoglycemia.

Hyperglycemia and glucose variability are associated with worse survival in mechanically ventilated COVID-19 patients: the prospective Maastricht Intensive Care Covid Cohort

BACKGROUND

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.

METHODS

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.

RESULTS

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)).

CONCLUSIONS

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

Classification-based deep neural network vs mixture density network models for insulin sensitivity prediction problem

Model-based glycemic control (GC) protocols are used to treat stress-induced hyperglycaemia in intensive care units (ICUs). The STAR (Stochastic-TARgeted) glycemic control protocol - used in clinical practice in several ICUs in New Zealand, Hungary, Belgium, and Malaysia - is a model-based GC protocol using a patient-specific, model-based insulin sensitivity to describe the patient's actual state. Two neural network based methods are defined in this study to predict the patient's insulin sensitivity parameter: a classification deep neural network and a Mixture Density Network based method. Treatment data from three different patient cohorts are used to train the network models. Accuracy of neural network predictions are compared with the current model- based predictions used to guide care. The prediction accuracy was found to be the same or better than the reference. The authors suggest that these methods may be a promising alternative in model-based clinical treatment for patient state prediction. Still, more research is needed to validate these findings, including in-silico simulations and clinical validation trials.

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.

Association between time in range 70-180 mg/dl in early stage and severity with in patients acute pancreatitis

BACKGROUND

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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

Preoperative carbohydrate load to reduce perioperative glycemic variability and improve surgical outcomes: A scoping review

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.

Glucometrics in the first week of critical illness and its association with mortality

OBJECTIVE

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

DESIGN

Prospective observational study.

SETTING

Mixed ICU of teaching hospital.

PATIENTS

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

MAIN VARIABLES OF INTEREST

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.

RESULTS

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).

CONCLUSIONS

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.

Machine-learning models for prediction of sepsis patients mortality

OBJECTIVES

Sepsis is an infection-caused syndrome, that leads to life-threatening organ damage. We aim to develop machine learning models with large-scale data to predict sepsis patients' mortality.

DESIGN

we extracted sepsis patients from two databases, Medical Information Mart for Intensive Care IV (MIMIC-IV) as a train set and Philips eICU Collaborative Research Database as a test set.

SETTING

ICUs in multicenter hospitals in the USA during 2012-2019.

PATIENTS OR PARTICIPANTS

A total of 21,680 sepsis-3 patients are included in the study, in which, 3771 patients were dead and 17,909 survived during hospitalization, respectively.

INTERVENTIONS

No interventions.

MAIN VARIABLES OF INTEREST

Basic information, examination items during hospitalization and some medication and treatment information are incorporated into analyzed. Seven different models were built with a Support vector machine, Decision Tree Classifier, Random Forest, Gradients Boosting, Multiple Layer Perception, Xgboost, light Gradients Boosting to predict dead or live during hospitalization.

RESULTS

Algorithms with an AUC value in the test set of the top three: light GBM, GBM, Xgboost. Considering the performance of the training set and the test set, the light GBM model performs best, and then the parameters of the model were adjusted, after that the AUC value was 0.99 in the train set, 0.96 in the test set, respectively.

CONCLUSIONS

Models built with light GBM algorithm from real-world sepsis patients from electronic health records accurately predict whether sepsis patients are dead and can be incorporated into clinical decision tools to enhance the prognosis of the patient and prevent adverse outcomes.

Identification of new co-diagnostic genes for sepsis and metabolic syndrome using single-cell data analysis and machine learning algorithms

Sepsis, a serious inflammatory response that can be fatal, has a poorly understood pathophysiology. The Metabolic syndrome (MetS), however, is associated with many cardiometabolic risk factors, many of which are highly prevalent in adults. It has been suggested that Sepsis may be associated with MetS in several studies. Therefore, this study investigated diagnostic genes and metabolic pathways associated with both diseases. In addition to microarray data for Sepsis, PBMC single cell RNA sequencing data for Sepsis and microarray data for MetS were downloaded from the GEO database. Limma differential analysis identified 122 upregulated genes and 90 downregulated genes in Sepsis and MetS. WGCNA identified brown co-expression modules as Sepsis and MetS core modules. Two machine learning algorithms, RF and LASSO, were used to screen seven candidate genes, namely, STOM, BATF, CASP4, MAP3K14, MT1F, CFLAR and UROD, all with an AUC greater than 0.9. XGBoost assessed the co-diagnostic efficacy of Hub genes in Sepsis and MetS. The immune infiltration results show that Hub genes were expressed at high levels in all immune cells. After performing Seurat analysis on PBMC from normal and Sepsis patients, six immune subpopulations were identified. The metabolic pathways of each cell were scored and visualized using ssGSEA, and the results show that CFLAR plays an important role in the glycolytic pathway. Our study identified seven Hub genes that serve as co-diagnostic markers for Sepsis and MetS and revealed that diagnostic genes play an important role in immune cell metabolic pathway.

Association between stress hyperglycemia and outcomes in patients with acute ischemic stroke due to large vessel occlusion

AIMS

This study aimed to evaluate the association between stress hyperglycemia ratio (SHR) and clinical outcomes at 90 days in acute ischemic stroke due to large vessel occlusion receiving endovascular treatment.

METHODS

The RESCUE BT trial was a multicenter, randomized, double-blind, placebo-controlled clinical trial, consisting of 948 stroke patients from 55 centers in China. A total of 542 patients with glucose and glycated hemoglobin (HbA1C) values at admission were included in this analysis. SHR, measured by glucose/HbA1C, was evaluated as both a tri-categorical variable (≤1.07 vs. 1.08-1.29 vs. ≥1.30) and a continuous variable. The primary outcome was a favorable functional outcome (modified Rankin Scale [mRS] score ≤2) at 90 days. The secondary outcome included excellent functional outcome (mRS score ≤1) and safety outcomes, such as 90-day mortality and intracranial hemorrhage. The study was registered with Chictr.org.cn (ChiCTR-INR-17014167).

RESULTS

Compared with patients in the lowest tertile of SHR, the highest tertile group had significantly lower odds of achieving favorable functional outcome of mRS score of 0-2 (adjusted odds ratio, 0.44; 95% confidence interval, 0.28-0.69; p < 0.001) and excellent clinical outcome of mRS score of 0-1 (adjusted odds ratio, 0.48; 95% confidence interval, 0.29-0.79; p = 0.004) at 90 days after adjusting for potential covariates. Similar results were observed after further adjustment for preexisting diabetes and Alberta Stroke Program Early Computed Tomography Score (ASPECTS).

CONCLUSION

Stress hyperglycemia ratio, as measured by the glucose/HbA1C, was associated with a decreased odds of achieving a favorable functional outcome in patients with acute large vessel occlusion stroke at 90 days.

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.

Glycemic control and blood gas sampling frequency during continuous glucose monitoring in the intensive care unit: A before-and-after study

BACKGROUND

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.

METHODS

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).

RESULTS

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.

CONCLUSIONS

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.

Critical care for multimorbid patients

Multimorbidity - the simultaneous presence of several chronic diseases - is very common in the critically ill patients. Its prevalence is roughly 40-85 % and continues to increase further. Certain chronic diseases such as diabetes, obesity, chronic heart, pulmonary, liver or kidney disease and malignancy are associated with higher risk of developing serious acute complications and therefore the possible need for intensive care. This review summarizes and discusses selected specifics of critical care for multimorbid patients.

Stress-Induced Hyperglycemia: Consequences and Management

Hyperglycemia during stress is a common occurrence seen in patients admitted to the hospital. It is defined as a blood glucose level above 180mg/dl in patients without pre-existing diabetes. Stress-induced hyperglycemia (SIH) occurs due to an illness that leads to insulin resistance and decreased insulin secretion. Such a mechanism causes elevated blood glucose and produces a complex state to manage with external insulin. This article compiles various studies to explain the development and consequences of SIH in the critically ill that ultimately lead to an increase in mortality while also discussing the dire impact of SIH on certain acute illnesses like myocardial infarction and ischemic stroke. It also evaluates multiple studies to understand the management of SIH with insulin and proper nutritional therapy in the hospitalized patients admitted to the Intensive care unit (ICU) alongside the non-critical care unit. While emphasizing the diverse effects of improper control of SIH in the hospital, this article elucidates and discusses the importance of formulating a discharge plan due to an increased risk of type 2 diabetes in the recovered.

Machine learning approaches to the human metabolome in sepsis identify metabolic links with survival

Background

Metabolic predictors and potential mediators of survival in sepsis have been incompletely characterized. We examined whether machine learning (ML) tools applied to the human plasma metabolome could consistently identify and prioritize metabolites implicated in sepsis survivorship, and whether these methods improved upon conventional statistical approaches.

Methods

Plasma gas chromatography–liquid chromatography mass spectrometry quantified 411 metabolites measured ≤ 72 h of ICU admission in 60 patients with sepsis at a single center (Brigham and Women’s Hospital, Boston, USA). Seven ML approaches were trained to differentiate survivors from non-survivors. Model performance predicting 28 day mortality was assessed through internal cross-validation, and innate top-feature (metabolite) selection and rankings were compared across the 7 ML approaches and with conventional statistical methods (logistic regression). Metabolites were consensus ranked by a summary, ensemble ML ranking procedure weighing their contribution to mortality risk prediction across multiple ML models.

Results

Median (IQR) patient age was 58 (47, 62) years, 45% were women, and median (IQR) SOFA score was 9 (6, 12). Mortality at 28 days was 42%. The models’ specificity ranged from 0.619 to 0.821. Partial least squares regression-discriminant analysis and nearest shrunken centroids prioritized the greatest number of metabolites identified by at least one other method. Penalized logistic regression demonstrated top-feature results that were consistent with many ML methods. Across the plasma metabolome, the 13 metabolites with the strongest linkage to mortality defined through an ensemble ML importance score included lactate, bilirubin, kynurenine, glycochenodeoxycholate, phenylalanine, and others. Four of these top 13 metabolites (3-hydroxyisobutyrate, indoleacetate, fucose, and glycolithocholate sulfate) have not been previously associated with sepsis survival. Many of the prioritized metabolites are constituents of the tryptophan, pyruvate, phenylalanine, pentose phosphate, and bile acid pathways.

Conclusions

We identified metabolites linked with sepsis survival, some confirming prior observations, and others representing new associations. The application of ensemble ML feature-ranking tools to metabolomic data may represent a promising statistical platform to support biologic target discovery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40635-022-00445-8.

Association of Blood Glucose Level and Glycemic Variability With Mortality in Sepsis Patients During ICU Hospitalization

Background

There was considerable debate regarding the effect of mean blood glucose (MBG) and glycemic variability (GV) on the mortality of septic patients. This retrospective cohort study aimed to assess the association between MBG and GV with ICU mortality of sepsis patients and to explore the optimal MBG range.

Methods

Sepsis patients were enrolled from the Medical Information Mart for Intensive Care IV database (MIMIC-IV). MBG and glycemic coefficient of variation (GluCV) were, respectively, calculated to represent the overall glycemic status and GV during ICU stay. The associations between MBG, GluCV, and ICU mortality of the septic patients were assessed by using multivariate logistic regression in different subgroups and the severity of sepsis. Restricted cubic splines evaluated the optimal MBG target.

Results

A total of 7,104 adult sepsis patients were included. The multivariate logistic regression results showed that increased MBG and GluCV were significantly correlated with ICU mortality. The adjusted odds ratios were 1.14 (95% CI 1.09-1.20) and 1.05 (95% CI 1.00-1.12). However, there was no association between hyperglycemia and ICU mortality among diabetes, liver disease, immunosuppression, and hypoglycemia patients. And the impact of high GluCV on ICU mortality was not observed in those with diabetes, immunosuppression, liver disease, and non-septic shock. The ICU mortality risk of severe hyperglycemia (≧200 mg/dl) and high GluCV (>31.429%), respectively, elevated 2.30, 3.15, 3.06, and 2.37, 2.79, 3.14-folds in mild (SOFA ≦ 3), middle (SOFA 3-7), and severe group (SOFA ≧ 7). The MBG level was associated with the lowest risk of ICU mortality and hypoglycemia between 120 and 140 mg/dl in the subgroup without diabetes. For the diabetic subset, the incidence of hypoglycemia was significantly reduced when the MBG was 140-190 mg/dl, but a glycemic control target effectively reducing ICU mortality was not observed.

Conclusion

MBG and GluCV during the ICU stay were associated with all-cause ICU mortality in sepsis patients; however, their harms are not apparent in some particular subgroups. The impact of hyperglycemia and high GV on death increased with the severity of sepsis. The risk of ICU mortality and hypoglycemia in those with no pre-existing diabetes was lower when maintaining the MBG in the range of 120-140 mg/dl.

Acute glycemic variability and risk of mortality in patients with sepsis: a meta-analysis

BACKGROUND

Acute glycemic variability (GV) has been correlated with the severity of sepsis. The aim of the study was to evaluate the potential association between acute GV and mortality risk in patients with sepsis.

METHODS

Cohort studies comparing the risk of death within 3 months between septic patients with higher versus lower acute GV were retrieved by systematic search of Medline, Embase, Web of Science, Wanfang and CNKI databases. We used a random-effect model to pool the data by incorporating the between-study heterogeneity. Sensitivity analyses were performed to evaluate the stability of the findings.

RESULTS

Ten studies including 4296 patients were available for the meta-analysis. Pooled results showed that septic patients with higher acute GV had significantly increased mortality risk compared to those with lower acute GV, as evidenced by results using different parameters including standard deviation of blood glucose (SDBG, risk ratio [RR]: 1.74, 95% confidence interval [CI] 1.36-2.24, p < 0.001; I2 = 0%), coefficient of variation of blood glucose (RR: 1.91, 95% CI 1.57-2.31, p < 0.001; I2 = 0%), mean amplitude of glycemic excursion (RR: 1.81. 95% CI 1.36-2.40, p < 0.001; I2 = 0%), and glycemic lability index (RR: 2.52, 95% CI 1.72-3.68, p < 0.001; I2 = 0%). Sensitivity analyses by excluding one study at a time did not significantly affect the results (p all < 0.05).

CONCLUSIONS

Higher acute GV may be a predictor of mortality risk in patients with sepsis.

Glycemic Variability Is Independently Associated With Poor Prognosis in Five Pediatric ICU Centers in Southwest China

Background

Glucose variability (GV) is a common complication of dysglycemia in critically ill patients. However, there are few studies on the role of GV in the prognosis of pediatric patients, and there is no consensus on the appropriate method for GV measurement. The objective of this study was to determine the “optimal” index of GV in non-diabetic critically ill children in a prospective multicenter cohort observational study. Also, we aimed to confirm the potential association between GV and unfavorable outcomes and whether this association persists after controlling for hypoglycemia or hyperglycemia.

Materials and Methods

Blood glucose values were recorded for the first 72 h and were used to calculate the GV for each participant. Four different metrics [SD, glycemic lability index (GLI), mean absolute glucose (MAG), and absolute change of percentage (ACACP)] were considered and compared to identify the “best” GV index associated with poor prognosis in non-diabetic critically ill children. Among the four metrics, the SD was most commonly used in previous studies, while GLI- and MAG-integrated temporal information, that is the rate and magnitude of change and the time interval between glucose measurements. The fourth metric, the average consecutive ACACP, was introduced in our study, which can be used in real-time clinical decisions. The primary outcome of this study was the 28-day mortality. The receiver operating characteristic (ROC) curve analysis was conducted to compare the predictive power of different metrics of GV for the primary outcome. The GV index with the largest area under ROC curve (AUC) was chosen for subsequent multivariate analyses. Multivariate Cox regression analysis was performed to identify the potential predictors of the outcome. To compare the contribution in 28-day mortality prognosis between glycemic variability and hyper- or hypoglycemia, performance metrics were calculated, which included AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results

Among 780 participants, 12.4% (n = 97) died within 28 days after admission to the pediatric intensive care unit (PICU). Statistically significant differences were found between survivors and non-survivors in terms of four GV metrics (SD, GLI, MAG, and ACACP), in which MAG (AUC: 0.762, 95% CI: 0.705–0.819, p < 0.001) achieved the largest AUC and showed a strong independent association with ICU mortality. Subsequent addition of MAG to the multivariate Cox model for hyperglycemia resulted in further quantitative evolution of the model statistics (AUC = 0.651–0.681, p = 0.001; IDI: 0.017, p = 0.044; NRI: 0.224, p = 0.186). The impact of hyperglycemia (adjusted hazard ratio [aHR]: 1.419, 95% CI: 0.815–2.471, p = 0.216) on outcome was attenuated and no longer statistically relevant after adjustment for MAG (aHR: 2.455, 95% CI: 1.411–4.270, p = 0.001).

Conclusions

GV is strongly associated with poor prognosis independent of mean glucose level, demonstrating more predictive power compared with hypoglycemia and hyperglycemia after adjusting for confounding factors. GV metrics that contain information, such as time and rate of change, are the focus of future research; thus, the MAG may be a good choice. The findings of this study emphasize the crucial role of GVs in children in the PICU. Clinicians should pay more attention to GV for clinical glucose management.

Divergent Sepsis Pathophysiology in Older Adults

Significance: Both incidence and mortality rates of sepsis significantly increase with advanced age, and the majority of sepsis patients are late middle-aged or older. With the proportion of older adults rapidly increasing in developed countries, age-dependent sepsis vulnerability is an urgent medical issue. Due to an increasing life expectancy, postsepsis complications and health care costs are expected to increase as well. Recent Advances: Older patients suffer from higher sepsis incidence and mortality rates, likely resulting from frequent comorbidities, increased coagulation, dysgylcemia, and altered immune responses. Critical Issues: Despite a large number of ongoing clinical and basic research studies, there is currently no effective therapeutic strategy targeting older patients with severe sepsis. The disparity between clinical and basic studies is a problem, and this is largely due to the use of animal models lacking clinical relevance. Although the majority of sepsis cases occur in older adults, most laboratory animals used for sepsis research are very young. Further, despite the wide use of combination fluid and antibiotic treatment in intensive care unit (ICU) patients, most animal research does not include such treatment. Future Directions: Because sepsis is a systemic disease with multiple organ dysfunction, combined therapy approaches, not those targeting single pathways or single organs, are essential. As for preclinical research, it is critical to confirm new findings using aged animal models with clinically relevant ICU-like medical treatments. Antioxid. Redox Signal. 35, 1358-1375.

Effect of Moderate and Severe Persistent Hyperglycemia on Outcomes in Patients With Intracerebral Hemorrhage

BACKGROUND AND PURPOSE

We evaluated the effect of persistent hyperglycemia on outcomes in 1000 patients with intracerebral hemorrhage enrolled within 4.5 hours of symptom onset.

METHODS

We defined moderate and severe hyperglycemia based on serum glucose levels ≥140 mg/dL-<180 and ≥180 mg/dL, respectively, measured at baseline, 24, 48, and 72 hours. Persistent hyperglycemia was defined by 2 consecutive (24 hours apart) serum glucose levels. We evaluated the relationship between moderate and severe hyperglycemia and death or disability (defined by modified Rankin Scale score of 4-6) at 90 days in the overall cohort and in groups defined by preexisting diabetes.

RESULTS

In the multivariate analysis, both moderate (odds ratio, 1.8 [95% CI, 1.1-2.8]) and severe (odds ratio, 1.8 [95% CI, 1.2-2.7]) hyperglycemia were associated with higher 90-day death or disability after adjusting for Glasgow Coma Scale score, hematoma volume, presence or absence of intraventricular hemorrhage, hyperlipidemia, cigarette smoking, and hypertension (no interaction between hyperglycemia and preexisting diabetes, P=0.996). Among the patients without preexisting diabetes, both moderate (odds ratio, 1.8 [95% CI, 1.0-3.2]) and severe (odds ratio, 2.0 [95% CI, 1.1-3.7]) hyperglycemia were associated with 90-day death or disability after adjusting for above mentioned potential confounders. Among the patients with preexisting diabetes, moderate and severe hyperglycemia were not associated with 90-day death or disability.

CONCLUSIONS

Persistent hyperglycemia, either moderate or severe, increased the risk of death or disability in nondiabetic patients with intracerebral hemorrhage.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT01176565.

Effect of Premorbid Beta-Blockers on Mortality in Patients With Sepsis: A Systematic Review and Meta-Analysis

OBJECTIVE

The aim of this study was to conduct a systematic review and meta-analysis to investigate the impact of premorbid beta-blockers on mortality in patients with sepsis.

DATA SOURCES

We searched EMBASE, the Cochrane Central Register of Controlled Trials, and MEDLINE for eligible studies. The protocol was registered at the PROSPERO (CRD42021256813).

STUDY SELECTION

Two authors independently evaluated the following inclusion criteria: (1) randomized controlled trials, cohort studies, cross-sectional studies; (2) patients with sepsis aged ≥18 years, and (3) premorbid beta-blocker use.

DATA EXTRACTION

Two authors extracted the patients' characteristics and outcomes independently. All analyses were performed using the random-effects models. The primary outcome was short-term mortality, defined as mortality within 30 days, in-hospital or intensive care unit mortality.

DATA SYNTHESIS

Ten studies (n = 24 748 patients) were included. The pooled odds ratio (OR) of short-term mortality associated with the premorbid use of beta-blockers was 0.85 (95% confidence interval [CI], 0.69-1.04; P = .12; I² = 50%). Five studies reported an adjusted OR of short-term mortality. The pooled adjusted OR of short-term mortality associated with the premorbid use of beta-blockers was 0.73 (95% CI, 0.65-0.83; P < .001; I² = 0%).

CONCLUSION

Premorbid beta-blockers were associated with a lower short-term mortality in patients with sepsis.

Pros and cons of continuous glucose monitoring in the intensive care unit

Diabetes mellitus affects people worldwide, and management of its acute complications or treatment-related adverse events is particularly important in critically ill patients. Previous reports have confirmed that hyperglycemia can increase the risk of mortality in patients cared in the intensive care unit (ICU). In addition, severe and multiple hypoglycemia increases the risk of mortality when using insulin or intensive antidiabetic therapy. The innovation of continuous glucose monitoring (CGM) may help to alert medical caregivers with regard to the development of hyperglycemia and hypoglycemia, which may decrease the potential complications in patients in the ICU. The major limitation of CGM is the measurement of interstitial glucose levels rather than real-time blood glucose levels; thus, there will be a delay in the treatment of hyperglycemia and hypoglycemia in patients. Recently, the European Union approved a state-of-art artificial intelligence directed loop system coordinated by CGM and a continuous insulin pump for diabetes control, which may provide a practical way to prevent acute adverse glycemic events related to antidiabetic therapy in critically ill patients. In this mini-review paper, we describe the application of CGM to patients in the ICU and summarize the pros and cons of CGM.

Glycemic lability index and mortality in critically ill patients-A multicenter cohort study

BACKGROUND

Emerging evidence indicates a relationship between glycemic variability during intensive care unit (ICU) admission and death. We assessed whether mean glucose, hypoglycemia occurrence, or premorbid glycemic control modified this relationship.

METHODS

In this retrospective, multicenter cohort study, we included adult patients admitted to five ICUs in Australia and Sweden with available preadmission glycated hemoglobin A1c (HbA1c) and three or more glucose readings. We calculated the glycemic lability index (GLI), a measure of glycemic variability, and the time-weighted average blood glucose (TWA-BG) from all glucose readings. We used logistic regression analysis with adjustment for hypoglycemia and admission characteristics to assess the independent association of GLI (above vs. below cohort median) and TWA-BG (above vs. below cohort median) with hospital mortality.

RESULTS

Among 2305 patients, 859 (37%) had diabetes, median GLI was 40 [mmol/L]² /h/week, median TWA-BG was 8.2 mmol/L, 171 (7%) developed hypoglycemia, and 371 (16%) died. The adjusted odds ratio for death was 1.61 (95% CI, 1.19-2.15; P = .002) for GLI above versus below median and 1.06 (95% CI, 0.80-1.41; P = .67) for TWA-BG above versus below median. The relationship between GLI and mortality was not modified by TWA-BG (P [interaction] = 0.66), a history of diabetes (P [interaction] = 0.89) or by HbA1c ≥52 mmol/mol (vs. <52 mmol/mol) (P [interaction] = 0.29).

CONCLUSION

In adult patients admitted to an ICU in Sweden and Australia, a high GLI was associated with increased hospital mortality irrespective of the level of mean glycemia, hypoglycemia occurrence, or premorbid glycemic control. These findings support the assessment of interventions to reduce glycemic variability during critical illness.

Hypoglycemia and mortality in sepsis patients: A systematic review and meta-analysis

BACKGROUND

Hypoglycemia frequently occurs in patients with sepsis. The status of prognosis of sepsis patients varies with the cause of hypoglycemia.

OBJECTIVE

A meta-analysis was performed to obtain a reliable basis for assessing the severity of disease in sepsis patients.

METHODS

A search of electronic databases was performed. The random-effects model was employed to calculate the overall odds ratio (OR) and 95% CI.

RESULTS

Five cohort studies were included. Decreased blood glucose level was associated with an increased risk of death [OR:1.68; 95% CI (1.12-2.53)]. Incidents of mortality were analyzed based on the causative factor of hypoglycemia. Patients with spontaneous hypoglycemia showed a significantly higher mortality rate than the control subjects[OR 1.65; 95% CI (1.20-2.28); p = 0.002].

CONCLUSION

In the early stages of sepsis, the occurrence of spontaneous hypoglycemia may be associated with the severity of the disease.

Blood glucose-related indicators are associated with in-hospital mortality in critically ill patients with acute pancreatitis

Acute pancreatitis (AP) results in potentially harmful blood glucose fluctuations, affecting patient prognosis. This study aimed to explore the relationship between blood glucose-related indicators and in-hospital mortality in critically ill patients with AP. We extracted data on AP patients from the Multiparameter Intelligent Monitoring in Intensive Care III database. Initial glucose (Glucose_initial), maximum glucose (Glucose_max), minimum glucose (Glucose_min), mean glucose (Glucose_mean), and glucose variability (glucose standard deviation [Glucose_SD] and glucose coefficient of variation [Glucose_CV]) were selected as blood glucose-related indicators. Logistic regression models and the Lowess smoothing curves were used to display the association between significant blood glucose-related indicators and in-hospital mortality. Survivors and non-survivors showed significant differences in Glucose_max, Glucose_mean, Glucose_SD, and Glucose_CV (P < 0.05). Glucose_max, Glucose_mean, Glucose_SD, and Glucose_CV were risk factors for in-hospital mortality in AP patients (OR > 1; P < 0.05). According to the Lowess smoothing curve, the overall trends of blood glucose-related indicators showed a non-linear correlation with in-hospital mortality. Glucose_max, Glucose_mean, Glucose_SD, and Glucose_CV were associated with in-hospital mortality in critically ill patients with AP.

GLP-1 Receptor: A New Target for Sepsis

Patients with sepsis often exhibit hyperglycemia, which increases mortality. glucagon-like peptide-1 receptor agonists (GLP-1RAs) not only regulate blood glucose homeostasis but also improve organ dysfunction, regulate immunity, and control inflammation and other functions in patients with sepsis. Here, we review the possible application of GLP-1RAs in sepsis, to provide a new perspective for the clinical diagnosis and treatment of patients with sepsis complicated with stress hyperglycemia.

Loss of FOXO transcription factors in the liver mitigates stress-induced hyperglycemia

Objective

Stress-induced hyperglycemia is associated with poor outcomes in nearly all critical illnesses. This acute elevation in glucose after injury or illness is associated with increased morbidity and mortality, including multiple organ failure. Stress-induced hyperglycemia is often attributed to insulin resistance as controlling glucose levels via exogenous insulin improves outcomes, but the mechanisms are unclear. Forkhead box O (FOXO) transcription factors are direct targets of insulin signaling in the liver that regulate glucose homeostasis via direct and indirect pathways. Loss of hepatic FOXO transcription factors reduces hyperglycemia in chronic insulin resistance; however, the role of FOXOs in stress-induced hyperglycemia is unknown.

Methods

We subjected mice lacking FOXO transcription factors in the liver to a model of injury known to cause stress-induced hyperglycemia. Glucose, insulin, glycerol, fatty acids, cytokines, and adipokines were assessed before and after injury. Liver and adipose tissue were analyzed for changes in glycogen, FOXO target gene expression, and insulin signaling.

Results

Stress-induced hyperglycemia was associated with reduced hepatic insulin signaling and increased hepatic FOXO target gene expression while loss of FOXO1, 3, and 4 in the liver attenuated hyperglycemia and prevented hyperinsulinemia. Mechanistically, the loss of FOXO transcription factors mitigated the stress-induced hyperglycemia response by directly altering gene expression and glycogenolysis in the liver and indirectly suppressing lipolysis in adipose tissue. Reductions were associated with decreased IL-6, TNF-α, and follistatin and increased FGF21, suggesting that cytokines and FOXO-regulated hepatokines contribute to the stress-induced hyperglycemia response.

Conclusions

This study implicates FOXO transcription factors as a predominant driver of stress-induced hyperglycemia through means that include cross-talk between the liver and adipose, highlighting a novel mechanism underlying acute hyperglycemia and insulin resistance in stress.

•

Liver forkhead box O (FOXO) target gene expression is increased in critical illness.

•

Loss of FOXO1, 3, and 4 in the liver mitigates stress-induced hyperglycemia (SIH).

•

Hepatic FOXO drives SIH via direct and indirect means in the liver and adipose.

GlucoSTRESS - A project to optimize glycemic control in a level C (III) Portuguese intensive care unit

Objective

To double the percentage of time within the 100 - 180mg/dL blood glucose range in the first three months following a phased implementation of a formal education program, and then, of an insulin therapy protocol, without entailing an increased incidence of hypoglycemia.

Methods

The pre-intervention glycemic control was assessed retrospectively. Next, were carried out the implementation of a formal education program, distribution of manual algorithms for intravenous insulin therapy - optimized by the users, based on the modified Yale protocol - and informal training of the nursing staff. The use of electronic blood glucose control systems was supported, and the results were recorded prospectively.

Results

The first phase of the program (formal education) lead to improvement of the time within the euglycemic interval (28% to 37%). In the second phase, euglycemia was achieved 66% of the time, and the incidence of hypoglycemia was decreased. The percentage of patients on intravenous insulin infusion at 48 hours from admission increased from 6% to 35%.

Conclusion

The phased implementation of a formal education program, fostering the use of electronic insulin therapy protocols and dynamic manuals, received good adherence and has shown to be safe and effective for blood glucose control in critically ill patients, with a concomitant decrease in hypoglycemia.

The Association of Diabetes and Hyperglycemia on Inpatient Readmissions

OBJECTIVE

To evaluate the association between inpatient glycemic control and readmission in individuals with diabetes and hyperglycemia (DM/HG).

METHODS

Two data sets were analyzed from fiscal years 2011 to 2013: hospital data using the International Classification of Diseases, Ninth Revision (ICD-9) codes for DM/HG and point of care (POC) glucose monitoring. The variables analyzed included gender, age, mean, minimum and maximum glucose, along with 4 measures of glycemic variability (GV), standard deviation, coefficient of variation, mean amplitude of glucose excursions, and average daily risk range.

RESULTS

Of 66 518 discharges in FY 2011-2013, 28.4% had DM/HG based on ICD-9 codes and 53% received POC monitoring. The overall readmission rate was 13.9%, although the rates for individuals with DM/HG were higher at 18.9% and 20.6% using ICD-9 codes and POC data, respectively. The readmitted group had higher mean glucose (169 ± 47 mg/dL vs 158 ± 46 mg/dL, P < .001). Individuals with severe hypoglycemia and hyperglycemia had the highest readmission rates. All 4 GV measures were consistent and higher in the readmitted group.

CONCLUSION

Individuals with DM/HG have higher 30-day readmission rates than those without. Those readmitted had higher mean glucose, more extreme glucose values, and higher GV. To our knowledge, this is the first report of multiple metrics of inpatient glycemic control, including GV, and their associations with readmission.

Relationship between 24-h venous blood glucose variation and mortality among patients with acute respiratory failure

Evidence indicates that glucose variation (GV) plays an important role in mortality of critically ill patients. We aimed to investigate the relationship between the coefficient of variation of 24-h venous blood glucose (24-hVBGCV) and mortality among patients with acute respiratory failure. The records of 1625 patients in the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC II) database were extracted. The 24-hVBGCV was calculated as the ratio of the standard deviation (SD) to the mean venous blood glucose level, expressed as a percentage. The outcomes included ICU mortality and in-hospital mortality. Participants were divided into three subgroups based on tertiles of 24-hVBGCV. Multivariable logistic regression models were used to evaluate the relationship between 24-hVBGCV and mortality. Sensitivity analyses were also performed in groups of patients with and without diabetes mellitus. Taking the lowest tertile as a reference, after adjustment for all the covariates, the highest tertile was significantly associated with ICU mortality [odds ratio (OR), 1.353; 95% confidence interval (CI), 1.018–1.797] and in-hospital mortality (OR, 1.319; 95% CI, 1.003–1.735), especially in the population without diabetes. The 24-hVBGCV may be associated with ICU and in-hospital mortality in patients with acute respiratory failure in the ICU, especially in those without diabetes.

Current concepts and clinical importance of glycemic variability

BACKGROUND AND AIMS

Evolving evidence indicate that variations in blood glucose levels are likely to be an important factor in developing diabetic complications. Monitoring glucose fluctuations in patients remains as a therapeutic challenge and more evidence needs to be created in order to bring GV into limelight. This review encapsulates the most important findings conducted and discusses on them to provide readers a better understanding on this emerging subject.

METHODS

Keyword-based comprehensive desktop search was conducted to gather the relevant literature. Triple-stage cascade type content analysis of the literature was conducted to draw relevant themes of discussions.

RESULTS

High glycemic variability is associated with an increased risk of development of diabetic complications especially in cardiac conditions. The widely used and accepted metrics to determine the variations in blood glucose are Standard deviation (SD), MAGE (Mean amplitude of glycemic excursions) and MODD (Mean of daily differences). Occurrence of blood glucose variations affects at a molecular level thereby causing more harm than the occurrence of hyperglycemia alone.

CONCLUSION

Available data suggest that Glycemic Variability should be used as an additional marker of glycemia. Additional research globally, and in India are required.

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.

Plasma levels of glucagon but not GLP-1 are elevated in response to inflammation in humans

OBJECTIVE

Glucagon and glucagon-like peptide-1 (GLP-1) originate from the common precursor, proglucagon, and their plasma concentrations have been reported to be increased during inflammatory conditions. Increased blood glucose levels are frequently observed in septic patients, and therefore we hypothesized that glucagon, but not GLP-1, is increased in individuals with inflammation.

DESIGN

Prospective longitudinal cohort study.

MATERIALS AND METHODS

We measured glucagon and GLP-1 in plasma sampled consecutively in three cohorts consisting of patients with infective endocarditis (n = 16), urosepsis (n = 28) and post-operative inflammation following percutaneous aortic valve implantation or thoracic endovascular aortic repair (n = 5). Correlations between C-reactive protein (CRP), a marker of systemic inflammation, and glucagon and GLP-1 concentrations were investigated. Additionally, glucagon and GLP-1 concentrations were measured after a bolus infusion of lipopolysaccharide (LPS, 1 ng/kg) in nine healthy young males.

RESULTS

Glucagon and CRP were positively and significantly correlated (r = 0.27; P = 0.0003), whereas no significant association between GLP-1 and CRP was found (r = 0.08, P = 0.30). LPS infusion resulted in acute systemic inflammation reflected by increased temperature, pulse, tumor necrosis factor-α (TNFα), interleukin-6 (IL-6) and concomitantly increased concentrations of glucagon (P < 0.05) but not GLP-1.

CONCLUSIONS

Systemic inflammation caused by bacterial infections or developed as a non-infected condition is associated with increased plasma concentration of glucagon, but not GLP-1. Hyperglucagonemia may contribute to the impaired glucose control in patients with systemic inflammatory diseases.

Insulin sensitivity in critically ill patients: are women more insulin resistant?

Background

Glycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care.

Methods

Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (P < 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal.

Results

Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent.

Conclusion

Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.

Insulin sensitivity in critically ill patients: are women more insulin resistant?

BACKGROUND

Glycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care.

METHODS

Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (P < 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal.

RESULTS

Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent.

CONCLUSION

Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.

Capacity of Infusion Lines for Insulin Adsorption: Effect of Flow Rate on Total Adsorption

BACKGROUND

Insulin adsorption to clinical materials has been well observed, but not well quantified. Insulin adsorption reduces expected and actual insulin delivery and is unaccounted for in insulin therapy or glycemic control. It may thus contribute to poor control and high glycemic variability. This research quantifies the problem in the context of clinical use.

METHOD

Experimental insulin adsorption data from literature is used to calculate insulin delivery and total insulin adsorption capacities for polyethylene (PE) and polyvinal chloride (PVC) lines at clinically relevant flow rates and concentrations.

RESULTS

Insulin adsorption capacity decreased hyperbolically with flow rate for both PE and PVC, where low flow scenarios result in greater insulin adherence to infusion lines. When the infusion flow rate was halved from 1 to 0.5 mL/h, twice as much insulin adsorbed to the line. Insulin loss to adsorption resulted in up to ~50% of intended insulin not delivered over 24 hours in a low flow and low concentration context.

CONCLUSION

Material capacity for insulin adsorption is not constant, but increases with decreasing flow. Different materials have different adsorption capacities. In low flow and low concentration contexts, such as in neonatal or pediatric intensive care, insulin loss to adsorption represents a significant proportion of daily insulin delivery, which needs to be accounted for.