The impact of early hypoglycemia and blood glucose variability on outcome in critical illness

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.

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.

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

BACKGROUND

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

METHOD

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

RESULTS

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

CONCLUSION

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

Prognostic Value of Serum Glucose Level in Critically Ill Septic Patients on Admission to Pediatric Intensive Care Unit

Background

Sepsis is one of the major causes of admission to the pediatric intensive care unit (PICU), as well as a primary cause of poor outcomes. Glycemic variation may occur because of sepsis resulting in either hypoglycemia or hyperglycemia. Measuring the random blood glucose (RBG) level of patients presenting with sepsis in PICU is an easy way to assess their prognosis.

Objectives

A prospective study was done from February 2023 to June 2023 to evaluate the relation between the outcome of pediatric septic patients and blood glucose level upon PICU admission.

Patients and methods

One hundred three children diagnosed with sepsis underwent clinical assessment upon admission to the PICU and initial labs including blood glucose levels were done. Pediatric Sequential Organ Failure Assessment (pSOFA) was calculated for every patient. The outcome of sepsis including length of stay, review of body systems, and mortality was documented.

Results

Hypoglycemic patients had the highest percentage of non-survivors (20.4%). They had a higher pSOFA score with a median of 11 (interquartile range-IQR 7-15), shorter PICU stay with a median of 2 (IQR 1-6) days, lower RBG with a median of 95 (45-120), a higher percentage of ventilation (55.1%), and a higher percentage of inotropic support (87.8%) with statistical significance with p-value (< 0.001, < 0.001, 0.001, < 0.001, 0.002), respectively.

Conclusion

Critically ill patients with abnormal random blood sugar (RBS) had a higher possibility of non-survival particularly those with hypoglycemia. Accordingly, RBS measurement is a rapid and cheap method that could be used in any emergency and as an early indicator to detect outcome.

How to cite this article

Mohamed AO, Abd El-Megied MA, Hosni YA. Prognostic Value of Serum Glucose Level in Critically Ill Septic Patients on Admission to Pediatric Intensive Care Unit. Indian J Crit Care Med 2023;27(10):754-758.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Malglycemia in the critical care setting. Part I: Defining hyperglycemia in the critical care setting using the glycemic ratio

INTRODUCTION

Stress-induced hyperglycemia (SIH) is conventionally represented by Blood Glucose (BG) although recent evidence indicates the Glycemic Ratio (GR, quotient of mean BG and estimated preadmission BG) is a superior prognostic marker. We assessed the association between in-hospital mortality and SIH, using BG and GR in an adult medical-surgical ICU.

METHODS

We included patients with hemoglobin A1c (HbA1c) and minimum four BGs in a retrospective cohort investigation (n = 4790).

RESULTS

A critical SIH threshold of GR 1.1 was identified. Mortality increased with increasing exposure to GR ≥ 1.1 (r² = 0.94, p = 0.0007). Duration of exposure to BG ≥ 180 mg/dL demonstrated a less robust association with mortality (r² = 0.75, p = 0.059). In risk-adjusted analyses, hours GR ≥ 1.1 (OR 1.0014, 95%CI (1.0003-1.0026), p = 0.0161) and hours BG ≥ 180 mg/dL (OR 1.0080, 95%CI (1.0034-1.0126), p = 0.0006) were associated with mortality. In the cohort with no exposure to hypoglycemia however, only hours GR ≥ 1.1 was associated with mortality (OR 1.0027, 95%CI (1.0012-1.0043), p = 0.0007), not BG ≥ 180 mg/dL (OR 1.0031, 95%CI (0.9949-1.0114), p = 0.50) and this relationship remained intact for those who never experienced BG outside the 70-180 mg/dL range (n = 2494).

CONCLUSIONS

Clinically significant SIH commenced above GR 1.1. Mortality was associated with hours of exposure to GR ≥ 1.1 which was a superior marker of SIH compared to BG.

Association of the time in targeted blood glucose range of 3.9-10 mmol/L with the mortality of critically ill patients with or without diabetes

Purpose

The relationship between the TIR and mortality may be influenced by the presence of diabetes and other glycemic indicators. The purpose of this study was to investigate the relationship between TIR and in-hospital mortality in diabetic and non-diabetic patients in ICU.

Methods

A total of 998 patients with severe diseases in the ICU were selected for this retrospective analysis. The TIR is defined as the percentage of time spent in the target blood glucose range of 3.9-10.0 mmol/L within 24 h. The relationship between TIR and in-hospital mortality in diabetic and non-diabetic patients was analyzed. The effect of glycemic variability was also analyzed.

Results

The binary logistic regression model showed that there was a significant association between the TIR and the in-hospital death of severely ill non-diabetic patients. Furthermore, TIR≥70% was significantly associated with in-hospital death (OR = 0.581, P = 0.003). The study found that the coefficient of variation (CV) was significantly associated with the mortality of severely ill diabetic patients (OR = 1.042, P = 0.027).

Conclusions

Both diabetic and non-diabetic critically ill patients should control blood glucose fluctuations and maintain blood glucose levels within the target range, it may be beneficial in reducing mortality.

Model-based subcutaneous insulin for glycemic control of pre-term infants in the neonatal intensive care unit

Hyperglycaemia is a common problem in neonatal intensive care units (NICUs). Achieving good control can result in better outcomes for patients. However, good control is difficult, where poor control and resulting hypoglycaemia reduces outcomes and confounds results. Clinically validated models can provide good control, and subcutaneous insulin delivery can provide more options for insulin therapy for clinicians. However, this combination has only been significantly utilised in adult outpatient diabetes, but could hold benefit for treating NICU infants. This research combines a well-validated NICU metabolic model with subcutaneous insulin kinetics models to assess the feasibility of a model-based approach. Clinical data from 12 very/extremely pre-mature infants was collected for an average study duration of 10.1 days. Blood glucose, interstitial and plasma insulin, as well as subcutaneous and local insulin were modelled, and patient-specific insulin sensitivity profiles were identified for each patient. Modelling error was low, where the cohort median [IQR] mean percentage error was 0.8 [0.3 3.4] %. For external validation, insulin sensitivity was compared to previous NICU cohorts using the same metabolic model, where overall levels of insulin sensitivity were similar. Overall, the combined system model accurately captured observed glucose and insulin dynamics, showing the potential for a model-based approach to glycaemic control using subcutaneous insulin in this cohort. The results justify further model validation and clinical trial research to explore a model-based protocol.

Association of fasting in the first 72 h of intensive care unit stay with outcomes of critically ill patients

BACKGROUND

Whether fasting early in critical illness course is acceptable is not clear and high-quality data on this topic are lacking. To generate equipoise for future clinical trials and bring additional data to current literature, we compared outcomes of patients fasted during the first 72 h of intensive care unit (ICU) stay to patients receiving any nutrition support during this period.

METHODS

Retrospective cohort study of a medical ICU from a tertiary academic center in Brazil. Adult patients treated between November 2017 and February 2022 with an ICU length of stay of ≥5 days were included. Baseline and daily data were retrieved from the prospectively collected administrative database. We did 1:1 propensity score matching to compare patients fasting for at least 72 h with controls. Primary outcome was hospital mortality and secondary outcomes were other resources' use.

RESULTS

During the study period, 1591 patients were cared for in this ICU, of which 998 stayed ≥5 days. After excluding readmissions and propensity score matching, 93 patients in the fasting group were matched to 93 controls. Hospital mortality was similar between fasting and matched control groups (odds ratio = 1.04; 95% CI = 0.56-1.94; P > 0.99). Secondary outcomes were not different between groups, including length of stay, days on mechanical ventilation, and incidence of new infections.

CONCLUSION

Withholding nutrition support in the first 72 h of ICU stay was not associated with worse outcomes in this cohort of severe critically ill patients.

Prognostic Significance of Baseline Blood Glucose Levels and Glucose Variability in Severe Acute Kidney Injury: A Secondary Analysis from the RENAL Study

Aim: To study the associations between baseline blood glucose levels (BGL), glycemic variability and clinical outcomes in patients with severe acute kidney injury (AKI) receiving continuous renal replacement therapy (CRRT). Methods: We performed a secondary analysis of the Randomized Evaluation of Normal versus Augmented Level of RRT (RENAL) study. A multivariate Cox regression model was used to assess the association between baseline BGL, glycemic variability and clinical outcomes. The primary outcome was all-cause mortality, and secondary outcomes were duration of hospital and intensive care unit (ICU) stay. Results: Baseline BGL data were available in 1404 out of 1508 patients from the RENAL study. Among them, 627 patients died within 90 days of randomization. Compared to patients in the second quartile (BGL 5.8−7.2 mmol/L), patients in the first quartile (BGL < 5.8 mmol/L) had increased mortality rate (90-day HR 1.48; p = 0.001; 28-day HR 1.47; p = 0.042). However, there were no significant differences in ICU and hospital length of stay (LOS) (p = 0.82 and p = 0.33, respectively). Glycemic variability data were from 1345 out of 1404 patients who had data for BG values within 28 days. Higher coefficient of variation (CV) (HR 1.02; P trend = 0.002) and standard deviation value (SD) (HR 1.29; P trend = 0.027) were associated with higher risk of death at day 90. Conclusions: We identified a low BGL within the normal physiological range at baseline and greater CV and SD values as significant modifiable risk factors for mortality in severe AKI patients in ICU, which may be a target for intervention.

Early glycaemic variability increases 28-day mortality and prolongs intensive care unit stay in critically ill patients with pneumonia

OBJECTIVE

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance

BACKGROUND

Critically ill ICU patients frequently experience acute insulin resistance and increased endogenous glucose production, manifesting as stress-induced hyperglycemia and hyperinsulinemia. STAR (Stochastic TARgeted) is a glycemic control protocol, which directly manages inter- and intra- patient variability using model-based insulin sensitivity (SI). The model behind STAR assumes a population constant for endogenous glucose production (EGP), which is not otherwise identifiable.

OBJECTIVE

This study analyses the effect of estimating EGP for ICU patients with very low SI (severe insulin resistance) and its impact on identified, model-based insulin sensitivity identification, modeling accuracy, and model-based glycemic clinical control.

METHODS

Using clinical data from 717 STAR patients in 3 independent cohorts (Hungary, New Zealand, and Malaysia), insulin sensitivity, time of insulin resistance, and EGP values are analyzed. A method is presented to estimate EGP in the presence of non-physiologically low SI. Performance is assessed via model accuracy.

RESULTS

Results show 22%-62% of patients experience 1+ episodes of severe insulin resistance, representing 0.87%-9.00% of hours. Episodes primarily occur in the first 24 h, matching clinical expectations. The Malaysian cohort is most affected. In this subset of hours, constant model-based EGP values can bias identified SI and increase blood glucose (BG) fitting error. Using the EGP estimation method presented in these constrained hours significantly reduced BG fitting errors.

CONCLUSIONS

Patients early in ICU stay may have significantly increased EGP. Increasing modeled EGP in model-based glycemic control can improve control accuracy in these hours. The results provide new insight into the frequency and level of significantly increased EGP in critical illness.

Effect of Treatment and Nutrition on Glycemic Variability in Critically Ill Patients

Nondiabetic hyperglycemia is a dangerous metabolic phenomenon in the intensive care unit. Inattentive treatment of glycemic disorders is a serious health hazard promoting negative outcomes. The aim of our study was to assess glycemic variability and its basic determinants, and to verify its relationship with mortality in patients hospitalized in a mixed ICU (intensive care unit). The medical records of 37 patients hospitalized 13 January−29 February 2020 were analyzed prospectively. The BG (blood glucose) variability during the stay was assessed using two definitions, i.e., the value of standard deviation (SD) from all the measurements performed and the coefficient of variation (CV). A correlation between the BG variability and insulin dose was observed (SD: R = 0.559; p < 0.01; CV: R = 0.621; p < 0.01). There was also a correlation between the BG variability and the total energy daily dose (SD: R = 0.373; p = 0.02; CV: R = 0.364; p = 0.03). Glycemic variability was higher among patients to whom treatment with adrenalin (p = 0.0218) or steroid (p = 0.0292) was applied. The BG variability, expressed using SD, was associated with ICU mortality (ROC = 0.806; 95% CI: 0.643−0.917; p = 0.0014). The BG variability in the ICU setting arises from the loss of balance between the supplied energy and the applied insulin dose and may be associated with a worse prognosis.

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.

Sepsis‐associated hypoglycemia on admission is associated with increased mortality in intensive care unit patients

Aim

Hyperglycemia is a common response to acute illness, but it is not often seen in critical conditions. The frequency and cause of hypoglycemia in septic patients have not been well elucidated. In this study, we focused on sepsis‐associated hypoglycemia in the early phase and evaluated the impact of hypoglycemia on mortality.

Methods

We performed a retrospective review of 265 patients with sepsis admitted to a tertiary medical center. Blood glucose levels on admission were evaluated and analyzed by a Cox proportional hazard model.

Results

We categorized patients with sepsis into five groups according to blood glucose levels. Seven patients (2.6%) were admitted with severe hypoglycemia (≤40 mg/dL), 19 (7.2%) with mild hypoglycemia (41–70 mg/dL), 103 (38.9%) with euglycemia (71–140 mg/dL), 58 (21.9%) with mild hyperglycemia (141–180 mg/dL), and 78 (29.4%) with hyperglycemia (>180 mg/dL). There was a significant difference in 28‐day mortality between those with severe hypoglycemia and euglycemia (71.4% versus 8.7%; P < 0.05). We analyzed the hazard ratios for the groups (relative to the reference of euglycemia) adjusted for sex, age, and Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment scores on admission. The hazard ratios for 28‐day mortality in patients with severe hypoglycemia and mild hypoglycemia compared with that in patients with euglycemia were 8.18 (95% confidence interval [CI], 2.39–27.96; P = 0.001) and 7.56 (95% CI, 2.96–19.35; P < 0.001), respectively.

Conclusion

Septic patients with severe hypoglycemia had significantly higher mortality compared with patients with euglycemia.

Perioperative glycemic status is linked to postoperative complications in non-intensive care unit patients with type-2 diabetes: a retrospective study

BACKGROUND

Perioperative hyperglycemia is a risk factor for postoperative complications in the general population. However, it has not been clarified whether perioperative hyperglycemia increases postoperative complications in patients with type-2 diabetes mellitus (T2D). Therefore, we aimed to analyze the relationship between perioperative glycemic status and postoperative complications in non-intensive care unit (non-ICU) hospitalized patients with T2D.

MATERIALS AND METHODS

Medical records of 1217 patients with T2D who were admitted to the non-ICU in our hospital were analyzed retrospectively. Relationships between clinical characteristics including perioperative glycemic status and postoperative complications were assessed using univariate and multivariate analyses. Perioperative glycemic status was evaluated by calculating the mean, standard deviation (SD), and coefficient of variation (CV) of blood glucose (BG) measurements in preoperative and postoperative periods for three contiguous days before and after surgery, respectively. Postoperative complications were defined as infections, delayed wound healing, postoperative bleeding, and/or thrombosis.

RESULTS

Postoperative complications occurred in 139 patients (11.4%). These patients showed a lower BG immediately before surgery (P = 0.04) and a higher mean postoperative BG (P = 0.009) than those without postoperative complications. There were no differences in the other perioperative BG parameters including BG variability and the frequency of hypoglycemia. The multivariate analysis showed that BG immediately before surgery (adjusted odds ratio (95% confidence interval [CI]), 0.91 (0.85-0.98), P = 0.01) and mean postoperative BG (1.11 (1.05-1.18), P < 0.001) were independently associated with postoperative complications.

CONCLUSION

Perioperative glycemic status, that is, a low BG immediately before surgery and a high mean postoperative BG, are associated with the increased incidence of postoperative complications in non-ICU patients with T2D.

Safety and efficacy of transitioning from intravenous to subcutaneous insulin in critically ill patients

Background Intravenous (IV) insulin is commonly used for the management of hyperglycemia in critically ill patients. However, an assessment of real-world practices for the transition process from IV to Subcutaneous (SC) is lacking. Objective The objective of this study was to describe the real-world practice during insulin transition from IV to SC in intensive care unit (ICU) patients. Setting ICUs at a tertiary medical center. Methods This was a retrospective cohort study. Data were obtained from electronic medical records for all ICU patients for whom insulin infusions were ordered between Nov 2017-2018. Adult ICU patients were included if they were transitioned to a SC insulin regimen after spending at least 6 h on IV insulin infusion. Data collected include blood glucose readings, transition percentage, and the type of insulin regimen used after transition. Main outcome measure Assessment of the transition percentage and dysglycemic events during the insulin transition process from IV to SC. Results Two hundred patients with 4702 blood glucose checks were included. Of the included patients, 65% (130/200) were transitioned to a basal insulin-containing regimen. The median transition percentage in those patients was 45% [IQR: 28 - 69]. In the overall cohort, the number of patients who developed moderate and severe hypoglycemia was significantly higher prior to transition, while hyperglycemia was significantly higher after insulin transition. Conclusion We observed that patients were converted to SC therapy using a lower transition percentage than previously described. More data are needed to optimize the transition process in critically ill patients.

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.

Estimating Increased EGP During Stress Response in Critically Ill Patients

BACKGROUND

Stress-induced hyperglycemia is frequently experienced by critically ill patients and the use of glycemic control (GC) has been shown to improve patient outcomes. For model-based approaches to GC, it is important to understand and quantify model parameter assumptions. This study explores endogenous glucose production (EGP) and the use of a population-based parameter value in the intensive care unit context.

METHOD

Hourly insulin sensitivity (SI) was fit to clinical data from 145 patients on the Specialized Relative Insulin and Nutrition Titration GC protocol for at least 24 hours. Constraint of SI at a lower bound was used to explore likely EGP variability due to stress response. Minimum EGP was estimated during times when the model SI was constrained, and time and duration of events were examined.

RESULTS

Constrained events occur for 1.6% of patient hours. About 70% of constrained events occur in the first 12 hours and most events (~80%) occur when there is no exogenous nutrition given. Enhanced EGP values ranged from 1.16 mmol/min (current population value) to 2.75 mmol/min, with most being below 1.5 mmol/min (21% increase).

CONCLUSION

The frequency of constrained events is low and the current population value of 1.16 mmol/min is sufficient for more than 98% of patient hours, however, some patients experience significantly raised EGP probably due to an extreme stress response early in patient stay.

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.

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.

Variability in Annual Fasting Glucose and the Risk of Peripheral Artery Disease in Patients with Diabetes Mellitus

PURPOSE

High glucose concentrations and swings are associated with endothelial dysfunction. We examined the effects of variability in fasting plasma glucose on peripheral artery disease (PAD) in patients with diabetes mellitus (DM).

PATIENTS AND METHODS

In this screening study for the risk factors of PAD, we retrospectively collected data on the ankle-brachial index (ABI) and the percentage of mean arterial pressure (%MAP) at the ankle between August 01, 2016 and July 31, 2017. We defined low ABI ≤0.90, high %MAP ≥45%, or both as high-risk PAD and others as low-risk PAD. We compared the standard deviation (SD) of the first fasting plasma glucose data available each year after January 01, 2007.

RESULTS

In 2577 patients, a higher SD of annual fasting glucose was observed in those with an ABI ≤0.90 than in patients with an ABI >0.90 (2.6 ± 2.1 vs 2.2 ± 2.3, P = 0.009), and in patients with %MAP ≥45% than in those with %MAP <45% (2.4 ± 2.1 vs 2.2 ± 2.3, P = 0.034). A high-risk PAD was significantly associated with the SD (P = 0.032) but not with the mean (P = 0.338) of annual fasting glucose. The former was an independent risk factor for high-risk PAD (odds ratio = 1.424; 95% CI = 1.118‒1.814; P = 0.004).

CONCLUSION

Variability but not mean of annual fasting plasma glucose was significantly associated with a high risk of PAD in patients with DM.

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.

Lipid metabolic signatures deviate in sepsis survivors compared to non-survivors

Sepsis remains a major cause of death despite advances in medical care. Metabolic deregulation is an important component of the survival process. Metabolomic analysis allows profiling of critical metabolic functions with the potential to classify patient outcome. Our prospective longitudinal characterization of 33 septic and non-septic critically ill patients showed that deviations, independent of direction, in plasma levels of lipid metabolites were associated with sepsis mortality. We identified a coupling of metabolic signatures between liver and plasma of a rat sepsis model that allowed us to apply a human kinetic model of mitochondrial beta-oxidation to reveal differing enzyme concentrations for medium/short-chain hydroxyacyl-CoA dehydrogenase (elevated in survivors) and crotonase (elevated in non-survivors). These data suggest a need to monitor cellular energy metabolism beyond the available biomarkers. A loss of metabolic adaptation appears to be reflected by an inability to maintain cellular (fatty acid) metabolism within a "corridor of safety".

Review of Intravenous and Subcutaneous Electronic Glucose Management Systems for Inpatient Glycemic Control

PURPOSE OF REVIEW

The goal of this review is to summarize current literature on electronic glucose management systems (eGMS) and discuss their benefits and disadvantages in the inpatient setting.

RECENT FINDINGS

We review different versions of commercially available eGMS: Glucommander™ (Glytec, Greenville, SC), EndoTool^R (MD Scientific LLC, Charlotte, NC), GlucoStabilizer™ (Medical Decision Network, Charlottesville, VA), GlucoCare™ (Pronia Medical Systems, KY), and discuss advantages such as reducing rates of hypoglycemia, hyperglycemia, and glycemic variability. In addition, eCGMs offer a uniform standard of care and may improve workflows across institutions as well reduce barriers. Despite ample literature on intravenous (IV) versions of eGMS, there is little published research on subcutaneous (SQ) insulin guidance. Although use of eGMS requires extensive training and institution-wide adoption, time spent on diabetes management is better facilitated by their use.

Mean amplitude of glycemic excursions in septic patients and its association with outcomes: A prospective observational study using continuous glucose monitoring

PURPOSE

To apply continuous glucose monitoring (CGM) and determine the mean amplitude of glycemic excursions (MAGE) in septic patients and to assess the associations of MAGE with outcomes and oxidative stress.

MATERIALS AND METHODS

This study was conducted in adult septic patients expected to require intensive care for >48 h. We continuously measured blood glucose level for the first 48 h in the ICU using FreeStyle Libre®. MAGE was calculated using glycemic information obtained by CGM during the study period of 48 h. The primary outcome was 90-day all-cause mortality. The secondary outcomes were 90-day ICU-free days and the concentration of urinary 8-isoprostaglandinF2α measured 48 h after commencement of the study as a surrogate of oxidative stress.

RESULTS

Forty patients were included in this study. Median of MAGE was higher in non-survivors than in survivors: 68.8 (IQR;39.7-97.2) vs. 39.3 (IQR;19.9-53.3), p = 0.02. In multivariate analysis, MAGE was independently associated with 90-day all-cause mortality rate (p = 0.02), urinary 8-isoprostaglandinF2α level (p = 0.03) and 90-day ICU-free survival days (p = 0.03).

CONCLUSIONS

In the current study, MAGE for the first 48 h of treatment that was obtained by using CGM was associated with 90-day all-cause mortality, 90-day ICU-free days and urinary 8-isoprostaglandinF2α level in septic patients.

Risk and reward: extending stochastic glycaemic control intervals to reduce workload

Background

STAR is a model-based, personalised, risk-based dosing approach for glycaemic control (GC) in critically ill patients. STAR provides safe, effective control to nearly all patients, using 1–3 hourly measurement and intervention intervals. However, the average 11–12 measurements per day required can be a clinical burden in many intensive care units. This study aims to significantly reduce workload by extending STAR 1–3 hourly intervals to 1 to 4-, 5-, and 6-hourly intervals, and evaluate the impact of these longer intervals on GC safety and efficacy, using validated in silico virtual patients and trials methods. A Standard STAR approach was used which allowed more hyperglycaemia over extended intervals, and a STAR Upper Limit Controlled approach limited nutrition to mitigate hyperglycaemia over longer intervention intervals.

Results

Extending STAR from 1–3 hourly to 1–6 hourly provided high safety and efficacy for nearly all patients in both approaches. For STAR Standard, virtual trial results showed lower % blood glucose (BG) in the safe 4.4–8.0 mmol/L target band (from 83 to 80%) as treatment intervals increased. Longer intervals resulted in increased risks of hyper- (15% to 18% BG > 8.0 mmol/L) and hypo- (2.1% to 2.8% of patients with min. BG < 2.2 mmol/L) glycaemia. These results were achieved with slightly reduced insulin (3.2 [2.0 5.0] to 2.5 [1.5 3.0] U/h) and nutrition (100 [85 100] to 90 [75 100] % goal feed) rates, but most importantly, with significantly reduced workload (12 to 8 measurements per day). The STAR Upper Limit Controlled approach mitigated hyperglycaemia and had lower insulin and significantly lower nutrition administration rates.

Conclusions

The modest increased risk of hyper- and hypo-glycaemia, and the reduction in nutrition delivery associated with longer treatment intervals represent a significant risk and reward trade-off in GC. However, STAR still provided highly safe, effective control for nearly all patients regardless of treatment intervals and approach, showing this unique risk-based dosing approach, modulating both insulin and nutrition, to be robust in its design. Clinical pilot trials using STAR with different measurement timeframes should be undertaken to confirm these results clinically.

Effect of duck hepatitis A virus genotype 3 infection on glucose metabolism of Pekin ducklings and underlying mechanism

Earlier works identified the second generation (Z8R2) of a resistant Pekin duck line to duck hepatitis A virus genotype 3 (DHAV-3), which displays significantly strong resistance than that of the second generation (Z8S2) of a susceptible Pekin duck line. To understand the genetic mechanisms that determine the different resistance/susceptibility of Z8R2 and Z8S2 to DHAV-3, transcriptome analysis on livers of infected Pekin ducklings was performed to screen differentially expressed genes (DEGs). We found that DHAV-3 infection has a great effect on metabolism of Z8S2 at the transcription level. Using a newly created fourth generation of the resistant Pekin duck line (Z8R4) and an unselected Pekin duck flock (Z7) as models, hypoglycemia and dramatically increased aspartate aminotransferase and alanine aminotransferase were shown to be noticeable signs of fatal cases caused by DHAV-3 infection. These findings, together with expression analysis and verification of DEGs, support the view that DHAV-3 infection results in glucose metabolic abnormalities in susceptible individuals and that there are significant differences in expression patterns of glucose metabolism-related DEGs between susceptible and resistant individuals. Notably, cytokines displayed a negative correlation with glucose synthesis in terms of expression in susceptible individuals following DHAV-3 infection. Mechanism analyses suggests that cytokines will activate PI3K-AKT pathway and/or JAK-STAT pathway by up-regulated expression of JAK2, and thereby causes down-regulated expression of G6PC and/or ACAT1. Cytokines can also cause down-regulated expression of HPGDS by JAK2. The present work contributes to the understanding of pathogenesis of DHAV-3 infection and the resistance breeding project against DHAV-3.

A finite element model for insulin adsorption in ICU infusion sets

Insulin adsorption has been observed in ICU delivery lines, and is especially problematic in the delivery of insulin within the neonatal ICU. This paper presents a two state model with adsorptive loss described as a predator-prey term with parameters K_i and B_eq. This model is discretized to N sub volumes along the length of an infusion set. The model was found to converge to a solution for N>~100-150. The model was fit to literature data, and it was found that the total adsorptive capacity of a material (B_eq, U/m²) was hyperbolically related to flow rate. If the average rate constant K_i was used with the hyperbolic relationship, the model was able to describe adsorption dynamics at all 3 examined flow rates for a polyethylene line.

Individualizing Glycemic Control in the Critically Ill

Hyperglycemia is a common phenomenon in critically ill patients, even in those without diabetes. Two landmark studies established the benefits of tight glucose control (blood glucose target 80-110 mg/dL) in surgical and medical patients. Since then, literature has consistently demonstrated that both hyperglycemia and hypoglycemia are independently associated with increased morbidity and mortality in a variety of critically ill patients. However, tight glycemic control has subsequently come into question due to risks of hypoglycemia and increased mortality. More recently, strategies targeting euglycemia (blood glucose ≤180 mg/dL) have been associated with improved outcomes, although the risk of hypoglycemia remains. More complex targets (ie, glycemic variability and time within target glucose range) and the impact of individual patient characteristics (ie, diabetic status and prehospital glucose control) have more recently been shown to influence the relationship between glycemic control and outcomes in critically ill patients. Although our understanding has increased, the optimal glycemic target is still unclear and glucose management strategies may require adjustment for individual patient characteristics. As glucose management increases in complexity, we realize that traditional means of using meters and strips and paper insulin titration algorithms are potential limitations to our success. To achieve these complex goals for glycemic control, the use of continuous or near-continuous glucose monitoring combined with computerized insulin titration algorithms may be required. The purpose of this review is to discuss the evidence surrounding the various domains of glycemic control and the emerging data supporting the need for individualized glucose targets in critically ill patients.

Glycaemic Control in Critically Ill Adult Patients: Is intensive insulin therapy beneficial?

OBJECTIVES Glycaemic control with intensive insulin therapy and its impact on patient outcomes have always been contentious in an intensive care setting. This study aims to assess the patterns of glycaemic control in critically ill patients at a tertiary care institution in Trinidad and its relationship to outcomes. METHODS All adult patients admitted to a multidisciplinary intensive care unit (ICU) for a period of two years were enrolled for a retrospective chart review. Data collected included demographics, admission blood glucose, mean morning blood glucose (MBG), the trend of glucose control, number of hypoglycaemic episodes, admission Simplified Acute Physiology Score (SAPS) II, ICU and hospital length of stay, duration of mechanical ventilation, anaemia, renal replacement therapy and hospital outcome. RESULTS A total of 104 patients were studied. Four different patterns of insulin therapy were practised at the ICU. The median age of patients was 55.5 years, the mean SAPS II was 49.3, the mean predicted mortality was 45.5% and the overall observed mortality was 38.5%. The majority of admissions had cardiovascular illnesses (25%), followed by sepsis (20.2%). Patients with multiple hypoglycaemic episodes had an increased mortality (p<0.01). Patients had a better outcome with a higher MBG (>100 mg/dL) (p<0.05). There was a significant difference in mortality among the four patterns of glycaemic control (p<0.001). Admission blood glucose, length of time of mechanical ventilation, ICU length of stay and renal replacement therapy were not found to be associated with adverse outcomes. CONCLUSION Intensive insulin therapy (IIT) may not benefit ICU patients but can be probably associated with higher mortality. Avoidance of hypoglycaemia as well as persistent hyperglycaemia may lead to a better outcome in critically ill patients.

Multi-input stochastic prediction of insulin sensitivity for tight glycaemic control using insulin sensitivity and blood glucose data

BACKGROUND

Glycaemic control in the intensive care unit is dependent on effective prediction of patient insulin sensitivity (SI). The stochastic targeted (STAR) controller uses a 2D stochastic model for prediction, with current SI as an input and future SI as an output.

METHODS

This paper develops an extension of the STAR 2D stochastic model into 3D by adding blood glucose (G) as an input. The performance of the 2D and 3D stochastic models is compared over a retrospective cohort of 65,269 data points across 1525 patients.

RESULTS

Under five-fold cross-validation, the 3D model was found to better match the expected potion of data points within, above and below various credible intervals, suggesting it provided a better representation of the underlying probability field. The 3D model was also found to provide an 18.1% narrower 90% credible interval on average, and a narrower 90% credible interval in 96.4% of cases, suggesting it provided more accurate predictions of future SI. Additionally, the 3D stochastic model was found to avoid the undesirable tendency of the 2D model to overestimate SI for patients with high G, and underestimate SI for patients with low G.

CONCLUSIONS

Overall, the 3D stochastic model is shown to provide clear potential benefits over the 2D model for minimal clinical cost or effort, though further exploration into whether these improvements in SI prediction translate into improved clinical outcomes is required.

Blood Glucose Levels and Mortality in Patients With Sepsis: Dose-Response Analysis of Observational Studies

BACKGROUND

We undertook a systematic review and meta-analysis to investigate the relationship between blood glucose levels and mortality in patients with sepsis.

METHODS

Medline and EMBASE were searched from inception to April 8, 2018. Cohort studies or case-control studies reported the association between blood glucose and mortality in patients with sepsis were selected. Study characteristics, baseline characteristics, definition of hyperglycemia, and outcomes of interest were extracted. We performed a dose-response meta-analysis to assess the effect of blood glucose level on mortality. We also conducted meta-analysis for patients with or without diabetes separately.

RESULTS

Ten cohort studies involving 26 429 patients were included, of which 5 were prospective studies and 5 retrospective studies. Dose-response analysis showed that the effect of blood glucose on mortality may differ in patients with versus without diabetes. There was a U-shaped relationship for patients with diabetes and a J-shaped relationship for patients without diabetes, with blood glucose at 145 to 155 mg/dL corresponding to lowest mortality both in patients with and without diabetes.

CONCLUSIONS

Current evidence suggested U-shaped relationship between blood glucose and mortality in all patients irrespective of their diabetes status. Diabetic patients with blood glucose below 145 mg/dL may have poorer prognosis compared to patients without established diabetes.

3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation

Background

The challenges of glycaemic control in critically ill patients have been debated for 20 years. While glycaemic control shows benefits inter- and intra-patient metabolic variability results in increased hypoglycaemia and glycaemic variability, both increasing morbidity and mortality. Hence, current recommendations for glycaemic control target higher glycaemic ranges, guided by the fear of harm. Lately, studies have proven the ability to provide safe, effective control for lower, normoglycaemic, ranges, using model-based computerised methods. Such methods usually identify patient-specific physiological parameters to personalize titration of insulin and/or nutrition. The Stochastic-Targeted (STAR) glycaemic control framework uses patient-specific insulin sensitivity and a stochastic model of its future variability to directly account for both inter- and intra-patient variability in a risk-based insulin-dosing approach.

Results

In this study, a more personalized and specific 3D version of the stochastic model used in STAR is compared to the current 2D stochastic model, both built using kernel-density estimation methods. Fivefold cross validation on 681 retrospective patient glycaemic control episodes, totalling over 65,000 h of control, is used to determine whether the 3D model better captures metabolic variability, and the potential gain in glycaemic outcome is assessed using validated virtual trials. Results show that the 3D stochastic model has similar forward predictive power, but provides significantly tighter, more patient-specific, prediction ranges, showing the 2D model over-conservative > 70% of the time. Virtual trial results show that overall glycaemic safety and performance are similar, but the 3D stochastic model reduced median blood glucose levels (6.3 [5.7, 7.0] vs. 6.2 [5.6, 6.9]) with a higher 61% vs. 56% of blood glucose within the 4.4–6.5 mmol/L range.

Conclusions

This improved performance is achieved with higher insulin rates and higher carbohydrate intake, but no loss in safety from hypoglycaemia. Thus, the 3D stochastic model developed better characterises patient-specific future insulin sensitivity dynamics, resulting in improved simulated glycaemic outcomes and a greater level of personalization in control. The results justify inclusion into ongoing clinical use of STAR.

Sepsis patients with complication of hypoglycemia and hypoalbuminemia are an early and easy identification of high mortality risk

Either hypoglycemia or hypoalbuminemia alone is an independent condition associated with increased risk of mortality in critical illness. This study evaluates whether the mortality risk increases in septic patients if these conditions are combined. Patients admitted to our hospital from 2008 to 2015 who satisfied the definition of sepsis were targeted (n = 336). We classified cases into three groups based on blood glucose (BG) level measured at admission: hypoglycemia (Hypo-G; BG < 80 mg/dl), intermediate glycemia (Inter-G; 80-199 mg/dl), and hyperglycemia (Hyper-G; ≥ 200 mg/dl) group, and then estimated mortality. We also compared the clinical data of these glycemic groups in combination with hypoalbuminemia (Hypo-A) or Inter-G with non-hypoalbuminemia (Inter-G + Nonhypo-A), as a secondary analysis. Diagnostic cut-off level of Hypo-A (< 2.8 mg/dl) was determined using the ROC curve between blood albumin and mortality. In Hypo-G group (n = 40), APACHE II/SOFA scores are significantly higher than in the Inter-G (n = 196) and Hyper-G groups (n = 100). Mortality is 52.5% in the Hypo-G and 60.0% in the Hypo-G with Hypo-A (Hypo-G + Hypo-A) groups. Significantly higher APACHE II or SOFA scores and mortality are observed in the Hypo-G + Hypo-A group compared to the Inter-G + Nonhypo-A group. A higher mortality risk is observed in cases with Hypo-G + Hypo-A (OR 5.065) than those with Hypo-G (OR 3.503), Inter-G (OR 1.175), Hyper-G (OR 1.756) or Hypo-A (OR 3.243), calculated by a single logistic-regression analysis. Hypo-G + Hypo-A in patients with sepsis is related to higher ICU mortality. Physicians should be keenly aware of these conditions to provide immediate intensive treatment after admission of septic patients.

Factors influencing glycaemic stability after neonatal hypoglycaemia and relationship to neurodevelopmental outcome

Higher and unstable glucose concentrations in the first 48 hours in neonates at risk of hypoglycaemia have been associated with neurosensory impairment. It is unclear what defines and contributes to instability. This was a prospective study of term and late preterm babies (N = 139) born at risk of neonatal hypoglycaemia who had interstitial glucose (IG) monitoring and ≥1 hypoglycaemic episode <48 hours after birth (blood glucose concentration <2.6 mmol/l [<47 mg/dl]). For 6-hour epochs after each hypoglycaemic episode, masked IG parameters (time to reach maximum IG concentration [hours]; range, average, maximum and minimum IG concentrations; proportion of IG measurements outside the central band of 3–4 mmol/l [54–72 md/dl]; and total duration [hours] of IG concentrations <2.6 mmol/l) were analysed in tertiles and related to: (i) glycaemic instability in the first 48 hours (defined as the proportion of blood glucose concentrations outside the central band in the first 48 hours); (ii) risk factors and treatment for each episode; and (iii) risk of neurosensory impairment at 4.5 years, or at 2 years if a child was not seen at 4.5 years. Glycaemic instability in the first 48 hours was related to IG instability after hypoglycaemia. Risk factors for hypoglycaemia were not related to IG parameters. Treatment with intravenous dextrose was associated with higher IG maximum and range, and lower minimum compared to treatment with dextrose gel plus breast milk, breast milk alone or formula alone. The risk of neurosensory impairment was increased with both shorter and longer time to reach maximum epoch IG (P = 0.04; lower tertile [0.4–2.2 hours] vs middle [2.3–4.2 hours] OR 3.10 [95% CI 1.03; 9.38]; higher tertile [4.3–6.0 hours] vs middle OR 3.07; [95% CI 1.01; 9.24]). Glycaemic response to hypoglycaemia contributes to overall glycaemic instability in newborns and is influenced by treatment. Slow or rapid recovery of hypoglycaemia appears to be associated with neurosensory impairment.

Critical Illness Associated Fatal Hypoglycemia in a Nondiabetic Male

We present here the case of a 55-year-old male who presented to the emergency department with severe abdominal pain and respiratory distress secondary to mesenteric ischemia. His critical illness on preexisting chronic kidney disease, previously undiagnosed alcoholic cirrhosis, and congestive heart failure led to a rare yet fatal consequence of refractory hypoglycemia. Critical illness associated hypoglycemia generally occurs as a result of high metabolic consumption with relative insulin excess and insufficient nutritional intake that is seen frequently in critically ill patients. This, along with reduced hepatic and renal gluconeogenesis seen in preexisting liver and renal disease, can cumulate to life-threatening hypoglycemia and is seen as a poor prognostic factor in the ICU setting.

The practice of glycaemic control in intensive care units: A multicentre survey of nursing and medical professionals

AIMS AND OBJECTIVES

To determine the views of nurses and physicians working in intensive care units (ICU) about the aims of glycaemic control and use of their protocols.

BACKGROUND

Evidence about the optimal aims and methods for glycaemic control in ICU is controversial, and current local protocols guiding practice differ between ICUs, both nationally and internationally. The views of professionals on glycaemic control can influence their practice.

DESIGN

Cross-sectional, multicentre, survey-based study.

METHODS

An online short survey was sent to all physicians and nurses of seven ICUs, including questions on effective glycaemic control, treatment of hypoglycaemia and deviations from protocols' instructions. STROBE reporting guidelines were followed.

RESULTS

Over half of the 40 respondents opined that a patient spending <75% admission time within the target glycaemic levels constituted poor glycaemic control. Professionals with more than 5 years of experience were more likely to rate a patient spending 50%-74% admission time within target glycaemic levels as poor than less experienced colleagues. Physicians were more likely to rate a patient spending <50% admission time within target as poor than nurses. There was general agreement on how professionals would rate most deviations from their protocols. Nurses were more likely to rate insulin infusions restarted late and incorrect dosage of rescue glucose as major deviations than physicians. Most professionals agreed on when they would treat hypoglycaemia.

CONCLUSIONS

When surveyed on various aspects of glycaemic control, ICU nurses and physicians often agreed, although there were certain areas of disagreement, in which their profession and level of experience seemed to play a role.

RELEVANCE TO CLINICAL PRACTICE

Differing views on glycaemic control amongst professionals may affect their practice and, thus, could lead to health inequalities. Clinical leads and the multidisciplinary ICU team should assess and, if necessary, address these differing opinions.

High blood glucose variability is associated with bacteremia and mortality in patients hospitalized with acute infection

BACKGROUND

Limited data are available regarding the association between glucose levels variability (GV) and outcomes of patients hospitalized with acute infectious diseases.

AIM

To determine the association between GV and bacteremia, length of stay (LOS) and mortality.

METHODS

A retrospective study of patients hospitalized in departments of medicine with respiratory tract, urinary tract and skin and soft tissue infections during 2011-17. GV was assessed by the coefficient of variation (CV) of glucose levels during hospitalization and was divided into tertiles (CV ≤ 16%, 17-29%, >29%). LOS, bacteremia rates and all-cause mortality (30 days, 90 days and after 5 years) were evaluated for the patients with and without DM according the three GV categories.

RESULTS

The study consisted of 1485 patients, 838 (56%) were diabetic. There was no significant association between GV and LOS. Bacteremia rates were higher in the upper GV tertile compared with the lower one (6% vs. 2%, P = 0.007). Mid and upper tertiles compared with the lower one were significantly associated with increased 30-day mortality (13% vs. 5%, P = 0.005; and 40% vs. 5%, P = 0.002, respectively). A decreased 5 years survival was observed for both diabetic and non-diabetic patients in the mid and upper GV tertiles [adjusted HRs 0.8 (95% CI, 0.6-1.04) and 0.6 (95% CI, 0.5-0.9) in diabetic patients and 0.7 (95% CI, 0.5-0.9) and 0.5 (95% CI, 0.3-0.7) in the non-diabetic ones].

CONCLUSION

In diabetic and non-diabetic patients, hospitalized in non-ICU setting with acute infectious diseases, increased GV is associated with increased risk of bacteremia, short and long-term mortality.