Glucose control positively influences patient outcome: A retrospective study

Glucose evaluation and management in the ICU (GEM-ICU): Protocol for a bi-centre cohort study

INTRODUCTION

Hyperglycaemia is common in intensive care unit (ICU) patients. Glycaemic monitoring and effective glycaemic control with insulin are crucial in the ICU to improve patient outcomes. However, glycaemic control and insulin use vary between ICU patients and hypo- and hyperglycaemia occurs. Therefore, we aim to provide contemporary data on glycaemic control and management, and associated outcomes, in adult ICU patients. We hypothesise that the occurrence of hypoglycaemia in acutely admitted ICU patients is lower than that of hyperglycaemia.

METHODS

We will conduct a bi-centre cohort study of 300 acutely admitted adult ICU patients. Routine data will be collected retrospectively at baseline (ICU admission) and daily during ICU stay up to a maximum of 30 days. The primary outcome will be the number of patients with hypoglycaemia during their ICU stay. Secondary outcomes will be occurrence of severe hypoglycaemia, occurrence of hyperglycaemia, time below blood glucose target range, time above target range, all-cause mortality at Day 30, number of days alive without life support at Day 30 and number of days alive and out of hospital at Day 30. Process outcomes include the number of in-ICU days, glucose measurements (number of measurements and method) and use of insulin (including route of administration and dosage). All statistical analyses will be descriptive.

CONCLUSIONS

This cohort study will provide a contemporary overview of glucose evaluation and management practices in adult ICU patients and, thus, highlight potential areas for improvement through future clinical trials in this area.

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.

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.

The goldilocks problem: Nutrition and its impact on glycaemic control

BACKGROUND

Glucose intolerance and insulin resistance manifest as hyperglycaemia in intensive care, which is associated with mortality and morbidities. Glycaemic control (GC) may improve outcomes, though safe and effective control has proven elusive. Nutritional glucose intake affects blood glucose (BG) outcomes, but few protocols actively control it. This study aims to examine BG outcomes in the context of nutritional management during GC.

METHODS

Retrospective cohort analysis of 5 glycaemic control cohorts spanning 4 years (n = 273) from Christchurch Hospital Intensive Care Unit (ICU). GC is delivered using a single model-based protocol (STAR), with default 4.4-8.0 mmol/L target range via. modulation of insulin and nutrition. Clinical adaptations/cohorts include variations on upper target (UL-9 with 9.0 mmol/L, reducing workload and nutrition responsiveness), and insulin only (IO) with clinically set nutrition at 3 glucose concentrations (71 g/L vs. 120 and 180 g/L in the TARGET study).

RESULTS

Percent of BG hours in the 4.4-8.0 mmol/L range highest under standard STAR conditions (78%), and was lower at 64% under UL-9, likely due to reduced time-responsiveness of nutrition-insulin changes. By comparison, IO only resulted in 64-69% BG in range across different nutrition types. A subset of patients receiving high glucose nutrition under IO were persistently hyperglycaemic, indicating patient-specific glucose tolerance.

CONCLUSION

STAR GC is most effective when nutrition and insulin are modulated together with timely responsiveness to persistent hyperglycaemia. Results imply modulation of nutrition alongside insulin improves GC, particularly in patients with persistent hyperglycaemia/low glucose tolerance.

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.

Assessment of Glycemic Control Protocol (STAR) Through Compliance Analysis Amongst Malaysian ICU Patients

Purpose

This paper presents an assessment of an automated and personalized stochastic targeted (STAR) glycemic control protocol compliance in Malaysian intensive care unit (ICU) patients to ensure an optimized usage.

Patients and Methods

STAR proposes 1–3 hours treatment based on individual insulin sensitivity variation and history of blood glucose, insulin, and nutrition. A total of 136 patients recorded data from STAR pilot trial in Malaysia (2017–quarter of 2019*) were used in the study to identify the gap between chosen administered insulin and nutrition intervention as recommended by STAR, and the real intervention performed.

Results

The results show the percentage of insulin compliance increased from 2017 to first quarter of 2019* and fluctuated in feed administrations. Overall compliance amounted to 98.8% and 97.7% for administered insulin and feed, respectively. There was higher average of 17 blood glucose measurements per day than in other centres that have been using STAR, but longer intervals were selected when recommended. Control safety and performance were similar for all periods showing no obvious correlation to compliance.

Conclusion

The results indicate that STAR, an automated model-based protocol is positively accepted among the Malaysian ICU clinicians to automate glycemic control and the usage can be extended to other hospitals already. Performance could be improved with several propositions.

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.

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.

Percentage of Time in Range 70 to 139 mg/dL Is Associated With Reduced Mortality Among Critically Ill Patients Receiving IV Insulin Infusion

BACKGROUND

In addition to hyperglycemia, hypoglycemia, and glycemic variability, reduced time in targeted blood glucose range (TIR) is associated with increased risk of death in critically ill patients. This relation between TIR and mortality may be confounded by diabetic status and antecedent glycemic control.

METHODS

This study retrospectively analyzed critically ill patients managed with the same IV insulin protocol at multiple centers. The percentage of TIR between 70 and 139 mg/dL was calculated. Patients with diabetic ketoacidosis, patients who had < 10 blood glucose readings, and patients with repeat admissions were excluded. The highest recorded glycosylated hemoglobin value in the preceding 3 months or up to 1 month following admission were used as a surrogate for the patient's preexisting glucose control. Stratified regression analyses were performed for 30-day mortality, with covariates of age, sex, TIR ≥ 80%, Acute Physiology Score, and Charlson Comorbidity Index.

RESULTS

A total of 9,028 patients, 53.2% of whom had diabetes, were studied. Median TIR was 84.1% for nondiabetic patients and 64.5% for patients with diabetes. Mortality was lower in those with TIR > 80% compared with those with TIR ≤ 80% (12.4% vs 19.2%; P < .001). TIR > 80% was independently associated with reduced mortality in nondiabetic patients (OR, 0.52; P < .001), patients with diabetes (OR, 0.69; P = .001), and patients with well-controlled disease (OR, 0.50; P < .001) but not in patients with poorly controlled disease (OR, 0.86; P = .40).

CONCLUSIONS

TIR was independently associated with mortality in critically ill patients, particularly those with good antecedent glucose control.

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.

Volume-Based vs Rate-Based Enteral Nutrition in the Intensive Care Unit: Impact on Nutrition Delivery and Glycemic Control

BACKGROUND

Underfeeding with enteral nutrition (EN) is prevalent in intensive care units (ICUs) and associated with negative outcomes. This study evaluated the impact of volume-based EN (VBEN) vs rate-based EN (RBEN) on delivery of prescribed energy and protein, and glycemic control (GC).

METHODS

This retrospective study included adult patients who require mechanical ventilation within 48 hours of ICU admission and with an RBEN (n = 85) or VBEN (n = 86) order for ≥3 consecutive days during the first 12 ICU days.

RESULTS

Patients receiving VBEN, vs RBEN, received more prescribed energy (RBEN, 67.6%; VBEN, 79.6%; P < .001) and protein (RBEN, 68.6%; VBEN, 79.3%; P < .001). Multiple linear regression analyses confirmed VBEN was significantly associated with an 8.9% increase in energy (P = .002) and 7.7% increase in protein (P = .004) received, after adjusting for age, Acute Physiology and Chronic Health Evaluation II score, duration of and initiation day for EN, and ICU admission location. Presence of hyperglycemia (P = .40) and glycemic variability (GV) (P = .99) were not different between the 2 groups. After adjusting for age, body mass index, diabetes history, primary diagnosis, and percent of days receiving corticosteroids, GC outcomes (presence of hyperglycemia, P = .27; GV, P = .67) remained unrelated to EN order type in multivariable regression models.

CONCLUSION

VBEN, compared with RBEN, was associated with increased energy and protein delivery without adversely affecting GC. These results suggest VBEN is an effective, safe strategy to enhance EN delivery in the ICU.

Improving glycemic control in critically ill patients: personalized care to mimic the endocrine pancreas

There is considerable physiological and clinical evidence of harm and increased risk of death associated with dysglycemia in critical care. However, glycemic control (GC) currently leads to increased hypoglycemia, independently associated with a greater risk of death. Indeed, recent evidence suggests GC is difficult to safely and effectively achieve for all patients. In this review, leading experts in the field discuss this evidence and relevant data in diabetology, including the artificial pancreas, and suggest how safe, effective GC can be achieved in critically ill patients in ways seeking to mimic normal islet cell function. The review is structured around the specific clinical hurdles of: understanding the patient’s metabolic state; designing GC to fit clinical practice, safety, efficacy, and workload; and the need for standardized metrics. These aspects are addressed by reviewing relevant recent advances in science and technology. Finally, we provide a set of concise recommendations to advance the safety, quality, consistency, and clinical uptake of GC in critical care. This review thus presents a roadmap toward better, more personalized metabolic care and improved patient outcomes.

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Continuous Glucose Monitoring Measures Can Be Used for Glycemic Control in the ICU: An In-Silico Study

BACKGROUND

Continuous glucose monitoring (CGM) technology has become more prevalent in the intensive care unit (ICU), offering potential benefits of increased safety and reduced workload in glycemic control (GC). The drift and higher point accuracy errors of CGM devices over traditional intermittent blood glucose (BG) measures have so far limited their application in the ICU. This study delineates the trade-offs of performance, safety and workload that CGM sensors provide in GC protocols.

METHODS

Clinical data from 236 patients were used for clinically validated virtual trials. A CGM-enabled version of the STAR GC protocol was used to evaluate the use of guard rails and rolling windows. Safety was assessed through percentage of patients who had a severe hypoglycemic episode (BG < 40 mg/dl) as well as percentage of resampled BG < 72 mg/dl. Performance was assessed as percentage of resampled measurements in the 80-126 mg/dl and the 80-144 mg/dl target bands. Workload was measured by number of manual BG measures per day.

RESULTS

CGM-enabled versions of STAR decreased the number of required blood draws by up to 74%, while maintaining performance (76.6% BG measurements in the 80-126 mg/dl range vs 62.8% clinically, 87.9% in the 80-144 mg/dl range vs 83.7% clinically) and maintaining patient safety (1.13% of patients experienced a severe hypoglycemic event vs 0.85% clinically, 1.37% of BG measurements were less than 72 mg/dl vs 0.51% clinically).

CONCLUSION

CGM sensor traces were reproduced in virtual trials to guide GC. Existing GC protocols such as STAR may need to be adjusted only slightly to gain the benefits of the increased temporal measurements of CGM sensors, through which workload may be significantly decreased while maintaining GC performance and safety.

Clinical Effectiveness of Intravenous Exenatide Infusion in Perioperative Glycemic Control after Coronary Artery Bypass Graft Surgery

Background

We aimed to assess the clinical effectiveness of intravenous exenatide compared to insulin in perioperative blood glucose control in coronary artery bypass grafting surgery patients.

Methods

Patients more than 18 yr old admitted for elective coronary artery bypass grafting were included in a phase II/III nonblinded randomized superiority trial. Current insulin use and creatinine clearance of less than 60 ml/min were exclusion criteria. Two groups were compared: the exenatide group, receiving exenatide (1-h bolus of 0.05 µg/min followed by a constant infusion of 0.025 µg/min), and the control group, receiving insulin therapy. The blood glucose target range was 100 to 139 mg/dl. The primary outcome was the proportion of patients who spent at least 50% of the study period within the target range. The consumption of insulin (Cinsulin) and the time to start insulin (Tinsulin) were compared between the two groups.

Results

In total, 53 and 51 patients were included and analyzed in the exenatide and control groups, respectively (age: 70 ± 9 vs. 68 ± 11 yr; diabetes mellitus: 12 [23%] vs. 10 [20%]). The primary outcome was observed in 38 (72%) patients in the exenatide group and in 41 (80%) patients in the control group (odds ratio [95% CI] = 0.85 [0.34 to 2.11]; P = 0.30). Cinsulin was significantly lower (60 [40 to 80] vs. 92 [63 to 121] U, P &lt; 0.001), and Tinsulin was significantly longer (12 [7 to 16] vs. 7 [5 to 10] h, P = 0.02) in the exenatide group.

Conclusions

Exenatide alone at the dose used was not enough to achieve adequate blood glucose control in coronary artery bypass grafting patients, but it reduces overall consumption of insulin and increases the time to initiation of insulin.

Effect of insulin infusion line on glycaemic variability in a perioperative high dependency unit (HDU): a prospective randomised controlled trial

BACKGROUND

Glucose control is an important issue in post-operative patients. The objective here was to compare two insulin infusion lines by syringe pumps to assess the impact of medical devices on glycaemic variability in surgical patients under intensive insulin therapy. This open, prospective, single-centre randomised study was conducted in a fifteen-bed perioperative high dependency unit (HDU) in a university hospital. In total, 172 eligible patients receiving insulin therapy agreed to participate in the study. Subcutaneous continuous glucose monitoring was set up for all patients and an optimised system with a dedicated insulin infusion line for half of the patients.

RESULTS

Eighty-six patients were infused via the optimised infusion line and 86 patients via the standard infusion line. No significant difference was found according to the glycaemic lability index score [mean difference between groups (95% CI): -0.09 (-0.34; 0.16), p = 0.49 after multiple imputation]. A glucose control monitoring system indicated a trend towards differences in the duration of hypoglycaemia (blood glucose level below 70 mg dl^-1 (3.9 mmol l^-1) over 1000 h of insulin infusion (9.7 ± 25.0 h in the standard group versus 4.4 ± 14.8 h in the optimised group, p = 0.059) and in the number of patients experiencing at least one hypoglycaemia incident (25.7 vs. 12.9%, p = 0.052). Time in the target range was similar for both groups.

CONCLUSIONS

The use of optimised infusion line with a dedicated insulin infusion line did not reduce glycaemic variability but minimised the incidence of hypoglycaemia events. The choice of the medical devices used to infuse insulin seems important for improving the safety of insulin infusion in perioperative HDU.

Untangling glycaemia and mortality in critical care

Background

Hyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often failed to replicate these results, and they were often unable to achieve consistent, safe control, raising questions about the benefit or harm of GC as well as the nature of the association of glycaemia with mortality and clinical outcomes. In this study, we evaluated if non-survivors are harder to control than survivors and determined if glycaemic outcome is a function of patient condition and eventual outcome or of the glycaemic control provided.

Methods

Clinically validated, model-based, hour-to-hour insulin sensitivity (SI) and its hour-to-hour variability (%ΔSI) were identified over the first 72 h of therapy in 145 patients (119 survivors, 26 non-survivors). In hypothesis testing, we compared distributions of SI and %ΔSI in 6-hourly blocks for survivors and non-survivors. In equivalence testing, we assessed if differences in these distributions, based on blood glucose measurement error, were clinically significant.

Results

SI level was never equivalent between survivors and non-survivors (95% CI of percentage difference in medians outside ±12%). Non-survivors had higher SI, ranging from 9% to 47% higher overall in 6-h blocks, and this difference became statistically significant as glycaemic control progressed. %ΔSI was equivalent between survivors and non-survivors for all 6-hourly blocks (95% CI of difference in medians within ±12%) and decreased in general over time as glycaemic control progressed.

Conclusions

Whereas non-survivors had higher SI levels, variability was equivalent to that of survivors over the first 72 h. These results indicate survivors and non-survivors are equally controllable, given an effective glycaemic control protocol, suggesting that glycaemia level and variability, and thus the association between glycaemia and outcome, are essentially determined by the control provided rather than by underlying patient or metabolic condition.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-017-1725-y) contains supplementary material, which is available to authorized users.

Comparison of 2 intravenous insulin protocols: Glycemia variability in critically ill patients

OBJECTIVE

Glycemic variability is an independent predictor of mortality in critically ill patients. The objective of this study was to compare two intravenous insulin protocols in critically ill patients regarding the glycemic variability.

MATERIAL AND METHODS

This was a retrospective observational study performed by reviewing clinical records of patients from a Critical Care Unit for 4 consecutive months. First, a simpler Scale-Based Intravenous Insulin Protocol (SBIIP) was reviewed and later it was compared for the same months of the following year with a Sliding Scale-Based Intravenous Insulin Protocol (SSBIIP). All adult patients admitted to the unit during the referred months were included. Patients in whom the protocol was not adequately followed were excluded. A total of 557 patients were reviewed, of whom they had needed intravenous insulin 73 in the first group and 52 in the second group. Four and two patients were excluded in each group respectively.

RESULTS

Glycemic variability for both day 1 (DS1) and total stay (DST) was lower in SSBIIP patients compared to SBIIP patients: SD1 34.88 vs 18.16 and SDT 36.45 vs 23.65 (P<.001).

CONCLUSION

A glycemic management protocol in critically ill patients based on sliding scales decreases glycemic variability.

Generalisability of a Virtual Trials Method for Glycaemic Control in Intensive Care

BACKGROUND

Elevated blood glucose (BG) concentrations (Hyperglycaemia) are a common complication in critically ill patients. Insulin therapy is commonly used to treat hyperglycaemia, but metabolic variability often results in poor BG control and low BG (hypoglycaemia).

OBJECTIVE

This paper presents a model-based virtual trial method for glycaemic control protocol design, and evaluates its generalisability across different populations.

METHODS

Model-based insulin sensitivity (SI) was used to create virtual patients from clinical data from three different ICUs in New Zealand, Hungary, and Belgium. Glycaemic results from simulation of virtual patients under their original protocol (self-simulation) and protocols from other units (cross simulation) were compared.

RESULTS

Differences were found between the three cohorts in median SI and inter-patient variability in SI. However, hour-to-hour intra-patient variability in SI was found to be consistent between cohorts. Self and cross-simulation results were found to have overall similarity and consistency, though results may differ in the first 24-48 h due to different cohort starting BG and underlying SI.

CONCLUSIONS AND SIGNIFICANCE

Virtual patients and the virtual trial method were found to be generalisable across different ICUs. This virtual trial method is useful for in silico protocol design and testing, given an understanding of the underlying assumptions and limitations of this method.

Manual versus Automated moNitoring Accuracy of GlucosE II (MANAGE II)

BACKGROUND

Intravascular continuous glucose monitoring (CGM) may facilitate glycemic control in the intensive care unit (ICU). We compared the accuracy of a CGM device (OptiScanner®) with a standard reference method.

METHODS

Adult patients who had blood glucose (BG) levels >150 mg/dl and required insertion of an arterial and central venous catheter were included. The OptiScanner® was inserted into a multiple-lumen central venous catheter. Patients were treated using a dynamic-scale insulin algorithm to achieve BG values between 80 and 150 mg/dl. The BG values measured by the OptiScanner® were plotted against BG values measured using a reference analyzer. The correlation between the BG values measured using the two methods and the clinical relevance of any differences were assessed using the coefficient of determination (r 2) and the Clarke error grid, respectively; bias was assessed by the mean absolute relative difference (MARD). Three different standards of glucose monitoring were used to assess accuracy. Glycemic control was assessed using the time in range (TIR). Six indices of glycemic variability were calculated.

RESULTS

The analysis included 929 paired samples from 88 patients, monitored for a total of 2584 hours. Reference BG values ranged between 60 and 484 mg/dl. The r 2 value was 0.89. The percentage of BG values within zones A and B of the Clarke error grid was 99.9%; the MARD was 7.7%. Using the ISO 15197 standard and Food and Drug Administration and consensus standards, respectively, 80.4% of measurements were within 15 mg/dl and 88.2% within 15% of reference values, 40% of measurements were within 7 mg/dl and 72.5% within 10% of reference values, and 65.2% of measurements were within 10 mg/dl and 82.7% within 12.5% of reference values. The TIR was slightly lower with the OptiScanner® than with the reference method. The J-index, standard deviation and maximal glucose change were the indices of glycemic variability least affected by the measurement device.

CONCLUSIONS

Based on the MARD, the performance of the OptiScanner® is adequate for use in ICU patients. Because recent standards for accuracy were not met, the OptiScanner® should not be used as a sole monitor. The assessment of glycemic variability is influenced by the time interval between BG determinations.

TRIAL REGISTRATION

Clinicaltrials.gov NCT01720381 . Registered 31 October 2012.

Glucose Control in the ICU: A Continuing Story

In the present era of near-continuous glucose monitoring (CGM) and automated therapeutic closed-loop systems, measures of accuracy and of quality of glucose control need to be standardized for licensing authorities and to enable comparisons across studies and devices. Adequately powered, good quality, randomized, controlled studies are needed to assess the impact of different CGM devices on the quality of glucose control, workload, and costs. The additional effects of continuing glucose control on the general floor after the ICU stay also need to be investigated. Current algorithms need to be adapted and validated for CGM, including effects on glucose variability and workload. Improved collaboration within the industry needs to be encouraged because no single company produces all the necessary components for an automated closed-loop system. Combining glucose measurement with measurement of other variables in 1 sensor may help make this approach more financially viable.

Glucose Levels in Patients With Acute Respiratory Failure Requiring Mechanical Ventilation

BACKGROUND

Recent studies suggest that patients with acute exacerbations of chronic obstructive pulmonary disease (COPD) frequently develop hyperglycemia, which has been linked to adverse outcomes.

METHODS

We retrospectively collected information about patient demographics, admission diagnosis, comorbidities, use of insulin, and glucose levels and related tests in 174 patients who required mechanical ventilation for acute respiratory failure.

RESULTS

These patients had a mean age of 57.8 ± 16.8 years, a mean Acute Physiology and Chronic Health Evaluation (APACHE II) score of 13.8 ± 6.1, and an overall mortality of 32.2%. The mean number of ventilator days was 7.5 ± 7.1. The mean highest glucose level was 239.3 ± 88.9 mg/dL in patients with COPD (n = 41) and 259.1 ± 131.7 mg/dL in patients without COPD (n =133). Patients with diabetes had higher glucose levels than patients without this diagnosis ( P < .05). Patients receiving corticosteroids did not have increased glucose levels ( P > .05). The mortality rate was higher in patients with glucose levels >140 mg/dL than in patients below 140 mg/dL (35.1% vs 10.5%, P < .05 unadjusted analysis).

CONCLUSION

In this study, hyperglycemia occurred in 89% of the patients with acute respiratory failure requiring mechanical ventilation. The most important risk factor for this was a premorbid diagnosis of diabetes.

Reporting on Glucose Control Metrics in the Intensive Care Unit

The 'diabetes of injury' typically associated with critical illness has recently been thoroughly revisited and much better characterised following major therapeutic advances. The occurrence of severe hyperglycaemia, moderate hypoglycaemia or high glycaemic variability has been associated with an increased mortality and rate of complications in large independent cohorts of acutely ill patients. Hence, current guidelines advocate the prevention and avoidance of each of these three dysglycaemic domains, and the use of a common metrics for a quantitative description of dysglycaemic events, such as the proportion of time spent in the target glycaemic range as a unifying variable. Using a common language will help to face the future challenges, including the definition of the most appropriate blood glucose (BG) target according to the category of admission, the time interval from the initial injury and the medical history. The clinical testing of technological improvements in the monitoring systems and the therapeutic algorithms should be assessed using the same metrics.