Glucose Control in the ICU: A Continuing Story

Flash Glucose Monitoring for Predicting Cardiogenic Shock Occurrence in Critically Ill Patients: A Retrospective Pilot Study

Background/Objectives: Continuous and flash glucose monitoring (CGM and FGM) may enhance glucose management by providing real-time glucose data. Furthermore, growing evidence is linking altered blood glucose concentrations and worse short-term outcomes in critically ill patients. While hyperglycemia is more common in these patients and is associated with an increased risk of adverse events, hypoglycemia is particularly concerning and significantly raises the risk of fatal outcomes. This exploratory study investigated the link between FGM variables and cardiogenic shock in critically ill Coronary Care Unit (CCU) patients. Methods: Twenty-eight CCU patients (1 May 2021-31 January 2022) were monitored using a Libre FreeStyle system. Analyzed data included patient demographic and laboratory data, left ventricular ejection fraction, standard glucose monitoring, APACHE IV scores, and cardiogenic shock occurrence. Analysis was performed using the χ2 test, Mann-Whitney U test, and logistic regression. Results: Among the patients, 13 (46.43%) developed cardiogenic shock. FGM detected hypoglycemia in 18 (64.29%) patients, while standard methods in 6 (21.43%) patients. FGM-detected hypoglycemia was more frequent in patients who developed cardiogenic shock (p = 0.0129, χ2 test) with a significantly higher time below range reading (p = 0.0093, Mann Withney U test), despite no differences in mean glucose values. In addition, hypoglycemia detected by FGM was an independent predictor of shock (p = 0.0390, logistic regression). Conclusions: FGM identified more hypoglycemic events compared to standard glucose monitoring in the CCU. Frequent FGM-detected hypoglycemic events were associated with cardiogenic shock, regardless of a history of diabetes. Due to a limited sample size, these results should be interpreted cautiously and further research in this area is justified.

Insulin Infusion Protocols for Blood Glucose Management in Critically Ill Patients: A Scoping Review

BACKGROUND

Continuous insulin infusion is a method for maintaining blood glucose stability in critically ill patients with hyperglycemia. Many insulin infusion protocols have been applied in intensive care units. Understanding the content of these protocols can help clinical staff choose the most appropriate and convenient protocol and promote best practices in managing glucose levels in critically ill adult patients.

OBJECTIVE

To examine the types of insulin infusion therapies performed for blood glucose management in critically ill patients.

METHODS

For this scoping review, 3 Chinese-language and 8 English-language databases were searched for articles published from May 25, 2016, to October 25, 2022.

RESULTS

Twenty-one articles met the inclusion criteria. Twenty-one insulin infusion protocols were examined. Most of the insulin infusion protocols were paper protocols. Fourteen glucose management indicators were included in the 21 protocols. The glucose target range for all 21 protocols ranged from 70 to 180 mg/dL (3.9-10.0 mmol/L). Nurses were primarily responsible for protocol implementation in most protocol development processes. The roles of nurses differed in nurse-led insulin infusion protocols and non-nurse-led insulin infusion protocols.

DISCUSSION

This scoping review indicates an urgent need for more comprehensive glycemic control guidelines for patients receiving critical care. Because insulin infusion protocols are core aspects of blood glucose management guidelines, different population subgroups should also be considered.

CONCLUSIONS

Nurse-led guidelines must be based on the best available evidence and should include other variables related to glucose management (eg, patient disease type, medication, and nutrition) in addition to insulin infusion.

Accuracy and Potential Interferences of Continuous Glucose Monitoring Sensors in the Hospital

For years, the standard of care for monitoring dysglycemia in hospitalized patients was capillary blood glucose (CBG) testing with point-of-care glucose meters. Recently, there has been a revolution in novel factory-calibrated continuous glucose monitoring (CGM) systems. Newer CGMs are smaller and less expensive, have improved accuracy and longer wear time, and do not require fingerstick CBG for calibration, resulting in increased utilization in ambulatory settings. Consequently, hospitals have noticed increased usability of CGMs among hospitalized patients and expect a progressive continued increase. During the COVID-19 pandemic, there was a critical need for innovative approaches to glycemic monitoring, with several pilot implementation projects using CGM in the intensive care unit and non-intensive care unit settings, further boosting the evidence in this area. Hence, recent guidelines have provided recommendations for the use of CGM in specific hospital scenarios and highlighted the potential of CGM to overcome CBG limitations for glucose monitoring in the inpatient setting. In this review, we provide the following: 1) an up-to-date review of the accuracy of the newer CGMs in hospitalized patients, 2) a discussion of standards for CGM accuracy metrics, 3) a contemporary overview of potential interferences that may cause inaccuracies or poor CGM performance, and 4) required steps for full regulatory approval of CGMs in the hospital and future research steps to advance the field forward.

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

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

Implementation of Continuous Glucose Monitoring in Critical Care: A Scoping Review

PURPOSE OF REVIEW

The aim of this review is to identify the implementation approaches, strategies, and outcomes for continuous glucose monitoring (CGM) in the intensive care unit (ICU). Medline and Web of Science databases were searched to report relevant literature published between September 12, 2016 and September 12, 2021. Implementation outcomes and strategies, defined by the Expert Recommendations for Implementing Change (ERIC) project, were extracted.

RECENT FINDINGS

Of the 324 titles reviewed, 16 articles were included in the review. While no studies were identified as implementation research, 14 of 16 identified implementation strategies that aligned with ERIC definitions. Included studies described a multi-disciplinary approach. Clinical outcomes included Mean Absolute Relative Difference (MARD), ranging from 7.5 to 15.3%, and 33-71% reduction in frequency of point-of-care (POC) blood glucose monitoring (BGM) using hybrid protocols. This scoping review provides valuable insight into the process of CGM implementation in the ICU. Continued research should include implementation outcomes to inform widespread utilization.

Nursing Perspectives on the Use of Continuous Glucose Monitoring in the Intensive Care Unit

BACKGROUND

The COVID-19 pandemic necessitated rapid implementation of continuous glucose monitoring (CGM) in the intensive care unit (ICU). Although rarely reported, perceptions from nursing staff who used the systems are critical for successful implementation and future expanded use of CGM in the inpatient setting.

METHODS

A 22-item survey focused on CGM use was distributed to ICU nurses at two large academic medical centers in the United States in 2022. Both institutions initiated inpatient CGM in the spring of 2020 using the same CGM+point of care (POC) hybrid protocol. The survey employed a 1- to 5-point Likert scale regarding CGM sensor insertion, accuracy, acceptability, usability, training, and perceptions on workload.

RESULTS

Of the 71 surveys completed, 68 (96%) nurses reported they cared for an ICU patient on CGM and 53% reported they had independently performed CGM sensor insertion. The ICU nurses overwhelmingly reported that CGM was accurate, reduced their workload, provided safer patient care, and was preferred over POC glucose testing alone. Interestingly, nearly half of nurses (49%) reported that they considered trend arrows in dosing decisions although trends were not included in the CGM+POC hybrid protocol. Nurses received training through multiple modalities, with the majority (80%) of nurses reporting that CGM training was sufficient and prepared them for its use.

CONCLUSION

These results confirm nursing acceptance and preference for CGM use within a hybrid glucose monitoring protocol in the ICU setting. These data lay a blueprint for successful implementation and training strategies for future widespread use.

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.

Glycemic control in critically ill patients with or without diabetes

Background

Early randomized controlled trials have demonstrated the benefits of tight glucose control. Subsequent NICE-SUGAR study found that tight glucose control increased mortality. The optimal glucose target in diabetic and nondiabetic patients remains unclear. This study aimed to evaluate the relationship between blood glucose levels and outcomes in critically ill patients with or without diabetes.

Methods

This was a retrospective analysis of the eICU database. Repeat ICU stays, ICU stays of less than 2 days, patients transferred from other ICUs, those with less than 2 blood glucose measurements, and those with missing data on hospital mortality were excluded. The primary outcome was hospital mortality. Generalised additive models were used to model relationship between glycemic control and mortality. Models were adjusted for age, APACHE IV scores, body mass index, admission diagnosis, mechanical ventilation, and use of vasopressor or inotropic agents.

Results

There were 52,107 patients in the analysis. Nondiabetes patients exhibited a J-shaped association between time-weighted average glucose and hospital mortality, while this association in diabetes patients was right-shifted and flattened. Using a TWA glucose of 100 mg/dL as the reference value, the adjusted odds ratio (OR) of TWA glucose of 140 mg/dL was 3.05 (95% confidence interval (CI) 3.03–3.08) in nondiabetes and 1.14 (95% CI 1.08–1.20) in diabetes patients. The adjusted OR of TWA glucose of 180 mg/dL were 4.20 (95% CI 4.07–4.33) and 1.49 (1.41–1.57) in patients with no diabetes and patients with diabetes, respectively. The adjusted ORs of TWA glucose of 80 mg/dL compared with 100 mg/dL were 1.74 (95% CI 1.57–1.92) in nondiabetes and 1.36 (95% CI 1.12–1.66) in patients with diabetes. The glucose ranges associated with a below-average risk of mortality were 80–120 mg/dL and 90–150 mg/dL for nondiabetes and diabetes patients, respectively. Hypoglycemia was associated with increased hospital mortality in both groups but to a lesser extent in diabetic patients. Glucose variability was positively associated with hospital mortality in nondiabetics.

Conclusions

Time-weighted average glucose, hypoglycemia, and glucose variability had different impacts on clinical outcomes in patients with and without diabetes. Compared with nondiabetic patients, diabetic patients showed a more blunted response to hypo- and hyperglycemia and glucose variability. Glycemic control strategies should be reconsidered to avoid both hypoglycemia and hyperglycemia.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-022-01769-4.

Practice of hyperglycaemia control in intensive care units of the Military Hospital, Sudan—Needs of a protocol

Hyperglycaemia is a major risk factor in critically ill patients leading to adverse outcomes and mortality in diabetic and non-diabetic patients. The target blood glucose remained controversial; this study aimed to contribute in assessing the practice of hyperglycaemia control in intensive care units of the Military Hospital. Furthermore, the study proposed a protocol for hyperglycaemia control based on findings. A hospital-based cross-sectional study assessed the awareness and practice towards hyperglycaemia management in a sample 83 healthcare staff selected through stratified random sampling technique. In addition, 55 patients were enrolled, through quota sampling, after excluding those with diabetic ketoacidosis, hyperosmolar-hyperglycaemic state and patients < 18 years. A self-administrated questionnaire enabled to collect data from health staff and patient data were extracted from the medical records. SPSS-23 was used to analyze the collected data. Chi-square and ANOVA tests assessed the association among variables, these tests were considered statistically significant when p ≤ 0.05. The training on hyperglycaemia control differed (p = 0.017) between doctors and nurses. The target glycaemic level (140–180 mg/dl) was known by 11.1% of the study participants. Neither the knowledge nor the practice of hyperglycaemia control methods differed among staff (p> 0.05). The use of sliding scale was prevalent (79.3%) across the ICUs (p = 0.002). 31.5% of the patients had received different glycaemic control methods, 11.8% were in the targeted blood glucose level. Sliding scale was the method used by doctors and nurses (71.4% and 81.6% respectively). Lack of awareness about hyperglycaemia management methods was prevalent among ICU healthcare staff. Use of obsolete methods was the common practice in the ICUS of the Military Hospital. Target blood glucose for patients were unmet. Development of a local protocol for glycaemic control in all ICUs is needed along with sustained training programs on hyperglycaemia control for ICU healthcare staff.

Long-term normothermic machine preservation of human livers: what is needed to succeed?

Although short-term machine perfusion (≤24 h) allows for resuscitation and viability assessment of high-risk donor livers, the donor organ shortage might be further remedied by long-term perfusion machines. Extended preservation of injured donor livers may allow reconditioning, repairing, and regeneration. This review summarizes the necessary requirements and challenges for long-term liver machine preservation, which requires integrating multiple core physiological functions to mimic the physiological environment inside the body. A pump simulates the heart in the perfusion system, including automatically controlled adjustment of flow and pressure settings. Oxygenation and ventilation are required to account for the absence of the lungs combined with continuous blood gas analysis. To avoid pressure necrosis and achieve heterogenic tissue perfusion during preservation, diaphragm movement should be simulated. An artificial kidney is required to remove waste products and control the perfusion solution's composition. The perfusate requires an oxygen carrier, but will also be challenged by coagulation and activation of the immune system. The role of the pancreas can be mimicked through closed-loop control of glucose concentrations by automatic injection of insulin or glucagon. Nutrients and bile salts, generally transported from the intestine to the liver, have to be supplemented when preserving livers long term. Especially for long-term perfusion, the container should allow maintenance of sterility. In summary, the main challenge to develop a long-term perfusion machine is to maintain the liver's homeostasis in a sterile, carefully controlled environment. Long-term machine preservation of human livers may allow organ regeneration and repair, thereby ultimately solving the shortage of donor livers.

GLU: a software package for analysing continuously measured glucose levels in epidemiology

Continuous glucose monitors (CGM) record interstitial glucose levels 'continuously', producing a sequence of measurements for each participant (e.g. the average glucose level every 5 min over several days, both day and night). To analyse these data, researchers tend to derive summary variables such as the area under the curve (AUC), to then use in subsequent analyses. To date, a lack of consistency and transparency of precise definitions used for these summary variables has hindered interpretation, replication and comparison of results across studies. We present GLU, an open-source software package for deriving a consistent set of summary variables from CGM data. GLU performs quality control of each CGM sample (e.g. addressing missing data), derives a diverse set of summary variables (e.g. AUC and proportion of time spent in hypo-, normo- and hyper- glycaemic levels) covering six broad domains, and outputs these (with quality control information) to the user. GLU is implemented in R and is available on GitHub at https://github.com/MRCIEU/GLU. Git tag v0.2 corresponds to the version presented here.

Glucose variability and diabetes complications: Risk factor or biomarker? Can we disentangle the "Gordian Knot"?

« Variability in glucose homoeostasis » is a better description than « glycaemic variability » as it encompasses two categories of dysglycaemic disorders: i) the short-term daily glucose fluctuations and ii) long-term weekly, monthly or quarterly changes in either HbA1c, fasting or postprandial plasma glucose. Presently, the relationship between the "variability in glucose homoeostasis" and diabetes complications has never been fully clarified because studies are either observational or limited to retrospective analysis of trials not primarily designed to address this issue. Despite the absence of definitive evidence from randomized controlled trials (RCTs), it is most likely that acute and long-term glucose homoeostasis "cycling", akin to weight and blood pressure "cycling" in obese and hypertensive individuals, are additional risk factors for diabetes complications in the presence of sustained ambient hyperglycaemia. As hypoglycaemic events are strongly associated with short- and long-term glucose variability, two relevant messages can be formulated. Firstly, due consideration should be given to avoid within-day glucose fluctuations in excess of 36% (coefficient of variation) at least for minimizing the inconvenience and dangers associated with hypoglycaemia. Secondly, it seems appropriate to consider that variability in glucose homoeostasis is not only associated with cardiovascular events but is also a causative risk factor via hypoglycaemic episodes as intermediary step. Untangling the" Gordian Knot", to provide confirmation about the impact of variability in glucose homoeostasis and diabetes complications remains a daunting prospect.

Inpatient Hyperglycemia Management and COVID-19

Diabetes is one of the most common comorbidities in hospitalized patients with coronavirus disease 2019 (COVID-19). Inpatient hyperglycemia during this pandemic has been associated with worse outcomes, so it is mandatory to implement effective glycemic control treatment approaches for inpatients with COVID-19. The shortage of personal protective equipment, the need to prevent staff exposure, or the fact that many of the healthcare professionals might be relatively unfamiliar with the management of hyperglycemia may lead to worse glycemic control and, consequently, a worse prognosis. In order to reduce these barriers, we intend to adapt established recommendations to manage hyperglycemia during this pandemic in critical and noncritical care settings.

Managing hyperglycemia during the COVID-19 pandemic: Improving outcomes using new technologies in intensive care

Hyperglycemia is a significant risk for mortality in COVID-19 infections and is most dramatically noted in critically ill patients. Hyperglycemia and/or diabetes are noted in approximately 30%-40% of patients admitted with COVID-19 infections. Previous studies have shown a marked increase in mortality related to increased glucose concentrations and reduction with improved glucose control. In vivo and in vitro studies reveal the mechanisms by which hyperglycemia increases virulence and how glucose control and insulin reduce it. Optimal glucose control in intensive care is limited by manual sampling of glucose and intravenous insulin adjustment, as well as increased nursing workload and the need of protective equipment. Tools for safe and effective automation of glucose control in intensive care are discussed. A suitable closed loop device could save the lives of thousands of hospitalized hyperglycemic individuals infected with COVID-19 while protecting medical professionals from infection risk.

Practice of Hyperglycaemia Control in Intensive Care Units of the Military Hospital, Sudan – Needs of a Protocol.. [DOI: 10.1101/2020.08.17.20176453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Hyperglycaemia is a major risk factor in critically ill patients as it leads to adverse outcomes and mortality in diabetic and non-diabetic patients. The target blood glucose remained controversial; this study aimed to contribute in assessing the practice of hyperglycaemia control in intensive care units of Khartoum Military Hospital. Furthermore, it proposed a protocol for hyperglycaemia control based on findings. A hospital-based cross-sectional study assessed the awareness and practice towards hyperglycaemia management in a sample of 83 healthcare staff selected through stratified random sampling technique. In addition, 55 patients were enrolled, through quota sampling, after excluding those with diabetic ketoacidosis, hyperosmolar-hyperglycaemic state and patients < 18 years. A self-administrated questionnaire enabled to collect data from healthcare staff, patients data were extracted from medical records. SPSS 23 was used to analyse the collected data. Chi-square and ANOVA tests assessed the association among variables. All statistical tests were considered statistically significant when p < 0.05. The training on hyperglycaemia control differed statistically (p = 0.017) among healthcare staff. The target glycaemic level (140-180 mg/dl) was knew by 11.1% of the study participants. Neither the knowledge nor the practice of hyperglycaemia control methods differed among staff (p> 0.05). The use of sliding scale was 79.3% across the ICUs with a statistically significant difference (p = 0.002). 31.5% of patients had received glycaemic control based on different methods and 11.8% were in the targeted blood glucose level. Sliding scale was the prevalent method used by doctors (71.4%) and nurses (81.6%). A patient benefited from insulin infusion method, which achieved the NICE-SUGAR target. The poor knowledge and lack of awareness towards hyperglycaemia monitoring led to inappropriate implementation of glycaemia control methods across the Military Hospital ICUs. Sustained training programs on hyperglycaemia control to ICU staff and the availability of a protocol on glycaemia control are highly required.

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.

Dynamic properties of glucose complexity during the course of critical illness: a pilot study

Methods to control the blood glucose (BG) levels of patients in intensive care units (ICU) improve the outcomes. The development of continuous BG levels monitoring devices has also permitted to optimize these processes. Recently it was shown that a complexity loss of the BG signal is linked to poor clinical outcomes. Thus, it becomes essential to decipher this relation to design efficient BG level control methods. In previous studies the BG signal complexity was calculated as a single index for the whole ICU stay. Although, these approaches did not grasp the potential variability of the BG signal complexity. Therefore, we setup this pilot study using a continuous monitoring of central venous BG levels in ten critically ill patients (EIRUS platform, Maquet Critical CARE AB, Solna, Sweden). Data were processed and the complexity was assessed by the detrended fluctuation analysis and multiscale entropy (MSE) methods. Finally, recordings were split into 24 h overlapping intervals and a MSE analysis was applied to each of them. The MSE analysis on time intervals revealed an entropy variation and allowed periodic BG signal complexity assessments. To highlight differences of MSE between each time interval we calculated the MSE complexity index defined as the area under the curve. This new approach could pave the way to future studies exploring new strategies aimed at restoring blood glucose complexity during the ICU stay.

Higher glycemic variability within the first day of ICU admission is associated with increased 30-day mortality in ICU patients with sepsis

Background

High glycemic variability (GV) is common in critically ill patients; however, the prevalence and mortality association with early GV in patients with sepsis remains unclear.

Methods

This retrospective cohort study was conducted in a medical intensive care unit (ICU) in central Taiwan. Patients in the ICU with sepsis between January 2014 and December 2015 were included for analysis. All of these patients received protocol-based management, including blood sugar monitoring every 2 h for the first 24 h of ICU admission. Mean amplitude of glycemic excursions (MAGE) and coefficient of variation (CoV) were used to assess GV.

Results

A total of 452 patients (mean age 71.4 ± 14.7 years; 76.7% men) were enrolled for analysis. They were divided into high GV (43.4%, 196/452) and low GV (56.6%, 256/512) groups using MAGE 65 mg/dL as the cut-off point. Patients with high GV tended to have higher HbA1c (6.7 ± 1.8% vs. 5.9 ± 0.9%, p < 0.01) and were more likely to have diabetes mellitus (DM) (50.0% vs. 23.4%, p < 0.01) compared with those in the low GV group. Kaplan–Meier analysis showed that a high GV was associated with increased 30-day mortality (log-rank test, p = 0.018). The association remained strong in the non-DM (log-rank test, p = 0.035), but not in the DM (log-rank test, p = 0.254) group. Multivariate Cox proportional hazard regression analysis identified that high APACHE II score (adjusted hazard ratio (aHR) 1.045, 95% confidence interval (CI) 1.013–1.078), high serum lactate level at 0 h (aHR 1.009, 95% CI 1.003–1.014), having chronic airway disease (aHR 0.478, 95% CI 0.302–0.756), high mean day 1 glucose (aHR 1.008, 95% CI 1.000–1.016), and high MAGE (aHR 1.607, 95% CI 1.008–2.563) were independently associated with increased 30-day mortality. The association with 30-day mortality remained consistent when using CoV to assess GV.

Conclusions

We found that approximately 40% of the septic patients had a high early GV, defined as MAGE > 65 mg/dL. Higher GV within 24 h of ICU admission was independently associated with increased 30-day mortality. These findings highlight the need to monitor GV in septic patients early during an ICU admission.

Updates in Glycemic Management in the Hospital

PURPOSE OF REVIEW

To provide an update of glycemic management during metabolic stress related to surgery or critical illness.

RECENT FINDINGS

There is a clear association between severe hyperglycemia, hypoglycemia, and high glycemic variability and poor outcomes of postoperative or critically ill patients. However, the impressive beneficial effects of tight glycemic management (TGM) by intensive insulin therapy reported in one study were never reproduced. Hence, the recommendation of TGM is now replaced by more liberal blood glucose (BG) targets (< 180 mg/dL or 10 mM). Recent data support the concept of targeting individualized blood glucose (BG) values according to the presence of diabetes mellitus/chronic hyperglycemia, the presence of brain injury, and the time from injury. A more liberal glycemic management goal is currently advised during metabolic stress and could be switched to individualized glycemic management once validated by prospective trials.

Near-Continuous Glucose Monitoring Makes Glycemic Control Safer in ICU Patients

OBJECTIVES

Tight glycemic control using intermittent blood glucose measurements is associated with a risk of hypoglycemia. Glucose concentrations can now be measured near continuously (every 5-15 min). We assessed the quality and safety of glycemic control guided by a near-continuous glucose monitoring system in ICU patients.

DESIGN

Prospective, cluster-randomized, crossover study.

SETTING

Thirty-five-bed medico-surgical department of intensive care with four separate ICUs.

PATIENTS

Adult patients admitted to the department and expected to stay for at least 3 days were considered for inclusion if they had persistent hyperglycemia (blood glucose > 150 mg/dL) up to 6 hours after admission and/or were receiving insulin therapy.

INTERVENTIONS

A peripheral venous catheter was inserted in all patients and connected to a continuous glucose monitoring sensor (GlucoClear; Edwards Lifesciences, Irvine, CA). The four ICUs were randomized in pairs in a crossover design to glycemic control using unblinded or blinded continuous glucose monitoring monitors. The insulin infusion rate was adjusted to keep blood glucose between 90 and 150 mg/dL using the blood glucose values displayed on the continuous glucose monitor (continuous glucose monitoring group-unblinded units) or according to intermittent blood glucose readings (intermittent glucose monitoring group-blinded units).

MEASUREMENTS AND MAIN RESULTS

The quality and safety of glycemic control were assessed using the proportion of time in range, the frequency of blood glucose less than 70 mg/dL, and the time spent with blood glucose less than 70 mg/dL (TB70), using blood glucose values measured by the continuous glucose monitoring device. Seventy-seven patients were enrolled: 39 in the continuous glucose monitoring group and 38 in the intermittent glucose monitoring group. A total of 43,107 blood glucose values were recorded. The time in range was similar in the two groups. The incidence of hypoglycemia (8/39 [20.5%] vs 15/38 [39.5%]) and the TB70 (0.4% ± 0.9% vs 1.6% ± 3.4%; p < 0.05) was lower in the continuous glucose monitoring than in the intermittent glucose monitoring group.

CONCLUSIONS

Use of a continuous glucose monitoring-based strategy decreased the incidence and severity of hypoglycemia, thus improving the safety of glycemic control.

Blood glucose control in the intensive care unit: Where is the data?

Blood glucose control, including hyperglycemia correction, maintaining glucose at optimal level and avoiding hypoglycemia, is a challenge clinicians face every day in intensive care units (ICUs). If managed inadequately, its related mortality can increase. Prior to 2001, no relevant data from randomized, controlled studies assessing glucose control in the ICU were available. In the past 18 years, however, many clinical trials have defined criteria for managing abnormal blood glucose levels, as well as provided suggestions for glycemic monitoring. Point-of-care blood glucose monitors have become the preferred bedside technology to aid in glycemic management. In addition, in some institutions, continuous glucose monitoring is now available. Cost-effectiveness of adequate glycemic control in the ICU must be taken into consideration when addressing this complex issue. Newer types of glycemic monitoring may reduce nursing staff fatigue and shorten times for the treatment of hyperglycemia or hypoglycemia. There are a variety of glycemic care protocols available. However, not all ICU clinicians are aware of them. The following minireview describes some of these concepts.

Advancing the Use of CGM Devices in a Non-ICU Setting

Improvements in glycemic control using continuous glucose monitoring (CGM) systems have been demonstrated in the outpatient setting. Among hospitalized patients the use of CGM is largely investigational, particularly in the non-ICU setting. Although there is no commercially available closed-loop system, it has recently been evaluated in the non-critical care setting. Both CGMs and closed-loop systems may lead to improved glycemic control, decreased length of stay, reduced risk of adverse events related to severe hypoglycemia or hyperglycemia. Limitations of inpatient use of CGM and closed-loop systems include lack of FDA approvals, inexperience with this technology, and costs related to supplies. Significant investment may be necessary for hospital staff training and for development of infrastructure to support inpatient use. Additional limitations for CGM systems includes potential inaccuracy of interstitial glucose measurements due to medication interferences, sensor lag, or sensor drift. Limitations for closed-loop systems also includes need for routine monitoring to detect infusion site issues as well as monitoring to ensure adequate insulin supply in reservoir to avoid abrupt cessation of insulin infusion leading to severe hyperglycemia. Hospital staff must be familiar with trouble-shooting and conversion to alternative mode of insulin delivery in the event of insulin pump malfunction. Given these complexities, implementation of closed-loop systems may require involvement of an endocrinology team, limiting widespread adoption. This article reviews current state of CGM and closed-loop system use in the non-ICU setting, available literature, advantages and limitations, as well as suggestions for future CGM design, specifically for the inpatient setting.

Glucose Management Technologies for the Critically Ill

Hyperglycemia is common in the intensive care unit (ICU) both in patients with and without a previous diagnosis of diabetes. The optimal glucose range in the ICU population is still a matter of debate. Given the risk of hypoglycemia associated with intensive insulin therapy, current recommendations include treating hyperglycemia after two consecutive glucose >180 mg/dL with target levels of 140-180 mg/dL for most patients. The optimal method of sampling glucose and delivery of insulin in critically ill patients remains elusive. While point of care glucose meters are not consistently accurate and have to be used with caution, continuous glucose monitoring (CGM) is not standard of care, nor is it generally recommended for inpatient use. Intravenous insulin therapy using paper or electronic protocols remains the preferred approach for critically ill patients. The advent of new technologies, such as electronic glucose management, CGM, and closed-loop systems, promises to improve inpatient glycemic control in the critically ill with lower rates of hypoglycemia.

Glucose control in the ICU

PURPOSE OF REVIEW

Critically ill patients usually develop hyperglycemia, which is associated with adverse outcome. Controversy exists whether the relationship is causal or not. This review summarizes recent evidence regarding glucose control in the ICU.

RECENT FINDINGS

Despite promising effects of tight glucose control in pioneer randomized controlled trials, the benefit has not been confirmed in subsequent multicenter studies and one trial found potential harm. This discrepancy could be explained by methodological differences between the trials rather than by a different case mix. Strategies to improve the efficacy and safety of tight glucose control have been developed, including the use of computerized treatment algorithms.

SUMMARY

The ideal blood glucose target remains unclear and may depend on the context. As compared with tolerating severe hyperglycemia, tight glucose control is well tolerated and effective in patients receiving early parenteral nutrition when provided with a protocol that includes frequent, accurate glucose measurements and avoids large glucose fluctuations. All patient subgroups potentially benefit, with the possible exception of patients with poorly controlled diabetes, who may need less aggressive glucose control. It remains unclear whether tight glucose control is beneficial or not in the absence of early parenteral nutrition.

Understanding of type 1 diabetes mellitus: what we know and where we go

The incidence of type 1 diabetes mellitus (T1DM) in children and adolescents is increasing worldwide. Combined effects of genetic and environmental factors cause T1DM, which make it difficult to predict whether an individual will inherit the disease. Due to the level of self-care necessary in T1DM maintenance, it is crucial for pediatric settings to support achieving optimal glucose control, especially when adolescents are beginning to take more responsibility for their own health. Innovative insulin delivery systems, such as continuous subcutaneous insulin infusion (CSII), and noninvasive glucose monitoring systems, such as continuous glucose monitoring (CGM), allow patients with T1DM to achieve a normal and flexible lifestyle. However, there are still challenges in achieving optimal glucose control despite advanced technology in T1DM administration. In this article, disease prediction and current management of T1DM are reviewed with special emphasis on biomarkers of pancreatic β-cell stress, CSII, glucose monitoring, and several other adjunctive therapies.

Designing the Glucose Telemetry for Hospital Management: From Bedside to the Nursing Station

PURPOSE OF THE REVIEW

Hospitalized patients with diabetes are monitored with point-of-care glucose testing. Continuous glucose monitoring (CGM) devices represent an alternative way to monitor glucose values; however, the in-hospital CGM use is still considered experimental. Most inpatient studies used "blinded" CGM properties and only few used the real-time/unblinded CGM features. One major limitation of the CGM devices is that they need to be placed at the patients' bedside, limiting any therapeutic interventions. In this article, we review the real-time/unblinded CGM use and share our thoughts about the development of future inpatient CGM systems.

RECENT FINDINGS

We recently reported that glucose values can be wirelessly transmitted to the nursing station, providing remote continuous glucose monitoring. Future inpatient CGM devices may be utilized for patients at risk for hypoglycemia similarly to the way that we use cardiac telemetry to monitor hospitalized patients who are at increased risk for cardiac arrhythmias.

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.

Bridging technology and clinical practice: innovating inpatient hyperglycaemia management in non-critical care settings

Emerging evidence shows that suboptimal glycaemic control is associated with increased morbidity and length of stay in hospital. Various guidelines for safe and effective inpatient glycaemic control in the non-critical care setting have been published. In spite of this, implementation in practice remains limited because of the increasing number of people with diabetes admitted to hospital and staff work burden. The use of technology in the outpatient setting has led to improved glycaemic outcomes and quality of life for people with diabetes. There remains an unmet need for technology utilisation in inpatient hyperglycaemia management in the non-critical care setting. Novel technologies have the potential to provide benefits in diabetes care in hospital by improving efficacy, safety and efficiency. Rapid analysis of glucose measurements by point-of-care devices help facilitate clinical decision-making and therapy adjustment in the hospital setting. Glucose treatment data integration with computerized glucose management systems underpins the effective use of decision support systems and may streamline clinical staff workflow. Continuous glucose monitoring and automation of insulin delivery through closed-loop systems may provide a safe and efficacious tool for hospital staff to manage inpatient hyperglycaemia whilst reducing staff workload. This review summarizes the evidence with regard to technological methods to manage inpatient glycaemic control, their limitations and the future outlook, as well as potential strategies by healthcare organizations such as the National Health Service to mediate the adoption, procurement and use of diabetes technologies in the hospital setting.

Hypoglycemia Prevention by Algorithm Design During Intravenous Insulin Infusion

PURPOSE OF REVIEW

This review examines algorithm design features that may reduce risk for hypoglycemia while preserving glycemic control during intravenous insulin infusion. We focus principally upon algorithms in which the assignment of the insulin infusion rate (IR) depends upon maintenance rate of insulin infusion (MR) or a multiplier.

RECENT FINDINGS

Design features that may mitigate risk for hypoglycemia include use of a mid-protocol bolus feature and establishment of a low BG threshold for temporary interruption of infusion. Computer-guided dosing may improve target attainment without exacerbating risk for hypoglycemia. Column assignment (MR) within a tabular user-interpreted algorithm or multiplier may be specified initially according to patient characteristics and medical condition with revision during treatment based on patient response. We hypothesize that a strictly increasing sigmoidal relationship between MR-dependent IR and BG may reduce risk for hypoglycemia, in comparison to a linear relationship between multiplier-dependent IR and BG. Guidelines are needed that curb excessive up-titration of MR and recommend periodic pre-emptive trials of MR reduction. Future research should foster development of recommendations for "protocol maxima" of IR appropriate to patient condition.

Prednosti in slabosti različnih protokolov vodenja vrednosti glukoze v krvi pri kritično bolnih pacientih

Uvod: Medicinske sestre v enotah intenzivne terapije uravnavajo ciljno vrednost glukoze v krvi pri kritično bolnih po sprejetih in veljavnih protokolih. Namen raziskave je bil raziskati prednosti in slabosti različnih protokolov vodenja  vrednosti glukoze v krvi pri kritično bolnih.Metode: Uporabljen je bil sistematični pregled znanstvene in strokovne literature. Iskanje literature je potekalo od 1. 2. 2017 do 8. 8. 2017. V pregled so bile vključene naslednje baze: COBIB.SI, Digitalna knjižnica Slovenije – Dlib.si, CINAHL, ProQuest, PubMed in Google Učenjak. Iskanje je potekalo z različnimi kombinacijami ključnih besed v slovenskem in angleškem jeziku: prednosti, slabosti, medicinske sestre, kritično bolni, glukoza v krvi in protokoli za vodenje vrednosti glukoze v krvi. Uporabljen je bil Boolov operater AND. Iz iskalnega nabora 1064 zadetkov je bilo v končno analizo  vključenih 15 člankov. Za obdelavo podatkov je bil uporabljen model analize konceptov.Rezultati: Identificirana so bila tri tematska področja: (1) primernost različnih protokolov za vodenje vrednosti glukoze v krvi, (2) delovne obremenitve medicinskih sester pri teh protokolih in (3) varnost protokolov. Prednosti računalniško podprtega protokola za vodenje vrednosti glukoze v krvi so v boljšem doseganju ciljne vrednosti koncentracije glukoze v krvi, slabosti pa v pojavu odstopanj v zvezi z načrtovanim časom za merjenje glukoze v krvi.Diskusija in zaključek: Nekatere raziskave ugotavljajo prednosti računalniško podprtih protokolov za vodenje vrednosti glukoze v krvi v smislu tehnoloških izboljšav, zmanjšanja delovnih obremenitev medicinskih sester in izboljšanja varnosti pacientov. Raziskava prispeva k izboljševanju klinične prakse pri delu s kritično bolnimi pacienti.

Diabetes Is Associated With Reduced Stress Hyperlactatemia in Cardiac Surgery

OBJECTIVE

Hyperglycemia and hyperlactatemia are associated with increased morbidity and mortality in critical illness. We evaluated the relationship among hyperlactatemia, glycemic control, and diabetes mellitus (DM) after cardiac surgery.

RESEARCH DESIGN AND METHODS

This was a retrospective cohort study of 4,098 cardiac surgery patients treated between 2011 and 2015. Patients were stratified by DM and glucose-lowering medication history. Hyperglycemia (glucose >180 mg/dL), hypoglycemia (<70 mg/dL), and the hyperglycemic index were assessed postoperatively (48 h). The relationship between lactate and glucose levels was modeled using generalized linear regression. Mortality was analyzed using an extended Cox regression model.

RESULTS

Hyperglycemia occurred in 26.0% of patients without DM (NODM), 46.5% with DM without prior drug treatment (DMNT), 62.8% on oral medication (DMOM), and 73.8% on insulin therapy (DMIT) (P < 0.0001). Hypoglycemia occurred in 6.3%, 9.1%, 8.8%, and 10.8% of NODM, DMNT, DMOM, and DMIT, respectively (P = 0.0012). The lactate levels of all patients were temporarily increased with surgery. This increase was greater in patients who also had hyperglycemia or hypoglycemia and was markedly attenuated in patients with DM. Peak lactate was 5.8 mmol/L (95% CI 5.6, 6.0) in NODM with hyperglycemia vs. 3.3 (95% CI 3.2, 3.4) without hyperglycemia; in DMNT: 4.8 (95% CI 4.4, 5.2) vs. 3.4 (95% CI 3.1, 3.6); in DMOM: 3.8 (95% CI 3.5, 4.1) vs. 2.9 (95% CI 2.7, 3.1); and in DMIT: 3.3 (95% CI 3.0, 3.5) vs. 2.7 (95% CI 2.3, 3.0). Increasing lactate levels were associated with increasing mortality; increasing glucose reduced this effect in DM but not in NODM (P = 0.0069 for three-way interaction).

CONCLUSIONS

Stress hyperlactatemia is markedly attenuated in patients with DM. There is a three-way interaction among DM, stress hyperlactatemia, and stress hyperglycemia associated with mortality after cardiac surgery.

Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor

BACKGROUND

Continuous glucose monitoring (CGM) devices have been effective in managing diabetes and offer potential benefits for use in the intensive care unit (ICU). Use of CGM devices in the ICU has been limited, primarily due to the higher point accuracy errors over currently used traditional intermittent blood glucose (BG) measures. General models of CGM errors, including drift and random errors, are lacking, but would enable better design of protocols to utilize these devices. This article presents an autoregressive (AR) based modeling method that separately characterizes the drift and random noise of the GlySure CGM sensor (GlySure Limited, Oxfordshire, UK).

METHODS

Clinical sensor data (n = 33) and reference measurements were used to generate 2 AR models to describe sensor drift and noise. These models were used to generate 100 Monte Carlo simulations based on reference blood glucose measurements. These were then compared to the original CGM clinical data using mean absolute relative difference (MARD) and a Trend Compass.

RESULTS

The point accuracy MARD was very similar between simulated and clinical data (9.6% vs 9.9%). A Trend Compass was used to assess trend accuracy, and found simulated and clinical sensor profiles were similar (simulated trend index 11.4° vs clinical trend index 10.9°).

CONCLUSION

The model and method accurately represents cohort sensor behavior over patients, providing a general modeling approach to any such sensor by separately characterizing each type of error that can arise in the data. Overall, it enables better protocol design based on accurate expected CGM sensor behavior, as well as enabling the analysis of what level of each type of sensor error would be necessary to obtain desired glycemic control safety and performance with a given protocol.

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.

Hypoglycemia Reduction Strategies in the ICU

PURPOSE OF REVIEW

We reviewed the strategies associated with hypoglycemia risk reduction among critically ill non-pregnant adult patients.

RECENT FINDINGS

Hypoglycemia in the ICU has been associated with increased mortality in a number of studies. Insulin dosing and glucose monitoring rules, response to impending hypoglycemia, use of computerization, and attention to modifiable factors extrinsic to insulin algorithms may affect the risk for hypoglycemia. Recurring use of intravenous (IV) bolus doses of insulin in insulin-resistant cases may reduce reliance upon higher IV infusion rates. In order to reduce the risk for hypoglycemia in the ICU, caregivers should define responses to interruption of continuous carbohydrate exposure, incorporate transitioning strategies upon initiation and interruption of IV insulin, define modifications of antihyperglycemic therapy in the presence of worsening renal function or chronic kidney disease, and anticipate the effects traceable to other medications and substances. Institutional and system-wide quality improvement efforts should assign priority to hypoglycemia prevention.

Consensus Statement on Inpatient Use of Continuous Glucose Monitoring

In June 2016, Diabetes Technology Society convened a panel of US experts in inpatient diabetes management to discuss the current and potential role of continuous glucose monitoring (CGM) in the hospital. This discussion combined with a literature review was a follow-up to a meeting, which took place in May 2015. The panel reviewed evidence on use of CGM in 3 potential inpatient scenarios: (1) the intensive care unit (ICU), (2) non-ICU, and (3) transitioning outpatient CGM use into the hospital setting. Panel members agreed that data from limited studies and theoretical considerations suggested that use of CGM in the hospital had the potential to improve patient clinical outcomes, and in particular reduction of hypoglycemia. Panel members discussed barriers to widespread adoption of CGM, which patients would benefit most from use of this technology, and what type of outcome studies are needed to guide use of CGM in the inpatient setting.

Inpatient Continuous Glucose Monitoring and Glycemic Outcomes

Continuous glucose monitoring (CGM) is commonly used in the outpatient setting to improve diabetes management. CGM can provide real-time glucose trends, detecting hyperglycemia and hypoglycemia before the onset of clinical symptoms. In 2011, at the time the Endocrine Society CGM guidelines were published, the society did not recommend inpatient CGM as its efficacy and safety were unknown. While many studies have subsequently evaluated inpatient CGM accuracy and reliability, glycemic outcome studies have not been widely published. In the non-ICU setting, investigational CGM studies have commonly blinded providers and patients to glucose data. Retrospective review of the glucose data reflects increased hypoglycemia detection with CGM. In the ICU setting, data are inconsistent whether CGM can improve glycemic outcomes. Studies have not focused on hospitalized patients with type 1 diabetes mellitus, the population most likely to benefit from inpatient CGM. This article reviews inpatient CGM glycemic outcomes in the non-ICU and ICU setting.

Closed-loop Administration of General Anaesthesia: From Sensor to Medical Device

Closed-loop administration devices for general anaesthesia have become a common subject of clinical research over the last decade and appear more and more acceptable in clinical practice. They encompass various therapeutic needs of the anesthetized patient, e. g. fluid administration, hypnotic and analgesic drug administration, myorelaxation. Multiple clinical trials involving closed-loop devices have underscored their safety, but data concerning their clinical benefit to the patient are still lacking. As the marketing of various devices increases, clinicians need to understand how comparisons between these devices can be made: the measure of performance error and wobble are technical but have also a clinical meaning, to which clinical outcomes can be added, such as drug consumption and maintenance of hemodynamic parameters (e. g. heart rate and blood pressure) within predefined ranges. Clinicians using closed-loop devices need especially to understand how various physiological signals lead to specific drug adaptations, which means that they switch from decision making to supervision of general anaesthesia.

Continuous glucose monitoring in the ICU: clinical considerations and consensus

Glucose management in intensive care unit (ICU) patients has been a matter of debate for almost two decades. Compared to intermittent monitoring systems, continuous glucose monitoring (CGM) can offer benefit in the prevention of severe hyperglycemia and hypoglycemia by enabling insulin infusions to be adjusted more rapidly and potentially more accurately because trends in glucose concentrations can be more readily identified. Increasingly, it is apparent that a single glucose target/range may not be optimal for all patients at all times and, as with many other aspects of critical care patient management, a personalized approach to glucose control may be more appropriate. Here we consider some of the evidence supporting different glucose targets in various groups of patients, focusing on those with and without diabetes and neurological ICU patients. We also discuss some of the reasons why, despite evidence of benefit, CGM devices are still not widely employed in the ICU and propose areas of research needed to help move CGM from the research arena to routine clinical use.

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.

Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating Improved Glycemic Outcomes

Continuous Glucose Monitoring (CGM) has been demonstrated to be clinically valuable, reducing risks of hypoglycemia and hyperglycemia, glycemic variability (GV), and improving patient quality of life for a wide range of patient populations and clinical indications. Use of CGM can help reduce HbA1c and mean glucose. One CGM device, with accuracy (%MARD) of approximately 10%, has recently been approved for self-adjustment of insulin dosages (nonadjuvant use) and approved for reimbursement for therapeutic use in the United States. CGM had previously been used off-label for that purpose. CGM has been demonstrated to be clinically useful in both type 1 and type 2 diabetes for patients receiving a wide variety of treatment regimens. CGM is beneficial for people using either multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII). CGM is used both in retrospective (professional, masked) and real-time (personal, unmasked) modes: both approaches can be beneficial. When CGM is used to suspend insulin infusion when hypoglycemia is detected until glucose returns to a safe level (low-glucose suspend), there are benefits beyond sensor-augmented pump (SAP), with greater reduction in the risk of hypoglycemia. Predictive low-glucose suspend provides greater benefits in this regard. Closed-loop control with insulin provides further improvement in quality of glycemic control. A hybrid closed-loop system has recently been approved by the U.S. FDA. Closed-loop control using both insulin and glucagon can reduce risk of hypoglycemia even more. CGM facilitates rigorous evaluation of new forms of therapy, characterizing pharmacodynamics, assessing frequency and severity of hypo- and hyperglycemia, and characterizing several aspects of GV.

The Clinical Benefits and Accuracy of Continuous Glucose Monitoring Systems in Critically Ill Patients-A Systematic Scoping Review

Continuous Glucose Monitoring (CGM) systems could improve glycemic control in critically ill patients. We aimed to identify the evidence on the clinical benefits and accuracy of CGM systems in these patients. For this, we performed a systematic search in Ovid MEDLINE, from inception to 26 July 2016. Outcomes were efficacy, accuracy, safety, workload and costs. Our search retrieved 356 articles, of which 37 were included. Randomized controlled trials on efficacy were scarce (n = 5) and show methodological limitations. CGM with automated insulin infusion improved time in target and mean glucose in one trial and two trials showed a decrease in hypoglycemic episodes and time in hypoglycemia. Thirty-two articles assessed accuracy, which was overall moderate to good, the latter mainly with intravascular devices. Accuracy in critically ill children seemed lower than in adults. Adverse events were rare. One study investigated the effect on workload and cost, and showed a significant reduction in both. In conclusion, studies on the efficacy and accuracy were heterogeneous and difficult to compare. There was no consistent clinical benefit in the small number of studies available. Overall accuracy was moderate to good with some intravascular devices. CGM systems seemed however safe, and might positively affect workload and costs.

Glucose: archetypal biomarker in diabetes diagnosis, clinical management and research

The clinical utility of diabetes biomarkers can be considered in terms of diagnosis, management and prediction of long-term vascular complications. Glucose satisfies all of these requirements. Thresholds of hyperglycemia diagnostic of diabetes reflect inflections that confer a risk of developing long-term microvascular complications. Degrees of hyperglycemia (impaired fasting glucose, impaired glucose tolerance) that lie below the diagnostic threshold for diabetes identify individuals at risk of progression to diabetes and/or development of atherothrombotic cardiovascular disease. Self-measured glucose levels usefully complement hemoglobin A1c levels to guide daily management decisions. Continuous glucose monitoring provides detailed real-time data that is of value in clinical decision making, assessing response to new diabetes drugs and the development of closed-loop artificial pancreas technology.

Round Table Discussion on Inpatient Use of Continuous Glucose Monitoring at the International Hospital Diabetes Meeting

In May 2015 the Diabetes Technology Society convened a panel of 27 experts in hospital medicine and endocrinology to discuss the current and potential future roles of continuous glucose monitoring (CGM) in delivering optimum health care to hospitalized patients in the United States. The panel focused on 3 potential settings for CGM in the hospital, including (1) the intensive care unit (ICU), (2) non-ICU, and (3) continuation of use of home CGM in the hospital. The group reviewed barriers to use and solutions to overcome the barriers. They concluded that CGM has the potential to improve the quality of patient care and can provide useful information to help health care providers learn more about glucose management. Widespread adoption of CGM by hospitals, however, has been limited by added costs and insufficient outcome data.