Computerized intensive insulin dosing can mitigate hypoglycemia and achieve tight glycemic control when glucose measurement is performed frequently and on time

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.

Feasibility of intraoperative continuous glucose monitoring: An observational study in general surgery patients

BACKGROUND

Perioperative hyperglycemia is associated with adverse outcomes in surgical patients, and major societies recommend intraoperative monitoring and treatment targeting glucose <180-200 mg/dL. However, compliance with these recommendations is poor, in part due to fear of unrecognized hypoglycemia. Continuous Glucose Monitors (CGMs) measure interstitial glucose with a subcutaneous electrode and can display the results on a receiver or smartphone. Historically CGMs have not been utilized for surgical patients. We investigated the use of CGM in the perioperative setting compared to current standard practices.

METHOD

This study evaluated the use of Abbott Freestyle Libre 2.0 and/or Dexcom G6 CGMs in a prospective cohort of 94 participants with diabetes mellitus undergoing surgery of ≥3 h duration. CGMs were placed preoperatively and compared to point of care (POC) BG checks obtained by capillary samples analyzed with a NOVA glucometer. Frequency of intraoperative blood glucose measurement was at the discretion of the anesthesia care team, with a recommendation of once per hour targeting BG of 140-180 mg/dL. Of those consented, 18 were excluded due to lost sensor data, surgery cancellation, or rescheduling to a satellite campus resulting in 76 enrolled subjects. There were zero occurrences of failure with sensor application. Paired POC BG and contemporaneous CGM readings were compared with Pearson product-moment correlation coefficients, and Bland-Altman plots.

RESULTS

Data for use of CGM in perioperative period was analyzed for 50 participants with Freestyle Libre 2.0, 20 participants with Dexcom G6, and 6 participants with both devices worn simultaneously. Lost sensor data occurred in 3 participants (15%) wearing Dexcom G6, 10 participants wearing Freestyle Libre 2.0 (20%) and 2 of the participants wearing both devices simultaneously. The overall agreement of the two CGM's utilized had a Pearson correlation coefficient of 0.731 in combined groups with 0.573 in Dexcom arm evaluating 84 matched pairs and 0.771 in Libre arm with 239 matched pairs. Modified Bland-Altman plot of the difference of CGM and POC BG indicated for the overall dataset a bias of -18.27 (SD 32.10).

CONCLUSIONS

Both Dexcom G6 and Freestyle Libre 2.0 CGMs were able to be utilized and functioned well if no sensor error occurred at time of initial warmup. CGM provided more glycemic data and further characterized glycemic trends more than individual BG readings. Required time of CGM warm up was a barrier for intraoperative use as well as unexplained sensor failure. CGMs had a fixed warm of time, 1 h for Libre 2.0 and 2 h for Dexcom G6 CGM, before glycemic data obtainable. Sensor application issues did not occur. It is anticipated that this technology could be used to improve glycemic control in the perioperative setting. Additional studies are needed to evaluate use intraoperatively and assess further if any interference from electrocautery or grounding devices may contribute to initial sensor failure. It may be beneficial in future studies to place CGM during preoperative clinic evaluation the week prior to surgery. Use of CGMs in these settings is feasible and warrants further evaluation of this technology on perioperative glycemic management.

A Systematic Review Supporting the Endocrine Society Guidelines: Management of Diabetes and High Risk of Hypoglycemia

CONTEXT

Interventions targeting hypoglycemia in people with diabetes are important for improving quality of life and reducing morbidity and mortality.

OBJECTIVE

To support development of the Endocrine Society Clinical Practice Guideline for management of individuals with diabetes at high risk for hypoglycemia.

METHODS

We searched several databases for studies addressing 10 questions provided by a guideline panel from the Endocrine Society. Meta-analysis was conducted when feasible. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess certainty of evidence.

RESULTS

We included 149 studies reporting on 43 344 patients. Continuous glucose monitoring (CGM) reduced episodes of severe hypoglycemia in patients with type 1 diabetes (T1D) and reduced the proportion of patients with hypoglycemia (blood glucose [BG] levels <54 mg/dL). There were no data on use of real-time CGM with algorithm-driven insulin pumps vs multiple daily injections with BG testing in people with T1D. CGM in outpatients with type 2 diabetes taking insulin and/or sulfonylureas reduced time spent with BG levels under 70 mg/dL. Initiation of CGM in hospitalized patients at high risk for hypoglycemia reduced episodes of hypoglycemia with BG levels lower than 54 mg/dL and time spent under 54 mg/dL. The proportion of patients with hypoglycemia with BG levels lower than 70 mg/dL and lower than 54 mg/dL detected by CGM was significantly higher than point-of-care BG testing. We found no data evaluating continuation of personal CGM in the hospital. Use of an inpatient computerized glycemic management program utilizing electronic health record data was associated with fewer patients with and episodes of hypoglycemia with BG levels lower than 70 mg/dL and fewer patients with severe hypoglycemia compared with standard care. Long-acting basal insulin analogs were associated with less hypoglycemia. Rapid-acting insulin analogs were associated with reduced severe hypoglycemia, though there were more patients with mild to moderate hypoglycemia. Structured diabetes education programs reduced episodes of severe hypoglycemia and time below 54 mg/dL in outpatients taking insulin. Glucagon formulations not requiring reconstitution were associated with longer times to recovery from hypoglycemia, although the proportion of patients who recovered completely from hypoglycemia was not different between the 2 groups.

CONCLUSION

This systematic review summarized the best available evidence about several interventions addressing hypoglycemia in people with diabetes. This evidence base will facilitate development of clinical practice guidelines by the Endocrine Society.

American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update

OBJECTIVE

The objective of this clinical practice guideline is to provide updated and new evidence-based recommendations for the comprehensive care of persons with diabetes mellitus to clinicians, diabetes-care teams, other health care professionals and stakeholders, and individuals with diabetes and their caregivers.

METHODS

The American Association of Clinical Endocrinology selected a task force of medical experts and staff who updated and assessed clinical questions and recommendations from the prior 2015 version of this guideline and conducted literature searches for relevant scientific papers published from January 1, 2015, through May 15, 2022. Selected studies from results of literature searches composed the evidence base to update 2015 recommendations as well as to develop new recommendations based on review of clinical evidence, current practice, expertise, and consensus, according to established American Association of Clinical Endocrinology protocol for guideline development.

RESULTS

This guideline includes 170 updated and new evidence-based clinical practice recommendations for the comprehensive care of persons with diabetes. Recommendations are divided into four sections: (1) screening, diagnosis, glycemic targets, and glycemic monitoring; (2) comorbidities and complications, including obesity and management with lifestyle, nutrition, and bariatric surgery, hypertension, dyslipidemia, retinopathy, neuropathy, diabetic kidney disease, and cardiovascular disease; (3) management of prediabetes, type 2 diabetes with antihyperglycemic pharmacotherapy and glycemic targets, type 1 diabetes with insulin therapy, hypoglycemia, hospitalized persons, and women with diabetes in pregnancy; (4) education and new topics regarding diabetes and infertility, nutritional supplements, secondary diabetes, social determinants of health, and virtual care, as well as updated recommendations on cancer risk, nonpharmacologic components of pediatric care plans, depression, education and team approach, occupational risk, role of sleep medicine, and vaccinations in persons with diabetes.

CONCLUSIONS

This updated clinical practice guideline provides evidence-based recommendations to assist with person-centered, team-based clinical decision-making to improve the care of persons with diabetes mellitus.

Glycemic targets in critically ill adults: A mini-review

Illness-induced hyperglycemia impairs neutrophil function, increases pro-inflammatory cytokines, inhibits fibrinolysis, and promotes cellular damage. In turn, these mechanisms lead to pneumonia and surgical site infections, prolonged mechanical ventilation, prolonged hospitalization, and increased mortality. For optimal glucose control, blood glucose measurements need to be done accurately, frequently, and promptly. When choosing glycemic targets, one should keep the glycemic variability < 4 mmol/L and avoid targeting a lower limit of blood glucose < 4.4 mmol/L. The upper limit of blood glucose should be set according to casemix and the quality of glucose control. A lower glycemic target range (i.e., blood glucose 4.5-7.8 mmol/L) would be favored for patients without diabetes mellitus, with traumatic brain injury, or who are at risk of surgical site infection. To avoid harm from hypoglycemia, strict adherence to glycemic control protocols and timely glucose measurements are required. In contrast, a higher glycemic target range (i.e., blood glucose 7.8-10 mmol/L) would be favored as a default choice for medical-surgical patients and patients with diabetes mellitus. These targets may be modified if technical advances for blood glucose measurement and control can be achieved.

Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU

Objective

Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care.

Materials and Methods

Using electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals. The model uses as inputs irregular multivariate time series data relating to recent in-patient medical history and glycemic control, including previous blood glucose, nutrition, and insulin dosing.

Results

Our forecasting model using routinely collected EMRs achieves performance comparable to previous models developed in planned research studies using continuous blood glucose monitoring. Model error, expressed as mean absolute percentage error is 16.5%–16.8%, with Clarke error grid analysis demonstrating that 97% of predictions would be clinically acceptable. The 95% prediction intervals achieve near intended coverage at 93%–94%.

Discussion

ML algorithms built on observational data sources, such as EMRs, present a promising approach for personalization and automation of glycemic control in critical care. Future research may benefit from applying a combination of methodologies and data sources to develop robust methodologies that account for the variations seen in ICU patients and difficultly in detecting the extremes of observed blood glucose values.

Conclusion

We demonstrate that EMRs can be used to train ML algorithms that may be suitable for incorporation into ICU decision support systems.

Adherence to a Hypoglycemia Protocol in Hospitalized Patients: A Retrospective Analysis

BACKGROUND

Hypoglycemia can be a common occurrence in hospitalized patients, both those with and without diabetes. Hypoglycemia poses significant risks to hospitalized patients, including increased mortality.

OBJECTIVES

This was a retrospective pre-post study of hypoglycemic patients in an academic medical center of an intervention to improve timely staff nurse adherence to a hypoglycemia protocol. The number of mild and severe hypoglycemia events pre- and postintervention, timeliness of adherence to the hypoglycemia protocol, the number of treatment interventions, and time to return patients to euglycemia were analyzed.

METHODS

Data from hospitalizations of patients who experienced hypoglycemia (<70 mg/dl) and met inclusion criteria 1 year prior to intervention and 3 years postintervention were extracted, including demographics, glycemic control medications, diagnostic-related group, length of stay, and Charlson comorbidity index. For clarity and to determine if any significant change was sustained, the analysis compared data from 1 year prior to intervention to the second-year postintervention.

RESULTS

A total of 7,895 unique hypoglycemic events in 3,819 patients experiencing 20,094 hypoglycemic measures were included in the analysis. Patients were primarily adult, female, and White. Only 58.7% of the sample had diabetes; the median Charlson comorbidity index was 6. Results demonstrated improvement postintervention to registered nurse hypoglycemia protocol adherence regardless of age category or hypoglycemia severity. There was a significant reduction in median time from the first hypoglycemia measure to the second measure. In addition, there was a significant difference in the number of treatment interventions and reduction in time from the first hypoglycemia measure to return of patient to a blood glucose of ≥70 mg/dl.

DISCUSSION

These study results support that the use of a standardized hypoglycemia protocol and appropriate nurse workflows enables nurses to manage hypoglycemia promptly and effectively in most acute and critically ill hospitalized patients. Results also supported a differentiation in nurse workflow for patients with mild versus severe hypoglycemia. Implementing these interventions may result in avoidance or mitigation of the potential consequences of severe and/or sustained hypoglycemia.

Outcomes and adverse effects of extremely high dose insulin infusions in ICU patients

PURPOSE

Describe the characteristics, hospital course, and outcomes of adult ICU patients receiving extremely high dose insulin infusions compared to those with lower insulin requirements.

MATERIALS AND METHODS

Retrospective observational study of 128 adult ICU patients receiving IV insulin infusions at a large academic medical center. Extremely high dose insulin infusions were defined as maximum rate ≥ 35 units/h. The primary endpoint was rate of hypoglycemia (BG < 70 mg/dL) and time to glucose control. A post-hoc matching analysis was performed for baseline imbalances.

RESULTS

Analysis included 32 patents with extremely high dose insulin infusions and 96 patients without, and most had a goal BG 100-150 mg/dL. Patients in the extreme group were more likely to have type 2 diabetes, a higher median hemoglobin A1c, preadmission insulin, be admitted for a medical reason, and receive inpatient steroids. The extreme group were more likely to experience hypoglycemia (<70 mg/dL, 63% v. 34%, p = 0.005), longer time to glucose control (19.8 h v. 5.7 h, p < 0.001) and higher mortality (34% v. 15%, p = 0.014).

CONCLUSIONS

ICU patients with extremely high dose insulin infusions had more hypoglycemia and took longer to achieve glucose targets compared to those with lower requirements. An individualized approach may be required for appropriate management.

Association Between Achieving Inpatient Glycemic Control and Clinical Outcomes in Hospitalized Patients With COVID-19: A Multicenter, Retrospective Hospital-Based Analysis

OBJECTIVE

Diabetes and hyperglycemia are important risk factors for poor outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19). We hypothesized that achieving glycemic control soon after admission, in both intensive care unit (ICU) and non-ICU settings, could affect outcomes in patients with COVID-19.

RESEARCH DESIGN AND METHODS

We analyzed pooled data from the Glytec national database including 1,544 patients with COVID-19 from 91 hospitals in 12 states. Patients were stratified according to achieved mean glucose category in mg/dL (≤7.77, 7.83-10, 10.1-13.88, and >13.88 mmol/L; ≤140, 141-180, 181-250, and >250 mg/dL) during days 2-3 in non-ICU patients or on day 2 in ICU patients. We conducted a survival analysis to determine the association between glucose category and hospital mortality.

RESULTS

Overall, 18.1% (279/1,544) of patients died in the hospital. In non-ICU patients, severe hyperglycemia (blood glucose [BG] >13.88 mmol/L [250 mg/dL]) on days 2-3 was independently associated with high mortality (adjusted hazard ratio [HR] 7.17; 95% CI 2.62-19.62) compared with patients with BG <7.77 mmol/L (140 mg/dL). This relationship was not significant for admission glucose (HR 1.465; 95% CI 0.683-3.143). In patients admitted directly to the ICU, severe hyperglycemia on admission was associated with increased mortality (adjusted HR 3.14; 95% CI 1.44-6.88). This relationship was not significant on day 2 (HR 1.40; 95% CI 0.53-3.69). Hypoglycemia (BG <70 mg/dL) was also associated with increased mortality (odds ratio 2.2; 95% CI 1.35-3.60).

CONCLUSIONS

Both hyperglycemia and hypoglycemia were associated with poor outcomes in patients with COVID-19. Admission glucose was a strong predictor of death among patients directly admitted to the ICU. Severe hyperglycemia after admission was a strong predictor of death among non-ICU patients.

Maintaining Blood Glucose Levels in Range (70-150 mg/dL) is Difficult in COVID-19 Compared to Non-COVID-19 ICU Patients-A Retrospective Analysis

Beta cell dysfunction is suggested in patients with COVID-19 infections. Poor glycemic control in ICU is associated with poor patient outcomes. This is a single center, retrospective analysis of 562 patients in an intensive care unit from 1 March to 30 April 2020. We review the time in range (70–150 mg/dL) spent by critically ill COVID-19 patients and non-COVID-19 patients, along with the daily insulin use. Ninety-three in the COVID-19 cohort and 469 in the non-COVID-19 cohort were compared for percentage of blood glucose TIR (70–150 mg/dL) and average daily insulin use. The COVID-19 cohort spent significantly less TIR (70–150 mg/dL) compared to the non-COVID-19 cohort (44.4% vs. 68.5%). Daily average insulin use in the COVID-19 cohort was higher (8.37 units versus 6.17 units). ICU COVID-19 patients spent less time in range (70–150 mg/dL) and required higher daily insulin dose. A higher requirement for ventilator and days on ventilator was associated with a lower TIR. Mortality was lower for COVID-19 patients who achieved a higher TIR.

Individualizing Glycemic Control in the Critically Ill

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

Diabetes Technology in the Inpatient Setting for Management of Hyperglycemia

In past decades, a rapid evolution of diabetes technology led to increased popularity and use of continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) in the ambulatory setting for diabetes management, and recently, the artificial pancreas became available. Efforts to translate this technology to the hospital setting have shown accuracy and reliability of CGM, safety of CSII in appropriate populations, improvement of inpatient glycemic control with computerized glycemic management systems, and feasibility of inpatient CGM-CSII closed-loop systems. Several ongoing studies are focusing on continued translation of this technology to improve glycemic control and outcomes in hospitalized patients.

Comparison of Routine and Computer-Guided Glucose Management for Glycemic Control in Critically Ill Patients

BACKGROUND

Glycemic control is crucial for reducing morbidity and mortality in critically ill patients. A standardized approach to glycemic control using a computer-guided protocol may help maintain blood glucose level within a target range and prevent human-induced medical errors.

OBJECTIVE

To determine the effectiveness of a computer-guided glucose management protocol for glycemic control in intensive care patients.

METHODS

This controlled, open-label implementation study involved 66 intensive care patients: 33 in the intervention group and 33 in the control group. The blood glucose level target range was established as 120 to 180 mg/dL. The control group received the clinic's routine glycemic monitoring approach, and the intervention group received monitoring using newly developed glycemic control software. At the end of the study, nurse perceptions and satisfaction were determined using a questionnaire.

RESULTS

The rates of hyperglycemia and hypoglycemia were lower and the blood glucose level was more successfully maintained in the target range in the intervention group than in the control group (P < .001). The time to achieve the target range was shorter and less insulin was used in the intervention group than in the control group (P < .05). Nurses reported higher levels of satisfaction with the computerized protocol, which they found to be more effective and reliable than routine clinical practice.

CONCLUSIONS

The computerized protocol was more effective than routine clinical practice in achieving glycemic control. It was also associated with higher nurse satisfaction levels.

An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment

BACKGROUND

We aimed to describe the outcome of a computerized intravenous insulin infusion (CII) protocol integrated to the electronic health record (EHR) system and to improve the CII protocol in silico using the EHR-based predictors of the outcome.

METHODS

Clinical outcomes of the patients who underwent the CII protocol between July 2016 and February 2017 and their matched controls were evaluated. In the CII protocol group (n=91), multivariable binary logistic regression analysis models were used to determine the independent associates with a delayed response (taking ≥6.0 hours for entering a glucose range of 70 to 180 mg/dL). The CII protocol was adjusted in silico according to the EHR-based parameters obtained in the first 3 hours of CII.

RESULTS

Use of the CII protocol was associated with fewer subjects with hypoglycemia alert values (P=0.003), earlier (P=0.002), and more stable (P=0.017) achievement of a glucose range of 70 to 180 mg/dL. Initial glucose level (P=0.001), change in glucose during the first 2 hours (P=0.026), and change in insulin infusion rate during the first 3 hours (P=0.029) were independently associated with delayed responses. Increasing the insulin infusion rate temporarily according to these parameters in silico significantly reduced delayed responses (P<0.0001) without hypoglycemia, especially in refractory patients.

CONCLUSION

Our CII protocol enabled faster and more stable glycemic control than conventional care with minimized risk of hypoglycemia. An EHR-based adjustment was simulated to reduce delayed responses without increased incidence of hypoglycemia.

SCING-Spinal Cord Injury Neuroprotection with Glyburide: a pilot, open-label, multicentre, prospective evaluation of oral glyburide in patients with acute traumatic spinal cord injury in the USA

INTRODUCTION

Acute traumatic spinal cord injury (tSCI) is a devastating neurological disorder with no pharmacological neuroprotective strategy proven effective to date. Progressive haemorrhagic necrosis (PHN) represents an increasingly well-characterised mechanism of secondary injury after tSCI that negatively impacts neurological outcomes following acute tSCI. Preclinical studies evaluating the use of the Food and Drug Administration-approved sulfonylurea receptor 1-transient receptor potential melastatin 4 channel blocker glyburide in rodent models have shown reduced secondary microhaemorrhage formation and the absence of capillary fragmentation, the pathological hallmark of PHN.

METHODS AND ANALYSIS

In this initial phase multicentre open-label pilot study, we propose to enrol 10 patients with acute cervical tSCI to primarily assess the feasibility, and safety of receiving oral glyburide within 8 hours of injury. Secondary objectives include pharmacokinetics and preliminary evaluations on neurological recovery as well as blood and MRI-based injury biomarkers. Analysis will be performed using the descriptive and non-parametric statistics.

ETHICS AND DISSEMINATION

Glyburide has been shown as an effective neuroprotective agent in preclinical tSCI models and in the treatment of ischaemic stroke with the additional risk of a hypoglycaemic response. Given the ongoing secondary injury and the traumatic hyperglycaemic stress response seen in patients with tSCI, glyburide; thus, offers an appealing neuroprotective strategy to supplement standard of care treatment. The study protocol was approved by the Ohio State University Biomedical Institutional Review Board. The protocol was amended in February 2017 with changes related to study feasibility and patient recruitment. Specifically, the route of administration was changed to the oral form to allow for streamlined and rapid drug administration, and the injury-to-drug time window was extended to 8 hours in an effort to further enhance enrolment. Participants or legally authorised representatives are informed about the trial and its anticipated risks orally and in written form using an approved informed consent form prior to inclusion. The findings of this study will be disseminated to the participants and to academic peers through scientific conferences and peer-reviewed journal publications.

TRIAL REGISTRATION NUMBERS

NCT02524379 and 2014H0335.

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.

Software-guided insulin dosing improves intrapartum glycemic management in women with diabetes mellitus

BACKGROUND

During labor, maintenance of maternal euglycemia is critical to decrease the risk of neonatal hypoglycemia and associated morbidities. When continuous intravenous insulin infusion is needed, standardized insulin dosing charts have been used for titration of insulin to maintain glucose in target range. The GlucoStabilizer software program (Indiana University Health Inc, Indianapolis, IN) is a software-guided insulin dosing system that calculates the dose of intravenous insulin that is needed based on metabolic parameters, target glucose concentration, and an individual's response to insulin. Although this tool has been validated and shown to reduce both hypoglycemia and errors in critical care settings, the utility of this software has not been examined in obstetrics.

OBJECTIVE

The purpose of this study was to determine whether the use of intravenous insulin dosing software in women with pregestational or gestational diabetes mellitus that requires intrapartum insulin infusion can improve the rate of glucose concentration in target range (70-100 mg/dL; 3.9-5.5 mmol/L) at the time delivery.

STUDY DESIGN

We performed a retrospective cohort study comparing laboring patients with diabetes mellitus that required insulin infusion who were dosed by standard insulin dosing chart vs the GlucoStabilizer software program from January 2012 to December 2017. The GlucoStabilizer software program, which was implemented in May 2016, replaced the standard intravenous insulin dosing chart. Inclusion criteria were women with pregestational or gestational diabetes mellitus who were treated with an intravenous insulin infusion intrapartum for at least 2 hours. Maternal characteristics, glucose values in labor, and neonatal outcomes were extracted from delivery and neonatal records. The primary outcome was the percentage of women who achieved the target glucose range (defined as a blood glucose between 70-100 mg/dL; 3.9-5.5 mmol/L) before delivery. Parametric and nonparametric statistics were used to compare both groups; a probability value of <.05 was considered statistically significant.

RESULTS

We identified 22 patients who were dosed by a standard insulin dosing chart and 11 patients who were dosed by the GlucoStabilizer software program during intrapartum management. The GlucoStabilizer software program was superior in achieving glucose values in target range at delivery (81.8% vs 9.1%; P<.001) compared with standard insulin dosing without increasing maternal hypoglycemia (0% vs 4.3%; P=.99). Patients whose insulin dosing was managed by the GlucoStabilizer software program also had lower mean capillary blood glucose values compared with the standard insulin infusion (102.9±5.9 mg/dL [5.7±0.33 mmol/L] vs 121.7±5.9 mg/dL [6.8±0.33 mmol/L]; P=.02). Before the initiation of the infusion, both groups demonstrated mean capillary blood glucose values outside of target range (122.6±8.8 mg/dL [6.7±0.49 mmol/L] for the GlucoStabilizer software program vs 131.9±10.1 mg/dL [7.3±0.56 mmol/L] for standard insulin treatment group; P=not significant). There were no significant differences in baseline maternal characteristics between the groups or neonatal outcomes.

CONCLUSION

This study is the first to demonstrate that the use of software-guided intravenous insulin dosing in obstetrics can improve intrapartum glycemic management without increasing hypoglycemia in women with both pregestational and gestational diabetes mellitus that is treated with an insulin infusion.

Lower Glucose Target Is Associated With Improved 30-Day Mortality in Cardiac and Cardiothoracic Patients

BACKGROUND

Practice guidelines recommend against intensive insulin therapy in patients who are critically ill based on trials that had high rates of severe hypoglycemia. Intermountain Healthcare uses a computerized IV insulin protocol that allows choice of blood glucose (BG) targets (80-110 vs 90-140 mg/dL) and has low rates of severe hypoglycemia. We sought to study the effects of BG target on mortality in adult patients in cardiac ICUs that have very low rates of severe hypoglycemia.

METHODS

Critically ill patients receiving IV insulin were treated with either of two BG targets (80-110 vs 90-140 mg/dL). We created a propensity score for BG target using factors thought to have influenced clinicians' choice, and then we performed a propensity score-adjusted regression analysis for 30-day mortality.

RESULTS

There were 1,809 patients who met inclusion criteria. Baseline patient characteristics were similar. Median glucose was lower in the 80-110 mg/dL group (104 vs 122 mg/dL, P < .001). Severe hypoglycemia occurred at very low rates in both groups (1.16% vs 0.35%, P = .051). Unadjusted 30-day mortality was lower in the 80-110 mg/dL group (4.3% vs 9.2%, P < .001). This remained after propensity score-adjusted regression (OR, 0.65; 95% CI, 0.43-0.98; P = .04).

CONCLUSIONS

Tight glucose control can be achieved with low rates of severe hypoglycemia and is associated with decreased 30-day mortality in a cohort of largely patients in cardiac ICUs. Although such findings should not be used to guide clinical practice at present, the use of tight glucose control should be reexamined using a protocol that has low rates of severe hypoglycemia.

A Bedside Computerized Decision-Support Tool for Intravenous Insulin Infusion Management in Critically Ill Patients

Intravenous (IV) insulin infusions using a validated protocol are the recommended method for blood glucose control in critically ill patients. Computerized decision-support tools improve quality over manual paper-based protocols. However, nonproprietary computerized tools targeting the recommended blood glucose range of 140-180 mg/dL are not readily available. A bedside computerized decision-support tool was developed at a US Department of Veterans Affairs health system to assist the nursing staff with the management of patients requiring IV insulin infusion. Initial evaluation showed that the tool was useful in the safe and effective management of an IV insulin infusion protocol for blood glucose control targeting the updated blood glucose range.

A systematic scoping review on the consequences of stress-related hyperglycaemia

Background

Stress-related hyperglycaemia (SHG) is commonly seen in acutely ill patients and has been associated with poor outcomes in many studies performed in different acute care settings. We aimed to review the available evidence describing the associations between SHG and different outcomes in acutely ill patients admitted to an ICU. Study designs, populations, and outcome measures used in observational studies were analysed.

Methods

We conducted a systematic scoping review of observational studies following the Joanna Briggs methodology. Medline, Embase, and the Cochrane Library were searched for publications between January 2000 and December 2015 that reported on SHG and mortality, infection rate, length of stay, time on ventilation, blood transfusions, renal replacement therapy, or acquired weakness.

Results

The search yielded 3,063 articles, of which 43 articles were included (totalling 536,476 patients). Overall, the identified studies were heterogeneous in study conduct, SHG definition, blood glucose measurements and monitoring, treatment protocol, and outcome reporting. The most frequently reported outcomes were mortality (38 studies), ICU and hospital length of stay (23 and 18 studies, respectively), and duration of mechanical ventilation (13 studies). The majority of these studies (40 studies) compared the reported outcomes in patients who experienced SHG with those who did not. Fourteen studies (35.9%) identified an association between hyperglycaemia and increased mortality (odds ratios ranging from 1.13 to 2.76). Five studies identified hyperglycaemia as an independent risk factor for increased infection rates, and one identified it as an independent predictor of increased ICU length of stay.

Discussion

SHG was consistently associated with poor outcomes. However, the wide divergences in the literature mandate standardisation of measuring and monitoring SHG and the creation of a consensus on SHG definition. A better comparability between practices will improve our knowledge on SHG consequences and management.

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.

Evaluating the Implementation of the EndoTool Glycemic Control Software System

PURPOSE

The purpose of this study was to compare achievement of glycemic control on insulin drips before and after the implementation of EndoTool, a glucose management software system used in a community hospital setting.

METHODS

A retrospective chart review was performed of patients on an insulin drip who were managed before and after implementation of the EndoTool software. Fifty patients were selected for each group. Statistical analyses were run to compare metrics gathered between groups.

RESULTS

Patients in the standard care group were on an insulin drip for an average of 23.9 hours compared to 20.9 hours in the EndoTool group (P = 0.38). Hypoglycemia occurred at an average rate of 0.036 events per patient in the standard group and 0.007 events per patient in the EndoTool group (P = 0.17). The average rate of hyperglycemia was 0.358 events per patient in the standard group and 0.283 events per patient in the EndoTool group (P = 0.25). The average time to achieve the blood glucose target was 2.78 and 3.67 hours in the standard and EndoTool groups, respectively (P = 0.27). Total patient values were within target range 45.2% of the time in the standard care group and 47.3% of the time in the EndoTool group (P = 0.71).

CONCLUSION

Analysis of the implementation of EndoTool in the community hospital setting found no statistically significant differences between groups, although rates of hypo- and hyperglycemia showed a trend toward improved safety in the EndoTool group. These results could be attributed to the conservative parameters the hospital set in the initial phase of EndoTool implementation.

Impact of a Paper-Based Dynamic Insulin Infusion Protocol on Glycemic Variability, Time in Target, and Hypoglycemic Risk: A Stepped Wedge Trial in Medical Intensive Care Unit Patients

BACKGROUND

Stress-induced hyperglycemia is a common feature of intensive care unit (ICU) patients. Besides mean blood glucose (BG) level, glucose variability and hypoglycemia have been highlighted as independent predictors of ICU and hospital mortality. Recent ICU recommendations suggest using insulin infusion protocols that can minimize glucose variability and hypoglycemic risk. Our aim was to assess the efficacy, safety, and acceptance by nurses of a paper-based simple dynamic insulin protocol compared with those by nurses of a paper-based static protocol.

METHODS

This is a 1 year stepped-wedge study that compared a static sliding scale protocol (SP - static protocol) with a validated dynamic paper-based intravenous insulin infusion protocol (DP - dynamic protocol) in medical ICU patients of a single university hospital. Patients with stress-induced hyperglycemia >9.9 mmol/L and ≥48 h intravenous insulin infusion were included in this trial.

RESULTS

One hundred thirty-one patients were included and received continuous intravenous insulin infusion managed with SP (n = 65) or DP (n = 66). Glucose variability was significantly higher in the SP group than in the DP group (mean average glucose excursion index: 0.90 [0.00-1.91] mmol/L vs. 0.00 [0.00-0.90] mmol/L, respectively; P = 0.001). The percentage of time spent in the target range (7.7-9.9 mmol/L) was lower in the SP group than in the DP group (42.5% [28.8%-54.2%] vs. 47.5% [36.6%-57.1%]; P = 0.037). Low BG (<4.4 mmol/L) and hypoglycemia (<3.3 mmol/L) were more frequent in the SP group than in the DP group. According to a satisfaction survey, this protocol was well accepted by nurses.

CONCLUSIONS

Our simple and feasible paper-based, dynamic insulin infusion protocol reduced glycemic variability and hypoglycemic risk in a medical ICU.

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.

Comparison of an Electronic Glycemic Management System Versus Provider-Managed Subcutaneous Basal Bolus Insulin Therapy in the Hospital Setting

BACKGROUND

American Diabetes Association (ADA) guidelines recommend a basal bolus correction insulin regimen as the preferred method of treatment for non-critically ill hospitalized patients. However, achieving ADA glucose targets safely, without hypoglycemia, is challenging. In this study we evaluated the safety and efficacy of basal bolus subcutaneous (SubQ) insulin therapy managed by providers compared to a nurse-directed Electronic Glycemic Management System (eGMS).

METHOD

This retrospective crossover study evaluated 993 non-ICU patients treated with subcutaneous basal bolus insulin therapy managed by a provider compared to an eGMS. Analysis compared therapy outcomes before Glucommander (BGM), during Glucommander (DGM), and after Glucommander (AGM) for all patients. The blood glucose (BG) target was set at 140-180 mg/dL for all groups. The safety of each was evaluated by the following: (1) BG averages, (2) hypoglycemic events <40 and <70 mg/dL, and (3) percentage of BG in target.

RESULT

Percentage of BG in target was BGM 47%, DGM 62%, and AGM 36%. Patients' BGM BG average was 195 mg/dL, DGM BG average was 169 mg/dL, and AGM BG average was 174 mg/dL. Percentage of hypoglycemic events <70 mg/dL was 2.6% BGM, 1.9% DGM, and 2.8% AGM treatment.

CONCLUSION

Patients using eGMS in the DGM group achieved improved glycemic control with lower incidence of hypoglycemia (<40 mg/dL and <70 mg/dl) compared to both BGM and AGM management with standard treatment. These results suggest that an eGMS can safely maintain glucose control with less hypoglycemia than basal bolus treatment managed by a provider.

USE OF A COMPUTER-GUIDED GLUCOSE MANAGEMENT SYSTEM TO IMPROVE GLYCEMIC CONTROL AND ADDRESS NATIONAL QUALITY MEASURES: A 7-YEAR, RETROSPECTIVE OBSERVATIONAL STUDY AT A TERTIARY CARE TEACHING HOSPITAL

OBJECTIVE

Inpatient hyperglycemia, hypoglycemia, and glucose variability are associated with increased mortality. The use of an electronic glucose management system (eGMS) to guide intravenous (IV) insulin infusion has been found to significantly improve blood glucose (BG) control. This retrospective observational study evaluated the 7-year (January 2009-December 2015) impact of the EndoTool^® eGMS in intensive and intermediate units at Vidant Medical Center, a 900-bed tertiary teaching hospital.

METHODS

Patients assigned to eGMS had indications for IV insulin infusion, including uncontrolled diabetes, stress hyperglycemia, and/or postoperative BG levels >140 mg/dL. This study evaluated time required to achieve BG control (<180 mg/dL; <140 mg/dL for cardiovascular surgery patients); hypoglycemia incidence (<70 and <40 mg/dL); glucose variability (assessed by SD and coefficient of variation percentage [CV%]); excursions (BG levels >180 mg/dL after control attained); and the impact of eGMS on hospital-acquired condition (HAC)-8 rates.

RESULTS

Data were available for all treated patients (492,078 BG readings from 16,850 patients). With eGMS, BG levels were brought to target within 1.5 to 2.3 hours (4.5 to 4.8 hours for cardiovascular patients). Minimal hypoglycemia was observed (BG values <70 mg/dL, 0.93%; <40 mg/dL, 0.03%), and analysis of variance of BG values <70 mg/dL showed significant reductions over time in hypoglycemia frequency, from 1.04% in 2009 to 0.46% in 2015 (P<.0001). The CV% per patient visit was 26.5 (±12.9)%, and 4% of patients experienced glucose excursions (defined as BG levels >180 mg/dL once control was attained). HAC-8 rates were reduced from 0.083 per 1,000 patients (2008) to 0.032 per 1,000 patients (2011).

CONCLUSION

The use of eGMS resulted in rapid, effective control of inpatient BG levels, including significantly reduced hypoglycemia rates.

ABBREVIATIONS

BG = blood glucose CMS = Centers for Medicare and Medicaid Services CV = coefficient of variation CV% = coefficient of variation percentage eGMS = electronic glucose management system GV = glycemic variability HAC = Hospital-Acquired Condition ICU = intensive care unit IU = intermediate unit IV = intravenous LOS = length of stay VMC = Vidant Medical Center.

Glucose management in the intensive care unit: are we looking for the right sweet spot?

In a recently published issue of Critical Care Medicine, Kar and colleagues investigated glucose management of critically ill patients with type 2 diabetes. In this commentary, we discuss the challenges of investigating glucose control in the critically ill, why so many internally valid studies in this field lead to conflicting results, and the obstacles preventing investigators from reaching a conclusive answer.

An in silico method to identify computer-based protocols worthy of clinical study: An insulin infusion protocol use case

OBJECTIVE

Develop an efficient non-clinical method for identifying promising computer-based protocols for clinical study. An in silico comparison can provide information that informs the decision to proceed to a clinical trial. The authors compared two existing computer-based insulin infusion protocols: eProtocol-insulin from Utah, USA, and Glucosafe from Denmark.

MATERIALS AND METHODS

The authors used eProtocol-insulin to manage intensive care unit (ICU) hyperglycemia with intravenous (IV) insulin from 2004 to 2010. Recommendations accepted by the bedside clinicians directly link the subsequent blood glucose values to eProtocol-insulin recommendations and provide a unique clinical database. The authors retrospectively compared in silico 18,984 eProtocol-insulin continuous IV insulin infusion rate recommendations from 408 ICU patients with those of Glucosafe, the candidate computer-based protocol. The subsequent blood glucose measurement value (low, on target, high) was used to identify if the insulin recommendation was too high, on target, or too low.

RESULTS

Glucosafe consistently provided more favorable continuous IV insulin infusion rate recommendations than eProtocol-insulin for on target (64% of comparisons), low (80% of comparisons), or high (70% of comparisons) blood glucose. Aggregated eProtocol-insulin and Glucosafe continuous IV insulin infusion rates were clinically similar though statistically significantly different (Wilcoxon signed rank test P = .01). In contrast, when stratified by low, on target, or high subsequent blood glucose measurement, insulin infusion rates from eProtocol-insulin and Glucosafe were statistically significantly different (Wilcoxon signed rank test, P < .001), and clinically different.

DISCUSSION

This in silico comparison appears to be an efficient nonclinical method for identifying promising computer-based protocols.

CONCLUSION

Preclinical in silico comparison analytical framework allows rapid and inexpensive identification of computer-based protocol care strategies that justify expensive and burdensome clinical trials.

Population-Specific Models of Glycemic Control in Intensive Care: Towards a Simulation-Based Methodology for Protocol Optimization

Stress-induced hyperglycemia is common in critically ill patients, where elevated blood glucose and glycemic variability have been found to contribute to infection, slow wound healing, and short-term mortality. Early clinical studies demonstrated improvement in mortality and morbidity resulting from intensive insulin therapy targeting euglycemia. Follow-up clinical studies have shown mixed results suggesting that the risk of hypoglycemia may outweigh the benefits of aggressive glycemic control. None of the prior studies clarify whether euglycemic targets are in themselves harmful, or if the danger lies in the inadequacy of the available methods for achieving desired glycemic outcomes. In this paper, we use a recently developed simulation model of stress hyperglycemia to demonstrate that given an insulin protocol glycemic outcomes are specific to the patient population under consideration, and that there is a need to optimize insulin therapy at the population level. Next, we use the simulator to demonstrate that the performance of Adaptive Proportional Feedback (APF), a popular format for computerized insulin therapy, is sensitive to its parameters, especially to the parameters that govern the aggressiveness of adaptation. Finally, we propose a framework for simulation-based protocol optimization using an objective function that penalizes below-range deviations more heavily than comparable deviations above.

Blood glucose response to rescue dextrose in hypoglycemic, critically ill patients receiving an insulin infusion

BACKGROUND

There is inadequate guidance for clinicians on selection of the optimal dextrose 50% (D50W) dose for hypoglycemia correction in critically ill patients.

OBJECTIVE

The purpose of this study was to determine the blood glucose (BG) response to D50W in critically ill patients.

METHODS

A retrospective analysis was conducted of critically ill patients who received D50W for hypoglycemia (BG < 70 mg/dL) while on an insulin infusion. The primary objective of this study was to determine the BG response to D50W. The relationship between participant characteristics and the dose-adjusted change in BG following D50W was analyzed using simple and multiple linear mixed-effects models.

RESULTS

There were 470 hypoglycemic events (BG < 70 mg/dL) corrected with D50W. The overall median BG response was 4.0 (2.53, 6.08) mg/dL per gram of D50W administered. Administration of D50W per protocol resulted in 32 episodes of hyperglycemia (BG > 150 mg/dL), resulting in a 6.8% rate of overcorrection; 49% of hypoglycemic episodes (230/470) corrected to a BG >100 mg/dL. A multivariable GEE analysis showed a significantly higher BG response in participants with diabetes (0.002) but a lower response in those with recurrent hypoglycemia (P = 0.049). The response to D50W increased with increasinginsulin infusion rate (P = 0.022). Burn patients experienced a significantly larger BG response compared with cardiac, medical, neurosurgical, or surgical patients.

CONCLUSIONS

The observed median effect of D50W on BG was approximately 4 mg/dL per gram of D50W administered. Application of these data may aid in rescue protocol development that may reduce glucose variability associated with hypoglycemic episodes and the correction.

Time in blood glucose range 70 to 140 mg/dl >80% is strongly associated with increased survival in non-diabetic critically ill adults

Introduction

Hyperglycemia, hypoglycemia and increased glucose variability are independently associated with increased risk of death in critically ill adults. The relationship between time in targeted blood glucose range (TIR) and mortality is not well described and may be a factor that has confounded the results of the major interventional trials of intensive insulin therapy.

Methods

We conducted a retrospective analysis of prospectively collected data involving 3,297 patients with intensive care unit (ICU) lengths of stay (LOS) of ≥1.0 day who were admitted between 1 January 2009 and 31 December 2013 to a single mixed medical-surgical ICU. We investigated the relationship between TIR 70 to 140 mg/dl with mortality and compared outcomes of non-diabetics (NON) and individuals with diabetes mellitus (DM), including stratifying by TIR above (TIR-hi) and below (TIR-lo) the median value for the NON and DM groups.

Results

There were 85,799 blood glucose (BG) values for the NON group and 32,651 for the DM group, and we found that 75.5% and 54.8%, respectively, were between 70 and 140 (P <0.0001). The median (interquartile range) TIR (%) values for the NON and DM groups were 80.6% (61.4% to 94.0%) and 55.0% (35.5% to 71.1%), respectively (P <0.0001). For the NON group, mortality was 8.47% and 15.71% for TIR-hi and TIR-lo, respectively (P <0.0001). For the DM group, mortality was 16.09% and 14.44% for TIR-hi and TIR-lo, respectively (P = NS). We observed similar relationships for the NON group when we stratified by ICU LOS or severity of illness, especially in the most severely ill patients. There was a cumulative interaction of indices of hypoglycemia, hyperglycemia or glucose variability with TIR. Multivariable analysis demonstrated, for the NON group, that TIR-hi was independently associated with increased survival (P =0.0019). For the NON group, the observed-to-expected mortality ratios for TIR-hi and TIR-lo, based on Acute Physiology and Chronic Health Evaluation IV methodology, were 0.53 and 0.78, respectively. In contrast, among those in the DM group, there was no clear relationship between TIR 70 to 140 mg/dl and survival.

Conclusions

Independently of ICU LOS and severity of illness, TIR 70 to 140 mg/dl >80% is strongly associated with survival in critically ill patients without diabetes. These findings have implications for the design of clinical protocols for glycemic control in critically ill patients as well for the design of future interventional trials of intensive insulin therapy.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-015-0908-7) contains supplementary material, which is available to authorized users.

The impact of measurement frequency on the domains of glycemic control in the critically ill--a Monte Carlo simulation

The role of blood glucose (BG) measurement frequency on the domains of glycemic control is not well defined. This Monte Carlo mathematical simulation of glycemic control in a cohort of critically ill patients modeled sets of 100 patients with simulated BG-measuring devices having 5 levels of measurement imprecision, using 2 published insulin infusion protocols, for 200 hours, with 3 different BG-measurement intervals-15 minutes (Q15'), 1 hour (Q1h), and 2 hours (Q2h)-resulting in 1,100,000 BG measurements for 3000 simulated patients. The model varied insulin sensitivity, initial BG value and rate of gluconeogenesis. The primary outcomes included rates of hyperglycemia (BG > 180 mg/dL), hypoglycemia (BG < 70 and 40 mg/dL), proportion of patients with elevated glucose variability (within-patient coefficient of variation [CV] > 20%), and time in range (BG ranges 80-150 mg/dL and 80-180 mg/dL). Percentages of hyperglycemia, hypoglycemia at both thresholds, and patients with elevated glucose variability as well as time outside glycemic targets were substantially higher in simulations with measurement interval Q2h compared to those with measurement interval Q1h and moderately higher in simulations with Q1h than in those with Q15'. Higher measurement frequency mitigated the deleterious effect of high measurement imprecision, defined as CV ≥ 15%. This Monte Carlo simulation suggests that glycemic control in critically ill patients is more optimal with a BG measurement interval no longer than 1h, with further benefit obtained with use of measurement interval of 15'. These findings have important implications for the development of glycemic control standards.

Insulin infusion therapy in critically ill patients

While dysglycemia (hyperglycemia, hypoglycemia and glucose variability) is clearly associated with increased mortality in critically ill patients, target range of blood glucose control remains controversial. Standardized insulin infusion protocols constitute the basis of treatment of these patients. The choice of protocol and its implementation is a great challenge. In this article, we review the published data to help define the essential elements that compose a good protocol and apply the right conditions to make it safe and effective.

The benefits of tight glycemic control in critical illness: Sweeter than assumed?

Hyperglycemia has long been observed amongst critically ill patients and associated with increased mortality and morbidity. Tight glycemic control (TGC) is the clinical practice of controlling blood glucose (BG) down to the “normal” 4.4–6.1 mmol/L range of a healthy adult, aiming to avoid any potential deleterious effects of hyperglycemia. The ground-breaking Leuven trials reported a mortality benefit of approximately 10% when using this technique, which led many to endorse its benefits. In stark contrast, the multi-center normoglycemia in intensive care evaluation–survival using glucose algorithm regulation (NICE-SUGAR) trial, not only failed to replicate this outcome, but showed TGC appeared to be harmful. This review attempts to re-analyze the current literature and suggests that hope for a benefit from TGC should not be so hastily abandoned. Inconsistencies in study design make a like-for-like comparison of the Leuven and NICE-SUGAR trials challenging. Inadequate measures preventing hypoglycemic events are likely to have contributed to the increased mortality observed in the NICE-SUGAR treatment group. New technologies, including predictive models, are being developed to improve the safety of TGC, primarily by minimizing hypoglycemia. Intensive Care Units which are unequipped in trained staff and monitoring capacity would be unwise to attempt TGC, especially considering its yet undefined benefit and the deleterious nature of hypoglycemia. International recommendations now advise clinicians to ensure critically ill patients maintain a BG of <10 mmol/L. Despite encouraging evidence, currently we can only speculate and remain optimistic that the benefit of TGC in clinical practice is sweeter than assumed.

Glucose Meter Use in the Intensive Care Unit: Much Ado About Something

Glucose meters are a fast and convenient way to measure circulating blood glucose. Like many technologies in healthcare, the use of glucose meters within the hospital has evolved significantly over the last few decades. This change has been driven predominantly by changes in the approach to glycemic control for critically ill patients. Both glycemic control in the intensive care unit (ICU), and use of glucose meters to manage insulin dosing during glycemic control, are likely to remain controversial topics in the years to come. This review will elaborate on the evidence for and against use of glucose meters in the ICU to monitor glucose concentrations during glycemic control, and provide some tips for point of care programs on how to evaluate glucose monitors for this purpose.

Glucose control in intensive care: usability, efficacy and safety of Space GlucoseControl in two medical European intensive care units

BACKGROUND

The Space GlucoseControl system (SGC) is a nurse-driven, computer-assisted device for glycemic control combining infusion pumps with the enhanced Model Predictive Control algorithm (B. Braun, Melsungen, Germany). We aimed to investigate the performance of the SGC in medical critically ill patients.

METHODS

Two open clinical investigations in tertiary centers in Graz, Austria and Zurich, Switzerland were performed. Efficacy was assessed by percentage of time within the target range (4.4-8.3 mmol/L; primary end point), mean blood glucose, and sampling interval. Safety was assessed by the number of hypoglycemic episodes (≤2.2 mmol/L) and the percentage of time spent below this cutoff level. Usability was analyzed with a standardized questionnaire given to involved nursing staff after the trial.

RESULTS

Forty medical critically ill patients (age, 62 ± 15 years; body mass index, 30.0 ± 8.9 kg/m2; APACHE II score, 24.8 ± 5.4; 27 males; 8 with diabetes) were included for a period of 6.5 ± 3.7 days (n = 20 in each center). The primary endpoint (time in target range 4.4 to 8.3 mmol/l) was reached in 88.3% ± 9.3 of the time and mean arterial blood glucose was 6.7 ± 0.4 mmol/l. The sampling interval was 2.2 ± 0.4 hours. The mean daily insulin dose was 87.2 ± 64.6 IU. The adherence to the given insulin dose advice was high (98.2%). While the percentage of time spent in a moderately hypoglycemic range (2.2 to 3.3 mmol/L) was low (0.07 ± 0.26% of the time), one severe hypoglycemic episode (<2.2 mmol/L) occurred (2.5% of patients or 0.03% of glucose readings).

CONCLUSIONS

SGC is a safe and efficient method to control blood glucose in critically ill patients as assessed in two European medical intensive care units.

Innovative designs for the smart ICU: Part 3: Advanced ICU informatics

This third and final installment of this series on innovative designs for the smart ICU addresses the steps involved in conceptualizing, actualizing, using, and maintaining the advanced ICU informatics infrastructure and systems. The smart ICU comprehensively and electronically integrates the patient in the ICU with all aspects of care, displays data in a variety of formats, converts data to actionable information, uses data proactively to enhance patient safety, and monitors the ICU environment to facilitate patient care and ICU management. The keys to success in this complex informatics design process include an understanding of advanced informatics concepts, sophisticated planning, installation of a robust infrastructure capable of both connectivity and interoperability, and implementation of middleware solutions that provide value. Although new technologies commonly appear compelling, they are also complicated and challenging to incorporate within existing or evolving hospital informatics systems. Therefore, careful analysis, deliberate testing, and a phased approach to the implementation of innovative technologies are necessary to achieve the multilevel solutions of the smart ICU.

A quadruply-asymmetric sigmoid to describe the insulin-glucose relationship during intravenous insulin infusion

For hospitalized patients requiring intravenous insulin therapy, an objective is to quantify the intravenous insulin infusion rate (IR) across the domain of blood glucose (BG) values at a single timepoint. The algorithm parameters include low BG (70 mg/dL), critical high BG, target range BG limits, and maintenance rate (MR) of insulin infusion, which, after initialization, depends on rate of change of blood glucose, previous IR, and other inputs. The restraining rate (RR) is a function of fractional completeness of ascent of BG (FCABG) from BG 70 mg/dL to target. The correction rate (CR) is a function of fractional elevation of BG (FEBG), in comparison to elevation of a critical high BG, above target. IR = RR + CR. The proposed mathematical model describing a sigmoidal relationship between IR and BG may offer a safety advantage over the linear relationship currently employed in some intravenous glucose management systems.

The Association of Glucose Variability and Home Discharge Among Survivors of Critical Illness Managed With a Computerized Decision-Support Tool for Glycemic Control

In-intensive care unit (ICU) glucose variability (GV) is associated with increased mortality. However, the impact of GV on hospital survivors' morbidity and associated changes in destination at time of hospital discharge are unknown. We studied a retrospective patient cohort in a medical/surgical ICU, requiring insulin infusion, using computer-guided insulin dosing software. Standard deviation (Glu_SD) and coefficient of variation (Glu_CV) were used as GV measures. We examined rates of home discharge (H) in the whole cohort and selected subgroups across GV quartiles, between patients with and without H, determinants of H, and determinants of GV and its association with patients' ICU length of stay (LOS). A total of 351 patients met study criteria. The association of GV and H varied among examined subgroups. H increased with GV quartile (Glu_SD; P = .004). GV was higher in patients with H than non-H (Glu_SD 36.1 vs 30.0 mg/dl, respectively; P = .002). Increased GV was not a predictor of reduced H on multivariate analysis. GV was inversely associated with patients' ICU LOS in all examined subgroups. Increased number of hypoglycemic events and time to attain target glycemia were independent predictors of reduced H. GV was not associated with adverse impact on H in the present cohort, and its prognostic impact should be considered in the context of ICU LOS of examined patient populations. Further studies are needed to examine the morbidity effects of GV and other glycemia-related measures among hospital survivors of critical illness across varying ICU populations, glycemic control approaches, and glycemic targets.

Software-guided insulin dosing: tight glycemic control and decreased glycemic derangements in critically ill patients

OBJECTIVE

To determine whether glycemic derangements are more effectively controlled using software-guided insulin dosing compared with paper-based protocols.

PATIENTS AND METHODS

We prospectively evaluated consecutive critically ill patients treated in a tertiary hospital surgical intensive care unit (ICU) between January 1 and June 30, 2008, and between January 1 and September 30, 2009. Paper-based protocol insulin dosing was evaluated as a baseline during the first period, followed by software-guided insulin dosing in the second period. We compared glycemic metrics related to hyperglycemia, hypoglycemia, and glycemic variability during the 2 periods.

RESULTS

We treated 110 patients by the paper-based protocol and 87 by the software-guided protocol during the before and after periods, respectively. The mean ICU admission blood glucose (BG) level was higher in patients receiving software-guided intensive insulin than for those receiving paper-based intensive insulin (181 vs 156 mg/dL; P=.003, mean of the per-patient mean). Patients treated with software-guided intensive insulin had lower mean BG levels (117 vs 135 mg/dL; P=.0008), sustained greater time in the desired BG target range (95-135 mg/dL; 68% vs 52%; P=.0001), had less frequent hypoglycemia (percentage of time BG level was <70 mg/dL: 0.51% vs 1.44%; P=.04), and showed decreased glycemic variability (BG level per-patient standard deviation from the mean: ±29 vs ±42 mg/dL; P=.01).

CONCLUSION

Surgical ICU patients whose intensive insulin infusions were managed using the software-guided program achieved tighter glycemic control and fewer glycemic derangements than those managed with the paper-based insulin dosing regimen.

Moderate glucose control is associated with increased mortality compared with tight glucose control in critically ill patients without diabetes

BACKGROUND

Optimal glucose management in the ICU remains unclear. In 2009, many clinicians at Intermountain Healthcare selected a moderate glucose control (90-140 mg/dL) instead of tight glucose control (80-110 mg/dL). We hypothesized that moderate glucose control would affect patients with and without preexisting diabetes differently.

METHODS

We performed a retrospective cohort analysis of all patients treated with eProtocol-insulin from November 2006 to March 2011, stratifying for diabetes. We performed multivariate logistic regression for 30-day mortality with covariates of age, modified APACHE (Acute Physiology and Chronic Health Evaluation) II score, Charlson Comorbidity score, and target glucose.

RESULTS

We studied 3,529 patients in 12 different ICUs in eight different hospitals. Patients with diabetes had higher mean glucose (132 mg/dL vs 124 mg/dL) and greater glycemic variability (SD = 41 mg/dL vs 29 mg/dL) than did patients without diabetes (P &lt; .01 for both comparisons). Tight glucose control was associated with increased frequency of moderate and severe hypoglycemia (30.3% and 3.6%) compared with moderate glucose control (14.3% and 2.0%, P &lt; .01 for both). Multivariate analysis demonstrated that the moderate glucose target was independently associated with increased risk of mortality in patients without diabetes (OR, 1.36; 95% CI, 1.01-1.84; P = .05) but decreased risk of mortality in patients with diabetes (OR, 0.65; 95% CI, 0.45-0.93; P = .01).

CONCLUSIONS

Moderate glucose control (90-140 mg/dL) may confer greater mortality in critically ill patients without diabetes compared with tight glucose control (80-110 mg/dL). A single glucose target does not appear optimal for all critically ill patients. These data have important implications for the design of future interventional trials as well as for the glycemic management of critically ill patients.

The future is now: software-guided intensive insulin therapy in the critically ill

Since the development of intensive insulin therapy for the critically ill adult, tight glycemic control (TGC) has become increasingly complicated to apply and achieve. Software-guided (SG) algorithms for insulin dosing represent a new method to achieve euglycemia in critical illness. We provide an overview of the state of SG TGC with an eye to the future. The current milieu is disorganized, with little research that incorporates newer variables of dysglycemia, such as glycemic variability. To develop and implement better algorithms, scientists, programmers, and clinicians need to standardize measurements and variables.