Circadian rhythm of blood glucose values in critically ill patients

Daily variation in blood glucose levels during continuous enteral nutrition in patients on the intensive care unit: a retrospective observational study

BACKGROUND

The circadian timing system coordinates daily cycles in physiological functions, including glucose metabolism and insulin sensitivity. Here, the aim was to characterise the 24-h variation in glucose levels in critically ill patients during continuous enteral nutrition after controlling for potential sources of bias.

METHODS

Time-stamped clinical data from adult patients who stayed in the Intensive Care Unit (ICU) for at least 4 days and received enteral nutrition were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Linear mixed-effects and XGBoost modelling were used to determine the effect of time of day on blood glucose values.

FINDINGS

In total, 207,647 glucose measurements collected during enteral nutrition were available from 6,929 ICU patients (3,948 males and 2,981 females). Using linear mixed-effects modelling, time of day had a significant effect on blood glucose levels (p < 0.001), with a peak of 9.6 [9.5-9.6; estimated marginal means, 95% CI] mmol/L at 10:00 in the morning and a trough of 8.6 [8.5-8.6] mmol/L at 02:00 at night. A similar impact of time of day on glucose levels was found with the XGBoost regression model.

INTERPRETATION

These results revealed marked 24-h variation in glucose levels in ICU patients even during continuous enteral nutrition. This 24-h pattern persists after adjustment for potential sources of bias, suggesting it may be the result of endogenous biological rhythmicity.

FUNDING

This work was supported by a VENI grant from the Netherlands Organisation for Health Research and Development (ZonMw), an institutional project grant, and by the Dutch Research Council (NWO).

Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges

A wealth of data is available from electronic health records (EHR) that are collected as part of routine clinical care in hospitals worldwide. These rich, longitudinal data offer an attractive object of study for the field of circadian medicine, which aims to translate knowledge of circadian rhythms to improve patient health. This narrative review aims to discuss opportunities for EHR in studies of circadian medicine, highlight the methodological challenges, and provide recommendations for using these data to advance the field. In the existing literature, we find that data collected in real-world clinical settings have the potential to shed light on key questions in circadian medicine, including how 24-hour rhythms in clinical features are associated with-or even predictive of-health outcomes, whether the effect of medication or other clinical activities depend on time of day, and how circadian rhythms in physiology may influence clinical reference ranges or sampling protocols. However, optimal use of EHR to advance circadian medicine requires careful consideration of the limitations and sources of bias that are inherent to these data sources. In particular, time of day influences almost every interaction between a patient and the healthcare system, creating operational 24-hour patterns in the data that have little or nothing to do with biology. Addressing these challenges could help to expand the evidence base for the use of EHR in the field of circadian medicine.

Circadian rhythm in critically ill patients: Insights from the eICU Database

Objective

To investigate the circadian variation among critically ill patients and its association with clinical characteristics and survival to hospital discharge in a large population of patients in the intensive care unit (ICU).

Methods

Circadian variation was analyzed by fitting cosinor models to hourly blood pressure (BP) measurements in patients of the eICU Collaborative Research Database with an ICU length of stay of at least 3 days. We calculated the amplitude of the 24-hour circadian rhythm and time of the day when BP peaked. We determined the association between amplitude and time of peak BP and severity of illness, medications, mechanical intubation, and survival to hospital discharge.

Results

Among 23,355 patients (mean age 65 years, 55% male), the mean amplitude of the 24-hour rhythm was 4.5 ± 3.1 mm Hg. Higher APACHE-IV scores, sepsis, organ dysfunction, and mechanical ventilation were associated with a lower amplitude and a shifted circadian rhythm (P < .05 for all). The timing of the BP peak was associated with in-hospital mortality (P < .001). Higher BP amplitude was associated with shorter ICU (2 mm Hg amplitude: 7.0 days, 8 mm Hg amplitude: 6.7 days) and hospital (2 mm Hg amplitude: 11.8 days, 8 mm Hg amplitude: 11.3 days) lengths of stay and lower in-hospital mortality (2 mm Hg amplitude: 18.2%, 8 mm Hg amplitude: 15.2%) (P < .001 for all).

Conclusion

The 24-hour rhythm is dampened and phase-shifted in sicker patients and those on mechanical ventilation, vasopressors, or inotropes. Dampening and phase shifting are associated with a longer length of stay and higher in-hospital mortality.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Impact of Mean Arterial Pressure Fluctuation on Mortality in Critically Ill Patients

OBJECTIVE

The purpose of this study was to investigate the association between mean arterial pressure fluctuations and mortality in critically ill patients admitted to the ICU.

DESIGN

Retrospective cohort.

SETTING

All adult ICUs at a tertiary care hospital.

PATIENTS

All adult patients with complete mean arterial pressure records were selected for analysis in the Multiparameter Intelligent Monitoring in Intensive Care II database. Patients in the external cohort were newly recruited adult patients in the Medical Information Mart for Intensive Care III database.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The records of 8,242 patients were extracted. Mean arterial pressure fluctuation was calculated as follows: (mean nighttime mean arterial pressure - mean daytime mean arterial pressure)/mean arterial pressure. Patients were divided into two groups according to the degree of mean arterial pressure fluctuation: group A (between -5% and 5%) and group B (<-5% and >5%). The endpoints of this study were ICU and hospital mortality. Patients in group A (n = 4,793) had higher ICU and hospital mortality than those in group B (n = 3,449; 11.1% vs 8.1%, p < 0.001 and 13.8% vs 10.1%, p < 0.001, respectively). After adjusting for other covariates, the mean arterial pressure fluctuations between -5% and 5% were significantly correlated with ICU mortality (odds ratio, 1.296; 95% CI, 1.103-1.521; p = 0.002) and hospital mortality (odds ratio, 1.323; 95% CI, 1.142-1.531; p < 0.001). This relationship remained remarkable in patients with low or high Sequential Organ Failure Assessment scores in the sensitive analysis. Furthermore, external validation on a total of 4,502 individuals revealed that patients in group A still had significantly higher ICU (p < 0.001) and hospital mortality (p < 0.001) than those in group B.

CONCLUSIONS

The reduced mean arterial pressure fluctuation (within -5% and 5%) may be associated with ICU and hospital mortality in critically ill patients.

Dysglycemia in the critically ill patient: current evidence and future perspectives

Dysglycemia in critically ill patients (hyperglycemia, hypoglycemia, glycemic variability and time in range) is a biomarker of disease severity and is associated with higher mortality. However, this impact appears to be weakened in patients with previous diabetes mellitus, particularly in those with poor premorbid glycemic control; this phenomenon has been called "diabetes paradox". This phenomenon determines that glycated hemoglobin (HbA1c) values should be considered in choosing glycemic control protocols on admission to an intensive care unit and that patients' target blood glucose ranges should be adjusted according to their HbA1c values. Therefore, HbA1c emerges as a simple tool that allows information that has therapeutic utility and prognostic value to be obtained in the intensive care unit.

Post-operative stress hyperglycemia is a predictor of mortality in liver transplantation

BACKGROUND

A significant association is known between increased glycaemic variability and mortality in critical patients. To ascertain whether glycaemic profiles during the first week after liver transplantation might be associated with long-term mortality in these patients, by analysing whether diabetic status modified this relationship.

METHOD

Observational long-term survival study includes 642 subjects undergoing liver transplantation from July 1994 to July 2011. Glucose profiles, units of insulin and all variables with influence on mortality are analysed using joint modelling techniques.

RESULTS

Patients registered a survival rate of 85% at 1 year and 65% at 10 years, without differences in mortality between patients with and without diabetes. In glucose profiles, however, differences were observed between patients with and without diabetes: patients with diabetes registered lower baseline glucose values, which gradually rose until reaching a peak on days 2-3 and then subsequently declined, diabetic subjects started from higher values which gradually decreased across the first week. Patients with diabetes showed an association between mortality and age, Model for End-Stage Liver Disease score (MELD) score and hepatitis C virus; among non-diabetic patients, mortality was associated with age, body mass index, malignant aetiology, red blood cell requirements and parenteral nutrition. Glucose profiles were observed to be statistically associated with mortality among patients without diabetes (P = 0.022) but not among patients who presented with diabetes prior to transplantation (P = 0.689).

CONCLUSIONS

Glucose profiles during the first week after liver transplantation are different in patients with and without diabetes. While glucose profiles are associated with long-term mortality in patients without diabetes, after adjusting for potential confounding variables such as age, cause of transplantation, MELD, nutrition, immunosuppressive drugs, and units of insulin administered, this does not occur among patients with diabetes.

Poststroke glycemic variability increased recurrent cardiovascular events in diabetic patients

BACKGROUND AND PURPOSE

The association between blood glucose fluctuation and poststroke cardiovascular outcome has been largely unknown. This study attempted to evaluate whether initial glycemic variability increases cardiovascular events and mortality in diabetic patients with acute ischemic stroke.

METHODS

We recruited consecutive patients with acute ischemic stroke or transient ischemic attack from March 2005 to December 2014. A total of 674 patients with diabetes within 72 hours from stroke onset were included. The serum glucose levels were checked 4 times per day during the initial 3 hospital days. J-index, coefficients of variation and standard deviation were calculated for glycemic variability. Composite outcome (nonfatal stroke, nonfatal myocardial infarction, cardiovascular death) and all-cause mortality at 3 months were prospectively captured. Multivariable logistic regression analyses were done adjusting for covariates which can influence on cardiovascular outcomes.

RESULTS

Cardiovascular composite outcomes at 3 months were identified in 71 (10.5%): 11 (6.5%), 15 (8.9%), 18 (10.7%) and 27 (16.0%) in each J-index quartiles (P = .035). The highest quartile of J-index had significantly higher cardiovascular death (4.2%, 3.6%, 6.5% and 11.8%; P = .008). In multivariable logistic regression, age (odds ratio [OR] 1.045; 95% confidence interval [CI] 1.006-1.084), P = .021), NIH stroke scale (OR 1.078; 95% CI 1.024-1.134, P = .004), and the highest J-index (OR 12.058; 95% 1.890-76.912, P = .008) were significantly associated with 3-month cardiovascular composite outcome. Increased cardiovascular outcomes in highest J-index quartile were similar in both euglycemic and hyperglycemic groups.

CONCLUSION

The initial glycemic variability might increase cardiovascular events in acute ischemic stroke patients with diabetes.

Circadian rhythms in anesthesia and critical care medicine: potential importance of circadian disruptions

The rotation of the earth and associated alternating cycles of light and dark--the basis of our circadian rhythms--are fundamental to human biology and culture. However, it was not until 1971 that researchers first began to describe the molecular mechanisms for the circadian system. During the past few years, groundbreaking research has revealed a multitude of circadian genes affecting a variety of clinical diseases, including diabetes, obesity, sepsis, cardiac ischemia, and sudden cardiac death. Anesthesiologists, in the operating room and intensive care units, manage these diseases on a daily basis as they significantly affect patient outcomes. Intriguingly, sedatives, anesthetics, and the intensive care unit environment have all been shown to disrupt the circadian system in patients. In the current review, we will discuss how newly acquired knowledge of circadian rhythms could lead to changes in clinical practice and new therapeutic concepts.

Insulin effects on glucose tolerance, hypermetabolic response, and circadian-metabolic protein expression in a rat burn and disuse model

Insulin controls hyperglycemia after severe burns, and its use opposes the hypermetabolic response. The underlying molecular mechanisms are poorly understood, and previous research in this area has been limited because of the inadequacy of animal models to mimic the physiological effects seen in humans with burns. Using a recently published rat model that combines both burn and disuse components, we compare the effects of insulin treatment vs. vehicle on glucose tolerance, hypermetabolic response, muscle loss, and circadian-metabolic protein expression after burns. Male Sprague-Dawley rats were assigned to three groups: cage controls (n = 6); vehicle-treated burn and hindlimb unloading (VBH; n = 11), and insulin-treated burn and hindlimb unloading (IBH; n = 9). With the exception of cage controls, rats underwent a 40% total body surface area burn with hindlimb unloading, then IBH rats received 12 days of subcutaneous insulin injections (5 units·kg(-1)·day(-1)), and VBH rats received an equivalent dose of vehicle. Glucose tolerance testing was performed on day 14, after which blood and tissues were collected for analysis. Body mass loss was attenuated by insulin treatment (VBH = 265 ± 17 g vs. IBH = 283 ± 14 g, P = 0.016), and glucose clearance capacity was increased. Soleus and gastrocnemius muscle loss was decreased in the IBH group. Insulin receptor substrate-1, AKT, FOXO-1, caspase-3, and PER1 phosphorylation was altered by injury and disuse, with levels restored by insulin treatment in almost all cases. Insulin treatment after burn and during disuse attenuated the hypermetabolic response, increased glucose clearance, and normalized circadian-metabolic protein expression patterns. Therapies aimed at targeting downstream effectors may provide the beneficial effects of insulin without hypoglycemic risk.

Glucose sensing module - is it time to integrate it into real-time perioperative monitoring? An observational pilot study with subcutaneous sensors

AIMS

To explore the feasibility of subcutaneous continuous glucose monitoring (CGM) in perioperative settings and to evaluate the perioperative development of glycaemia in persons with diabetes mellitus or impaired glucose tolerance by means of CGM.

METHODS

Monitoring by means of Guardian REAL-Time CGMS (Medtronic, Nortridge, USA) in 20 perioperative periods. Sensor was inserted on the day before surgery and continued for 3 days with some exceptions.

RESULTS

Full implementation of the method was successful in the intensive care unit setting only. No electromagnetic interference and no side effects were found. The Wilcoxon signed-rank test revealed no significant difference between sensor and laboratory analyser values. Pearson's correlation coefficients of the values obtained by sensor and the Wellion Linus glucometer were 0.875 for the whole perioperative period, 0.866 for the intraoperative period and 0.903 for the first perioperative day. A decline in sensor accuracy on the 6(th) day was registered in one case. 16 monitored cases (80%) did not meet the criteria for safe plasma glucose range. Hypoglycaemia was found in 4 (20%) cases. There was an association between grade of the perioperative dysglycaemia and need for reoperation within the next 3 months. The most frequent perioperative glycaemic patterns are demonstrated.

CONCLUSION

Subcutaneous CGM is safe offering detailed insight into glucose homeostasis in the dynamic perioperative period. Laboratory confirmation of sensor plasma glucose concentration by approved laboratory analyser is still necessary. The potentional benefits of maintaining patients within a safe glucose range should be comfirmed by future studies.

Circadian rhythms: from basic mechanisms to the intensive care unit

OBJECTIVE

: Circadian rhythms are intrinsic timekeeping mechanisms that allow for adaptation to cyclic environmental changes. Increasing evidence suggests that circadian rhythms may influence progression of a variety of diseases as well as effectiveness and toxicity of drugs commonly used in the intensive care unit. In this perspective, we provide a brief review of the molecular mechanisms of circadian rhythms and its relevance to critical care. DATA SOURCES, STUDY SELECTION, DATA EXTRACTION, AND DATA SYNTHESIS:: Articles related to circadian rhythms and organ systems in normal and disease conditions were searched through the PubMed library with the goal of providing a concise review.

CONCLUSIONS

: Critically ill patients may be highly vulnerable to disruption of circadian rhythms as a result of the severity of their underlying diseases as well as the intensive care unit environment where noise and frequent therapeutic/diagnostic interventions take place. Further basic and clinical research addressing the importance of circadian rhythms in the context of critical care is warranted to develop a better understanding of the complex pathophysiology of critically ill patients as well as to identify novel therapeutic approaches for these patients.

Glycemic control in critically ill patients

Hyperglycemia is common in critically ill patients and can be caused by various mechanisms, including nutrition, medications, and insufficient insulin. In the past, hyperglycemia was thought to be an adaptive response to stress, but hyperglycemia is no longer considered a benign condition in patients with critical illnesses. Indeed, hyperglycemia can increase morbidity and mortality in critically ill patients. Correction of hyperglycemia may improve clinical outcomes. To date, a definite answer with regard to glucose management in general intensive care unit patients, including treatment thresholds and glucose target is undetermined. Meta-analyses of randomized controlled trials suggested no survival benefit of tight glycemic control and a significantly increased incidence of hypoglycemia. Studies have shown a J- or U-shaped relationship between average glucose values and mortality; maintaining glucose levels between 100 and 150 mg/dL was likely to be associated with the lowest mortality rates. Recent studies have shown glycemic control < 180 mg/dL is not inferior to near-normal glycemia in critically ill patients and is clearly safer. Glycemic variability is also an important aspect of glucose management in the critically ill patients. Higher glycemic variability may increase the mortality rate, even in patients with the same mean glucose level. Decreasing glucose variability is an important issue for glycemic control in critically ill patients. Continuous measurements with automatic closed-loop systems could be considered to ensure that blood glucose levels are controlled within a specific range and with minimal variability.

Interface design and human factors considerations for model-based tight glycemic control in critical care

INTRODUCTION

Tight glycemic control (TGC) has shown benefits but has been difficult to implement. Model-based methods and computerized protocols offer the opportunity to improve TGC quality and compliance. This research presents an interface design to maximize compliance, minimize real and perceived clinical effort, and minimize error based on simple human factors and end user input.

METHOD

The graphical user interface (GUI) design is presented by construction based on a series of simple, short design criteria based on fundamental human factors engineering and includes the use of user feedback and focus groups comprising nursing staff at Christchurch Hospital. The overall design maximizes ease of use and minimizes (unnecessary) interaction and use. It is coupled to a protocol that allows nurse staff to select measurement intervals and thus self-manage workload.

RESULTS

The overall GUI design is presented and requires only one data entry point per intervention cycle. The design and main interface are heavily focused on the nurse end users who are the predominant users, while additional detailed and longitudinal data, which are of interest to doctors guiding overall patient care, are available via tabs. This dichotomy of needs and interests based on the end user's immediate focus and goals shows how interfaces must adapt to offer different information to multiple types of users.

CONCLUSIONS

The interface is designed to minimize real and perceived clinical effort, and ongoing pilot trials have reported high levels of acceptance. The overall design principles, approach, and testing methods are based on fundamental human factors principles designed to reduce user effort and error and are readily generalizable.

Data entry errors and design for model-based tight glycemic control in critical care

INTRODUCTION

Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. Model-based methods and computerized protocols offer the opportunity to improve TGC quality but require human data entry, particularly of blood glucose (BG) values, which can be significantly prone to error. This study presents the design and optimization of data entry methods to minimize error for a computerized and model-based TGC method prior to pilot clinical trials.

METHOD

To minimize data entry error, two tests were carried out to optimize a method with errors less than the 5%-plus reported in other studies. Four initial methods were tested on 40 subjects in random order, and the best two were tested more rigorously on 34 subjects. The tests measured entry speed and accuracy. Errors were reported as corrected and uncorrected errors, with the sum comprising a total error rate. The first set of tests used randomly selected values, while the second set used the same values for all subjects to allow comparisons across users and direct assessment of the magnitude of errors. These research tests were approved by the University of Canterbury Ethics Committee.

RESULTS

The final data entry method tested reduced errors to less than 1-2%, a 60-80% reduction from reported values. The magnitude of errors was clinically significant and was typically by 10.0 mmol/liter or an order of magnitude but only for extreme values of BG < 2.0 mmol/liter or BG > 15.0-20.0 mmol/liter, both of which could be easily corrected with automated checking of extreme values for safety.

CONCLUSIONS

The data entry method selected significantly reduced data entry errors in the limited design tests presented, and is in use on a clinical pilot TGC study. The overall approach and testing methods are easily performed and generalizable to other applications and protocols.

Hypoglycemia at admission is associated with inhospital mortality in Ugandan patients with severe sepsis

OBJECTIVE

Dysglycemia during sepsis is associated with poor outcomes in resource-rich settings. In resource-limited settings, hypoglycemia is often diagnosed clinically without the benefit of laboratory support. We studied the utility of point-of-care glucose monitoring to predict mortality in severely septic patients in Uganda.

DESIGN

Prospective observational study.

SETTING

One national and two regional referral hospitals in Uganda.

PATIENTS

We enrolled 532 patients with sepsis at three hospitals in Uganda. The analysis included 418 patients from the three sites with inhospital mortality data, a documented admission blood glucose concentration, and evidence of organ dysfunction at admission (systolic blood pressure≤100 mm Hg, lactate>4 mmol/L, platelet number<100,000/μL, or altered mental status).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We evaluated the association between admission point-of-care blood glucose concentration and inhospital mortality. We also assessed the accuracy of altered mental status as a predictor of hypoglycemia. Euglycemia occurred in 33.5% (140 of 418) of patients, whereas 16.3% (68 of 418) of patients were hypoglycemic and 50.2% (210 of 418) were hyperglycemic. Univariate Cox regression analyses comparing in-hospital mortality among hypoglycemic (35.3% [24 of 68], hazard ratio 2.0, 95% confidence interval 1.2-3.6, p=.013) and hyperglycemic (29.5% [62 of 210], hazard ratio 1.5, 95% confidence interval 0.96-2.4, p=.08) patients to euglycemic (19.3% [27 of 140]) patients showed statistically significantly higher rates of inhospital mortality for patients with hypoglycemia. Hypoglycemia (adjusted hazard ratio 1.9, 95% confidence interval 1.1-3.3, p=.03) remained significantly and independently associated with inhospital mortality in the multivariate model. The sensitivity and specificity of altered mental status for hypoglycemia were 25% and 86%, respectively.

CONCLUSION

Hypoglycemia is an independent risk factor for inhospital mortality in patients with severe sepsis and cannot be adequately assessed by clinical examination. Correction of hypoglycemia may improve outcomes of critically ill patients in resource-limited settings.

The interaction of chronic and acute glycemia with mortality in critically ill patients with diabetes

OBJECTIVES

The relationship between hyperglycemia and mortality is altered by the presence of diabetes mellitus. Biological adjustment to preexisting hyperglycemia might explain this phenomenon. We tested whether the degree of preexisting hyperglycemia would modulate the association between glycemia and outcome during critical illness in patients with diabetes mellitus.

DESIGN

Retrospective observational study.

SETTING

Two tertiary intensive care units.

PATIENTS

Four hundred fifteen critically ill diabetic patients with HbA1c levels measured within 3 months of intensive care unit admission.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

There were 9,946 blood glucose measurements in this study cohort (glucose measured 6.7 times per day; every 3.6 hrs on average). The median preadmission HbA1c level was 7.0%. There was no significant difference in HbA1c levels (p = .17) or time-weighted average of blood glucose concentrations (p = .49) between survivors and nonsurvivors. The time-weighted average of blood glucose concentrations during intensive care unit stay for nonsurvivors was lower than that of survivors when the HbA1c was >6.8%. In multivariate analysis, we found that there was a significant interaction between HbA1c and the time-weighted glucose level, indicating that the relationship between HbA1c and mortality changed according to the levels of time-weighted average of blood glucose concentrations (p = .008). As a consequence, in patients with higher (>7%) preadmission levels of HbA1c, the higher the time-weighted acute glucose concentration during intensive care unit stay (>10 mmol/L), the lower the hospital mortality compared with the lower HbA1c cohort (<7%).

CONCLUSIONS

In patients with diabetes mellitus admitted to intensive care units, there was a significant interaction between preexisting hyperglycemia and the association between acute glycemia and mortality. These observations generate the hypothesis that glucose levels that are considered safe and desirable in other patients might be undesirable in diabetic patients with chronic hyperglycemia. Further studies are required to confirm or refute our findings.

Time and degree of glycemic derangement are associated with increased mortality in trauma patients in the setting of tight glycemic control

BACKGROUND

Tight glucose control (TGC) may reduce mortality in critically ill trauma patients. We hypothesize that euglycemia is beneficial, and a measure considering time and degree of hyperglycemia is most associated with mortality.

METHODS

We performed a review of intensive care unit trauma patients admitted for more than 3 days between January 2005 and December 2007 on a TGC protocol with a goal of 80 to 110 mg/dL. Hyperglycemic, hypoglycemic, and euglycemic time ranges, and area of interpolated curves above and below 80 to 110 mg/dL were assessed. Associations with mortality were based on logistic regression models adjusted for age, injury severity score, and admission Glasgow Coma Scale score.

RESULTS

A total of 546 patients were identified, and 68 (13%) died. Time spent as hyperglycemic (P = .29) and hyperglycemic area under the curve (P = .58) were not associated with mortality; hyperglycemic area/time (P = .01) was associated with mortality. Regarding hypoglycemia, area over the curve (P = .009) and time spent as hypoglycemic (P = .002) were associated with mortality.

CONCLUSIONS

TGC prevents prolonged, high degrees of hyperglycemia; avoiding hypoglycemia likely provides mortality benefit for trauma patients.

Taking the septic patient to the operating room

The acutely septic patient is a multifaceted challenge for the anesthetist. Unlike most elective surgery patients, acutely septic patients have severe systemic disease before the physiologic insults of anesthesia and surgery. The decision to operate is usually informed by the urgent or emergent need to correct a severe surgical problem and weighed against the higher risks of morbidity and mortality from the procedure itself. The care of the septic patient in the intensive care unit can help guide operating room management. However, the acuity and time course of intraoperative events, including hemorrhage and drug-induced shock states, compel the anesthetist to respond aggressively with therapies that may or may not be strongly substantiated with long-term data in the intensive care unit setting. The anesthesiologist must place considerations concerning short-term survival from the acute insult of surgery ahead of longer-term considerations.

Hypoglycemia and outcome in critically ill patients

OBJECTIVE

To determine whether mild or moderate hypoglycemia that occurs in critically ill patients is independently associated with an increased risk of death.

PATIENTS AND METHODS

Of patients admitted to 2 hospital intensive care units (ICUs) in Melbourne and Sydney, Australia, from January 1, 2000, to October 14, 2004, we analyzed all those who had at least 1 episode of hypoglycemia (glucose concentration, <81 mg/dL). The independent association between hypoglycemia and outcome was statistically assessed.

RESULTS

Of 4946 patients admitted to the ICUs, a cohort of 1109 had at least 1 episode of hypoglycemia (blood glucose level, <81 mg/dL). Of these 1109 patients (22.4% of all admissions to the intensive care unit), hospital mortality was 36.6% compared with 19.7% in the 3837 nonhypoglycemic control patients (P<.001). Even patients with a minimum blood glucose concentration between 72 and 81 mg/dL had a greater unadjusted mortality rate than did control patients (25.9% vs 19.7%; unadjusted odds ratio, 1.42; 95% confidence interval, 1.12-1.80; P=.004.) Mortality increased significantly with increasing severity of hypoglycemia (P<.001). After adjustment for insulin therapy, hypoglycemia was independently associated with increased risk of death, cardiovascular death, and death due to infectious disease.

CONCLUSION

In critically ill patients, an association exists between even mild or moderate hypoglycemia and mortality. Even after adjustment for insulin therapy or timing of hypoglycemic episode, the more severe the hypoglycemia, the greater the risk of death.

Glycemic control in critically ill patients before and after institution of an intensive insulin infusion protocol: circadian rhythm and the quality duration calculator

INTRODUCTION

A circadian rhythm of blood glucose values has been recently reported in critically ill patients, but there are no reports of how this rhythm is altered by a continuous intensive insulin infusion therapy protocol (IIT). We wished to examine the effect of IIT on this rhythm as well as to describe the use of the quality duration calculator (QDC) for the evaluation of glycemic control before and after IIT.

METHODS

This was a retrospective multihospital observational study that took place in the medical and surgical intensive care units (ICUs) of 2 tertiary care hospitals. Cohorts of consecutively admitted critically ill patients from 2-year periods before and after institution of an IIT protocol were examined. Laboratory, demographic, and outcome data were extracted from hospital databases.

RESULTS

We studied 167,645 blood glucose measurements from 8,327 patients. We observed a circadian rhythm of blood glucose control in the pre-IIT cohort that was greatly attenuated in the post-IIT cohort. The difference between the morning and the average daily blood glucose in the pre-IIT cohort was 3.53 mg/dL (P < .001), and the difference between these values in the post-IIT cohort was 1.10 mg/dL (P = .031). In addition, the circadian nature of hyperglycemia incidence observed in the pre-IIT cohort was not seen in the post-IIT cohort. The amount of time spent in goal glycemic range increased from 23.69% (95% CI 23.01-24.38) in the pre-IIT cohort to 29.67% (95% CI 29.04-30.31) in the post-IIT cohort as estimated by the QDC. The amount of time spent in the hyperglycemic decreased from 20.17% (95% CI 19.33-20.99) in the pre-IIT cohort to 14.80% (95% CI 14.15-15.39) in the post-IIT cohort.

CONCLUSIONS

The circadian rhythm of blood glucose control confirmed in our pre-IIT cohort was lost after institution of IIT. The morning blood glucose value appears to be a reasonable surrogate of overall glycemic control in a critically ill population on IIT, although this may vary based on the degree of control achieved. The QDC method is useful for analyzing glycemic control in patients on IIT.

Reducing glycemic variability in intensive care unit patients: a new therapeutic target?

Acute hyperglycemia is common in critically ill patients. Strict control of blood glucose (BG) concentration has been considered important because hyperglycemia is associated independently with increased intensive care unit mortality. After intensive insulin therapy was reported to reduce mortality in selected surgical critically ill patients, lowering of BG levels was recommended as a means of improving patient outcomes. However, a large multicenter multination study has found that intensive insulin therapy increased mortality significantly. A difference in variability of BG control may be one possible explanation why the effect of intensive insulin therapy varied from beneficial to harmful. Several studies have confirmed significant associations between variability of BG levels and patient outcomes. Decreasing the variability of the BG concentration may be an important dimension of glucose management. If reducing swings in the BG concentration is a major biologic mechanism behind the putative benefits of glucose control, it may not be necessary to pursue lower glucose levels with their attendant risk of hypoglycemia.

Challenges to glycemic measurement in the perioperative and critically ill patient: a review

Accurate monitoring of glucose in the perioperative environment has become increasingly important over the last few years. Because of increased cost, turnaround time, and sample volume, the use of central laboratory devices for glucose measurement has been somewhat supplanted by point-of-care (POC) glucose devices. The trade-off in moving to these POC systems has been a reduction in accuracy, especially in the hypoglycemic range. Furthermore, many of these POC devices were originally developed, marketed, and received Food and Drug Administration regulatory clearance as home use devices for patients with diabetes. Without further review, many of these POC glucose measurement devices have found their way into the hospital environment and are used frequently for measurement during intense insulin therapy, where accurate measurements are critical. This review covers the technology behind glucose measurement and the evidence questioning the use of many POC devices for perioperative glucose management.

Glycemic control in the burn intensive care unit: focus on the role of anemia in glucose measurement

Glycemic control with intensive insulin therapy (IIT) has received widespread adoption secondary to findings of improved clinical outcomes and survival in the burn population. Severe burn as a model for trauma is characterized by a hypermetabolic state, hyperglycemia, and insulin resistance. In this article, we review the findings of a burn center research facility in terms of understanding glucose management. The conferred benefits from IIT, our findings of poor outcomes associated with glycemic variability, advantages from preserved diurnal variation of glucose and insulin, and impacts of glucometer error and hematocrit correction factor are discussed. We conclude with direction for further study and the need for a reliable continuous glucose monitoring system. Such efforts will further the endeavor for achieving adequate glycemic control in order to assess the efficacy of target ranges and use of IIT.

Early blood glucose control and mortality in critically ill patients in Australia

OBJECTIVE

To measure temporal trends in blood glucose (BG) control and describe their association with hospital mortality in a cohort of critically ill patients from Australia.

DESIGN

Interrogation of prospectively collected data from the Australia New Zealand Intensive Care Society Adult Patient Database.

SETTING

Twenty-four intensive care units (ICU) across Australia.

PATIENTS AND PARTICIPANTS

A cohort of 66,184 adult ICU admissions for >or=24 hours from January 1, 2000, to December 31, 2005.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Highest and lowest BG values within 24 hours of ICU admission, standard demographic, clinical, and physiologic data, and hospital mortality. Medical, mechanically ventilated surgical, cardiac surgical, and septic subgroups were evaluated. Average BG was evaluated as a continuous variable and by quartiles (low [<5.6 mmol/L], near normal [5.6-8.69 mmol/L], high [8.69-11.79 mmol/L], and highest [>11.79 mmol/L]). There were 132,368 BG values, with a mean (95% confidence intervals) value 8.69 mmol/L (8.66-8.73). There was no trend in BG for the entire cohort (p = 0.66) over the study period; yet, BG increased after 2002 (0.17 mmol/L, p < 0.0001). The mechanically ventilated surgical and cardiac surgical subgroups had decreasing trends in BG (p < 0.001), whereas the septic subgroup had an increasing BG trend (p < 0.001). BG in the low, high, and highest quartiles, compared with the near-normal quartile, were consistently associated with higher hospital mortality in crude (odds ratio 1.31, 1.58, and 2.00) and multivariable analysis (odds ratio 1.29, 1.07, and 1.10), respectively. This association was similarly shown for the mechanically ventilated surgical and cardiac surgical subgroups.

CONCLUSIONS

In a large cohort of ICU patients from Australia, there was no significant change in early glycemic control from 2000 to 2005. There were differences in selected subgroups. Average BG decreased in surgical subgroups, whereas it increased in septic patients. Both high and early low BG values were independently associated with hospital mortality.

Is reducing variability of blood glucose the real but hidden target of intensive insulin therapy?

Since the first report that intensive insulin therapy reduced mortality in selected surgical critically ill patients, lowering of blood glucose levels has been recommended as a means of improving patient outcomes. In this initial Leuven trial, blood glucose control by protocol using insulin was applied to 98.7% of patients in the intensive group but to only 39.2% (P < 0.0001) of patients in the control group. If appropriately applied, such protocols should decrease both the mean blood glucose concentration and its variability (variation of blood glucose concentration). Thus, it is logically possible that the benefit of intensive insulin therapy in the first Leuven trial was due to a decrease in mean glucose levels, a decrease in their variability, or both. Several recent studies have confirmed significant associations between variability of blood glucose levels and patient outcomes. Decreasing the variability of blood glucose levels might be an important dimension of glucose management, a possible mechanism by which an intensive insulin protocol exerts its putative beneficial effects, and an important goal of glucose management in the intensive care unit. Clinicians need to be aware of this controversy when considering the application of intensive insulin therapy and interpreting future trials.

Temporal and geographic patterns of hypoglycemia among hospitalized patients with diabetes mellitus

BACKGROUND

Hypoglycemia is often cited as a barrier to achieving inpatient glycemic targets. We sought to characterize hypoglycemic events in our institution by work-shift cycle and by specific treatment area.

METHODS

Capillary (bedside) and blood (laboratory) glucose values of <70 mg/dl for patients with either a known diagnosis of diabetes or with evidence of hyperglycemia were abstracted from our laboratory database for hospitalizations between October 1, 2007, and February 3, 2008. Hypoglycemic events were analyzed by 12 h nursing work-shift cycles (day shift, 07:00 to 18:59; night shift, 19:00 to 06:59) and by the six medical, surgical, and intensive care areas in the hospital (designated areas 1 to 6).

RESULTS

We identified 206 individual patients with either diabetes or hyperglycemia (mean age, 67 years; 56% men; 83% white) who had 423 hypoglycemic events. There were 78% more hypoglycemic events during the night shift (n = 271 events in 128 individual patients) than during the day shift (n = 152 events in 96 individual patients). Most of the night-shift hypoglycemic measurements were detected between 04:00 and 04:59 or 06:00 and 06:59. The mean hypoglycemic level was comparable between shifts (p = .79) and across the six inpatient areas. The number of hypoglycemic events per person increased with lengths of hospital stay >5 days. The prevalence of hypoglycemia varied across patient care areas within the hospital, with most (28%) detected in one area of the hospital.

CONCLUSION

There are temporal and geographic patterns in the occurrence of hypoglycemia among patients with diabetes or hyperglycemia in our hospital. Further study should focus on the reasons underlying these variations so that specific interventions can address the risk of hypoglycemia during peak times and places.

Is reducing variability of blood glucose the real but hidden target of intensive insulin therapy?

Since the first report that intensive insulin therapy reduced mortality in selected surgical critically ill patients, lowering of blood glucose levels has been recommended as a means of improving patient outcomes. In this initial Leuven trial, blood glucose control by protocol using insulin was applied to 98.7% of patients in the intensive group but to only 39.2% (P < 0.0001) of patients in the control group. If appropriately applied, such protocols should decrease both the mean blood glucose concentration and its variability (variation of blood glucose concentration). Thus, it is logically possible that the benefit of intensive insulin therapy in the first Leuven trial was due to a decrease in mean glucose levels, a decrease in their variability, or both. Several recent studies have confirmed significant associations between variability of blood glucose levels and patient outcomes. Decreasing the variability of blood glucose levels might be an important dimension of glucose management, a possible mechanism by which an intensive insulin protocol exerts its putative beneficial effects, and an important goal of glucose management in the intensive care unit. Clinicians need to be aware of this controversy when considering the application of intensive insulin therapy and interpreting future trials.

Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change

Introduction

Stress-induced hyperglycaemia is prevalent in critical care. Control of blood glucose levels to within a 4.4 to 6.1 mmol/L range or below 7.75 mmol/L can reduce mortality and improve clinical outcomes. The Specialised Relative Insulin Nutrition Tables (SPRINT) protocol is a simple wheel-based system that modulates insulin and nutritional inputs for tight glycaemic control.

Methods

SPRINT was implemented as a clinical practice change in a general intensive care unit (ICU). The objective of this study was to measure the effect of the SPRINT protocol on glycaemic control and mortality compared with previous ICU control methods. Glycaemic control and mortality outcomes for 371 SPRINT patients with a median Acute Physiology And Chronic Health Evaluation (APACHE) II score of 18 (interquartile range [IQR] 15 to 24) are compared with a 413-patient retrospective cohort with a median APACHE II score of 18 (IQR 15 to 23).

Results

Overall, 53.9% of all measurements were in the 4.4 to 6.1 mmol/L band. Blood glucose concentrations were found to be log-normal and thus log-normal statistics are used throughout to describe the data. The average log-normal glycaemia was 6.0 mmol/L (standard deviation 1.5 mmol/L). Only 9.0% of all measurements were below 4.4 mmol/L, with 3.8% below 4 mmol/L and 0.1% of measurements below 2.2 mmol/L. On SPRINT, 80% more measurements were in the 4.4 to 6.1 mmol/L band and standard deviation of blood glucose was 38% lower compared with the retrospective control. The range and peak of blood glucose were not correlated with mortality for SPRINT patients (P >0.30). For ICU length of stay (LoS) of greater than or equal to 3 days, hospital mortality was reduced from 34.1% to 25.4% (-26%) (P = 0.05). For ICU LoS of greater than or equal to 4 days, hospital mortality was reduced from 34.3% to 23.5% (-32%) (P = 0.02). For ICU LoS of greater than or equal to 5 days, hospital mortality was reduced from 31.9% to 20.6% (-35%) (P = 0.02). ICU mortality was also reduced but the P value was less than 0.13 for ICU LoS of greater than or equal to 4 and 5 days.

Conclusion

SPRINT achieved a high level of glycaemic control on a severely ill critical cohort population. Reductions in mortality were observed compared with a retrospective hyperglycaemic cohort. Range and peak blood glucose metrics were no longer correlated with mortality outcome under SPRINT.

Glycemic penalty index for adequately assessing and comparing different blood glucose control algorithms

Introduction

Blood glucose (BG) control performed by intensive care unit (ICU) nurses is becoming standard practice for critically ill patients. New (semi-automated) 'BG control' algorithms (or 'insulin titration' algorithms) are under development, but these require stringent validation before they can replace the currently used algorithms. Existing methods for objectively comparing different insulin titration algorithms show weaknesses. In the current study, a new approach for appropriately assessing the adequacy of different algorithms is proposed.

Methods

Two ICU patient populations (with different baseline characteristics) were studied, both treated with a similar 'nurse-driven' insulin titration algorithm targeting BG levels of 80 to 110 mg/dl. A new method for objectively evaluating BG deviations from normoglycemia was founded on a smooth penalty function. Next, the performance of this new evaluation tool was compared with the current standard assessment methods, on an individual as well as a population basis. Finally, the impact of four selected parameters (the average BG sampling frequency, the duration of algorithm application, the severity of disease, and the type of illness) on the performance of an insulin titration algorithm was determined by multiple regression analysis.

Results

The glycemic penalty index (GPI) was proposed as a tool for assessing the overall glycemic control behavior in ICU patients. The GPI of a patient is the average of all penalties that are individually assigned to each measured BG value based on the optimized smooth penalty function. The computation of this index returns a number between 0 (no penalty) and 100 (the highest penalty). For some patients, the assessment of the BG control behavior using the traditional standard evaluation methods was different from the evaluation with GPI. Two parameters were found to have a significant impact on GPI: the BG sampling frequency and the duration of algorithm application. A higher BG sampling frequency and a longer algorithm application duration resulted in an apparently better performance, as indicated by a lower GPI.

Conclusion

The GPI is an alternative method for evaluating the performance of BG control algorithms. The blood glucose sampling frequency and the duration of algorithm application should be similar when comparing algorithms.

Patterns of exogenous insulin requirement reflect insulin sensitivity changes in trauma

INTRODUCTION

We investigated patterns of blood glucose and exogenous insulin requirement in the intensive care unit, and questioned whether they reflect fluctuations in insulin activity.

METHODS

Records for burn intensive care unit patients with 7 days of glucose control with insulin were reviewed. Hourly blood glucose and insulin dose were matched for time collected and analyzed with linear and cosine regression. Frequency analysis identified recurring patterns.

RESULTS

Diurnal patterns of blood glucose and insulin requirement were noted (insulin troughs = noon; insulin peaks = midnight; glucose troughs = 5 am; glucose peaks = 5 pm). Average insulin requirement increased at a constant linear rate (slope = .013, r2 = .57, P < or = .001).

CONCLUSIONS

Diurnal patterns in blood glucose and insulin requirement mirror those of healthy subjects and may reflect persistence of normal variability in insulin activity. The 5-hour offset in peaks and troughs is suggestive of complex interplay between insulin availability and receptor sensitivity. The insulin requirement to blood glucose ratio increased, evidence that insulin resistance progresses over time.