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1.  Stress Hyperglycaemia in Hospitalised Patients and Their 3-Year Risk of Diabetes: A Scottish Retrospective Cohort Study 
PLoS Medicine  2014;11(8):e1001708.
In a retrospective analysis of a national database of hospital admissions, David McAllister and colleagues identify the 3-year risk of diabetes of hospitalized patients with hyperglycemia in Scotland.
Please see later in the article for the Editors' Summary
Background
Hyperglycaemia during hospital admission is common in patients who are not known to have diabetes and is associated with adverse outcomes. The risk of subsequently developing type 2 diabetes, however, is not known.
We linked a national database of hospital admissions with a national register of diabetes to describe the association between admission glucose and the risk of subsequently developing type 2 diabetes.
Methods and Findings
In a retrospective cohort study, patients aged 30 years or older with an emergency admission to hospital between 2004 and 2008 were included. Prevalent and incident diabetes were identified through the Scottish Care Information (SCI)-Diabetes Collaboration national registry. Patients diagnosed prior to or up to 30 days after hospitalisation were defined as prevalent diabetes and were excluded.
The predicted risk of developing incident type 2 diabetes during the 3 years following hospital discharge by admission glucose, age, and sex was obtained from logistic regression models. We performed separate analyses for patients aged 40 and older, and patients aged 30 to 39 years.
Glucose was measured in 86,634 (71.0%) patients aged 40 and older on admission to hospital. The 3-year risk of developing type 2 diabetes was 2.3% (1,952/86,512) overall, was <1% for a glucose ≤5 mmol/l, and increased to approximately 15% at 15 mmol/l. The risks at 7 mmol/l and 11.1 mmol/l were 2.6% (95% CI 2.5–2.7) and 9.9% (95% CI 9.2–10.6), respectively, with one in four (21,828/86,512) and one in 40 (1,798/86,512) patients having glucose levels above each of these cut-points. For patients aged 30–39, the risks at 7 mmol/l and 11.1 mmol/l were 1.0% (95% CI 0.8–1.3) and 7.8% (95% CI 5.7–10.7), respectively, with one in eight (1,588/11,875) and one in 100 (120/11,875) having glucose levels above each of these cut-points.
The risk of diabetes was also associated with age, sex, and socio-economic deprivation, but not with specialty (medical versus surgical), raised white cell count, or co-morbidity. Similar results were obtained for pre-specified sub-groups admitted with myocardial infarction, chronic obstructive pulmonary disease, and stroke.
There were 25,193 deaths (85.8 per 1,000 person-years) over 297,122 person-years, of which 2,406 (8.1 per 1,000 person-years) were attributed to vascular disease. Patients with glucose levels of 11.1 to 15 mmol/l and >15 mmol/l had higher mortality than patients with a glucose of <6.1 mmol/l (hazard ratio 1.54; 95% CI 1.42–1.68 and 2.50; 95% CI 2.14–2.95, respectively) in models adjusting for age and sex.
Limitations of our study include that we did not have data on ethnicity or body mass index, which may have improved prediction and the results have not been validated in non-white populations or populations outside of Scotland.
Conclusion
Plasma glucose measured during an emergency hospital admission predicts subsequent risk of developing type 2 diabetes. Mortality was also 1.5-fold higher in patients with elevated glucose levels. Our findings can be used to inform patients of their long-term risk of type 2 diabetes, and to target lifestyle advice to those patients at highest risk.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Insulin—a hormone released by the pancreas after meals—controls blood glucose (sugar) levels in healthy individuals. However, many patients admitted to hospital because of an acute illness have hyperglycemia, an abnormally high blood glucose level. In this setting, hyperglycemia can be caused by the drugs that patients are taking for existing conditions or may be stress hyperglycemia, a reversible condition in which hormonal changes induced by acute illness stimulate glucose production by the liver. However, hyperglycemia detected during an acute illness may also indicate underlying or incipient type 2 diabetes, a common condition in which blood glucose control fails. Type 2 diabetes can initially be controlled by diet, exercise, and antidiabetic drugs but many patients eventually need insulin injections to control their blood sugar level. Long-term complications of type 2 diabetes, which include an increased risk of heart attacks and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes
Why Was This Study Done?
Prompt diagnosis of type 2 diabetes can minimize its long-term complications, so experts have designed several scoring systems based on lifestyle and other characteristics that allow primary care clinicians to identify the patients who should be tested for diabetes because they are at high risk of developing the condition. Unfortunately, these scoring systems cannot be used to interpret a high blood glucose result obtained during an acute illness so clinicians cannot currently advise their patients on the clinical significance of this type of abnormal glucose reading or make an informed decision about whether follow-up testing is needed. In this retrospective cohort study, the researchers investigate the association between blood glucose levels measured during emergency hospital admissions in Scotland and the risk of developing type 2 diabetes by linking together national databases of hospital admissions, laboratory test results, and people with diabetes. A retrospective cohort study examines the medical histories of a group of patients.
What Did the Researchers Do and Find?
The researchers used the databases to identify more than 100,000 patients aged 30 years or older who were admitted to a hospital for an acute illness between 2004 and 2008 in Scotland, to obtain information on blood glucose levels on admission for nearly three-quarters of these patients, and to identify which patients subsequently developed diabetes. They then used statistical models to estimate the patients' risk of developing type 2 diabetes during the 3 years following hospital discharge. Among patients aged 40 years or older, the overall 3-year risk of developing diabetes was 2.3%. The risk of developing diabetes increased linearly with increasing blood glucose level at admission. Specifically, the 3-year risks at blood glucose levels of 7 mmol/l and 11.1 mmol/l were 2.6% and 9.9%, respectively; because glucose levels fluctuate according to when an individual last ate, fasting blood glucose levels of 7 mmol/l and non-fasting blood glucose levels of 11.1 mmol/l are used as thresholds for the diagnosis of diabetes. The diabetes risk associated with blood glucose levels on admission among 30–39-year-old patients followed a similar pattern but was less marked. Finally, high glucose levels on admission were associated with increased mortality.
What Do These Findings Mean?
These findings indicate that blood glucose measured during an emergency hospital admission predicts the subsequent risk of type 2 diabetes among patients aged 40 years or older (the analysis specified in the researchers' original protocol). Importantly, however, they also suggest that a high blood glucose reading in these circumstances usually indicates stress hyperglycemia rather than type 2 diabetes. The accuracy and generalizability of these findings may be limited by the lack of data on ethnicity or body mass index (a measure of obesity), both of which affect diabetes risk, and by other aspects of the study design. Nevertheless, given their findings, the researchers recommend that any patient with a blood glucose level above 11.1 mmol/l on hospital admission for an acute illness (one in 40 patients in this study) should be offered follow-up testing. In addition, the researchers constructed a risk calculator using their findings that should help clinicians to inform their patients about their long-term risk of diabetes following hyperglycemia during an acute hospital admission and to target lifestyle advice to those patients at the highest risk of type 2 diabetes.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001708.
The US National Diabetes Information Clearinghouse provides information about diabetes and about diabetes prevention (in English and Spanish)
The UK National Health Service Choices website provides information about type 2 diabetes and about living with diabetes; it also provides people's stories about diabetes
The charity Diabetes UK provides information about diabetes in several languages, including information on healthy lifestyles for people with diabetes
Wikipedia has a page on stress hyperglycemia (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
More information about stress hyperglycemia is available in Diapedia, a living textbook of diabetes produced by the European Association for the Study of Diabetes
GUARD (Glucose on Unselected Admissions and Risk of Diabetes), a risk calculator that allows clinicians to estimate a patient's 3-year risk of diabetes following hyperglycemia at hospital admission for an acute illness, is available online
The UK-based non-profit organization Healthtalkonline has interviews with people about their experiences of diabetes
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention (in English and Spanish)
doi:10.1371/journal.pmed.1001708
PMCID: PMC4138030  PMID: 25136809
2.  Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change 
Critical Care  2008;12(2):R49.
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.
doi:10.1186/cc6868
PMCID: PMC2447603  PMID: 18412978
3.  Optimal glycemic control in neurocritical care patients: a systematic review and meta-analysis 
Critical Care  2012;16(5):R203.
Introduction
Hyper- and hypoglycemia are strongly associated with adverse outcomes in critical care. Neurologically injured patients are a unique subgroup, where optimal glycemic targets may differ, such that the findings of clinical trials involving heterogeneous critically ill patients may not apply.
Methods
We performed a systematic review and meta-analysis of randomized controlled trials (RCTs) comparing intensive insulin therapy with conventional glycemic control among patients with traumatic brain injury, ischemic or hemorrhagic stroke, anoxic encephalopathy, central nervous system infections or spinal cord injury.
Results
Sixteen RCTs, involving 1248 neurocritical care patients, were included. Glycemic targets with intensive insulin ranged from 70-140 mg/dl (3.9-7.8 mmol/L), while conventional protocols aimed to keep glucose levels below 144-300 mg/dl (8.0-16.7 mmol/L). Tight glycemic control had no impact on mortality (RR 0.99; 95% CI 0.83-1.17; p = 0.88), but did result in fewer unfavorable neurological outcomes (RR 0.91; 95% CI 0.84-1.00; p = 0.04). However, improved outcomes were only observed when glucose levels in the conventional glycemic control group were permitted to be relatively high [threshold for insulin administration > 200 mg/dl (> 11.1 mmol/L)], but not with more intermediate glycemic targets [threshold for insulin administration 140-180 mg/dl (7.8-10.0 mmol/L)]. Hypoglycemia was far more common with intensive therapy (RR 3.10; 95% CI 1.54-6.23; p = 0.002), but there was a large degree of heterogeneity in the results of individual trials (Q = 47.9; p<0.0001; I2 = 75%). Mortality was non-significantly higher with intensive insulin in studies where the proportion of patients developing hypoglycemia was large (> 33%) (RR 1.17; 95% CI 0.79-1.75; p = 0.44).
Conclusions
Intensive insulin therapy significantly increases the risk of hypoglycemia and does not influence mortality among neurocritical care patients. Very loose glucose control is associated with worse neurological recovery and should be avoided. These results suggest that intermediate glycemic goals may be most appropriate.
doi:10.1186/cc11812
PMCID: PMC3682305  PMID: 23082798
4.  Strict glycaemic control in patients hospitalised in a mixed medical and surgical intensive care unit: a randomised clinical trial 
Critical Care  2008;12(5):R120.
Introduction
Critically ill patients can develop hyperglycaemia even if they do not have diabetes. Intensive insulin therapy decreases morbidity and mortality rates in patients in a surgical intensive care unit (ICU) and decreases morbidity in patients in a medical ICU. The effect of this therapy on patients in a mixed medical/surgical ICU is unknown. Our goal was to assess whether the effect of intensive insulin therapy, compared with standard therapy, decreases morbidity and mortality in patients hospitalised in a mixed ICU.
Methods
This is a prospective, randomised, non-blinded, single-centre clinical trial in a medical/surgical ICU. Patients were randomly assigned to receive either intensive insulin therapy to maintain glucose levels between 80 and 110 mg/dl (4.4 to 6.1 mmol/l) or standard insulin therapy to maintain glucose levels between 180 and 200 mg/dl (10 and 11.1 mmol/l). The primary end point was mortality at 28 days.
Results
Over a period of 30 months, 504 patients were enrolled. The 28-day mortality rate was 32.4% (81 of 250) in the standard insulin therapy group and 36.6% (93 of 254) in the intensive insulin therapy group (Relative Risk [RR]: 1.1; 95% confidence interval [CI]: 0.85 to 1.42). The ICU mortality in the standard insulin therapy group was 31.2% (78 of 250) and 33.1% (84 of 254) in the intensive insulin therapy group (RR: 1.06; 95%CI: 0.82 to 1.36). There was no statistically significant reduction in the rate of ICU-acquired infections: 33.2% in the standard insulin therapy group compared with 27.17% in the intensive insulin therapy group (RR: 0.82; 95%CI: 0.63 to 1.07). The rate of hypoglycaemia (≤ 40 mg/dl) was 1.7% in the standard insulin therapy group and 8.5% in the intensive insulin therapy group (RR: 5.04; 95% CI: 1.20 to 21.12).
Conclusions
IIT used to maintain glucose levels within normal limits did not reduce morbidity or mortality of patients admitted to a mixed medical/surgical ICU. Furthermore, this therapy increased the risk of hypoglycaemia.
Trial Registration
clinicaltrials.gov Identifiers: 4374-04-13031; 094-2 in 000966421
doi:10.1186/cc7017
PMCID: PMC2592751  PMID: 18799004
5.  Effects of tight computerized glucose control on neurological outcome in severely brain injured patients: a multicenter sub-group analysis of the randomized-controlled open-label CGAO-REA study 
Critical Care  2014;18(5):498.
Introduction
Hyperglycemia is a marker of poor prognosis in severe brain injuries. There is currently little data regarding the effects of intensive insulin therapy (IIT) on neurological recovery.
Methods
A sub-group analysis of the randomized-controlled CGAO-REA study (NCT01002482) in surgical intensive care units (ICU) of two university hospitals. Patients with severe brain injury, with an expected ICU length of stay ≥48 hours were included. Patients were randomized between a conventional glucose management group (blood glucose target between 5.5 and 9 mmol.L−1) and an IIT group (blood glucose target between 4.4 and 6 mmol.L−1). The primary outcome was the day-90 neurological outcome evaluated with the Glasgow outcome scale.
Results
A total of 188 patients were included in this analysis. In total 98 (52%) patients were randomized in the control group and 90 (48%) in the IIT group. The mean Glasgow coma score at baseline was 7 (±4). Patients in the IIT group received more insulin (130 (68 to 251) IU versus 74 (13 to 165) IU in the control group, P = 0.01), had a significantly lower morning blood glucose level (5.9 (5.1 to 6.7) mmol.L−1 versus 6.5 (5.6 to 7.2) mmol.L−1, P <0.001) in the first 5 days after ICU admission. The IIT group experienced more episodes of hypoglycemia (P <0.0001). In the IIT group 24 (26.6%) patients had a favorable neurological outcome (good recovery or moderate disability) compared to 31 (31.6%) in the control group (P = 0.4). There were no differences in day-28 mortality. The occurrence of hypoglycemia did not influence the outcome.
Conclusions
In this sub-group analysis of a large multicenter randomized trial, IIT did not appear to alter the day-90 neurological outcome or ICU morbidity in severe brain injured patients or ICU morbidity.
doi:10.1186/s13054-014-0498-9
PMCID: PMC4174656  PMID: 25189764
6.  Implementation of the Glucommander Method of Adjusting Insulin Infusions in Critically Ill Patients 
Background:
Intensive glycemic control has been associated with reduced morbidity and mortality in critically ill patients. Web-based, patient-specific insulin nomograms may facilitate improved glucose control.
Objective:
To compare 2 algorithms for individualizing insulin infusion therapy (a web-based system [Glucommander method] and a standard paper-based nomogram) in a cardiovascular surgery intensive care unit (ICU).
Methods:
In this prospective, before–after cohort study, measures of glycemic control for 50 patients receiving insulin according to the Glucommander system were compared with a control group (n = 50) who received insulin according to the standard paper-based nomogram used in the cardiovascular surgery ICU.
Results:
There was no significant difference between the 2 groups with respect to time to target blood glucose (5.1–8.0 mmol/L), percentage of time within the target range, or mean amplitude of glucose excursion. Patients in the intervention group spent less time above the target range (p = 0.007) and more time below the target range (p < 0.001), and the mean glucose was lower in this group compared with the control group (7.9 versus 8.6 mmol/L, p = 0.002). The percentage of blood glucose measurements below 4 mmol/L was higher in the intervention group than in the control group (3.7% versus 1.4%, p = 0.003). Satisfaction surveys revealed that the program was well accepted by the nursing staff in the cardiovascular surgery ICU.
Conclusions:
A web-based insulin nomogram was an easy-to-use instrument for achieving tighter glucose control for patients in the cardiovascular surgery ICU. Use of the Glucommander system led to lower mean blood glucose but an increase in episodes of hypoglycemia.
PMCID: PMC3203825  PMID: 22479085
tight glycemic control; critically ill patients; régulation serrée de la glycémie; patients gravement malades
7.  Intensive insulin therapy and mortality among critically ill patients: a meta-analysis including NICE-SUGAR study data 
Background
Hyperglycemia is associated with increased mortality in critically ill patients. Randomized trials of intensive insulin therapy have reported inconsistent effects on mortality and increased rates of severe hypoglycemia. We conducted a meta-analysis to update the totality of evidence regarding the influence of intensive insulin therapy compared with conventional insulin therapy on mortality and severe hypoglycemia in the intensive care unit (ICU).
Methods
We conducted searches of electronic databases, abstracts from scientific conferences and bibliographies of relevant articles. We included published randomized controlled trials conducted in the ICU that directly compared intensive insulin therapy with conventional glucose management and that documented mortality. We included in our meta-analysis the data from the recent NICE-SUGAR (Normoglycemia in Intensive Care Evaluation — Survival Using Glucose Algorithm Regulation) study.
Results
We included 26 trials involving a total of 13 567 patients in our meta-analysis. Among the 26 trials that reported mortality, the pooled relative risk (RR) of death with intensive insulin therapy compared with conventional therapy was 0.93 (95% confidence interval [CI] 0.83–1.04). Among the 14 trials that reported hypoglycemia, the pooled RR with intensive insulin therapy was 6.0 (95% CI 4.5–8.0). The ICU setting was a contributing factor, with patients in surgical ICUs appearing to benefit from intensive insulin therapy (RR 0.63, 95% CI 0.44–0.91); patients in the other ICU settings did not (medical ICU: RR 1.0, 95% CI 0.78–1.28; mixed ICU: RR 0.99, 95% CI 0.86–1.12). The different targets of intensive insulin therapy (glucose level ≤ 6.1 mmol/L v. ≤ 8.3 mmol/L) did not influence either mortality or risk of hypoglycemia.
Interpretation
Intensive insulin therapy significantly increased the risk of hypoglycemia and conferred no overall mortality benefit among critically ill patients. However, this therapy may be beneficial to patients admitted to a surgical ICU.
doi:10.1503/cmaj.090206
PMCID: PMC2665940  PMID: 19318387
8.  Value of Continuous Glucose Monitoring For Minimizing Severe Hypoglycemia during Tight Glycemic Control 
Objective
Tight glycemic control (TGC) can potentially reduce morbidity and mortality in the ICU but increases the risk of hypoglycemia. The most effective means to avoid hypoglycemia is to obtain frequent blood glucose (BG) samples, but this increases the burden to nursing staff. The objective of this study was to assess the ability of a real-time continuous glucose monitor (CGM-RT) to reduce hypoglycemia (BG<60 mg/dL [3.3 mmol/L]) during standard care (STD) or TGC effected with a proportional integral derivative (PID) insulin titration algorithm.
Design
CGM-RT profiles obtained from an ongoing prospective randomized trial of TGC were retrospectively analyzed to determine if the continuous glucose measure had prevented instances of hypoglycemia.
Setting
Cardiac ICU.
Patients
Children 3 years of age or less undergoing cardiac surgery were studied.
Interventions
Intravenous insulin infusion and rescue glucose with guided by CGM-RT and the PID algorithm in the TGC arm (N=155; target glucose 80–110 mg/dL [4.4–6.1 mmol/L); CGM-RT in the STD care arm (N=156).
Measurements and Main Results
No reduction in hypoglycemia was observed with CGM-RT alarms set at 60 mg/dL [3.3 mmol/L] (0 of 19 occurrences of BG<60 mg/dL [3.3 mmol/L] detected); 18 of 40 subsequent incidences of hypoglycemia were detected after increasing the alarm threshold to 70 mg/dL [3.9 mmol/L]. In the TGC arm, 8 incidences were reduced in duration and an additional 8 events were prevented with intravenous glucose. In the STD arm, 3 of 9 occurrences of hypoglycemia were detected with the duration reduced in all cases. On average, one to two false hypoglycemia alarms were observed in each patient.
Conclusions
CGM-RT in combination with PID control can reduce hypoglycemia during TGC. CGM-RT can also reduce hypoglycemia during STD. However, false alarms increase the overall nursing workload.
doi:10.1097/PCC.0b013e31821926a5
PMCID: PMC3705721  PMID: 21499183
tight glycemic control; continuous glucose monitoring; intensive care unit
9.  Improvement in Glycemic Control and Outcome Corresponding to Intensive Insulin Therapy Protocol Development 
Background
Intensive insulin therapy (IIT) has been shown to reduce mortality and morbidity in longer stay, critically ill patients. However, this has been demonstrated in a single site, whereas two multicentric studies have been terminated prematurely mainly due to hypoglycemia. Other difficulties with IIT include efficacy of glycemic control. This report describes how IIT can be improved by protocol simplification and removal of glucose supplementation.
Methods
A clinical information system established at each bedspace guided staff through the IIT algorithms. Time spent within predefined glycemic ranges was calculated assuming a linear trend between successive measurements. Three groups were investigated retrospectively: IIT1 protocol,1 an updated IIT2 version, and intuitive nurse dosing of conventional insulin therapy (CIT).
Results
Fifty consecutive, critically ill patients were included in each study group. Patient characteristics were similar in each group. The frequency of CIT and IIT2 blood glucose measurements were 11.6 and 11.5 measurements per day, respectively, while the IIT1 measurements were more frequent (14.5 measurements per day). The mean proportion of time spent in the target glycemic range (4.4–6.1 mmol/liter) was highest in the IIT2 group (34.9%), as compared to the IIT1 (22.9%) and CIT groups (20.3%) (p <.001). Survival at 28 days was 74.5% for IIT2 (highest), 68% for IIT1, and 48% for CIT (p = .02). There were a similar number of those experiencing a severe hypoglycemic event in each group.
Conclusions
IIT protocol optimization was associated with increased glycemic control and improved 28-day survival. The better optimized IIT2 protocol provided tighter control than either the IIT1 or CIT protocol, without increased sampling or incidence of hypoglycemia. The clinical effectiveness of the IIT algorithm appeared to be improved by simplifying the protocol to meet the needs of the critical care unit.
PMCID: PMC2769749  PMID: 19885203
blood glucose; critically ill; insulin; tight glycemic control
10.  Tight Glycemic Control versus Standard Care after Pediatric Cardiac Surgery 
The New England journal of medicine  2012;367(13):1208-1219.
BACKGROUND
In some studies, tight glycemic control with insulin improved outcomes in adults undergoing cardiac surgery, but these benefits are unproven in critically ill children at risk for hyperinsulinemic hypoglycemia. We tested the hypothesis that tight glycemic control reduces morbidity after pediatric cardiac surgery.
METHODS
In this two-center, prospective, randomized trial, we enrolled 980 children, 0 to 36 months of age, undergoing surgery with cardiopulmonary bypass. Patients were randomly assigned to either tight glycemic control (with the use of an insulin-dosing algorithm targeting a blood glucose level of 80 to 110 mg per deciliter [4.4 to 6.1 mmol per liter]) or standard care in the cardiac intensive care unit (ICU). Continuous glucose monitoring was used to guide the frequency of blood glucose measurement and to detect impending hypoglycemia. The primary outcome was the rate of health care–associated infections in the cardiac ICU. Secondary outcomes included mortality, length of stay, organ failure, and hypoglycemia.
RESULTS
A total of 444 of the 490 children assigned to tight glycemic control (91%) received insulin versus 9 of 490 children assigned to standard care (2%). Although normoglycemia was achieved earlier with tight glycemic control than with standard care (6 hours vs. 16 hours, P<0.001) and was maintained for a greater proportion of the critical illness period (50% vs. 33%, P<0.001), tight glycemic control was not associated with a significantly decreased rate of health care–associated infections (8.6 vs. 9.9 per 1000 patient-days, P = 0.67). Secondary outcomes did not differ significantly between groups, and tight glycemic control did not benefit high-risk subgroups. Only 3% of the patients assigned to tight glycemic control had severe hypoglycemia (blood glucose <40 mg per deciliter [2.2 mmol per liter]).
CONCLUSIONS
Tight glycemic control can be achieved with a low hypoglycemia rate after cardiac surgery in children, but it does not significantly change the infection rate, mortality, length of stay, or measures of organ failure, as compared with standard care. (Funded by the National Heart, Lung, and Blood Institute and others; SPECS ClinicalTrials.gov number, NCT00443599.)
doi:10.1056/NEJMoa1206044
PMCID: PMC3501680  PMID: 22957521
11.  Computerized intensive insulin dosing can mitigate hypoglycemia and achieve tight glycemic control when glucose measurement is performed frequently and on time 
Critical Care  2009;13(5):R163.
Introduction
Control of blood glucose (BG) in critically ill patients is considered important, but is difficult to achieve, and often associated with increased risk of hypoglycemia. We examined the use of a computerized insulin dosing algorithm to manage hyperglycemia with particular attention to frequency and conditions surrounding hypoglycemic events.
Methods
This is a retrospective analysis of adult patients with hyperglycemia receiving intravenous (IV) insulin therapy from March 2006 to December 2007 in the intensive care units of 2 tertiary care teaching hospitals. Patients placed on a glycemic control protocol using the Clarian GlucoStabilizer™ IV insulin dosing calculator with a target range of 4.4-6.1 mmol/L were analyzed. Metrics included time to target, time in target, mean blood glucose ± standard deviation, % measures in hypoglycemic ranges <3.9 mmol/L, per-patient hypoglycemia, and BG testing interval.
Results
4,588 ICU patients were treated with the GlucoStabilizer to a BG target range of 4.4-6.1 mmol/L. We observed 254 severe hypoglycemia episodes (BG <2.2 mmol/L) in 195 patients, representing 0.1% of all measurements, and in 4.25% of patients or 0.6 episodes per 1000 hours on insulin infusion. The most common contributing cause for hypoglycemia was measurement delay (n = 170, 66.9%). The median (interquartile range) time to achieve the target range was 5.9 (3.8 - 8.9) hours. Nearly all (97.5%) of patients achieved target and remained in target 73.4% of the time. The mean BG (± SD) after achieving target was 5.4 (± 0.52) mmol/L. Targeted blood glucose levels were achieved at similar rates with low incidence of severe hypoglycemia in patients with and without diabetes, sepsis, renal, and cardiovascular disease.
Conclusions
Glycemic control to a lower glucose target range can be achieved using a computerized insulin dosing protocol. With particular attention to timely measurement and adjustment of insulin doses the risk of hypoglycemia experienced can be minimized.
doi:10.1186/cc8129
PMCID: PMC2784393  PMID: 19822000
12.  Markers of Dysglycaemia and Risk of Coronary Heart Disease in People without Diabetes: Reykjavik Prospective Study and Systematic Review 
PLoS Medicine  2010;7(5):e1000278.
Background
Associations between circulating markers of dysglycaemia and coronary heart disease (CHD) risk in people without diabetes have not been reliably characterised. We report new data from a prospective study and a systematic review to help quantify these associations.
Methods and Findings
Fasting and post-load glucose levels were measured in 18,569 participants in the population-based Reykjavik study, yielding 4,664 incident CHD outcomes during 23.5 y of mean follow-up. In people with no known history of diabetes at the baseline survey, the hazard ratio (HR) for CHD, adjusted for several conventional risk factors, was 2.37 (95% CI 1.79–3.14) in individuals with fasting glucose ≥7.0 mmol/l compared to those <7 mmol/l. At fasting glucose values below 7 mmol/l, adjusted HRs were 0.95 (0.89–1.01) per 1 mmol/l higher fasting glucose and 1.03 (1.01–1.05) per 1 mmol/l higher post-load glucose. HRs for CHD risk were generally modest and nonsignificant across tenths of glucose values below 7 mmol/l. We did a meta-analysis of 26 additional relevant prospective studies identified in a systematic review of Western cohort studies that recorded fasting glucose, post-load glucose, or glycated haemoglobin (HbA1c) levels. In this combined analysis, in which participants with a self-reported history of diabetes and/or fasting blood glucose ≥7 mmol/l at baseline were excluded, relative risks for CHD, adjusted for several conventional risk factors, were: 1.06 (1.00–1.12) per 1 mmol/l higher fasting glucose (23 cohorts, 10,808 cases, 255,171 participants); 1.05 (1.03–1.07) per 1 mmol/l higher post-load glucose (15 cohorts, 12,652 cases, 102,382 participants); and 1.20 (1.10–1.31) per 1% higher HbA1c (9 cohorts, 1639 cases, 49,099 participants).
Conclusions
In the Reykjavik Study and a meta-analysis of other Western prospective studies, fasting and post-load glucose levels were modestly associated with CHD risk in people without diabetes. The meta-analysis suggested a somewhat stronger association between HbA1c levels and CHD risk.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Among people diagnosed with type 2 diabetes mellitus (the commonest type of diabetes worldwide), poor management or lack of appropriate treatment can lead to long-term complications resulting from persistently high sugar levels in the blood. The long-term complications of type 2 diabetes are generally divided into two main groups: microvascular problems (such as nerve damage, kidney disease, and eye disorders), and macrovascular disease (such as heart disease, strokes, and peripheral vascular disease). A major goal of diabetes treatment is to keep glucose control as normal as possible through diet, weight control, exercise, and pharmacological treatments. However, it is unclear whether the link between high blood sugar and macrovascular disease (principally heart disease and strokes) also holds for people who have slightly higher than normal blood sugar levels, but in whom this level does not reach the diabetic threshold. Some previous research studies have suggested that a continuous relationship exists between blood sugar level and the risk of heart disease across the spectrum, i.e., below the diabetic threshold as well as above it. If such a relationship were confirmed this might have important implications for the management of high blood sugar levels even among people who would not normally meet the usual definition for a diagnosis of diabetes (the “diabetic threshold”).
Why Was This Study Done?
Studies which examine the risk of serious, but relatively common, outcomes (such as a nonfatal heart attack or fatal heart disease), often suffer from insufficient statistical power: a large number of participants need to be recruited, and followed up over a long time, to find out whether certain factors measured at baseline (e.g., fasting glucose) are indeed associated with a particular outcome (e.g., heart attack) or not during follow up. Given the inconclusive nature of some previous studies in this area, the researchers who carried out this work wanted to gather evidence from a large prospective cohort, and a reappraisal of all existing evidence, in relation to the possible link between high blood sugar and risk of heart disease in people without diabetes.
What Did the Researchers Do and Find?
In this study, the researchers report results from a prospective population-based study (in which participants are followed forward in time) from Reykjavik, Iceland. In the study, men and women without history of heart disease aged between 31 and 57 in 1966 were first invited to join the cohort, and were followed forward in time using national registries that recorded deaths (and causes of death), and incidence of heart disease. A total of 8,888 male and 9,681 female participants were recruited. At baseline, laboratory measurements were taken to record blood sugar levels using two different methods: fasting blood glucose and post-load glucose. Among the group of participants, 4,664 people were recorded as having either a nonfatal heart attack or fatal heart disease, during approximately 23 years of follow-up. In addition, the researchers attempted to identify from the published medical literature previous prospective studies conducted in Western populations that had looked at the association between blood sugar levels and risk of coronary heart disease. They requested, and obtained, re-analyses of data conducted in accordance with a common protocol for most of the identified studies and then analysed these, together with the results of the Reykjavik cohort, to produce a summary estimate (meta-analysis) of the association between blood sugar levels and risk of coronary heart disease in people without diabetes.
In the Reykjavik cohort, the researchers confirmed an increased risk of coronary heart disease among individuals with blood sugar above the diabetic threshold, as compared to those below it. However, when they looked at blood sugar in people below the diabetic threshold, they found no evidence that higher levels were strongly linked with greater risk of coronary heart disease. This held for both methods of measuring blood sugar levels (fasting and post-load).
In the meta-analysis, the researchers obtained data for 27 different studies, comprising 303,961 participants and 16,982 cases of heart disease. In this meta-analysis, very small increases in risk of heart disease were found with higher levels of blood sugar, when measured using fasting blood glucose or post-load glucose. However, studies using glycated haemoglobin (a measure of average sugar levels over the past 1–3 months or so) found this measure to be associated with a somewhat higher risk of heart disease.
What Do these Findings Mean?
In this prospective cohort and wider meta-analysis, the researchers did not find evidence of a strong or continuous association between blood sugar levels and risk of heart disease amongst people without diabetes. The prospective study, and analysis of other cohorts, was large, but only looked at participants of European decent, so it is not clear whether the findings will also hold for non-European groups.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000278.
Information is available from the US National Diabetes Information Clearinghouse about diabetes, heart disease, and stroke
Centers for Disease Control provides information for the public and professionals about diabetes on their diabetes minisite
Medline Plus encyclopedia has an entry about coronary heart disease
doi:10.1371/journal.pmed.1000278
PMCID: PMC2876150  PMID: 20520805
13.  Glycaemic control in Australia and New Zealand before and after the NICE-SUGAR trial: a translational study 
Critical Care  2013;17(5):R215.
Introduction
There is no information on the uptake of Intensive Insulin Therapy (IIT) before the Normoglycemia in Intensive Care Evaluation and Surviving Using Glucose Algorithm Regulation (NICE-SUGAR) trial in Australia and New Zealand (ANZ) and on the bi-national response to the trial, yet such data would provide important information on the evolution of ANZ practice in this field. We aimed to study ANZ glycaemic control before and after the publication of the results of the NICE-SUGAR trial.
Methods
We analysed glucose control in critically ill patients across Australia and New Zealand during a two-year period before and after the publication of the NICE-SUGAR study. We used the mean first day glucose (Glu1) (a validated surrogate of ICU glucose control) to define practice. The implementation of an IIT protocol was presumed if the median of Glu1 measurements was <6.44 mmol/L for a given ICU. Hypoglycaemia was categorised as severe (glucose ≤2.2 mmol/L) or moderate (glucose ≤3.9 mmol/L).
Results
We studied 49 ICUs and 176,505 patients. No ICU practiced IIT before or after NICE-SUGAR. Overall, Glu1 increased from 7.96 (2.95) mmol/L to 8.03 (2.92) mmol/L (P <0.0001) after NICE-SUGAR. Similar increases were noted in all patient subgroups studied (surgical, medical, insulin dependent diabetes mellitus, ICU stay >48/<48 hours). The rate of severe and moderate hypoglycaemia before and after NICE-SUGAR study were 0.59% vs. 0.55% (P =0.33) and 6.62% vs. 5.68% (P <0.0001), respectively. Both crude and adjusted mortalities declined over the study period.
Conclusions
IIT had not been adopted in ANZ before the NICE-SUGAR study and glycaemic control corresponded to that delivered in the control arm of NICE-SUGAR trial. There were only minor changes in practice after the trial toward looser glycaemic control. The rate of moderate hypoglycaemia and mortality decreased along with such changes.
doi:10.1186/cc13030
PMCID: PMC4056083  PMID: 24088368
14.  The epidemiology of intensive care unit-acquired hyponatraemia and hypernatraemia in medical-surgical intensive care units 
Critical Care  2008;12(6):R162.
Introduction
Although sodium disturbances are common in hospitalised patients, few studies have specifically investigated the epidemiology of sodium disturbances in the intensive care unit (ICU). The objectives of this study were to describe the incidence of ICU-acquired hyponatraemia and hypernatraemia and assess their effects on outcome in the ICU.
Methods
We identified 8142 consecutive adults (18 years of age or older) admitted to three medical-surgical ICUs between 1 January 2000 and 31 December 2006 who were documented to have normal serum sodium levels (133 to 145 mmol/L) during the first day of ICU admission. ICU acquired hyponatraemia and hypernatraemia were respectively defined as a change in serum sodium concentration to below 133 mmol/L or above 145 mmol/L following day one in the ICU.
Results
A first episode of ICU-acquired hyponatraemia developed in 917 (11%) patients and hypernatraemia in 2157 (26%) patients with an incidence density of 3.1 and 7.4 per 100 days of ICU admission, respectively, during 29,142 ICU admission days. The incidence of both ICU-acquired hyponatraemia (age, admission diagnosis, Acute Physiology and Chronic Health Evaluation (APACHE) II score, length of ICU stay, level of consciousness, serum glucose level, body temperature, serum potassium level) and ICU-acquired hypernatraemia (baseline creatinine, APACHE II score, mechanical ventilation, length of ICU stay, body temperature, serum potassium level, level of care) varied according to patients' characteristics. Compared with patients with normal serum sodium levels, hospital mortality was increased in patients with ICU-acquired hyponatraemia (16% versus 28%, p < 0.001) and ICU-acquired hypernatraemia (16% versus 34%, p < 0.001).
Conclusions
ICU-acquired hyponatraemia and hypernatraemia are common in critically ill patients and are associated with increased risk of hospital mortality.
doi:10.1186/cc7162
PMCID: PMC2646327  PMID: 19094227
15.  Pilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control 
Introduction
Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) < 4.0 mmol/L. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in pilot trials.
Methods
Seven patients covering 660 hours. Insulin and nutrition interventions are given 1-3 hourly as chosen by the nurse to allow them to manage workload. Interventions are calculated by using clinically validated computer models of human metabolism and its variability in critical illness to maximize the overlap of the model-predicted (5-95th percentile) range of BG outcomes with the 4.0-6.5 mmol/L band while ensuring a maximum 5% risk of BG < 4.0 mmol/L. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of SCCM/ACCP goal (25 kg/kcal/h). Maximum insulin doses and dose changes were limited for safety. Measurements were made with glucometers. Results are compared to those for the SPRINT study, which reduced mortality 25-40% for length of stay ≥3 days. Written informed consent was obtained for all patients, and approval was granted by the NZ Upper South A Regional Ethics Committee.
Results
A total of 402 measurements were taken over 660 hours (~14/day), because nurses showed a preference for 2-hourly measurements. Median [interquartile range, (IQR)] cohort BG was 5.9 mmol/L [5.2-6.8]. Overall, 63.2%, 75.9%, and 89.8% of measurements were in the 4.0-6.5, 4.0-7.0, and 4.0-8.0 mmol/L bands. There were no hypoglycemic events (BG < 2.2 mmol/L), and the minimum BG was 3.5 mmol/L with 4.5% < 4.4 mmol/L. Per patient, the median [IQR] hours of TGC was 92 h [29-113] using 53 [19-62] measurements (median, ~13/day). Median [IQR] results: BG, 5.9 mmol/L [5.8-6.3]; carbohydrate nutrition, 6.8 g/h [5.5-8.7] (~70% goal feed median); insulin, 2.5 U/h [0.1-5.1]. All patients achieved BG < 6.1 mmol/L. These results match or exceed SPRINT and clinical workload is reduced more than 20%.
Conclusions
STAR TGC modulating insulin and nutrition inputs provided very tight control with minimal variability by managing intra- and inter- patient variability. Performance and safety exceed that of SPRINT, which reduced mortality and cost in the Christchurch ICU. The use of glucometers did not appear to impact the quality of TGC. Finally, clinical workload was self-managed and reduced 20% compared with SPRINT.
doi:10.1186/2110-5820-1-38
PMCID: PMC3224394  PMID: 21929821
16.  Intensive insulin therapy and mortality in critically ill patients 
Critical Care  2008;12(1):R29.
Introduction
Intensive insulin therapy (IIT) with tight glycemic control may reduce mortality and morbidity in critically ill patients and has been widely adopted in practice throughout the world. However, there is only one randomized controlled trial showing unequivocal benefit to this approach and that study population was dominated by post-cardiac surgery patients. We aimed to determine the association between IIT and mortality in a mixed population of critically ill patients.
Methods
We conducted a cohort study comparing three consecutive time periods before and after IIT protocol implementation in a Level 1 trauma center: period I (no protocol); period II, target glucose 80 to 130 mg/dL; and period III, target glucose 80 to 110 mg/dL. Subjects were 10,456 patients admitted to intensive care units (ICUs) between 1 March 2001 and 28 February 2005. The main study endpoints were ICU and hospital mortality, Sequential Organ Failure Assessment score, and occurrence of hypoglycemia. Multivariable regression analysis was used to evaluate mortality and organ dysfunction during periods II and III relative to period I.
Results
Insulin administration increased over time (9% period I, 25% period II, and 42% period III). Nonetheless, patients in period III had a tendency toward higher adjusted hospital mortality (odds ratio [OR] 1.15, 95% confidence interval [CI] 0.98, 1.35) than patients in period I. Excess hospital mortality in period III was present primarily in patients with an ICU length of stay of 3 days or less (OR 1.47, 95% CI 1.11, 1.93 There was an approximately fourfold increase in the incidence of hypoglycemia from periods I to III.
Conclusion
A policy of IIT in a group of ICUs from a single institution was not associated with a decrease in hospital mortality. These results, combined with the findings from several recent randomized trials, suggest that further study is needed prior to widespread implementation of IIT in critically ill patients.
doi:10.1186/cc6807
PMCID: PMC2374630  PMID: 18312617
17.  Differential temporal profile of lowered blood glucose levels (3.5 to 6.5 mmol/l versus 5 to 8 mmol/l) in patients with severe traumatic brain injury 
Critical Care  2008;12(4):R98.
Introduction
Hyperglycaemia is detrimental, but maintaining low blood glucose levels within tight limits is controversial in patients with severe traumatic brain injury, because decreased blood glucose levels can induce and aggravate underlying brain injury.
Methods
In 228 propensity matched patients (age, sex and injury severity) treated in our intensive care unit (ICU) from 2000 to 2004, we retrospectively evaluated the influence of different predefined blood glucose targets (3.5 to 6.5 versus 5 to 8 mmol/l) on frequency of hypoglycaemic and hyperglycaemic episodes, insulin and norepinephrine requirement, changes in intracranial pressure and cerebral perfusion pressure, mortality and length of stay on the ICU.
Results
Mortality and length of ICU stay were similar in both blood glucose target groups. Blood glucose values below and above the predefined levels were significantly increased in the 3. 5 to 6.5 mmol/l group, predominantly during the first week. Insulin and norepinephrine requirements were markedly increased in this group. During the second week, the incidences of intracranial pressure exceeding 20 mmHg and infectious complications were significantly decreased in the 3.5 to 6.5 mmol/l group.
Conclusion
Maintaining blood glucose within 5 to 8 mmol/l appears to yield greater benefit during the first week. During the second week, 3.5 to 6.5 mmol/l is associated with beneficial effects in terms of reduced intracranial hypertension and decreased rate of pneumonia, bacteraemia and urinary tract infections. It remains to be determined whether patients might profit from temporally adapted blood glucose limits, inducing lower values during the second week, and whether concomitant glucose infusion to prevent hypoglycaemia is safe in patients with post-traumatic oedema.
doi:10.1186/cc6974
PMCID: PMC2575586  PMID: 18680584
18.  Computer-assisted glucose control in critically ill patients 
Intensive Care Medicine  2008;34(8):1421-1427.
Objective
Intensive insulin therapy is associated with the risk of hypoglycemia and increased costs of material and personnel. We therefore evaluated the safety and efficiency of a computer-assisted glucose control protocol in a large population of critically ill patients.
Design and setting
Observational cohort study in three intensive care units (32 beds) in a 1,300-bed university teaching hospital.
Patients
All 2,800 patients admitted to the surgical, neurosurgical, and cardiothoracic units; the study period started at each ICU after implementation of Glucose Regulation for Intensive Care Patients (GRIP), a freely available computer-assisted glucose control protocol.
Measurements and results
We analysed compliance in relation to recommended insulin pump rates and glucose measurement frequency. Patients were on GRIP-ordered pump rates 97% of time. Median measurement time was 5 min late (IQR 20 min early to 34 min late). Hypoglycemia was uncommon (7% of patients for mild hypoglycemia, < 3.5 mmol/l; 0.86% for severe hypoglycemia, < 2.2 mmol/l). Our predefined target range (4.0–7.5 mmol/l) was reached after a median of 5.6 h (IQR 0.2–11.8) and maintained for 89% (70–100%) of the remaining stay at the ICU. The number of measurements needed was 5.9 (4.8–7.3) per patient per day. In-hospital mortality was 10.1%.
Conclusions
Our computer-assisted glucose control protocol provides safe and efficient glucose regulation in routine intensive care practice. A low rate of hypoglycemic episodes was achieved with a considerably lower number of glucose measurements than used in most other schemes.
Electronic supplementary material
The online version of this article (doi:10.1007/s00134-008-1091-y) contains supplementary material, which is available to authorized users.
doi:10.1007/s00134-008-1091-y
PMCID: PMC2491417  PMID: 18389221
Insulin; Glucose; Critically ill; Computer decision support
19.  Trial design: Computer guided normal-low versus normal-high potassium control in critically ill patients: Rationale of the GRIP-COMPASS study 
BMC Anesthesiology  2010;10:23.
Background
Potassium depletion is common in hospitalized patients and can cause serious complications such as cardiac arrhythmias. In the intensive care unit (ICU) the majority of patients require potassium suppletion. However, there are no data regarding the optimal control target in critically ill patients. After open-heart surgery, patients have a strongly increased risk of atrial fibrillation or atrial flutter (AFF). In a novel trial design, we examined if in these patients different potassium control-targets within the normal range may have different effects on the incidence of AFF.
Methods/Design
The "computer-driven Glucose and potassium Regulation program in Intensive care Patients with COMparison of PotASSium targets within normokalemic range (GRIP-COMPASS) trial" is a single-center prospective trial in which a total of 1200 patients are assigned to either a potassium control-target of 4.0 mmol/L or 4.5 mmol/L in consecutive alternating blocks of 50 patients each. Potassium levels are regulated by the computer-assisted potassium suppletion algorithm called GRIP-II (Glucose and potassium regulation for Intensive care Patients). Primary endpoint is the in-hospital incidence of AFF after cardiac surgery. Secondary endpoints are: in-hospital AFF in medical patients or patients after non-cardiac surgery, actually achieved potassium levels and their variation, electrolyte and glucose levels, potassium and insulin requirements, cumulative fluid balance, (ICU) length of stay, ICU mortality, hospital mortality and 90-day mortality.
Discussion
The GRIP-COMPASS trial is the first controlled clinical trial to date that compares potassium targets. Other novel methodological elements of the study are that it is performed in ICU patients where both targets are within the normal range and that a computer-assisted potassium suppletion algorithm is used.
Trial registration
NCT 01085071 at ClinicalTrials.gov
doi:10.1186/1471-2253-10-23
PMCID: PMC3022901  PMID: 21194419
20.  The impact of early hypoglycemia and blood glucose variability on outcome in critical illness 
Critical Care  2009;13(3):R91.
Introduction
In critical illness, the association of hypoglycemia, blood glucose (BG) variability and outcome are not well understood. We describe the incidence, clinical factors and outcomes associated with an early hypoglycemia and BG variability in critically ill patients.
Methods
Retrospective interrogation of prospectively collected data from the Australia New Zealand Intensive Care Society Adult Patient Database on 66184 adult admissions to 24 intensive care units (ICUs) from 1 January 2000 to 31 December 2005. Primary exposure was hypoglycemia (BG < 4.5 mmol/L) and BG variability (BG < 4.5 and ≥ 12.0 mmol/L) within 24 hours of admission. Primary outcome was all-cause mortality.
Results
The cumulative incidence of hypoglycemia and BG variability were 13.8% (95% confidence interval (CI) = 13.5 to 14.0; n = 9122) and 2.9% (95%CI = 2.8 to 3.0, n = 1913), respectively. Several clinical factors were associated with both hypoglycemia and BG variability including: co-morbid disease (P < 0.001), non-elective admissions (P < 0.001), higher illness severity (P < 0.001), and primary septic diagnosis (P < 0.001). Hypoglycemia was associated with greater odds of adjusted ICU (odds ratio (OR) = 1.41, 95% CI = 1.31 to 1.54) and hospital death (OR = 1.36, 95% CI = 1.27 to 1.46). Hypoglycemia severity was associated with 'dose-response' increases in mortality. BG variability was associated with greater odds of adjusted ICU (1.5, 95% CI = 1.4 to 1.6) and hospital (1.4, 95% CI = 1.3 to 1.5) mortality, when compared with either hypoglycemia only or neither.
Conclusions
In critically ill patients, both early hypoglycemia and early variability in BG are relatively common, and independently portend an increased risk for mortality.
doi:10.1186/cc7921
PMCID: PMC2717463  PMID: 19534781
21.  Intensive insulin treatment improves forearm blood flow in critically ill patients: a randomized parallel design clinical trial 
Critical Care  2009;13(6):R198.
Introduction
Intensive insulin treatment of critically ill patients was seen as a promising method of treatment, though recent studies showed that reducing the blood glucose level below 6 mmol/l had a detrimental outcome. The mechanisms of the effects of insulin in the critically ill are not completely understood. The purpose of the study was to test the hypothesis that intensive insulin treatment may influence forearm blood flow independently of global hemodynamic indicators.
Methods
The study encompassed 29 patients of both sexes who were admitted to the intensive care unit due to sepsis and required artificial ventilation as the result of acute respiratory failure. 14 patients were randomly selected for intensive insulin treatment (Group 1; blood glucose concentration 4.4-6.1 mmol/l), and 15 were selected for conventional insulin treatment (Group 2; blood glucose level 7.0 mmol/l-11.0 mmol/l). At the start of the study (t0, beginning up to 48 hours after admittance and the commencement of artificial ventilation), at 2 hours (t1), 24 hours (t2), and 72 hours (t3) flow in the forearm was measured for 60 minutes using the strain-gauge plethysmography method. Student's t-test of independent samples was used for comparisons between the two groups, and Mann-Whitney's U-test where appropriate. Linear regression analysis and the Pearson correlation coefficient were used to determine the levels of correlation.
Results
The difference in 60-minute forearm flow at the start of the study (t0) was not statistically significant between groups, while at t2 and t3 significantly higher values were recorded in Group 1 (t2; Group 1: 420.6 ± 188.8 ml/100 ml tissue; Group 2: 266.1 ± 122.2 ml/100 ml tissue (95% CI 30.9-278.0, P = 0.02); t3; Group 1: 369.9 ± 150.3 ml/100 ml tissue; Group 2: 272.6 ± 85.7 ml/100 ml tissue (95% CI 5.4-190.0, P = 0.04). At t1 a trend towards significantly higher values in Group 1 was noted (P = 0.05). The level of forearm flow was related to the amount of insulin infusion (r = 0.40).
Conclusions
Compared to standard treatment, intensive insulin treatment of critically ill patients increases forearm flow. Flow increase was weakly related to the insulin dose, though not to blood glucose concentration.
Trial Registration
Trial number: ISRCTN39026810.
doi:10.1186/cc8202
PMCID: PMC2811932  PMID: 20003200
22.  Changes in HbA1c Level over a 12-Week Follow-up in Patients with Type 2 Diabetes following a Medication Change 
PLoS ONE  2014;9(3):e92458.
Background
Current guidance about the interval needed before retesting HbA1c when monitoring for glycaemic control is based on expert opinion rather than well-powered studies. The aim of our work was to explore how fast HbA1c changes after a change in glucose-lowering medication. This has implications for whether routine HbA1c testing intervals before 12 weeks could inform diabetes medication adjustments.
Methods
This 12-week cohort study recruited patients from 18 general practices in the United Kingdom with non-insulin treated diabetes who were initiating or changing dose of oral glucose-lowering medication. HbA1c was measured at baseline and 2, 4, 8 and 12 weeks after recruitment. HbA1c levels at earlier time intervals were correlated with 12-week HbA1c. A ROC curve analysis was used to identify the 8-week threshold above which medication adjustment may be clinically appropriate.
Results
Ninety-three patients were recruited to the study. Seventy-nine patients with no change in medication and full 12-week follow-up had the following baseline characteristics: mean±standard deviation age of 61.3±10.8 years, 34% were female and diabetes duration of 6.0±4.3 years. Mean HbA1c at baseline, 2, 4, 8 and 12 weeks was 8.7±1.5%, (72.0±16.8 mmol/mol) 8.6±1.6% (70.7±17.0 mmol/mol), 8.4±1.5% (68.7±15.9 mmol/mol), 8.2±1.4% (66.3±15.8 mmol/mol) and 8.1±1.4% (64.8±15.7 mmol/mol) respectively. At the end of the study 61% of patients had sub-optimal glycaemic control (HbA1c>7.5% or 59 mmol/mol). The 8-week change correlated significantly with the 12-week change in HbA1c and an HbA1c above 8.2% (66 mmol/mol) at 8 weeks correctly classified all 28 patients who had not achieved glycaemic control by 12 weeks.
Conclusions/interpretation
This is the first study designed with sufficient power to examine short-term changes in HbA1c. The 12-week change in HbA1c can be predicted 8 weeks after a medication change. Many participants who had not achieved glycaemic control after 12 weeks may have benefitted from an earlier review of their HbA1c and medication.
doi:10.1371/journal.pone.0092458
PMCID: PMC3965408  PMID: 24667212
23.  Intensive Insulin Therapy in Critically Ill Hospitalized Patients: Making It Safe and Effective 
Intensive insulin therapy (IIT) for hyperglycemia in critically ill patients has become a standard practice. Target levels for glycemia have fluctuated since 2000, as evidence initially indicated that tight glycemic control to so-called normoglycemia (80–110 mg/dl) leads to the lowest morbidity and mortality without hypoglycemic complications. Subsequent studies have demonstrated minimal clinical benefit combined with greater hypoglycemic morbidity and mortality with tight glycemic control in this population. The consensus glycemic targets were then liberalized to the mid 100s (mg/dl).
Handheld POC blood glucose (BG) monitors have migrated from the outpatient setting to the hospital environment because they save time and money for managing critically ill patients who require IIT. These devices are less accurate than hospital-grade POC blood analyzers or central laboratory analyzers.
Three questions must be answered to understand the role of IIT for defined populations of critically ill patients: (1) How safe is IIT, with various glycemic targets, from the risk of hypoglycemia? (2) How tightly must BG be controlled for this approach to be effective? (3) What role does the accuracy of BG measurements play in affecting the safety of this method? For each state of impaired glucose regulation seen in the hospital, such as hyperglycemia, hypoglycemia, or glucose variability, the benefits, risks, and goals of treatment, including IIT, might differ.
With improved accuracy of BG monitors, IIT might be rendered even more intensive than at present, because patients will be less likely to receive inadvertent overdosages of insulin. Greater doses of insulin, but with dosing based on more accurate glucose levels, might result in less hypoglycemia, less hyperglycemia, and less glycemic variability.
PMCID: PMC3192642  PMID: 21722591
critical care; glucose; glucose monitoring; glucose variability; hyperglycemia; hypoglycemia; insulin; intensive; intensive care unit; point of care
24.  Differential influence of arterial blood glucose on cerebral metabolism following severe traumatic brain injury 
Critical Care  2009;13(1):R13.
Introduction
Maintaining arterial blood glucose within tight limits is beneficial in critically ill patients. Upper and lower limits of detrimental blood glucose levels must be determined.
Methods
In 69 patients with severe traumatic brain injury (TBI), cerebral metabolism was monitored by assessing changes in arterial and jugular venous blood at normocarbia (partial arterial pressure of carbon dioxide (paCO2) 4.4 to 5.6 kPa), normoxia (partial arterial pressure of oxygen (paO2) 9 to 20 kPa), stable haematocrit (27 to 36%), brain temperature 35 to 38°C, and cerebral perfusion pressure (CPP) 70 to 90 mmHg. This resulted in a total of 43,896 values for glucose uptake, lactate release, oxygen extraction ratio (OER), carbon dioxide (CO2) and bicarbonate (HCO3) production, jugular venous oxygen saturation (SjvO2), oxygen-glucose index (OGI), lactate-glucose index (LGI) and lactate-oxygen index (LOI). Arterial blood glucose concentration-dependent influence was determined retrospectively by assessing changes in these parameters within pre-defined blood glucose clusters, ranging from less than 4 to more than 9 mmol/l.
Results
Arterial blood glucose significantly influenced signs of cerebral metabolism reflected by increased cerebral glucose uptake, decreased cerebral lactate production, reduced oxygen consumption, negative LGI and decreased cerebral CO2/HCO3 production at arterial blood glucose levels above 6 to 7 mmol/l compared with lower arterial blood glucose concentrations. At blood glucose levels more than 8 mmol/l signs of increased anaerobic glycolysis (OGI less than 6) supervened.
Conclusions
Maintaining arterial blood glucose levels between 6 and 8 mmol/l appears superior compared with lower and higher blood glucose concentrations in terms of stabilised cerebral metabolism. It appears that arterial blood glucose values below 6 and above 8 mmol/l should be avoided. Prospective analysis is required to determine the optimal arterial blood glucose target in patients suffering from severe TBI.
doi:10.1186/cc7711
PMCID: PMC2688130  PMID: 19196488
25.  Defective Awakening Response to Nocturnal Hypoglycemia in Patients with Type 1 Diabetes Mellitus 
PLoS Medicine  2007;4(2):e69.
Background
Nocturnal hypoglycemia frequently occurs in patients with type 1 diabetes mellitus (T1DM). It can be fatal and is believed to promote the development of the hypoglycemia-unawareness syndrome. Whether hypoglycemia normally provokes awakening from sleep in individuals who do not have diabetes, and whether this awakening response is impaired in T1DM patients, is unknown.
Methods and Findings
We tested two groups of 16 T1DM patients and 16 healthy control participants, respectively, with comparable distributions of gender, age, and body mass index. In one night, a linear fall in plasma glucose to nadir levels of 2.2 mmol/l was induced by infusing insulin over a 1-h period starting as soon as polysomnographic recordings indicated that stage 2 sleep had been reached. In another night (control), euglycemia was maintained.
Only one of the 16 T1DM patients, as compared to ten healthy control participants, awakened upon hypoglycemia (p = 0.001). In the control nights, none of the study participants in either of the two groups awakened during the corresponding time. Awakening during hypoglycemia was associated with increased hormonal counterregulation. In all the study participants (from both groups) who woke up, and in five of the study participants who did not awaken (three T1DM patients and two healthy control participants), plasma epinephrine concentration increased with hypoglycemia by at least 100% (p < 0.001). A temporal pattern was revealed such that increases in epinephrine in all participants who awakened started always before polysomnographic signs of wakefulness (mean ± standard error of the mean: 7.5 ± 1.6 min).
Conclusions
A fall in plasma glucose to 2.2 mmol/l provokes an awakening response in most healthy control participants, but this response is impaired in T1DM patients. The counterregulatory increase in plasma epinephrine that we observed to precede awakening suggests that awakening forms part of a central nervous system response launched in parallel with hormonal counterregulation. Failure to awaken increases the risk for T1DM patients to suffer prolonged and potentially fatal hypoglycemia.
A study of 16 patients with type 1 diabetes and 16 healthy participants showed that the normal awakening response provoked by a fall in plasma glucose was impaired in diabetic individuals.
Editors' Summary
Background.
Hypoglycemia (low blood sugar) is a frequent complication of insulin-treated diabetes, affecting patients with type 1 diabetes mellitus in particular. In individuals who do not have diabetes, insulin secretion is modified naturally and continuously by the body's own regulatory systems, depending on the blood sugar. However, in diabetes patients there is a lack of natural insulin and so manufactured insulin has to be given by injection after blood sugar testing. Hence, it is not possible for patients with diabetes to modify insulin secretion naturally in response to a change in glucose levels, and so blood glucose levels can rise and fall beyond healthy levels. In individuals who have intensive insulin therapy, hypoglycemia can be a particular problem; each year about 25% of patients on intensive insulin therapy have at least one episode of severe hypoglycemia—which requires the assistance of another person.
When hypoglycemia occurs during the day, diabetes patients can recognize it by a variety of symptoms, e.g., feeling sweaty and lightheaded, and they may either seek help from another person or treat themselves with sugar. Hypoglycemia during sleep may be very common—it has been observed to occur in up to half of the nights when patients with diabetes were monitored. The particular problem with hypoglycemia occurring during sleep is that diabetes patients may not be aware of it and hence may not be able to treat themselves or to seek assistance. It is believed to contribute to some instances of sudden death during sleep in patients with diabetes.
Why Was This Study Done?
It has not been clear whether there is a certain level of blood glucose below which a signal is triggered that provokes awakening from sleep in either diabetes patients or in individuals who do not have diabetes. The authors of this study wanted to compare responses to lowered blood glucose in diabetes patients and in individuals who do not have diabetes and to see whether the responses differed. They also wanted to look at whether there were any other hormonal changes that preceded or followed awakening after hypoglycemia.
What Did the Researchers Do and Find?
They treated two groups; 16 type 1 diabetes mellitus patients and 16 healthy control participants. With careful monitoring, on one night once stage 2 sleep (as measured by a method known as polysomnography) had been reached, they gave insulin to lower the blood glucose to a specific level (2.2 mmol/l), which would when awake give symptoms of hypoglycemia. On another night (the control night) normal blood sugar levels were maintained.
They found that only one of the 16 diabetes patients, as compared to ten healthy control participants, woke when hypoglycemia occurred. In the control nights, none of the study participants in either of the two groups awakened during the corresponding time. Awakening during hypoglycemia was associated with substantial hormonal changes, especially with an increase in one hormone, epinephrine (also known as adrenaline), and the increases in this hormone occurred before polysomnographic signs of wakefulness.
What Do These Findings Mean?
It appears that patients with type 1 diabetes mellitus do not awake at a level of hypoglycemia that triggers waking in normal individuals. The hormonal responses that were seen in individuals who awoke may be part of a crucial response system to hypoglycemia. These results help us to understand how diabetes patients respond to hypoglycemia, but further work will need to be done to determine whether it is possible to improve the response. It should be noted, however, that the results are probably not generalizable to patients with type 2 diabetes mellitus, who represent the majority of patients with diabetes.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040069.
A related PLoS Medicine Perspective article by Ilan Gabriely and Harry Shamoon discusses this study and more about hypoglycemia in T1DM
MedlinePlus, the encyclopedia of health information from the US National Library of Medicine, has a collection of pages on hypoglycemia
Wikipedia pages on hypoglycemia (note that Wikipedia is a free online encyclopedia that anyone can edit)
National Diabetes Information Clearinghouse, a service of the US National Institute of Diabetes and Digestive and Kidney Diseases, has information on hypoglycemia
doi:10.1371/journal.pmed.0040069
PMCID: PMC1808097  PMID: 17326710

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