Tight Glycemic Control Protocols Research Articles

Tight glycaemic control and surgical site infections post cardiac surgery: a systematic review.

Surgical site infection (SSI) is one of the most serious potential complications post cardiac surgery among patients with diabetes and has a number of adverse health outcomes. The literature shows discrepancies regarding the effect of different glycaemic control protocols on reducing adverse health outcomes including SSIs. The aim of this study was to conduct a systematic review that investigated the effect of the optimal range of tight glycaemic control protocols using a continuous insulin infusion on reducing the incidence of SSIs in adult patients with diabetes undergoing cardiac surgery. A systematic review was conducted following the PRISMA statement and guidelines. Search terms were used to identify research studies published between 2000 and 2019 across five key databases, including CINAHL, Medline, PubMed, Cochrane Database and Google Scholar. A total of 12 studies met the review inclusion criteria. The reviewed literature tended to support the implementation of a tight glycaemic control protocol, particularly in the postoperative phase, that demonstrated fewer potential complications associated with cardiac surgery. On the other hand, the literature also supported the application of a moderate glycaemic control protocol in the intraoperative phase to obtain better glycaemic stability with fewer potential complications among those patients with diabetes undergoing cardiac surgery. This analysis concludes that tight glycaemic control is more effective than moderate glycaemic control intraoperatively in terms of glycaemic stability among patients with diabetes undergoing cardiac surgery. Results also emphasised the importance of time-based protocol implementation to ensure better health outcomes and better quality of care for patients.

Behavior Analysis of Sex based Cohorts Using the Toolset of Artificial Intelligence Based Insulin Sensitivity Prediction Methods

Abstract Tight glycaemic control (TGC) is a treatment in the intensive care in order to avoid stress-induced hyperglycaemia. The insulin sensitivity (SI) prediction is an essential step of the best performing, clinically applied so-called STAR (Stochastic-TARgeted) TGC protocol. Previous results showed performance improvement of the SI prediction using artificial intelligence methods. This study analyses the clinical performance of distinct artificial intelligence based SI prediction methods (2 different neural network based prediction methods: Classification Deep Network and Mixture Density Network with 3 different parametrizations and 2 variants: sex-specific and non sex-specific for each). In-silico validation was used for evaluation simulating the treatment of 171 virtual patients. Based on the results the number of input parameters involved into the prediction can effectively increase the reliability of the SI prediction. Improvements in the performance are also experienced in several cases by using sex-specific models.

Artificial Intelligence Based Insulin Sensitivity Prediction for Personalized Glycaemic Control in Intensive Care

Stress-induced hyperglycaemia is a frequent complication in the intensive therapy that can be safely and efficiently treated by using the recently developed model-based tight glycaemic control (TGC) protocols. The most widely applied TGC protocol is the STAR (Stochastic-TARgeted) protocol which uses the insulin sensitivity (SI) for the assessment of the patients state. The patient-specific metabolic variability is managed by the so-called stochastic model allowing the prediction of the 90% confidence interval of the future SI value of the patients. In this paper deep neural network (DNN) based methods (classification DNN and Mixture Density Network) are suggested to implement the patient state prediction. The deep neural networks are trained by using three years of STAR treatment data. The methods are validated by comparing the prediction statistics with the reference data set. The prediction accuracy was also compared with the stochastic model currently used in the clinical practice. The presented results proved the applicability of the neural network based methods for the patient state prediction in the model based clinical treatment. Results suggest that the methods’ prediction accuracy was the same or better than the currently used stochastic model. These results are the initial successful step in the validation process of the proposed methods which will be followed by in-silico simulation trials.

A Novel Approach to Assess Performance of Glucometer

Glucose meters are excellent tools for self-monitoring of blood glucose especially in the circumstances where continuous monitoring is mandatory and at decision-making levels. Tight glycemic control protocols are important for preventing ill effects of fluctuating glucose levels. As technology advances, glucometers are getting better in terms of quality of results. The purpose here is to study the quality of glucose meter results in terms of clinical outcomes. The study was conducted in a tertiary care referral hospital. A total of 125 patients were recruited from pediatric wards. Bland-Altman plot, Parke error grid, and surveillance error grid analysis were used for comparing the results of the glucose meters with a standard laboratory method. It was found that there was a significant difference between the results of the 2 methods. Although usually minimal, the glucose meter results deviated from the results of the standard laboratory method. This will affect overall patient care especially in emergency conditions. Sometimes, the risk may be so high that the patient may be mislabeled as hypoglycemic when they actually are hyperglycemic and vice versa. This study is the first of its kind because no similar studies have been reported in the pediatric population. For effective use of glucose meters, the devices should give as accurate results as possible. Results should not only be accurate but also precise, without which critical errors may be possible. We recommend that for any glucose meter there should be regular calibration so that agreement with the reference laboratory method is maintained and effective medical decisions are possible.

Assessment of analytical performance of glucose meter in pediatric age group at tertiary care referral hospital

BackgroundGlucometers are the excellent tools for self-monitoring of blood glucose (SMBG). They are important especially in the circumstances where continuous monitoring is mandatory and at decision making levels. Tight glycemic control protocols are important for preventing the ill effects of fluctuating glucose levels. This increases the use of glucometers in various healthcare settings. As technology advances, glucometers are getting better in terms of quality of results. But still some lacunae are there.MethodsPresent study was conducted in the tertiary care referral hospital. One hundred twenty five patients were recruited from pediatric wards. Bland-Altman plot, Parke error grid and Surveillance error grid analysis were used for comparing results of glucose meter with that of standard laboratory method.ResultsIt is found that there is significant difference between the results by two methods. Though minimal but glucose meter results deviate from the results of standard lab method. This will affect the overall patient care especially in emergency conditions.ConclusionsThis study is the first of its kind as no similar studies have been reported in the pediatric population. For effective use of glucose meter it should give as accurate as possible estimate of actual glucose levels. Results should not only be accurate but also precise without which critical errors may be possible. We recommend that for any glucose meter there should be regular maintenance as well as calibration is to be done. So that agreement with reference laboratory method is maintained and effective medical decisions are made.

FEASIBILITY OF AN INTENSIVE CONTROL INSULIN-NUTRITION GLUCOSE MODEL ‘ICING’ WITH MALAYSIAN CRITICALLY-ILL PATIENT

<p class="lead">A clinically verified patient-specific glucose-insulin metabolic model known as ICING is used to account for time-varying insulin sensitivity. ICING was developed and validated from critically-ill patients with various medical conditions in the intensive care unit in Christchurch Hospital, New Zealand. Hence, it is interesting and vital to analyse the compatibility of the model once fitted to Malaysian critically-ill data. Results were assessed in terms of percentage of model-fit error, both by cohort and per-patient analysis. The ICING model accomplished median fitting error of<1% over data from 63 patients. Most importantly, the median per-patients is at a low fitting error of 0.34% and per cohort is 0.35%. These results provide a promising avenue for near future simulations of developing tight glycaemic control protocol in the Malaysian intensive care unit.</p>

VIRTUAL TRIAL AND MONTE CARLO ANALYSIS OF MODEL-BASED GLYCAEMIC CONTROL PROTOCOL WITH REDUCED NURSING EFFORT

Tight glycaemic management has been shown to be beneficial to the outcomes of patients receiving intensive care. However, tight glycaemic control (TGC) protocol within intensive care (ICU) comes with a high clinical demand, namely high nursing effort. Thus, there is a need for a protocol that is safe, effective, robust, yet does not require a high nursing effort. A less intensive protocol is designed to use a combination of subcutaneous long-acting insulin (glargine) with IV insulin bolus and only requires blood glucose (BG) measurements every 4 hours while maintaining measurement within 4.0-6.1 mmol/L.

Slow Deadoptation of a Strategy: Was Tight Glycemic Control Truly Impractical?

Hyperglycemia is a prevalent scenario in critically ill patients. Hyperglycemia is associated with many adverse outcomes, including immune disorder, oxidative stress, susceptibility to infection, and endothelial dysfunction. Its impact is believed to be independently associated with increased mortality because it enhances the inflammatory responses. Some randomized controlled clinical trials have attempted to determine whether intensive insulin therapy targeted on establishing normoglycemia could benefit septic patients. Initial studies of adjustable insulin infusions to decrease blood glucose levels raised interest in inpatient glycemic control strategies (1,2), and several organizations called for implementing intensive insulin therapy (IIT) strategies using adjustable insulin infusions titrated to strict glycemic targets in the intensive care unit. Despite the early evidence of benefit from IIT (3-6), many subsequent trials, including the largest IIT trial to date, have not found a consistent benefit (7). Niven et al., in their article (8), evaluated glycemic control in critically ill patients before and after the publication of clinical trials, highlighting the fact that it was initially suggested that tight glycemic control reduced mortality (i.e. LEUVEN I study(6)), but subsequently it was suggested that tight glycemic control increased mortality (i.e. NICE SUGAR trial (7)). Before the publication of Leuven I, 17.2% (95% CI, 16.2%-18.2%) of ICU admissions had tight glycemic control, 3.0% (95% CI, 2.6%-3.5%) had hypoglycemia, and 40.2% (95% CI, 38.8%-41.5%) had hyperglycemia. After the publication of Leuven I, there were significant increases in the relative proportion of admissions with tight glycemic control (1.7% per quarter; 95% CI, 1.2%-2.3%; P < .001) and hypoglycemia (2.5% per quarter; 95% CI, 1.9%-3.2%; P <0.001) and decreases in those with hyperglycemia (0.6% per quarter; 95% CI, 0.4%-0.9%; P <0.001). Following the publication of NICE-SUGAR, there was no change in the proportion of patients with tight glycemic control or hyperglycemia. There was an immediate decrease in the relative proportion of patients with hypoglycemia (22.4%; 95% CI, 13.2%-30.1%; P <0.001) but no subsequent change over time. The authors reported that little or no deadoption from tight glycemic control was achieved following NICE SUGAR trial (1). It should be considered that there are fundamental differences between LEUVEN and NICE SUGAR trials. First, the level of therapy compliance in LEUVEN study was 70% compared to less than 50% in NICE SUGAR trial. Unsurprisingly, studies having managed to adequately achieve the blood glucose target have been associated with a reduced mortality compared to those not succeeding in therapy compliance. Second, in NICE SUGAR study, a variety of sampling sites and glucose meters were allowed most of which have recently been shown to be unsuitable for this purpose. Third, feeding strategies differed in two studies; in NICE SUGAR study feeding relied almost exclusively on the enteral route whereas in LEUVEN study, most of patients received supplemental parenteral nutrition which could be directed to a better response to insulin therapy. Fourth, most of the studied subjects in NICE SUGAR study were medical patients whereas in LEUVEN study, most of the studied subjects were surgical patients; these two different target groups would have had different responses to insulin therapy. Finally, normoglycemia was compared with distinctly different control targets (10-12 mmol/l in LEUVEN vs. 8-10 mmol/l in NICE SUGAR studies) which would have led to completely different results. As can be observed, there are numerous fundamental differences between these two studies which could have contributed to different outcomes. These obvious differences would definitely contribute to the unwillingness of the physicians for deadoption from tight glycemic control protocol. Further trials are required to evaluate this complex intervention which is occasionally incompletely implemented in recent trials.

A clinical and economic evaluation of Control of Hyperglycaemia in Paediatric intensive care (CHiP): a randomised controlled trial.

Early research in adults admitted to intensive care suggested that tight control of blood glucose during acute illness can be associated with reductions in mortality, length of hospital stay and complications such as infection and renal failure. Prior to our study, it was unclear whether or not children could also benefit from tight control of blood glucose during critical illness. This study aimed to determine if controlling blood glucose using insulin in paediatric intensive care units (PICUs) reduces mortality and morbidity and is cost-effective, whether or not admission follows cardiac surgery. Randomised open two-arm parallel group superiority design with central randomisation with minimisation. Analysis was on an intention-to-treat basis. Following random allocation, care givers and outcome assessors were no longer blind to allocation. The setting was 13 English PICUs. Patients who met the following criteria were eligible for inclusion: ≥ 36 weeks corrected gestational age; ≤ 16 years; in the PICU following injury, following major surgery or with critical illness; anticipated treatment > 12 hours; arterial line; mechanical ventilation; and vasoactive drugs. Exclusion criteria were as follows: diabetes mellitus; inborn error of metabolism; treatment withdrawal considered; in the PICU > 5 consecutive days; and already in CHiP (Control of Hyperglycaemia in Paediatric intensive care). The intervention was tight glycaemic control (TGC): insulin by intravenous infusion titrated to maintain blood glucose between 4.0 and 7.0 mmol/l. This consisted of insulin by intravenous infusion only if blood glucose exceeded 12.0 mmol/l on two samples at least 30 minutes apart; insulin was stopped when blood glucose fell below 10.0 mmol/l. The primary outcome was the number of days alive and free from mechanical ventilation within 30 days of trial entry (VFD-30). The secondary outcomes comprised clinical and economic outcomes at 30 days and 12 months and lifetime cost-effectiveness, which included costs per quality-adjusted life-year. CHiP recruited from May 2008 to September 2011. In total, 19,924 children were screened and 1369 eligible patients were randomised (TGC, 694; CM, 675), 60% of whom were in the cardiac surgery stratum. The randomised groups were comparable at trial entry. More children in the TGC than in the CM arm received insulin (66% vs. 16%). The mean VFD-30 was 23 [mean difference 0.36; 95% confidence interval (CI) -0.42 to 1.14]. The effect did not differ among prespecified subgroups. Hypoglycaemia occurred significantly more often in the TGC than in the CM arm (moderate, 12.5% vs. 3.1%; severe, 7.3% vs. 1.5%). Mean 30-day costs were similar between arms, but mean 12-month costs were lower in the TGC than in CM arm (incremental costs -£3620, 95% CI -£7743 to £502). For the non-cardiac surgery stratum, mean costs were lower in the TGC than in the CM arm (incremental cost -£9865, 95% CI -£18,558 to -£1172), but, in the cardiac surgery stratum, the costs were similar between the arms (incremental cost £133, 95% CI -£3568 to £3833). Lifetime incremental net benefits were positive overall (£3346, 95% CI -£11,203 to £17,894), but close to zero for the cardiac surgery stratum (-£919, 95% CI -£16,661 to £14,823). For the non-cardiac surgery stratum, the incremental net benefits were high (£11,322, 95% CI -£15,791 to £38,615). The probability that TGC is cost-effective is relatively high for the non-cardiac surgery stratum, but, for the cardiac surgery subgroup, the probability that TGC is cost-effective is around 0.5. Sensitivity analyses showed that the results were robust to a range of alternative assumptions. CHiP found no differences in the clinical or cost-effectiveness of TGC compared with CM overall, or for prespecified subgroups. A higher proportion of the TGC arm had hypoglycaemia. This study did not provide any evidence to suggest that PICUs should stop providing CM for children admitted to PICUs following cardiac surgery. For the subgroup not admitted for cardiac surgery, TGC reduced average costs at 12 months and is likely to be cost-effective. Further research is required to refine the TGC protocol to minimise the risk of hypoglycaemic episodes and assess the long-term health benefits of TGC. Current Controlled Trials ISRCTN61735247. This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 18, No. 26. See the NIHR Journals Library website for further project information.

What's in this issue?

What's in this issue?

Glycemic Control and Intensive Insulin Protocols for Neurologically Injured Patients

Hyperglycemia is a common complication that many critically ill patients experience. The increase in serum glucose is known to result in higher mortality and morbidity rates. Recent research has established that there is a benefit in using tight glycemic control protocols for critically ill patients. However, whether treatment guidelines benefit all patient populations remains unclear. Specifically, neurological injuries are known to require adequate glucose to repair cerebral metabolism, making the idea of tight glycemic control controversial. This article will analyze current research relevant to the use of strict glycemic control protocols for the neurologically injured patient by comparing population characteristics, methods, and outcomes to determine whether there is enough evidence to support its implementation in advanced nursing practice.

Variability of insulin sensitivity during the first 4 days of critical illness: implications for tight glycemic control

BackgroundEffective tight glycemic control (TGC) can improve outcomes in critical care patients, but it is difficult to achieve consistently. Insulin sensitivity defines the metabolic balance between insulin concentration and insulin-mediated glucose disposal. Hence, variability of insulin sensitivity can cause variable glycemia. This study quantifies and compares the daily evolution of insulin sensitivity level and variability for critical care patients receiving TGC.MethodsThis is a retrospective analysis of data from the SPRINT TGC study involving patients admitted to a mixed medical-surgical ICU between August 2005 and May 2007. Only patients who commenced TGC within 12 hours of ICU admission and spent at least 24 hours on the SPRINT protocol were included (N = 164). Model-based insulin sensitivity (SI) was identified each hour. Absolute level and hour-to-hour percent changes in SI were assessed on cohort and per-patient bases. Levels and variability of SI were compared over time on 24-hour and 6-hour timescales for the first 4 days of ICU stay.ResultsCohort and per-patient median SI levels increased by 34% and 33% (p < 0.001) between days 1 and 2 of ICU stay. Concomitantly, cohort and per-patient SI variability decreased by 32% and 36% (p < 0.001). For 72% of the cohort, median SI on day 2 was higher than on day 1. The day 1–2 results are the only clear, statistically significant trends across both analyses. Analysis of the first 24 hours using 6-hour blocks of SI data showed that most of the improvement in insulin sensitivity level and variability seen between days 1 and 2 occurred during the first 12–18 hours of day 1.ConclusionsCritically ill patients have significantly lower and more variable insulin sensitivity on day 1 than later in their ICU stay and particularly during the first 12 hours. This rapid improvement is likely due to the decline of counter-regulatory hormones as the acute phase of critical illness progresses. Clinically, these results suggest that while using TGC protocols with patients during their first few days of ICU stay, extra care should be afforded. Increased measurement frequency, higher target glycemic bands, conservative insulin dosing, and modulation of carbohydrate nutrition should be considered to minimize safely the outcome glycemic variability and reduce the risk of hypoglycemia.

Nurse workload in implementing a tight glycaemic control protocol in a UK hospital: a pilot time‐in‐motion study

The cumulative time that critical care nurses spend implementing a tight glycaemic control (TGC) protocol was estimated in a time-in-motion (TiM) study conducted in a hospital in the UK. TGC protocols were introduced to the critical care setting to reduce hyperglycaemic events in high-risk patients. The time burden to critical care nurses of implementing such protocols has not yet been studied in the UK. A prospective TiM pilot study was conducted in an eligible UK intensive care unit by four protocol-trained observers over five consecutive weekdays from 3 to 7 November 2008. Three nurses were also interviewed on site to gather their attitudes and perceptions about the benefits of and time associated with administering a TGC protocol. Independent observers shadowed nurses, observing when a blood glucose measurement was taken, when each predefined subtask was completed and the duration of each task. Semistructured interviews with nurses were conducted in-person and one-on-one by a trained study member. Considered together, the episodic median duration of all TGC activities was 6·65 min. Across a total shift, nurses devoted approximately 7% of their time to administering a TGC protocol. Nurses perceived that a TGC protocol is beneficial to patient safety and outcomes in a critical care setting but acknowledged that the tasks can be mildly to moderately tedious. This TiM analysis indicated that the additional responsibility of implementing a TGC protocol represents a substantive commitment of nursing time in a critical care setting. The episodic data of our pilot study in the UK contributes further evidence that TGC protocols may be arduous to maintain and constitute a substantial investment of nursing time.

Performance of the Roche Accu-Chek Active Glucose Meter to Screen for Gestational Diabetes Mellitus Using Fasting Capillary Blood

Point-of-care (POC) blood glucose measurement using glucose meters is used by diabetes patients to mange their disease. POC glucose testing also is also used in tight glycemic control protocols and as a screening tool for diabetes. We report the performance and effectiveness of the Accu-Chek® Active (Roche Diagnostics GmbH, Mannheim, Germany) glucose meter to screen for gestational diabetes mellitus (GDM) using blood fasting capillary glucose (FCG). To screen for GDM, 1,465 pregnant women underwent an oral glucose glucose tolerance test. Correlation between the FCG and fasting plasma glucose (FPG) levels was determined by Passing and Bablok regression analysis. Total error (TE) of the glucometer was ascertained using the Bland-Altman method with the DXC-800 analyzer (Beckman-Coulter Instruments, Brea, CA) as the reference method. The area under the receiver operator characteristic curve was used to analyze the performance of the FCG to predict GDM. FPG and FCG identified 361 (24.6%) and 338 (23%) women as having GDM, respectively. The Bland-Altman TE at 95% limits of agreement was -11.1% to 10.8%. The area under the receiver operator characteristic curve was 0.953 (95% confidence interval 0.943 to 0.964). The Roche Accu-Chek Active glucometer meets analytical and clinical quality requirements. A TE of±15% is acceptable for glucose meters used in ambulatory care, including home self-monitoring of blood glucose, and different TE targets should be set for acute critical care settings.

Endogenous insulin secretion and suppression during and after sepsis in critically ill patients: implications for tight glycemic control protocols

Insulin infusions over 2 U/hour can suppress endogenous insulin secretion in healthy subjects 30 to 45% [1]. Virtually all tight glycemic control (TGC) protocols deliver insulin via infusion. This study examines the impact of bolus delivery of insulin in TGC on the endogenous insulin secretion of critically ill patients.

Validation of a virtual patient and virtual trials method for accurate prediction of tight glycemic control protocol performance

Effective tight glycemic control (TGC) can improve outcomes, but is difficult to achieve. In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective TGC protocols. However, no such method has been fully validated. This study tests two matched cohorts from the Glucontrol trial treated with different protocols. The goal is to validate the ability of in-silico virtual patient models and methods to accurately predict patient-specific and clinical trial glycemic outcomes.

Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts. This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols. A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000 h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric ( S I ). Variation in S I provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/ S I ) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.

Implementation of a Safe Glycemic Control Protocol in Hospitalized Intensive Care Unit Patients

Implementation of tight glycemic control in hospitalized ICU patients presents a significant health care challenge. Many patients are involved, and implementation of tight glycemic control uses many hours of critical care nursing time. The current literature provides little practical guidance, older literature offers examples of tight glycemic control protocols, and the more recent NICE-SUGAR study involved the use of a computer-driven algorithm. This article provides the critical care physician direction for the organization of blood glucose control in the ICU. Discussion of an effective, safe, and user-friendly algorithm for intravenous insulin administration is presented, and safety considerations are discussed.

Incidence of Hypoglycemia with Tight Glycemic Control Protocols: A Comparative Study

Comparison of hypoglycemia incidence among tight glycemic control (TGC) protocols is a crucial aspect that has not been done in previous trials. This study compared the incidence of hypoglycemia using three TGC protocols in critically ill patients. This was a prospective study of 420 patients over 18 months. Patients were divided into three groups by TGC protocol: A (modified Leuven protocol), B (Georgia Hospital Association protocol, target blood glucose [BG] 80-110 mg/dL), and C (modified Georgia Hospital Association protocol, target BG 90-140 mg/dL). End points included differences in the incidence of first-degree hypoglycemia (BG <or= 60 mg/dL), second-degree hypoglycemia (BG <or= 40 mg/dL), and hyperglycemia (BG >or= 180 mg/dL). A total of 34,497 BG samples were analyzed: group A, 11,202 (32.47%); group B, 9,627 (27.91%); and group C, 13,668 (39.62%). First-degree hypoglycemia was more frequent in group A (348 episodes [3.11%]) compared to group B (209 episodes [2.17%] [odds ratio (OR) 1.45, 95% confidence interval (CI) 1.25-1.172, P = 0.001]) and group C (266 episodes [1.95%] [OR 1.66, 95% CI 1.37-1.89, P = 0.001]). Second-degree hypoglycemia was more frequent in group A (131 episodes [1.17%]) compared to group B (62 episodes [0.64%] [OR 1.83, 95% CI 1.22-1.72, P = 0.001]) and group C (58 episodes [0.42%] [OR 2.77, 95% CI 2.04-3.79, P = 0.001]). No significant difference was found when groups B and C were compared (P = 0.10 and P = 0.06, respectively). Hyperglycemia was significantly more common in group A (2,175 episodes [19.42%]) compared to group B (1,333 episodes [13.83%] [OR 1.49, 95% CI 1.39-1.62, P = 0.001], but there was no significant difference compared to group C (2,560 episodes [18.73%] [P = 0.17]). TGC protocols vary in their risk of inducing hypoglycemia. Whether this translates into differences in clinical outcomes such as mortality and adverse effects is still to be determined by future studies.

A Tight Glycemic Control Initiative in a Surgical Intensive Care Unit and Hospitalwide

In 2002, tight glycemic control (TGC) was mandated at Henry Ford Hospital (Detroit) to reduce surgical site infections (SSIs). THE FIVE STEPS FOR IMPROVEMENT: The TGC initiative was developed in terms of the five primary steps of the Institute for Healthcare Improvement (IHI) framework for leadership for improvement to drive practice change and maintain continuous improvement. In terms of Steps 1-3 (set direction, establish the foundation, and build will), in April 2002 the chief executive officer of the Henry Ford Hospital (Detroit) announced a hospitalwide initiative to reduce SSIs. For steps 4 and 5 (generate ideas and execute change), the 40-bed surgical intensive care unit (SICU) was designated the practice-change setting. TGC protocols were implemented in cardiothoracic patients, followed by all SICU patients, with target glucose ranges moving from the initial < 150 mg/dL to 80-110 mg/dL. Results showed decreases in SSIs and mortality. The project's success led initiation of hospitalwide TGC in the next two years. RESPONDING TO A CHANGING EVIDENCE BASE: In 2009, as studies began to show that the recommended glucose target of 80-110 mg/dL was not associated with clinical improvement in ICU patients and perhaps may cause harm (increased mortality), the target ranges were modified. Barriers to adoption of new practice change must be integrated into the planning process. Leadership champions are required across multiple levels of the organization to drive change to the bedside for effective and lasting improvement. A universal TGC protocol continues to be used throughout the hospital, with modifications and next-generation improvements occurring as evidence arises.