Computer-assisted glucose control in critically ill patients

Perioperative Glycemic Management in Cardiac Surgery: A Narrative Review

Diabetes and hyperglycemic events in cardiac surgical patients are associated with postoperative morbidity and mortality. The causes of dysglycemia, the abnormal fluctuations in blood glucose concentrations, in the perioperative period include surgical stress, surgical techniques, medications administered perioperatively, and patient factors. Both hyperglycemia and hypoglycemia lead to poor outcomes after cardiac surgery. While trying to control blood glucose concentration tightly for better postoperative outcomes, hypoglycemia is the main adverse event. Currently, there is no definite consensus on the optimum perioperative blood glucose concentration to be maintained in cardiac surgical patients. This review provides an overview of perioperative glucose homeostasis, the pathophysiology of dysglycemia, factors that affect glycemic control in cardiac surgery, and current practices for glycemic control in cardiac surgery.

Artificial intelligence in critically ill diabetic patients: current status and future prospects

Recent years have witnessed increasing numbers of artificial intelligence (AI) based applications and devices being tested and approved for medical care. Diabetes is arguably the most common chronic disorder worldwide and AI is now being used for making an early diagnosis, to predict and diagnose early complications, increase adherence to therapy, and even motivate patients to manage diabetes and maintain glycemic control. However, these AI applications have largely been tested in non-critically ill patients and aid in managing chronic problems. Intensive care units (ICUs) have a dynamic environment generating huge data, which AI can extract and organize simultaneously, thus analysing many variables for diagnostic and/or therapeutic purposes in order to predict outcomes of interest. Even non-diabetic ICU patients are at risk of developing hypo or hyperglycemia, complicating their ICU course and affecting outcomes. In addition, to maintain glycemic control frequent blood sampling and insulin dose adjustments are required, increasing nursing workload and chances of error. AI has the potential to improve glycemic control while reducing the nursing workload and errors. Continuous glucose monitoring (CGM) devices, which are Food and Drug Administration (FDA) approved for use in non-critically ill patients, are now being recommended for use in specific ICU populations with increased accuracy. AI based devices including artificial pancreas and CGM regulated insulin infusion system have shown promise as comprehensive glycemic control solutions in critically ill patients. Even though many of these AI applications have shown potential, these devices need to be tested in larger number of ICU patients, have wider availability, show favorable cost-benefit ratio and be amenable for easy integration into the existing healthcare systems, before they become acceptable to ICU physicians for routine use.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The association of early combined lactate and glucose levels with subsequent renal and liver dysfunction and hospital mortality in critically ill patients

BACKGROUND

The development of renal and liver dysfunction may be accompanied by initially subtle derangements in the gluconeogenetic function. Discrepantly low glucose levels combined with high lactate levels might indicate an impaired Cori cycle. Our objective was to examine the relation between early lactate and glucose levels with subsequent renal and liver dysfunction and hospital mortality in critically ill patients.

METHODS

Over a 4-year period (2011 to 2014), all adult patients admitted to our adult 48-bed teaching hospital intensive care unit (ICU) for at least 12 h were retrospectively analyzed. Lactate and glucose were regularly measured with point-of-care analyzers in all ICU patients. Lactate and glucose measurements were collected from 6 h before to 24 h after ICU admission. Patients with fewer than four lactate/glucose measurements were excluded. Patients received insulin according to a computer-guided control algorithm that aimed at a glucose level <8.0 mmol/L. Renal dysfunction was defined as the development of acute kidney injury (AKI) within 7 days, and liver function was based on the maximal bilirubin in the 7-day period following ICU admission. Mean lactate and mean glucose were classified into quintiles and univariate and multivariate analyses were related with renal and liver dysfunction and hospital mortality. Since glucose has a known U-shaped relation with outcome, we also accounted for this.

RESULTS

We analyzed 92,000 blood samples from 9074 patients (63% males) with a median age of 64 years and a hospital mortality of 11%. Both lactate quintiles (≤1.0; 1.0-1.3; 1.3-1.7; 1.7-2.3; >2.3 mmol/L) and glucose quintiles (≤7.0; 7.0-7.6; 7.6-8.2; 8.2-9.0; >9.0 mmol/L) were related with outcome in univariate analysis (p < 0.001). Acute Physiology and Chronic Health Evaluation (APACHE) IV, lactate, and glucose were associated with renal and liver dysfunction in multivariate analysis (p < 0.001), with a U-shaped relationship for glucose. The combination of the highest lactate quintile with the lowest glucose quintile was associated with the highest rates of renal dysfunction, liver dysfunction, and mortality (p < 0.001) with a significant interaction between lactate and glucose (p ≤ 0.001).

CONCLUSIONS

Abnormal combined lactate and glucose measurements may provide an early indication of organ dysfunction. In critically ill patients a 'normal' glucose with an elevated lactate should not be considered desirable, as this combination is related with increased mortality.

Development of a cross-disciplinary continuous insulin infusion protocol for non-critically ill patients in a French university hospital

RATIONALE, AIMS AND OBJECTIVES

In non-critically ill patients, the use of an insulin syringe pump allows the management of temporary situations during which other therapies cannot be used (failure of subcutaneous injections, awaiting advice from the diabetes team, emergency situations, prolonged corticosteroid therapy, initiation of an artificial nutrition, need for a fasting status, etc.). To manage the risks related to this «never event», the use of a standard validated protocol for insulin administration and monitoring is an essential prerequisite. To this end, a multidisciplinary approach is recommended.

METHOD

With the support of our subcommission «Endocrinology-Diabetology», we proceeded with a «step-by-step process» to create such a standardized protocol: (1) review of all existing protocols in our hospital; (2) overview of the literature data concerning insulin infusion protocols developed by multidisciplinary teams in France and abroad; (3) development of a standardized protocol for non-intensive care unit patients, respecting the current recommendations and adapting it to the working habits of health teams; and (4) validation of the protocol

RESULTS

Two protocols based on the same structure but adapted to the health status of the patient have been developed. The protocols are divided in to three parts: (1) golden rules to make the use of the protocol appropriate and safe; (2) the algorithm (a double entry table) corresponding to a dynamic adaptation of insulin doses, clearly defining the target and the 'at risk situations'; and (3) practical aspects of the protocol: preparation of the syringe, treatment initiation and traceability. The protocols have been validated by the institution.

CONCLUSION

Our standardized insulin infusion protocol is simple, easy to implement, safe and is likely to be applicable in diverse care units. However, the efficiency, safety and the workability of our protocols have to be clinically evaluated.

Computer-guided normal-low versus normal-high potassium control after cardiac surgery: No impact on atrial fibrillation or atrial flutter

INTRODUCTION

This study was designed to determine the effect of 2 different potassium regulation strategies with different targets (within the reference range) on atrial fibrillation (AF) or atrial flutter (AFL) in a cohort of intensive care unit patients after cardiac surgery.

METHODS

The GRIP-COMPASS study was a prospective double-blinded interventional study in 910 patients after cardiac surgery (coronary artery bypass grafting and/or valvular surgery). Patients were assigned to either the normal-low potassium target (nLP group, 4.0 mmol/L) or the normal-high potassium target (nHP group, 4.5 mmol/L) in alternating blocks of 50 patients. Potassium levels were regulated using a validated computer-assisted potassium replacement protocol (GRIP-II). The primary end point was the incidence of AF/AFL on a 12-lead electrocardiogram during the first postoperative week.

RESULTS

Of the 910 patients, 447 were assigned to the nLP group; and 463, to the nHP group, with no baseline differences between the 2 groups. The mean daily administered dose of potassium was 30 ± 23 mmol (nLP) versus 52 ± 27 mmol (nHP) (P < .001), which resulted in mean intensive care unit potassium concentration of 4.22 ± 0.36 mmol/L and 4.33 ± 0.34 mmol/L, respectively (P < .001). The incidence of AF/AFL after cardiac surgery did not differ: 38% in the nLP group and 41% in the nHP group. Also in several subgroups (eg, patients not known with prior AF/AFL or with valve surgery), there were no differences.

CONCLUSIONS

There were no differences in incidence of AF/AFL with 2 potassium regulation strategies with different potassium targets and different amounts of potassium administered in patients after cardiac surgery.

Space GlucoseControl system for blood glucose control in intensive care patients--a European multicentre observational study

BACKGROUND

Glycaemia control (GC) remains an important therapeutic goal in critically ill patients. The enhanced Model Predictive Control (eMPC) algorithm, which models the behaviour of blood glucose (BG) and insulin sensitivity in individual ICU patients with variable blood samples, is an effective, clinically proven computer based protocol successfully tested at multiple institutions on medical and surgical patients with different nutritional protocols. eMPC has been integrated into the B.Braun Space GlucoseControl system (SGC), which allows direct data communication between pumps and microprocessor. The present study was undertaken to assess the clinical performance and safety of the SGC for glycaemia control in critically ill patients under routine conditions in different ICU settings and with various nutritional protocols.

METHODS

The study endpoints were the percentage of time the BG was within the target range 4.4 - 8.3 mmol.l(-1), the frequency of hypoglycaemic episodes, adherence to the advice of the SGC and BG measurement intervals. BG was monitored, and insulin was given as a continuous infusion according to the advice of the SGC. Nutritional management (enteral, parenteral or both) was carried out at the discretion of each centre.

RESULTS

17 centres from 9 European countries included a total of 508 patients, the median study time was 2.9 (1.9-6.1) days. The median (IQR) time-in-target was 83.0 (68.7-93.1) % of time with the mean proposed measurement interval 2.0 ± 0.5 hours. 99.6% of the SGC advices on insulin infusion rate were accepted by the user. Only 4 episodes (0.01% of all BG measurements) of severe hypoglycaemia <2.2 mmol.l(-1) in 4 patients occurred (0.8%; 95% CI 0.02-1.6%).

CONCLUSION

Under routine conditions and under different nutritional protocols the Space GlucoseControl system with integrated eMPC algorithm has exhibited its suitability for glycaemia control in critically ill patients.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01523665.

Effectiveness, safety and feasibility of an evidence-based insulin infusion protocol targeting moderate glycaemic control in intensive cardiac care units

OBJECTIVES

To assess the effectiveness, safety and feasibility of the revised, simplified nurse-managed version of our insulin infusion protocol, adapted to the new recommended glycaemic target of 140 to 180 mg/dL (Desio Diabetes Diagram i.v. 140-180).

METHODS

All clinical responses to the Desio Diabetes Diagram i.v. 140-180 in use for 3 years were recorded in patients with diabetes or hyperglycaemia admitted to our intensive cardiac care unit. To assess the feasibility, we asked nurses to complete an ad hoc questionnaire anonymously when the new insulin infusion protocol had been in use for 2 years.

RESULTS

From December 2010 to December 2013, 276 patients (173 men, median age 75 years) were treated according to the Desio Diabetes Diagram i.v. 140-180. The median time to reach glycaemic target was 4 h (Q1-Q3 2-8) in 128 patients with blood glucose >180 mg/dL and 2 h (Q1-Q3 1-4) in 82 patients with blood glucose <140 mg/dL. Once the target had been reached, insulin infusion was maintained for a median of 38 h (Q1-Q3 24-48) with blood glucose between 140 and 180 mg/dL for 58.3% of the infusion time. Over a total of 11,863 h of infusion, seven blood glucose <70 mg/dL occurred. The Desio Diabetes Diagram i.v. 140-180 protocol was considered easy to use by 93% of nurses.

CONCLUSIONS

The Desio Diabetes Diagram i.v. 140-180 protocol, fully managed by nurses, with insulin and glucose intravenous infusion proved effective, safe and feasible in maintaining blood glucose between 140 and 180 mg/dL in patients with diabetes or hyperglycaemia admitted to the intensive cardiac care unit for acute cardiac events.

Effect of high-dose dexamethasone on perioperative lactate levels and glucose control: a randomized controlled trial

Introduction

Blood lactate levels are increasingly used to monitor patients. Steroids are frequently administered to critically ill patients. However, the effect of steroids on lactate levels has not been adequately investigated. We studied the effect of a single intraoperative high dose of dexamethasone on lactate and glucose levels in patients undergoing cardiac surgery.

Methods

The Dexamethasone for Cardiac Surgery (DECS) trial was a multicenter randomized trial on the effect of dexamethasone 1 mg/kg versus placebo on clinical outcomes after cardiac surgery in adults. Here we report a pre-planned secondary analysis of data from DECS trial participants included at the University Medical Center Groningen. The use of a computer-assisted glucose regulation protocol—Glucose Regulation for Intensive care Patients (GRIP)—was part of routine postoperative care. GRIP aimed at glucose levels of 4 to 8 mmol/L. Primary outcome parameters were area under the lactate and glucose curves over the first 15 hours of ICU stay (AUC₁₅). ICU length of stay and mortality were observed as well.

Results

The primary outcome could be determined in 497 patients of the 500 included patients. During the first 15 hours of ICU stay, lactate and glucose levels were significantly higher in the dexamethasone group than in the placebo group: lactate AUC₁₅ 25.8 (13.1) versus 19.9 (11.2) mmol/L × hour, P <0.001 and glucose AUC₁₅ 126.5 (13.0) versus 114.4 (13.9) mmol/L × hour, P <0.001. In this period, patients in the dexamethasone group required twice as much insulin compared with patients who had received placebo. Multivariate and cross-correlation analyses suggest that the effect of dexamethasone on lactate levels is related to preceding increased glucose levels. Patients in the placebo group were more likely to stay in the ICU for more than 24 hours (39.2%) compared with patients in the dexamethasone group (25.0%, P = 0.001), and 30-day mortality rates were 1.6% and 2.4%, respectively (P = 0.759).

Conclusions

Intraoperative high-dose dexamethasone increased postoperative lactate and glucose levels in the first 15 hours of ICU stay. Still, patients in the dexamethasone group had a shorter ICU length of stay and similar mortality compared with controls.

Trial registration

ClinicalTrials.gov NCT00293592. Registered 16 February 2006.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-015-0736-9) contains supplementary material, which is available to authorized users.

[Glucose control in the critically ill. Innovations and contemporary strategies]

Glucose control should be part of standard therapy in intensive care units (ICU) due to the proven association of hyperglycemia with increased morbidity and mortality. Due to the results of the latest randomized controlled multicentre trials blood glucose target levels of 140-180 mg/dl are currently recommended. In critically ill patients glucose monitoring should not be performed using point of care (POC) devices because of inacceptable inaccuracies. Blood gas analyzers have been shown to be accurate and are mostly available nearly at the bedside. Currently new continuous glucose monitoring devices for critically ill patients using multiple technologies are under development. Depending on the accuracy and reliability these new devices will add to selective blood glucose measurements to close the time gap between measurements or will even replace these measurements. Continuous, intravenous insulin therapy according to an algorithm should be performed by nursing staff. Computerized algorithms followed by so-called dynamic paper algorithms yield the best results. Besides mean glucose levels, glucose variability and glucose complexity are also associated with outcome in critically ill patients and might therefore be future target parameters. Critically ill diabetic patients might benefit from different glucose target levels depending on the preadmission glucose control. Hypoglycemic events <80 mg/dl should be avoided because of the association with poor outcome.

Computerization of the Yale insulin infusion protocol and potential insights into causes of hypoglycemia with intravenous insulin

BACKGROUND

The management of critically ill hyperglycemic patients in the intensive care unit (ICU) has been fraught with recent controversy. Only one randomized trial has demonstrated a mortality benefit to intensive glycemic control, with all subsequent studies failing to confirm this benefit and revealing a markedly increased risk of severe hypoglycemia (SH) in intensively treated patients. In most of these trials, adherence to the protocols were neither tracked nor reported.

METHODS

A retrospective analysis of all patients admitted to an ICU who were treated with an insulin infusion directed by the GlucoCare™ IGC System, an FDA-cleared insulin-dosing calculator (Yale 100-140 mg/dL protocol). Mean blood glucose (BG) levels, time to target range and incidence of SH (<40 mg/dL) and moderate hypoglycemia (MH) (40-69 mg/dL) were determined, and potential causes of hypoglycemic episodes were assessed.

RESULTS

Mean post-target BG was approximately 123 mg/dL. Of >55,000 readings in 1,657 patients, overall incidence of SH was 0.01% of readings and 0.3% of patients. MH occurred in 1.1% of readings and 17.6% of patients. The top potential causes of MH were: (1) Protocol-directed recommendations including continuation of insulin with BG <100 mg/dL and decreases in the frequency of BG checks (63.7%), and (2) Staff non-adherence to protocol directives (15.3%).

CONCLUSIONS

The results of the GlucoCare-directed Yale 100-140 mg/dL protocol experience revealed an extremely low incidence of SH and an incidence of MH of 1.1%. The incidence of SH in this study was lower than the control group of the NICE-SUGAR study and are supportive of the new Society of Critical Care guidelines to target BG levels of 100-150 mg/dL in critically ill patients. Further refinements to the original protocol and emphasis on staff adherence to protocol directives could potentially further reduce these very low hypoglycemia rates.

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

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

Guidelines for the use of an insulin infusion for the management of hyperglycemia in critically ill patients

OBJECTIVE

To evaluate the literature and identify important aspects of insulin therapy that facilitate safe and effective infusion therapy for a defined glycemic end point.

METHODS

Where available, the literature was evaluated using Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) methodology to assess the impact of insulin infusions on outcome for general intensive care unit patients and those in specific subsets of neurologic injury, traumatic injury, and cardiovascular surgery. Elements that contribute to safe and effective insulin infusion therapy were determined through literature review and expert opinion. The majority of the literature supporting the use of insulin infusion therapy for critically ill patients lacks adequate strength to support more than weak recommendations, termed suggestions, such that the difference between desirable and undesirable effect of a given intervention is not always clear.

RECOMMENDATIONS

The article is focused on a suggested glycemic control end point such that a blood glucose ≥ 150 mg/dL triggers interventions to maintain blood glucose below that level and absolutely <180 mg/dL. There is a slight reduction in mortality with this treatment end point for general intensive care unit patients and reductions in morbidity for perioperative patients, postoperative cardiac surgery patients, post-traumatic injury patients, and neurologic injury patients. We suggest that the insulin regimen and monitoring system be designed to avoid and detect hypoglycemia (blood glucose ≤ 70 mg/dL) and to minimize glycemic variability.Important processes of care for insulin therapy include use of a reliable insulin infusion protocol, frequent blood glucose monitoring, and avoidance of finger-stick glucose testing through the use of arterial or venous glucose samples. The essential components of an insulin infusion system include use of a validated insulin titration program, availability of appropriate staffing resources, accurate monitoring technology, and standardized approaches to infusion preparation, provision of consistent carbohydrate calories and nutritional support, and dextrose replacement for hypoglycemia prevention and treatment. Quality improvement of glycemic management programs should include analysis of hypoglycemia rates, run charts of glucose values <150 and 180 mg/dL. The literature is inadequate to support recommendations regarding glycemic control in pediatric patients.

CONCLUSIONS

While the benefits of tight glycemic control have not been definitive, there are patients who will receive insulin infusion therapy, and the suggestions in this article provide the structure for safe and effective use of this therapy.

[Glucose variability in intensive care unit]

Hyperglycemia is significantly associated with increased mortality in critically ill patients and then, strict control of blood glucose (BG) concentration is important. Lowering of BG levels with intensive insulin therapy (IIT) was recommended in order to improve patient outcomes. But recently, some recent prospective trials failed to confirm the initial data, showing conflicting results (significantly increased mortality with IIT, more hypoglycemic episodes). So there is no consensus about efficiency and safety of IIT. Significant associations between glucose variability and mortality have been confirmed by several recent studies. A difference in variability of BG control could explain why the effect of IIT varied from beneficial to harmful. Managing and decreasing this BG variability could be an important goal of BG control in critically ill patients. Clinicians have to consider definitions, physiopathology and impacts of glucose variability, in order to improve patient outcomes.

Glycemic variability and glycemic control in the acutely ill cardiac patient

The mechanisms for hyperglycemia-mediated harm in the hospitalized cardiac patient are poorly understood. Potential obstacles in the inpatient management of hyperglycemia in cardiac patients include rapidly changing clinical status, frequent procedures and interruptions in carbohydrate exposure, and short hospital length of stay. A patient's preadmission regimen is rarely suitable for inpatient glycemic control. Instead, an approach to a flexible, physiologic insulin regimen is described, which is intended to minimize glycemic excursions. When diabetes or hyperglycemia is addressed early and consistently, the hospital stay can serve as a potential window of opportunity for reinforcing self-care behaviors that reduce long-term complications.

Compliance with surgical care improvement project blood glucose--a marker for euglycemia, but does it put our patients at risk?

To improve outcomes in open heart surgery (OHS) patients, the Surgical Care Improvement Project (SCIP) requires 6 am postoperative day (POD) 1 and 2 blood glucose (BG) to be ≤200mg/dL. This study examined risk factors for SCIP noncompliance when using an insulin infusion protocol (IIP) and evaluated this SCIP metric as a surrogate for glycemic control. The authors divided 99 consecutive OHS patients, all subjected to 1 uniform IIP, into 2 groups: Group 1-SCIP compliant (n=79) and Group 2-SCIP noncompliant (n=20). They determined mean BG for the first 48 postoperative hours, percent of total time with hyperglycemia (% time BG >200mg/dL) for each group, and assessed risk of SCIP noncompliance as relates to multiple risk factors including intensity of IIP application, and switching to subcutaneous (SQ) insulin prior to 6 am on POD 2. Group 1 had lower mean BG than Group 2 and percent of total time with hyperglycemia, P<0.0001. Multivariate analysis showed diabetes, obesity in nondiabetics, and switching to SQ insulin prior to 6 am on POD 2 to be risk factors for SCIP noncompliance. The 6 am BG values on POD 1 or POD 2 each correlated with average postoperative BG, and compliance with the SCIP BG metric was associated with virtually uniform BG ≤200mg/dL. IIP application was not significantly different between groups (P=0.2). Only patients who had been switched to SQ insulin prior to 6 am POD 2 were noncompliant at 6 am on POD 2. There were hypoglycemic events (BG <70mg/dL) in 15 of 99 patients (15%), 12 of whom (80%) were in Group 1. Noncompliance with this SCIP measure occurred more frequently in patients with diabetes or, if nondiabetic, in those patients with obesity. A trend toward increased insulin assessments in the SCIP noncompliant group suggests that 1 uniform IIP for all patients may not be effective. By not requiring the reporting of hypoglycemia, SCIP may inadvertently be exposing patients to harm.

Diabetes, hyperglycaemia, and acute ischaemic stroke

Diabetes and ischaemic stroke often arise together. People with diabetes have more than double the risk of ischaemic stroke after correction for other risk factors, relative to individuals without diabetes. Multifactorial treatment of risk factors for stroke-in particular, lifestyle factors, hypertension, and dyslipidaemia-will prevent a substantial number of these disabling strokes. Hyperglycaemia occurs in 30-40% of patients with acute ischaemic stroke, also in individuals without a known history of diabetes. Admission hyperglycaemia is associated with poor functional outcome, possibly through aggravation of ischaemic damage by disturbing recanalisation and increasing reperfusion injury. Uncertainty surrounds the question of whether glucose-lowering treatment for early stroke can improve clinical outcome. Achievement of normoglycaemia in the early stage of stroke can be difficult, and the possibility of hypoglycaemia remains a concern. Phase 3 studies of glucose-lowering therapy in acute ischaemic stroke are underway.

The effect of a computerized prescribing and calculating system on hypo- and hyperglycemias and on prescribing time efficiency in neonatal intensive care patients

BACKGROUND

Prescribing glucose requires complex calculations because glucose is present in parenteral and enteral nutrition and drug vehicles, making it error prone and contributing to the burden of prescribing errors.

OBJECTIVE

Evaluation of the impact of a computerized physician order entry (CPOE) system with clinical decision support (CDS) for glucose control in neonatal intensive care patients (NICU) focusing on hypo- and hyperglycemic episodes and prescribing time efficiency.

METHODS

An interrupted time-series design to examine the effect of CPOE on hypo- and hyperglycemias and a crossover simulation study to examine the influence of CPOE on prescribing time efficiency. NICU patients at risk for glucose imbalance hospitalized at the University Medical Center Utrecht during 2001-2007 were selected. The risks of hypo- and hyperglycemias were expressed as incidences per 100 patient days in consecutive 3-month intervals during 3 years before and after CPOE implementation. To assess prescribing time efficiency, time needed to calculate glucose intake with and without CPOE was measured.

RESULTS

No significant difference was found between pre- and post-CPOE mean incidences of hypo- and hyperglycemias per 100 hospital days of neonates at risk in every 3-month period (hypoglycemias, 4.0 [95% confidence interval, 3.2-4.8] pre-CPOE and 3.1 [2.7-3.5] post-CPOE, P = .88; hyperglycemias, 6.0 [4.3-7.7] pre-CPOE and 5.0 [3.7-6.3] post-CPOE, P = .75). CPOE led to a significant time reduction of 16% (1.3 [0.3-2.3] minutes) for simple and 60% (8.6 [5.1-12.1] minutes) for complex calculations.

CONCLUSIONS

CPOE including a special CDS tool preserved accuracy for calculation and control of glucose intake and increased prescribing time efficiency.

ICU 2020: five interventions to revolutionize quality of care in the ICU

Intensive care units (ICUs) are an essential and unique component of modern medicine. The number of critically ill individuals, complexity of illness, and cost of care continue to increase with time. In order to meet future demands, maintain quality, and minimize medical errors, intensivists will need to look beyond traditional medical practice, seeking lessons on quality assurance from industry and aviation. Intensivists will be challenged to keep pace with rapidly advancing information technology and its diverse roles in ICU care delivery. Modern ICU quality improvement initiatives include ensuring evidence-based best practice, participation in multicenter ICU collaborations, employing state-of-the-art information technology, providing point-of-care diagnostic testing, and efficient organization of ICU care delivery. This article demonstrates that each of these initiatives has the potential to revolutionize the quality of future ICU care in the United States.

[Effectiveness and safety of goal directed nurse-led blood glucose control in an intensive care unit: a prospective observational study]

OBJECTIVE

to evaluate the effectiveness and safety of a nurse-led blood glucose control protocol in a medical ICU.

METHOD

a descriptive, prospective study was carried out for a period of 13 months. All blood glucose values from patients on insulin therapy for intensive glycemic control were recorded daily.

RESULTS

A total of 12,677 blood glucose determinations were performed on the 69 patients under glycemic control; 57.9% of the determinations had predetermined study target values for blood glucose (100-140 mg/dl) and 68.8% of the determinations had physiological blood glucose values (80-140 mg/dl); no values under 40 mg/dl were obtained, and only 0.2% were between 40-60 mg/dl.

CONCLUSIONS

For an adequate blood glucose control using intensive insulin therapy, individual management of insulin infusion regimen is essential, adjusted to the characteristics of each patient. A nurse-led intervention has allowed better results to be obtained in comparison with other studies in which different protocols for insulin infusion are used.

"One more thing to think about…" Cognitive burden experienced by intensive care unit nurses when implementing a tight glucose control protocol

Critically ill patients require intensive nursing care. Intensive care unit (ICU) nurses, who care for these physiologically unstable patients, are continuously occupied with the integration of assessments, monitoring, and interventions that are responsive to a patient's evolving state. Since 2005, numerous evidenced-based clinical protocols have been implemented in the critical care unit. Individually, each may not appear to be burdensome but, collectively, these clinical protocols add to the cognitive work of ICU nurses. While nurses are central to the successful implementation of these protocols, little is written about the cognitive burden imposed on them by the addition of these clinical protocols. This article explores the impact of clinical protocols on the cognitive burden of ICU nurses, using a tight glucose control (TGC) protocol as an exemplar case. Research from management, ergonomics, systems engineering, and nursing is used to build the concept of cognitive burden. Future research can build upon this understanding to facilitate successful implementation of clinical protocols.

Efficacy and safety of a tight glucose control protocol in critically ill term neonates

BACKGROUND

A large single-center randomized trial showed that treating hyperglycemia in critically ill children improved outcome, despite an increased incidence of hypoglycemia, especially in infants.

OBJECTIVES

We evaluated the efficacy and incidence of hypoglycemia using a tight glucose protocol in critically ill term neonates.

METHODS

Term hyperglycemic (>8 mmol·l(-1); >144 mg·dl(-1)) neonates treated with a tight glucose protocol during a 3.5-year period in a tertiary pediatric intensive care unit were retrospectively analyzed.

RESULTS

Seventy-three term hyperglycemic neonates [age 0 days (0-6), weight 3.2 ± 0.8 kg, PRISM 16 (11-20)] were included for analysis. Eighteen neonates died (25%). The initial mean (range) glucose level was 11.1 mmol·l(-1) [9.6-15.2; 200 mg·dl(-1) (173-274)], and normoglycemia (<8 mmol·l(-1); <144 mg·dl(-1)) was reached within 5.3 h (1-25) with an overall treatment duration of 27 h (10-57). Seven hypoglycemic incidents (5 times ≤2.2 mmol·l(-1); 40 mg·dl(-1), and 2 times <1.7 mmol·l(-1); 31 mg·dl(-1)) occurred in 5 (6.7%) infants, without severe clinical signs. Three hypoglycemic incidents were directly explained due to a protocol violation. One hypoglycemic incident occurred with the onset of sepsis, while no apparent cause was identified for three hypoglycemic incidents.

CONCLUSIONS

Our glucose protocol was effective, but hypoglycemia occurred more frequently than in older children reported previously. Potential differences in glucose and insulin metabolism in term neonates appear to justify additional safety approaches, while awaiting further studies assessing the benefits of tight glucose protocols in this population. Meanwhile, we have decreased the initial insulin starting doses in our protocol.

Barriers and facilitators to the use of computer-based intensive insulin therapy

PURPOSE

Computerized clinical decision support systems (CDSSs) for intensive insulin therapy (IIT) are increasingly common. However, recent studies question IIT's safety and mortality benefit. Researchers have identified factors influencing IIT performance, but little is known about how workflow affects computer-based IIT. We used ethnographic methods to evaluate IIT CDSS with respect to other clinical information systems and care processes.

METHODS

We conducted direct observation of and unstructured interviews with nurses using IIT CDSS in the surgical and trauma intensive care units at an academic medical center. We observed 49h of intensive care unit workflow including 49 instances of nurses using IIT CDSS embedded in a provider order entry system. Observations focused on the interaction of people, process, and technology. By analyzing qualitative field note data through an inductive approach, we identified barriers and facilitators to IIT CDSS use.

RESULTS

Barriers included (1) workload tradeoffs between computer system use and direct patient care, especially related to electronic nursing documentation, (2) lack of IIT CDSS protocol reminders, (3) inaccurate user interface design assumptions, and (4) potential for error in operating medical devices. Facilitators included (1) nurse trust in IIT CDSS combined with clinical judgment, (2) nurse resilience, and (3) paper serving as an intermediary between patient bedside and IIT CDSS.

CONCLUSION

This analysis revealed sociotechnical interactions affecting IIT CDSS that previous studies have not addressed. These issues may influence protocol performance at other institutions. Findings have implications for IIT CDSS user interface design and alerts, and may contribute to nascent general CDSS theory.

Computer-assisted glucose regulation during rapid step-wise increases of parenteral nutrition in critically ill patients: a proof of concept study

BACKGROUND

Early delivery of calories is important in critically ill patients, and the administration of parenteral nutrition (PN) is sometimes required to achieve this goal. However, PN can induce acute hyperglycemia, which is associated with adverse outcome. We hypothesized that initiation of PN using a rapid "step-up" approach, coupled with a computerized insulin-dosing protocol, would result in a desirable caloric intake within 24 hours without causing hyperglycemia.

METHODS

In our surgical intensive care unit (ICU), glucose is regulated by a nurse-centered computerized glucose regulation program. When adequate enteral feeding was not possible, PN was initiated according to a simple step-up rule at an infusion rate of 10 mL/h (approximately 10 kcal/h) and subsequently increased by steps of 10 mL/h every 4 hours, provided glucose was <10 mmol/L, until the target caloric intake (1 kcal/kg/h) was reached. All glucose levels and insulin doses were collected during the step-up period and for 24 hours after achieving target feeding.

RESULTS

In all 23 consecutive patients requiring PN, mean intake was 1 kcal/kg/h within 24 hours. Of the 280 glucose samples during the 48-hour study period, mean ± standard deviation glucose level was 7.4 ± 1.4 mmol/L. Only 4.5% of glucose measurements during the step-up period were transiently ≥10 mmol/L. After initiating PN, the insulin requirement rose from 1.1 ± 1.5 units/h to 2.9 ± 2.5 units/h (P < .001).

CONCLUSIONS

This proof of concept study shows that rapid initiation of PN using a step-up approach coupled with computerized glucose control resulted in adequate caloric intake within 24 hours while maintaining adequate glycemic control.

Characteristics and effects of nurse dosing over-rides on computer-based intensive insulin therapy protocol performance

OBJECTIVE

To determine characteristics and effects of nurse dosing over-rides of a clinical decision support system (CDSS) for intensive insulin therapy (IIT) in critical care units.

DESIGN

Retrospective analysis of patient database records and ethnographic study of nurses using IIT CDSS.

MEASUREMENTS

The authors determined the frequency, direction-greater than recommended (GTR) and less than recommended (LTR)- and magnitude of over-rides, and then compared recommended and over-ride doses' blood glucose (BG) variability and insulin resistance, two measures of IIT CDSS associated with mortality. The authors hypothesized that rates of hypoglycemia and hyperglycemia would be greater for recommended than over-ride doses. Finally, the authors observed and interviewed nurse users.

RESULTS

5.1% (9075) of 179,452 IIT CDSS doses were over-rides. 83.4% of over-ride doses were LTR, and 45.5% of these were ≥ 50% lower than recommended. In contrast, 78.9% of GTR doses were ≤ 25% higher than recommended. When recommended doses were administered, the rate of hypoglycemia was higher than the rate for GTR (p = 0.257) and LTR (p = 0.033) doses. When recommended doses were administered, the rate of hyperglycemia was lower than the rate for GTR (p = 0.003) and LTR (p < 0.001) doses. Estimates of patients' insulin requirements were higher for LTR doses than recommended and GTR doses. Nurses reported trusting IIT CDSS overall but appeared concerned about recommendations when administering LTR doses.

CONCLUSION

When over-riding IIT CDSS recommendations, nurses overwhelmingly administered LTR doses, which emphasized prevention of hypoglycemia but interfered with hyperglycemia control, especially when BG was >150 mg/dl. Nurses appeared to consider the amount of a recommended insulin dose, not a patient's trend of insulin resistance, when administering LTR doses overall. Over-rides affected IIT CDSS protocol performance.

The role of insulin therapy and glucose normalisation in patients with acute coronary syndrome

Patients with acute myocardial infarction (AMI) and diabetes mellitus, as well as patients admitted with elevated blood glucose without known diabetes, have impaired outcome. Therefore intensive glucose-lowering therapy with insulin (IGL) has been proposed in diabetic or hyperglycaemic patients and has been shown to improve survival and reduce incidence of adverse events. The current manuscript provides an overview of randomised controlled trials investigating the effect of IGL. Furthermore, systematic glucose-insulin-potassium infusion (GIK) has been studied to improve outcome after AMI. In spite of positive findings in some early studies, GIK did not show any beneficial effects in recent clinical trials and thus this concept has been abandoned. While IGL targeted to achieve normoglycaemia improves outcome in patients with AMI, achievement of glucose regulation is difficult and carries the risk of hypoglycaemia. More research is needed to determine the optimal glucose target levels in AMI and to investigate whether computerised glucose protocols and continuous glucose sensors can improve safety and efficacy of IGL.

Trial design: Computer guided normal-low versus normal-high potassium control in critically ill patients: Rationale of the GRIP-COMPASS study

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

Evaluation of a clinical decision support system for glucose control: impact of protocol modifications on compliance and achievement of glycemic targets

Treating hyperglycemia may improve patient outcome, but is a clinical challenge. Three variations of a computerized insulin protocol were compared with regard to protocol compliance and achievement of glucose target levels. In group 1, the existing protocol was applied, in group 2 the protocol was modified to account for decreasing glucose values; group 3 had a higher threshold for initiating insulin, wider glucose target ranges, and included instructions to regulate glucose around mealtimes. From July 28, 2008 until February 1, 2010, data from 1255 patients admitted to our Intensive Cardiac Care Unit with at least 2 glucose measurements were analyzed. Mean age was 64 +/- 15 years, 66% were male, 21% had diabetes. Groups 1 to 3 included 269, 814, and 142 patients, respectively. Protocol compliance in group 2 was lower with 44% of the glucose measurements performed on time versus 51% in group 1 (P < 0.001), and insulin was dosed correctly in 57% versus 67% (P < 0.001). In group 3, compliance increased, 52% of the measurements were done on time, and insulin was dosed correctly in 71%. Average glucose levels increased in group 3 due to a higher threshold for starting insulin and a wider target range: 70% (group 1), 66% (group 2), and 61% (group 3) had an average glucose of <8 mmol/L (P < 0.001). Also, we observed a decreasing trend in incidence of hypoglycemia and reporting of noncompliance. Further improvements in glucose measurement technology and protocols are needed to optimally treat hyperglycemia in the Intensive Cardiac Care Unit.

International recommendations for glucose control in adult non diabetic critically ill patients

INTRODUCTION

The purpose of this research is to provide recommendations for the management of glycemic control in critically ill patients.

METHODS

Twenty-one experts issued recommendations related to one of the five pre-defined categories (glucose target, hypoglycemia, carbohydrate intake, monitoring of glycemia, algorithms and protocols), that were scored on a scale to obtain a strong or weak agreement. The GRADE (Grade of Recommendation, Assessment, Development and Evaluation) system was used, with a strong recommendation indicating a clear advantage for an intervention and a weak recommendation indicating that the balance between desirable and undesirable effects of an intervention is not clearly defined.

RESULTS

A glucose target of less than 10 mmol/L is strongly suggested, using intravenous insulin following a standard protocol, when spontaneous food intake is not possible. Definition of the severe hypoglycemia threshold of 2.2 mmol/L is recommended, regardless of the clinical signs. A general, unique amount of glucose (enteral/parenteral) to administer for any patient cannot be suggested. Glucose measurements should be performed on arterial rather than venous or capillary samples, using central lab or blood gas analysers rather than point-of-care glucose readers.

CONCLUSIONS

Thirty recommendations were obtained with a strong (21) and a weak (9) agreement. Among them, only 15 were graded with a high level of quality of evidence, underlying the necessity to continue clinical studies in order to improve the risk-to-benefit ratio of glucose control.

Comparison of blood glucose management strategies to achieve control following cardiac surgery (computerized versus paper)

INTRODUCTION

Blood glucose control can be time-consuming and difficult to achieve. We hypothesized that a computerized system to obtain glucose control would enable faster "time to target" and produce less variability in blood glucose levels.

METHODS

Patients who underwent cardiac surgery at a community hospital between January and December 2007 (n = 1131) with glucose control obtained under a paper protocol were compared with similar patients operated on between January and December 2008 (n = 769) whose glucose control was obtained with a computer-driven protocol.

RESULTS

Glucose control was achieved in both groups. The computer group had less variability in glucose levels than the paper group. The mean time to target for the computer group was 3.5 (+/-1.3) hours. The time to target for the paper group was quite skewed; therefore, the median time to target was 6 hours.

CONCLUSIONS

The computer-driven protocol achieved excellent glycemic control.

Effects of blood glucose transcription mismatches on a computer-based intensive insulin therapy protocol

PURPOSE

Computerized clinical decision support systems (CDSS) for intensive insulin therapy (IIT) generate recommendations using blood glucose (BG) values manually transcribed from testing devices to computers, a potential source of error. We quantified the frequency and effect of blood glucose transcription mismatches on IIT protocol performance.

METHODS

We examined 38 months of retrospective data for patients treated with CDSS IIT in two intensive care units at one teaching hospital. A manually transcribed BG value not equal to a corresponding device value was deemed mismatched. For mismatches we recalculated CDSS recommendations using device BG values. We compared matched and mismatched data in terms of CDSS alerts, blood glucose variability, and dosing.

RESULTS

Of 189,499 CDSS IIT instances, 5.3% contained mismatched BG values. Mismatched data triggered 93 false alerts and failed to issue 170 alerts for nurses to notify physicians. Four of six BG variability measures differed between matched and mismatched data. Overall insulin dose was greater for matched than mismatched [matched 3.8 (1.6-6.0), median (interquartile range, IQR), versus 3.6 (1.6-5.7); p < 0.001], but recalculated and actual dose were similar. In mismatches preceding hypoglycemia, recalculated insulin dose was significantly lower than actual dose [recalculated 2.7 (0.4-5.0), median (IQR), versus 3.5 (1.4-5.6)]. In mismatches preceding hyperglycemia, recalculated insulin dose was significantly greater than actual dose [recalculated 4.7 (3.3-6.2), median (IQR), versus 3.3 (2.4-4.3); p < 0.001]. Administration of recalculated doses might have prevented blood glucose excursions.

CONCLUSIONS

Mismatched blood glucose values can influence CDSS IIT protocol performance.

Comparisons of different insulin infusion protocols: a review of recent literature

PURPOSE OF REVIEW

To provide an update on the currently available insulin infusion protocols for treatment of hyperglycemia in critically ill patients and to discuss the major differences and similarities among them.

RECENT FINDINGS

We identified a total of 26 protocols, 20 of which used manual blood-glucose calculations, and six that used computerized algorithms. The major differences and similarities among the insulin infusion protocols were in the following areas: patient characteristics, target glucose level, time to achieve target glucose level, incidence of hypoglycemia, rationale for adjusting the rates of insulin infusion, and methods of blood-glucose measurements. Several computerized protocols hold promise for safer achievement of glycemic targets.

SUMMARY

Insulin infusion is the most effective method for controlling hyperglycemia in critically ill patients. Clinicians should utilize a validated insulin infusion protocol that is well tolerated, and is most appropriate and practical for their institution based on the resources that are available.

Hypoglycemia aggravates critical illness-induced neurocognitive dysfunction

OBJECTIVE Tight glycemic control (TGC) in critically ill patients is associated with an increased risk of hypoglycemia. Whether those short episodes of hypoglycemia are associated with adverse morbidity and mortality is a matter of discussion. Using a case-control study design, we investigated whether hypoglycemia under TGC causes permanent neurocognitive dysfunction in patients surviving critical illness. RESEARCH DESIGN AND METHODS From our patient data management system, we identified adult survivors treated for >72 h in our surgical intensive care unit (ICU) between 2004 and 2007 (n = 4,635) without a history of neurocognitive dysfunction or structural brain abnormalities who experienced at least one episode of hypoglycemia during treatment (hypo group) (n = 37). For each hypo group patient, one patient stringently matched for demographic- and disease-related data were identified as a control subject. We performed a battery of neuropsychological tests investigating five areas of cognitive functioning in both groups at least 1 year after ICU discharge. Test results were compared with data from healthy control subjects and between groups. RESULTS Critical illness caused neurocognitive dysfunction in all tested domains in both groups. The dysfunction was aggravated in hypo group patients in one domain, namely that of visuospatial skills (P < 0.01). Besides hypoglycemia, both hyperglycemia (r = -0.322; P = 0.005) and fluctuations of blood glucose (r = -0.309; P = 0.008) were associated with worse test results in this domain. CONCLUSIONS Hypoglycemia was found to aggravate critical illness-induced neurocognitive dysfunction to a limited, but significant, extent; however, an impact of hyperglycemia and fluctuations of blood glucose on neurocognitive function cannot be excluded.

Is hypoglycaemia dangerous?

Tight glycaemic control (TGC) for patients treated in an intensive care unit ICU is associated with an increased risk for hypoglycaemia. Since hypoglycaemia mainly occurs in the sickest patients, no matter whether TGC is applied or not, it might be a marker for severity of illness or a harmful event in itself. Furthermore, it remains a matter of debate whether harmful effects of hypoglycaemia outbalance the clinical benefits of TGC. This review focusses on the clinical manifestations of hypoglycaemia in the critically ill and highlights its potential short- and long-term consequences specifically concerning neurocognitive function.

Hyperglycemia in acute ischemic stroke: pathophysiology and clinical management

Patients with acute ischemic stroke frequently test positive for hyperglycemia, which is associated with a poor clinical outcome. This association between poor glycemic control and an unfavorable prognosis is particularly evident in patients with persistent hyperglycemia, patients without a known history of diabetes mellitus, and patients with cortical infarction. To date, however, only one large clinical trial has specifically investigated the effect of glycemic control on stroke outcome. This trial failed to show a clinical benefit, but had several limitations. Despite a lack of clinical evidence supporting the use of glycemic control in the treatment of patients with stroke, international guidelines recommend treating this subset of critically ill patients for hyperglycemia in the hospital setting. This treatment regime is, however, particularly challenging in patients with stroke, and is associated with an increased risk of the patient developing hypoglycemia. Here we review the available evidence linking hyperglycemia to a poor clinical outcome in patients with ischemic stroke. We highlight the pathophysiological mechanisms that might underlie the deleterious effects of hyperglycemia on acute stroke prognosis and systematically review the literature concerning tight glycemic control after stroke. Finally, we provide directions on the use of insulin treatment strategies to control hyperglycemia in this patient group.

Implementation and evaluation of a nurse-centered computerized potassium regulation protocol in the intensive care unit--a before and after analysis

Background

Potassium disorders can cause major complications and must be avoided in critically ill patients. Regulation of potassium in the intensive care unit (ICU) requires potassium administration with frequent blood potassium measurements and subsequent adjustments of the amount of potassium administrated. The use of a potassium replacement protocol can improve potassium regulation. For safety and efficiency, computerized protocols appear to be superior over paper protocols. The aim of this study was to evaluate if a computerized potassium regulation protocol in the ICU improved potassium regulation.

Methods

In our surgical ICU (12 beds) and cardiothoracic ICU (14 beds) at a tertiary academic center, we implemented a nurse-centered computerized potassium protocol integrated with the pre-existent glucose control program called GRIP (Glucose Regulation in Intensive Care patients). Before implementation of the computerized protocol, potassium replacement was physician-driven. Potassium was delivered continuously either by central venous catheter or by gastric, duodenal or jejunal tube. After every potassium measurement, nurses received a recommendation for the potassium administration rate and the time to the next measurement. In this before-after study we evaluated potassium regulation with GRIP. The attitude of the nursing staff towards potassium regulation with computer support was measured with questionnaires.

Results

The patient cohort consisted of 775 patients before and 1435 after the implementation of computerized potassium control. The number of patients with hypokalemia (<3.5 mmol/L) and hyperkalemia (>5.0 mmol/L) were recorded, as well as the time course of potassium levels after ICU admission. The incidence of hypokalemia and hyperkalemia was calculated. Median potassium-levels were similar in both study periods, but the level of potassium control improved: the incidence of hypokalemia decreased from 2.4% to 1.7% (P < 0.001) and hyperkalemia from 7.4% to 4.8% (P < 0.001). Nurses indicated that they considered computerized potassium control an improvement over previous practice.

Conclusions

Computerized potassium control, integrated with the nurse-centered GRIP program for glucose regulation, is effective and reduces the prevalence of hypo- and hyperkalemia in the ICU compared with physician-driven potassium regulation.

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

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

Organ dysfunction is associated with hyperglycemia in critically ill children

OBJECTIVE

To determine the association between organ dysfunction and hyperglycemia in critically ill children receiving intravenous insulin.

DESIGN

Retrospective chart review (cohort study).

SETTING

Pediatric intensive care unit in a university hospital.

PATIENTS

n = 110 patients; inclusion criteria: ICU hospitalization from May 2005 to May 2006; insulin drip to manage hyperglycemia.

EXCLUSION CRITERIA

insulin drip <48 h; diabetic patients.

MEASUREMENTS

Duration of hyperglycemia: sum of hours of hyperglycemia (> or =126 mg/dl). Hypoglycemia (blood glucose <40 mg/dl). Organ dysfunction was determined per International Pediatric Sepsis Consensus Conference criteria. Multiple logistic regression models determined the association between > or =3 compared to <3 organ dysfunctions and hyperglycemia, hypoglycemia, and mortality, after adjustment for confounding variables (age, gender, PRISM score, vasopressors, steroids).

MAIN RESULTS

Organ dysfunction > or =3 compared to <3 after adjustment for confounders was associated with intermittent hyperglycemia of > or =24 h (OR 6.1, CI 1.8-21.2; p = 0.004). Hyperglycemia trended towards significance with mortality [3.2 (CI 0.9-11.6, p = 0.079)]. Hypoglycemia, after adjusting for the above, was not associated with mortality.

CONCLUSIONS

Organ dysfunction (> or =3 versus <3) was significantly associated with hyperglycemia for > or =24 h and hypoglycemia. Hyperglycemia trended toward significance with mortality in critically ill children. There was no association between hypoglycemia and mortality.

Health technology assessment review: Computerized glucose regulation in the intensive care unit--how to create artificial control

Current care guidelines recommend glucose control (GC) in critically ill patients. To achieve GC, many ICUs have implemented a (nurse-based) protocol on paper. However, such protocols are often complex, time-consuming, and can cause iatrogenic hypoglycemia. Computerized glucose regulation protocols may improve patient safety, efficiency, and nurse compliance. Such computerized clinical decision support systems (Cuss) use more complex logic to provide an insulin infusion rate based on previous blood glucose levels and other parameters. A computerized CDSS for glucose control has the potential to reduce overall workload, reduce the chance of human cognitive failure, and improve glucose control. Several computer-assisted glucose regulation programs have been published recently. In order of increasing complexity, the three main types of algorithms used are computerized flowcharts, Proportional-Integral-Derivative (PID), and Model Predictive Control (MPC). PID is essentially a closed-loop feedback system, whereas MPC models the behavior of glucose and insulin in ICU patients. Although the best approach has not yet been determined, it should be noted that PID controllers are generally thought to be more robust than MPC systems. The computerized Cuss that are most likely to emerge are those that are fully a part of the routine workflow, use patient-specific characteristics and apply variable sampling intervals.

How to implement a clinical pathway for intensive glucose regulation in acute coronary syndromes

Hyperglycemia upon admission of myocardial infarction patients predicts inferior clinical outcomes. Current strategies investigating hyperglycemia correction mostly use glucose-driven protocols. Implementation of these often labor-intensive protocols might be facilitated with the approach of a clinical pathway. Therefore, we evaluated the implementation of our glucose-driven protocol.We adapted a protocol for use in our coronary care unit (CCU), which was implemented according to the steps of a clinical pathway. To compensate for carbohydrates in meals we additionally developed a regimen of subcutaneous insulin.Protocol adherence was facilitated with a Web-based insulin calculator. All hyperglycemic patients admitted to the CCU were eligible for treatment according to this protocol.In a 4-month period, 643 glucose measurements were obtained in hyperglycemic patients admitted to our CCU. Patients were treated intensively with IV insulin for 35 hours and had 23 glucose measurements in this time span on average. This regimen achieved a median glucose of 6.2 mmol/L. Severe hypoglycemia occurred in only 1.1% of measurements and was without severe clinical side effects.Introduction of new intensive insulin protocol according to the steps of a clinical pathway is safe and feasible. The presence of a clinical pathway coordinator and sound communication are important conditions for successful introduction, which can be further aided with a computerized calculator.

Social, organizational, and contextual characteristics of clinical decision support systems for intensive insulin therapy: a literature review and case study

INTRODUCTION

Evaluations of computerized clinical decision support systems (CDSS) typically focus on clinical performance changes and do not include social, organizational, and contextual characteristics explaining use and effectiveness. Studies of CDSS for intensive insulin therapy (IIT) are no exception, and the literature lacks an understanding of effective computer-based IIT implementation and operation.

RESULTS

This paper presents (1) a literature review of computer-based IIT evaluations through the lens of institutional theory, a discipline from sociology and organization studies, to demonstrate the inconsistent reporting of workflow and care process execution and (2) a single-site case study to illustrate how computer-based IIT requires substantial organizational change and creates additional complexity with unintended consequences including error.

DISCUSSION

Computer-based IIT requires organizational commitment and attention to site-specific technology, workflow, and care processes to achieve intensive insulin therapy goals. The complex interaction between clinicians, blood glucose testing devices, and CDSS may contribute to workflow inefficiency and error. Evaluations rarely focus on the perspective of nurses, the primary users of computer-based IIT whose knowledge can potentially lead to process and care improvements.

CONCLUSION

This paper addresses a gap in the literature concerning the social, organizational, and contextual characteristics of CDSS in general and for intensive insulin therapy specifically. Additionally, this paper identifies areas for future research to define optimal computer-based IIT process execution: the frequency and effect of manual data entry error of blood glucose values, the frequency and effect of nurse overrides of CDSS insulin dosing recommendations, and comprehensive ethnographic study of CDSS for IIT.

Computer protocols: how to implement

Variation in clinical practice impedes control, is associated with unwanted and widespread error, and may preclude replicability. Methodologic replicability enhances our ability to detect signals of interest by both increasing the signal through consistent application of the intervention, and by reducing the obscuring effects of noise. Decision-support tools are intended to standardize some aspect of clinical care and thereby help lead to uniform implementation of clinical interventions. This is realized by explicit replicable computer protocols that can produce appropriate patient-specific decisions and introduce control of process into clinical care. Development of such protocols has required around-the-clock implementation for patient management because of the influence of patient history and previous patient states on the output of the computer protocol. Three successful computer protocols for management of blood glucose provide compelling examples. This clinician driven "bottom-up" approach complements the common information technology service driven "top-down" approach to clinical problems.