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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Glycemic targets in critically ill adults: A mini-review

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

A practical guidance on the use of intravenous insulin infusion for management of inpatient hyperglycemia: Intravenous Insulin Infusion for Management of Inpatient Hyperglycemia

BACKGROUND

We aim to provide a practical guidance on the use of intravenous insulin infusion for managing inpatient hyperglycemia.

METHODS AND RESULTS

This document was formulated based on the review of available literature and personal experience of authors. We have used various case scenarios to illustrate variables which should be taken into account when deciding adjustments in infusion rate, including but not restricted to ambient blood glucose level and magnitude of blood glucose change in the previous hour.

CONCLUSION

The guidance can be generalized to any situation where dedicated protocols are lacking, trained manpower is not available and resource constraints are present.

Optimal Blood Glucose Monitoring Interval for Insulin Infusion in Critically Ill Non-Cardiothoracic Patients: A Pilot Study

Objective:

The American Diabetes Association and the Society of Critical Care Medicine recommend monitoring blood glucose (BG) every 1-2 hours in patients receiving insulin infusion to guide titration of insulin infusion to maintain serum glucose in the target range; however, this is based on weak evidence. We evaluated the compliance of hourly BG monitoring and relation of less frequent BG monitoring to glycemic status.

Materials and Methods:

Retrospective chart review performed on 56 consecutive adult patients who received intravenous insulin infusion for persistent hyperglycemia in the ICU at Saint Vincent Hospital, a tertiary care community hospital an urban setting in Northeast region of USA. The frequency of fingerstick blood glucose (FSBG) readings was reviewed for compliance with hourly FSBG monitoring per protocol and the impact of FSBG testing at different time intervals on the glycemic status. Depending on time interval of FSBG monitoring, the data was divided into three groups: Group A (<90 min), Group B (91-179 min) and Group C (≥180 min).

Results:

The mean age was 69 years (48% were males), 77% patients had preexisting type 2 diabetes mellitus (T2DM). The mean MPM II score was 41. Of the 1411 readings for BG monitoring on insulin infusion, 467 (33%) were in group A, 806 (57%) in group B and 138 (10%) in group C; hourly BG monitoring compliance was 12.6%. The overall glycemic status was similar among all groups. There were 14 (0.99%) hypoglycemic episodes observed. The rate of hypoglycemic episodes was similar in all three groups (p=0.55).

Conclusion:

In patients requiring insulin infusion for sustained hyperglycemia in ICU, the risk of hypoglycemic episodes was not significantly different with less frequent BG monitoring. The compliance to hourly blood glucose monitoring and ICU was variable, and hypoglycemic episodes were similar across the groups despite the variation in monitoring.

Significance of the Study:

The importance of glycemic control in ICU has been well established and it is a resource intensive venture. However, there are no major studies highlighting the most optimal time interval for blood glucose checks in critically ill patients on insulin infusion. With this study we hypothesize that time duration between blood glucose checks can be increased safely without any untoward effects. Our study provides evidence for effective resource management with reducing the time spent with every glucose check and directly translating into high value care.

The effects of clinical decision support systems on insulin use: A systematic review

BACKGROUND

A clinical decision support system (CDSS) is a computerized system using case-based reasoning to assist clinicians in assessing disease status, in selecting appropriate therapy or in making other clinical decisions. Previous randomized controlled trials (RCTs or trials) have shown that CDSSs have the potential to improve the insulin use, but the evidence was conflicting and uncertain. The purpose of our study was to determine whether a CDSS improves the use of insulin.

METHOD

PubMed, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov were searched from their inception to October 2018. The quality assessment was based on the risk of bias criteria of the Cochrane Handbook.

RESULTS

Twenty-four RCTs, involving 7653 participants, were included. Thirteen of those trials (54.2%) used a computerized algorithm or a computer-assisted insulin protocol for insulin dose and therapy adjustment, of which 30.8% (four of 13) found significant changes. Of 10 trials that measured mean blood glucose levels and the 11 trials reported HbA1c, the computerized insulin dose adjustment resulted in lower mean blood glucose levels in 70.0% (seven of 10) and 36.4% (four of 11) of RCTs, respectively. Additionally, a significant reduction of hyperglycaemia events was reported in three of six RCTs. The evidence in a majority of the 24 RCTs was of moderate quality.

CONCLUSIONS

CDSSs have the potential to improve the insulin use and blood glucose control in a clinical setting. The methodologies in these studies were of mixed quality. Better designed and longer-term studies are required to ensure a larger and more reliable evidence base on the effects of CDSS intervention on insulin use.

Individualizing Glycemic Control in the Critically Ill

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

Diabetes Technology in the Inpatient Setting for Management of Hyperglycemia

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Glycemic control outcomes of manual and computerized insulin titration protocols: a systematic review protocol

OBJECTIVE

The object of this systematic review is to determine the effectiveness of computerized insulin titration protocols compared to manual insulin titration protocols for glycemic control in hospitalized adult patients.

INTRODUCTION

Hyperglycemia is common during acute illness, and current recommendations for patients with altered glucose metabolism is the use of intravenous insulin therapy. Due to the narrow therapeutic index of insulin, euglycemia is difficult to achieve and requires frequent dose titrations and blood glucose checks. Dose titrations can be accomplished through the use of manual or computerized insulin titration protocols.

INCLUSION CRITERIA

This review will consider studies that compare manual and computerized insulin titration protocols for hospitalized adult patients requiring intravenous insulin therapy for hyperglycemia. Studies must have considered one or more glycemic control outcomes.

METHODS

This systematic review will use the JBI methodology for evidence of effectiveness. The search will be limited to studies published in English from 1984, as this was the approximate year that the first pilot study of a computerized titration protocol was implemented. The databases to be searched include: Cochrane Central Register of Controlled Trials, CINAHL, PubMed, Embase, Health Technology Assessments and Ovid Healthstar. The trial registers to be searched include: US National Library of Medicine (ClinicalTrials.gov). The search for unpublished studies will include ProQuest Dissertations and Theses, and MedNar. Retrieval of full-text studies, assessment of methodological quality and data extraction will be performed independently by two reviewers. Meta-analysis will be performed if possible, and a Grading of Recommendations, Assessment, Development and Evaluation (GRADE) Summary of Findings presented.

SYSTEMATIC REVIEW REGISTRATION NUMBER

PROSPERO CRD42019142776.

Hypoglycemia Prevention by Algorithm Design During Intravenous Insulin Infusion

PURPOSE OF REVIEW

This review examines algorithm design features that may reduce risk for hypoglycemia while preserving glycemic control during intravenous insulin infusion. We focus principally upon algorithms in which the assignment of the insulin infusion rate (IR) depends upon maintenance rate of insulin infusion (MR) or a multiplier.

RECENT FINDINGS

Design features that may mitigate risk for hypoglycemia include use of a mid-protocol bolus feature and establishment of a low BG threshold for temporary interruption of infusion. Computer-guided dosing may improve target attainment without exacerbating risk for hypoglycemia. Column assignment (MR) within a tabular user-interpreted algorithm or multiplier may be specified initially according to patient characteristics and medical condition with revision during treatment based on patient response. We hypothesize that a strictly increasing sigmoidal relationship between MR-dependent IR and BG may reduce risk for hypoglycemia, in comparison to a linear relationship between multiplier-dependent IR and BG. Guidelines are needed that curb excessive up-titration of MR and recommend periodic pre-emptive trials of MR reduction. Future research should foster development of recommendations for "protocol maxima" of IR appropriate to patient condition.

Insulin Regimens to Treat Hyperglycemia in Hospitalized Patients on Nutritional Support: Systematic Review and Meta-Analyses

BACKGROUND

The best insulin regimen to treat hyperglycemia in hospitalized patients on nutritional support (NS) is unclear.

METHODS

We searched electronic databases to identify cohort studies or randomized clinical trials in order to evaluate the efficacy of different insulin regimens used to treat hyperglycemia in hospitalized patients on NS on diverse outcomes: mean blood glucose (MBG), hypoglycemia, length of stay in hospital, and mortality.

RESULTS

Seventeen studies from a total of 5,030 were included. Enteral Group included 8 studies; 1,203 patients using rapid, glargine, NPH, or Premix insulin; MBG 108-225 mg/dL; hypoglycemia 0-13%. In indirect meta-analyses, NPH insulin ranked best for glucose control (MD 95% CI -2.50 mg/dL [2.65 to -2.35]). Parenteral Group included 4 studies; 228 patients using regular and glargine or NPH insulin; MBG 137-202 mg/dL; hypoglycemia 0-40%. In meta-analyses comparing regular insulin added to parenteral nutrition bag with glargine, MBG (MD 95% CI -3.78 mg/dL [-11.93 to 4.37]; I2 = 0%) or hypoglycemia frequency (RR 95% CI 1.37 [0.43-4.32]; I2 = 70.7%) did not differ. The description related to hospital length of stay and mortality was inconsistent between groups.

CONCLUSIONS

The best insulin regimen to treat hyperglycemia in hospitalized patients on NS has not been established; best results using insulin regimens with NPH in enteral nutrition do not seem to be clinically relevant.

Sepsis and Septic Shock Strategies

Three therapeutic principles most substantially improve organ dysfunction and survival in sepsis: early, appropriate antimicrobial therapy; restoration of adequate cellular perfusion; timely source control. The new definitions of sepsis and septic shock reflect the inadequate sensitivity, specify, and lack of prognostication of systemic inflammatory response syndrome criteria. Sequential (sepsis-related) organ failure assessment more effectively prognosticates in sepsis and critical illness. Inadequate cellular perfusion accelerates injury and reestablishing perfusion limits injury. Multiple organ systems are affected by sepsis and septic shock and an evidence-based multipronged approach to systems-based therapy in critical illness results in improve outcomes.

Clinical Practice Guideline: Safe Medication Use in the ICU

OBJECTIVE

To provide ICU clinicians with evidence-based guidance on safe medication use practices for the critically ill.

DATA SOURCES

PubMed, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, CINAHL, Scopus, and ISI Web of Science for relevant material to December 2015.

STUDY SELECTION

Based on three key components: 1) environment and patients, 2) the medication use process, and 3) the patient safety surveillance system. The committee collectively developed Population, Intervention, Comparator, Outcome questions and quality of evidence statements pertaining to medication errors and adverse drug events addressing the key components. A total of 34 Population, Intervention, Comparator, Outcome questions, five quality of evidence statements, and one commentary on disclosure was developed.

DATA EXTRACTION

Subcommittee members were assigned selected Population, Intervention, Comparator, Outcome questions or quality of evidence statements. Subcommittee members completed their Grading of Recommendations Assessment, Development, and Evaluation of the question with his/her quality of evidence assessment and proposed strength of recommendation, then the draft was reviewed by the relevant subcommittee. The subcommittee collectively reviewed the evidence profiles for each question they developed. After the draft was discussed and approved by the entire committee, then the document was circulated among all members for voting on the quality of evidence and strength of recommendation.

DATA SYNTHESIS

The committee followed the principles of the Grading of Recommendations Assessment, Development, and Evaluation system to determine quality of evidence and strength of recommendations.

CONCLUSIONS

This guideline evaluates the ICU environment as a risk for medication-related events and the environmental changes that are possible to improve safe medication use. Prevention strategies for medication-related events are reviewed by medication use process node (prescribing, distribution, administration, monitoring). Detailed considerations to an active surveillance system that includes reporting, identification, and evaluation are discussed. Also, highlighted is the need for future research for safe medication practices that is specific to critically ill patients.

Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016

OBJECTIVE

To provide an update to "Surviving Sepsis Campaign Guidelines for Management of Sepsis and Septic Shock: 2012."

DESIGN

A consensus committee of 55 international experts representing 25 international organizations was convened. Nominal groups were assembled at key international meetings (for those committee members attending the conference). A formal conflict-of-interest (COI) policy was developed at the onset of the process and enforced throughout. A stand-alone meeting was held for all panel members in December 2015. Teleconferences and electronic-based discussion among subgroups and among the entire committee served as an integral part of the development.

METHODS

The panel consisted of five sections: hemodynamics, infection, adjunctive therapies, metabolic, and ventilation. Population, intervention, comparison, and outcomes (PICO) questions were reviewed and updated as needed, and evidence profiles were generated. Each subgroup generated a list of questions, searched for best available evidence, and then followed the principles of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to assess the quality of evidence from high to very low, and to formulate recommendations as strong or weak, or best practice statement when applicable.

RESULTS

The Surviving Sepsis Guideline panel provided 93 statements on early management and resuscitation of patients with sepsis or septic shock. Overall, 32 were strong recommendations, 39 were weak recommendations, and 18 were best-practice statements. No recommendation was provided for four questions.

CONCLUSIONS

Substantial agreement exists among a large cohort of international experts regarding many strong recommendations for the best care of patients with sepsis. Although a significant number of aspects of care have relatively weak support, evidence-based recommendations regarding the acute management of sepsis and septic shock are the foundation of improved outcomes for these critically ill patients with high mortality.

Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016

OBJECTIVE

To provide an update to "Surviving Sepsis Campaign Guidelines for Management of Sepsis and Septic Shock: 2012".

DESIGN

A consensus committee of 55 international experts representing 25 international organizations was convened. Nominal groups were assembled at key international meetings (for those committee members attending the conference). A formal conflict-of-interest (COI) policy was developed at the onset of the process and enforced throughout. A stand-alone meeting was held for all panel members in December 2015. Teleconferences and electronic-based discussion among subgroups and among the entire committee served as an integral part of the development.

METHODS

The panel consisted of five sections: hemodynamics, infection, adjunctive therapies, metabolic, and ventilation. Population, intervention, comparison, and outcomes (PICO) questions were reviewed and updated as needed, and evidence profiles were generated. Each subgroup generated a list of questions, searched for best available evidence, and then followed the principles of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to assess the quality of evidence from high to very low, and to formulate recommendations as strong or weak, or best practice statement when applicable.

RESULTS

The Surviving Sepsis Guideline panel provided 93 statements on early management and resuscitation of patients with sepsis or septic shock. Overall, 32 were strong recommendations, 39 were weak recommendations, and 18 were best-practice statements. No recommendation was provided for four questions.

CONCLUSIONS

Substantial agreement exists among a large cohort of international experts regarding many strong recommendations for the best care of patients with sepsis. Although a significant number of aspects of care have relatively weak support, evidence-based recommendations regarding the acute management of sepsis and septic shock are the foundation of improved outcomes for these critically ill patients with high mortality.

Implementation of an institution-wide acute stroke algorithm: Improving stroke quality metrics

Background:

In May 2012, an updated stroke algorithm was implemented at Vanderbilt University Medical Center. The current study objectives were to: (1) describe the process of implementing a new stroke algorithm and (2) compare pre- and post-algorithm quality improvement (QI) metrics, specificaly door to computed tomography time (DTCT), door to neurology time (DTN), and door to tPA administration time (DTT).

Methods:

Our institutional stroke algorithm underwent extensive revision, with a focus on removing variability, streamlining care, and improving time delays. The updated stroke algorithm was implemented in May 2012. Three primary stroke QI metrics were evaluated over four separate 3-month time points, one pre- and three post-algorithm periods.

Results:

The following data points improved after algorithm implementation: average DTCT decreased from 39.9 to 12.8 min (P < 0.001); average DTN decreased from 34.1 to 8.2 min (P ≤ 0.001), and average DTT decreased from 62.5 to 43.5 min (P = 0.17).

Conclusion:

A new stroke protocol that prioritized neurointervention at our institution resulted in significant lowering in the DTCT and DTN, with a nonsignificant improvement in DTT.

A comparison of two insulin infusion protocols in the medical intensive care unit by continuous glucose monitoring

Background

Achieving good glycemic control in intensive care units (ICU) requires a safe and efficient insulin infusion protocol (IIP). We aimed to compare the clinical performance of two IIPs (Leuven versus modified Yale protocol) in patients admitted to medical ICU, by using continuous glucose monitoring (CGM). This is a pooled data analysis of two published prospective randomized controlled trials. CGM monitoring was performed in 57 MICU patients (age 64 ± 12 years, APACHE-II score 28 ± 7, non-diabetic/diabetic: 36/21). The main outcome measures were percentage of time in normoglycemia (80–110 mg/dl) and in hypoglycemia (<60 mg/dl), and glycemic variability (standard deviation, coefficient of variation, mean amplitude of glucose excursions, mean of daily differences).

Results

Twenty-two subjects were treated using the Leuven protocol and 35 by the Yale protocol; >63,000 CGM measurements were available. The percentage of time in normoglycemia (80–110 mg/dl) was higher (37 ± 15 vs. 26 ± 11%, p = 0.001) and percentage of time spent in hypoglycemia was lower (0[0–2] vs. 5[1–8]%, p = 0.001) in the Yale group. Median glycemia did not differ between groups (118[108–128] vs. 128[106–154] mg/dl). Glycemic variability was less pronounced in the Yale group (median SD 28[21–37] vs. 47[31–71] mg/dl, p = 0.001; CV 23[19–31] vs. 36[26–50]%, p = 0.001; MODD 35[26–41] vs. 60[33–94] mg/dl, p = 0.001). However, logistic regression could not identify type of IIP, diabetes status, age, BMI, or APACHE-II score as independent parameters for strict glucose control.

Conclusions

The Yale protocol provided better average glycemia, more time spent in normoglycemia, less time in hypoglycemia, and less glycemic variability than the Leuven protocol, but was not independently associated with strict glycemic control.

Electronic supplementary material

The online version of this article (doi:10.1186/s13613-016-0214-9) contains supplementary material, which is available to authorized users.

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.

Insulin resistance increases before ventilator-associated pneumonia in euglycemic trauma patients

BACKGROUND

Hyperglycemia caused by stress-induced insulin resistance is associated with both infection and mortality in critically injured patients. The onset of infection may increase stress-induced insulin resistance, leading to hyperglycemia. Hyperglycemia has been shown to precede the diagnosis of ventilator-associated pneumonia (VAP) in critically injured adults and has been suggested to have potential diagnostic importance. However, glycemic control (GC) protocols in critically ill patients limit the development of hyperglycemia despite increasing insulin resistance. Our computer-assisted GC protocol achieves excellent GC, limiting infection-related hyperglycemia while capturing prospectively all glucose values, insulin infusion rates, and the multiplier (M) used to calculate the insulin rate. We hypothesized that surrogate measures of insulin resistance, the insulin infusion rate and multiplier M, would increase prior to the clinical suspicion of VAP, even in euglycemic critically injured patients.

METHODS

All critically injured patients (2,656) on the computerized glycemic control protocol were included in the analysis and categorized by those developing VAP and those without pneumonia on days 3-10 of their intensive care unit (ICU) stay. Median blood glucose concentration (BG), insulin infusion rate (IDR), and multiplier (M) [Insulin Drip Rate=M*(BG-60)] were determined for VAP patients (n=329) and non-infected ventilated (NIV) patients (n=2,327) on each day of mechanical ventilation. The day of VAP diagnosis according to U.S. Centers for Disease Control and Prevention (CDC) criteria was defined as day zero and VAP patients matched with NIV patients according to ventilator day from -10 to +10. Comparisons were conducted using the Mann-Whitney U test.

RESULTS

Baseline characteristics between VAP and NIV groups did not differ. Measures of insulin resistance increased from the time of injury in both groups. Patients with VAP had significantly greater change in both measures of insulin resistance, IDR and M, in the 48 hours preceding the diagnosis of VAP. These changes occurred despite the fact that the computer-assisted GC protocol achieved lower glucose values in VAP patients for the majority of study days.

CONCLUSIONS

Measures of insulin resistance increase in the two days prior to the clinical suspicion of VAP for critically injured patients on the GC protocol. These changes occur despite the protocol maintaining euglycemia. This data suggests that markers of insulin resistance may provide clinically useful information in the early diagnosis of VAP.

Insulin Resistance in Critically Injured Adults: Contribution of Pneumonia, Diabetes, Nutrition, and Acuity

PURPOSE

Changes in insulin resistance (IR) cause stress-induced hyperglycemia after trauma, but the numerous factors involved in IR have not been delineated clearly. We hypothesized that a statistical model could help determine the relative contribution of different clinical co-variates to IR in critically injured patients.

PATIENTS AND METHODS

We retrospectively studied 726 critically injured patients managed with a computer-assisted glycemic protocol at an academic level I trauma center (639 ventilated controls without pneumonia (VWP) and 87 patients with ventilator-associated pneumonia (VAP). Linear regression using age, gender, body mass index (BMI), diabetes mellitus, pneumonia, and glycemic provision was used to estimate M, a marker of IR that incorporates both the serum blood glucose concentration (BG) and insulin dose.

RESULTS

Increasing M (p<0.001) was associated with age (1.62%; 95% confidence interval [CI] 1.27%-1.97% per decade), male gender (9.78%; 95% CI 8.28%-12.6%), BMI (4.32% [95% CI 4.02%-4.62%] per 5 points), diabetes mellitus (21.2%; 95% CI 19.2%-23.2%), pneumonia (10.9%; 95% CI 9.31%-12.6%), and glycemic provision (27.3% [95% CI 6.6%-28.1%] per 100 g of glucose). Total parenteral nutrition was associated with a decrease in M of 10.3%; 95% CI 8.52%-12.1%; p<0.001.

CONCLUSIONS

Clinical factors can be used to construct a model of IR. Prospective validation might enable early detection and treatment of infection or other conditions associated with increased IR.

Reducing the risk of hypoglycemia associated with intravenous insulin: experience with a computerized insulin infusion program in 4 adult intensive care units

Computerized insulin infusion protocols have facilitated more effective blood glucose (BG) control in intensive care units (ICUs). This is particularly important in light of the risks associated with hypoglycemia. End stage renal disease (ESRD) increases the risk of insulin-induced hypoglycemia. We evaluated BG control in 210 patients in 2 medical ICUs and in 2 surgical ICUs who were treated with a computerized insulin infusion program (CIIP). Our CIIP was programmed for a BG target of 140-180 mg/dL for medical ICU patients or 120-160 mg/dL for surgical ICU patients. In addition, we focused on BG control in the 11% of our patients with ESRD. Mean BG was 147 ± 20 mg/dL for surgical ICU patients and 171 ± 26 mg/dL for medical ICU patients. Of both surgical and medical ICU patients, 17% had 1 or more BG 60-79 mg/dL, while 3% of surgical ICU and 8% of medical ICU patients had 1 or more BG < 60 mg/dL. Mean BG in ESRD patients was 147 ± 16 mg/dL similar to 152 ± 23 mg/dL in patients without ESRD. Of ESRD patients, 41% had 1 or more BG < 79 mg/dL as compared with 17.8% of non-ESRD patients (P < .01). A higher BG target for medical ICU patients as compared with surgical ICU patients yielded comparably low rates of moderate or severe hypoglycemia. However, hypoglycemia among ESRD patients was more common compared to non-ESRD patients, suggesting a need for a higher BG target specific to ESRD patients.

Continuous glucose control in the ICU: report of a 2013 round table meeting

Achieving adequate glucose control in critically ill patients is a complex but important part of optimal patient management. Until relatively recently, intermittent measurements of blood glucose have been the only means of monitoring blood glucose levels. With growing interest in the possible beneficial effects of continuous over intermittent monitoring and the development of several continuous glucose monitoring (CGM) systems, a round table conference was convened to discuss and, where possible, reach consensus on the various aspects related to glucose monitoring and management using these systems. In this report, we discuss the advantages and limitations of the different types of devices available, the potential advantages of continuous over intermittent testing, the relative importance of trend and point accuracy, the standards necessary for reporting results in clinical trials and for recognition by official bodies, and the changes that may be needed in current glucose management protocols as a result of a move towards increased use of CGM. We close with a list of the research priorities in this field, which will be necessary if CGM is to become a routine part of daily practice in the management of critically ill patients.

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

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

Stress-induced hyperglycemia: is it harmful following trauma?

There is an established association between the presence of SIH and worse morbidity and mortality after trauma. However, given the limitations of existing data, no definitive statements can be made as to whether aggressive treatment of hyperglycemia actually benefits outcome. Although early studies seemed to show a clear benefit in surgical ICU patients, subsequent studies have not duplicated these results. In addition, severe hypoglycemic episodes associated with glycemic control protocols have provided further concern, because they have been associated with higher rates of mortality. These disparate outcomes in prospective, randomized trials have not allowed definitive conclusions to be drawn regarding the exact glucose levels that should be maintained. Regardless, some postinjury control of glucose levels is likely necessary. Without data to support the practice, tight glycemic control keeping glucose levels below 110 mg/dL is likely not necessary and probably detrimental to patient outcome. It seems that a more moderate level of glycemic control, aimed at providing stabilization of glucose levels while reducing hyperglycemic and hypoglycemic events, is being practiced in most institutions. Performance of prospective, randomized trials in the trauma population along with further advancement and refinement of techniques to more precisely reduce glucose variability will further clarify the level of glucose control associated with improved outcomes.

Computerized advice on drug dosage to improve prescribing practice

BACKGROUND

Maintaining therapeutic concentrations of drugs with a narrow therapeutic window is a complex task. Several computer systems have been designed to help doctors determine optimum drug dosage. Significant improvements in health care could be achieved if computer advice improved health outcomes and could be implemented in routine practice in a cost-effective fashion. This is an updated version of an earlier Cochrane systematic review, first published in 2001 and updated in 2008.

OBJECTIVES

To assess whether computerized advice on drug dosage has beneficial effects on patient outcomes compared with routine care (empiric dosing without computer assistance).

SEARCH METHODS

The following databases were searched from 1996 to January 2012: EPOC Group Specialized Register, Reference Manager; Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Ovid; EMBASE, Ovid; and CINAHL, EbscoHost. A "top up" search was conducted for the period January 2012 to January 2013; these results were screened by the authors and potentially relevant studies are listed in Studies Awaiting Classification. The review authors also searched reference lists of relevant studies and related reviews.

SELECTION CRITERIA

We included randomized controlled trials, non-randomized controlled trials, controlled before-and-after studies and interrupted time series analyses of computerized advice on drug dosage. The participants were healthcare professionals responsible for patient care. The outcomes were any objectively measured change in the health of patients resulting from computerized advice (such as therapeutic drug control, clinical improvement, adverse reactions).

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data and assessed study quality. We grouped the results from the included studies by drug used and the effect aimed at for aminoglycoside antibiotics, amitriptyline, anaesthetics, insulin, anticoagulants, ovarian stimulation, anti-rejection drugs and theophylline. We combined the effect sizes to give an overall effect for each subgroup of studies, using a random-effects model. We further grouped studies by type of outcome when appropriate (i.e. no evidence of heterogeneity).

MAIN RESULTS

Forty-six comparisons (from 42 trials) were included (as compared with 26 comparisons in the last update) including a wide range of drugs in inpatient and outpatient settings. All were randomized controlled trials except two studies. Interventions usually targeted doctors, although some studies attempted to influence prescriptions by pharmacists and nurses. Drugs evaluated were anticoagulants, insulin, aminoglycoside antibiotics, theophylline, anti-rejection drugs, anaesthetic agents, antidepressants and gonadotropins. Although all studies used reliable outcome measures, their quality was generally low.This update found similar results to the previous update and managed to identify specific therapeutic areas where the computerized advice on drug dosage was beneficial compared with routine care:1. it increased target peak serum concentrations (standardized mean difference (SMD) 0.79, 95% CI 0.46 to 1.13) and the proportion of people with plasma drug concentrations within the therapeutic range after two days (pooled risk ratio (RR) 4.44, 95% CI 1.94 to 10.13) for aminoglycoside antibiotics;2. it led to a physiological parameter more often within the desired range for oral anticoagulants (SMD for percentage of time spent in target international normalized ratio +0.19, 95% CI 0.06 to 0.33) and insulin (SMD for percentage of time in target glucose range: +1.27, 95% CI 0.56 to 1.98);3. it decreased the time to achieve stabilization for oral anticoagulants (SMD -0.56, 95% CI -1.07 to -0.04);4. it decreased the thromboembolism events (rate ratio 0.68, 95% CI 0.49 to 0.94) and tended to decrease bleeding events for anticoagulants although the difference was not significant (rate ratio 0.81, 95% CI 0.60 to 1.08). It tended to decrease unwanted effects for aminoglycoside antibiotics (nephrotoxicity: RR 0.67, 95% CI 0.42 to 1.06) and anti-rejection drugs (cytomegalovirus infections: RR 0.90, 95% CI 0.58 to 1.40);5. it tended to reduce the length of time spent in the hospital although the difference was not significant (SMD -0.15, 95% CI -0.33 to 0.02) and to achieve comparable or better cost-effectiveness ratios than usual care;6. there was no evidence of differences in mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants.For all outcomes, statistical heterogeneity quantified by I(2) statistics was moderate to high.

AUTHORS' CONCLUSIONS

This review update suggests that computerized advice for drug dosage has some benefits: it increases the serum concentrations for aminoglycoside antibiotics and improves the proportion of people for which the plasma drug is within the therapeutic range for aminoglycoside antibiotics.It leads to a physiological parameter more often within the desired range for oral anticoagulants and insulin. It decreases the time to achieve stabilization for oral anticoagulants. It tends to decrease unwanted effects for aminoglycoside antibiotics and anti-rejection drugs, and it significantly decreases thromboembolism events for anticoagulants. It tends to reduce the length of hospital stay compared with routine care while comparable or better cost-effectiveness ratios were achieved.However, there was no evidence that decision support had an effect on mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants. In addition, there was no evidence to suggest that some decision support technical features (such as its integration into a computer physician order entry system) or aspects of organization of care (such as the setting) could optimize the effect of computerized advice.Taking into account the high risk of bias of, and high heterogeneity between, studies, these results must be interpreted with caution.

Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012

OBJECTIVE

To provide an update to the "Surviving Sepsis Campaign Guidelines for Management of Severe Sepsis and Septic Shock," last published in 2008.

DESIGN

A consensus committee of 68 international experts representing 30 international organizations was convened. Nominal groups were assembled at key international meetings (for those committee members attending the conference). A formal conflict of interest policy was developed at the onset of the process and enforced throughout. The entire guidelines process was conducted independent of any industry funding. A stand-alone meeting was held for all subgroup heads, co- and vice-chairs, and selected individuals. Teleconferences and electronic-based discussion among subgroups and among the entire committee served as an integral part of the development.

METHODS

The authors were advised to follow the principles of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to guide assessment of quality of evidence from high (A) to very low (D) and to determine the strength of recommendations as strong (1) or weak (2). The potential drawbacks of making strong recommendations in the presence of low-quality evidence were emphasized. Some recommendations were ungraded (UG). Recommendations were classified into three groups: 1) those directly targeting severe sepsis; 2) those targeting general care of the critically ill patient and considered high priority in severe sepsis; and 3) pediatric considerations.

RESULTS

Key recommendations and suggestions, listed by category, include: early quantitative resuscitation of the septic patient during the first 6 hrs after recognition (1C); blood cultures before antibiotic therapy (1C); imaging studies performed promptly to confirm a potential source of infection (UG); administration of broad-spectrum antimicrobials therapy within 1 hr of recognition of septic shock (1B) and severe sepsis without septic shock (1C) as the goal of therapy; reassessment of antimicrobial therapy daily for de-escalation, when appropriate (1B); infection source control with attention to the balance of risks and benefits of the chosen method within 12 hrs of diagnosis (1C); initial fluid resuscitation with crystalloid (1B) and consideration of the addition of albumin in patients who continue to require substantial amounts of crystalloid to maintain adequate mean arterial pressure (2C) and the avoidance of hetastarch formulations (1C); initial fluid challenge in patients with sepsis-induced tissue hypoperfusion and suspicion of hypovolemia to achieve a minimum of 30 mL/kg of crystalloids (more rapid administration and greater amounts of fluid may be needed in some patients) (1C); fluid challenge technique continued as long as hemodynamic improvement, as based on either dynamic or static variables (UG); norepinephrine as the first-choice vasopressor to maintain mean arterial pressure ≥ 65 mm Hg (1B); epinephrine when an additional agent is needed to maintain adequate blood pressure (2B); vasopressin (0.03 U/min) can be added to norepinephrine to either raise mean arterial pressure to target or to decrease norepinephrine dose but should not be used as the initial vasopressor (UG); dopamine is not recommended except in highly selected circumstances (2C); dobutamine infusion administered or added to vasopressor in the presence of a) myocardial dysfunction as suggested by elevated cardiac filling pressures and low cardiac output, or b) ongoing signs of hypoperfusion despite achieving adequate intravascular volume and adequate mean arterial pressure (1C); avoiding use of intravenous hydrocortisone in adult septic shock patients if adequate fluid resuscitation and vasopressor therapy are able to restore hemodynamic stability (2C); hemoglobin target of 7-9 g/dL in the absence of tissue hypoperfusion, ischemic coronary artery disease, or acute hemorrhage (1B); low tidal volume (1A) and limitation of inspiratory plateau pressure (1B) for acute respiratory distress syndrome (ARDS); application of at least a minimal amount of positive end-expiratory pressure (PEEP) in ARDS (1B); higher rather than lower level of PEEP for patients with sepsis-induced moderate or severe ARDS (2C); recruitment maneuvers in sepsis patients with severe refractory hypoxemia due to ARDS (2C); prone positioning in sepsis-induced ARDS patients with a PaO2/FIO2 ratio of ≤ 100 mm Hg in facilities that have experience with such practices (2C); head-of-bed elevation in mechanically ventilated patients unless contraindicated (1B); a conservative fluid strategy for patients with established ARDS who do not have evidence of tissue hypoperfusion (1C); protocols for weaning and sedation (1A); minimizing use of either intermittent bolus sedation or continuous infusion sedation targeting specific titration endpoints (1B); avoidance of neuromuscular blockers if possible in the septic patient without ARDS (1C); a short course of neuromuscular blocker (no longer than 48 hrs) for patients with early ARDS and a Pao2/Fio2 < 150 mm Hg (2C); a protocolized approach to blood glucose management commencing insulin dosing when two consecutive blood glucose levels are > 180 mg/dL, targeting an upper blood glucose ≤ 180 mg/dL (1A); equivalency of continuous veno-venous hemofiltration or intermittent hemodialysis (2B); prophylaxis for deep vein thrombosis (1B); use of stress ulcer prophylaxis to prevent upper gastrointestinal bleeding in patients with bleeding risk factors (1B); oral or enteral (if necessary) feedings, as tolerated, rather than either complete fasting or provision of only intravenous glucose within the first 48 hrs after a diagnosis of severe sepsis/septic shock (2C); and addressing goals of care, including treatment plans and end-of-life planning (as appropriate) (1B), as early as feasible, but within 72 hrs of intensive care unit admission (2C). Recommendations specific to pediatric severe sepsis include: therapy with face mask oxygen, high flow nasal cannula oxygen, or nasopharyngeal continuous PEEP in the presence of respiratory distress and hypoxemia (2C), use of physical examination therapeutic endpoints such as capillary refill (2C); for septic shock associated with hypovolemia, the use of crystalloids or albumin to deliver a bolus of 20 mL/kg of crystalloids (or albumin equivalent) over 5 to 10 mins (2C); more common use of inotropes and vasodilators for low cardiac output septic shock associated with elevated systemic vascular resistance (2C); and use of hydrocortisone only in children with suspected or proven "absolute"' adrenal insufficiency (2C).

CONCLUSIONS

Strong agreement existed among a large cohort of international experts regarding many level 1 recommendations for the best care of patients with severe sepsis. Although a significant number of aspects of care have relatively weak support, evidence-based recommendations regarding the acute management of sepsis and septic shock are the foundation of improved outcomes for this important group of critically ill patients.

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.

Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock, 2012

OBJECTIVE

To provide an update to the "Surviving Sepsis Campaign Guidelines for Management of Severe Sepsis and Septic Shock," last published in 2008.

DESIGN

A consensus committee of 68 international experts representing 30 international organizations was convened. Nominal groups were assembled at key international meetings (for those committee members attending the conference). A formal conflict of interest policy was developed at the onset of the process and enforced throughout. The entire guidelines process was conducted independent of any industry funding. A stand-alone meeting was held for all subgroup heads, co- and vice-chairs, and selected individuals. Teleconferences and electronic-based discussion among subgroups and among the entire committee served as an integral part of the development.

METHODS

The authors were advised to follow the principles of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system to guide assessment of quality of evidence from high (A) to very low (D) and to determine the strength of recommendations as strong (1) or weak (2). The potential drawbacks of making strong recommendations in the presence of low-quality evidence were emphasized. Recommendations were classified into three groups: (1) those directly targeting severe sepsis; (2) those targeting general care of the critically ill patient and considered high priority in severe sepsis; and (3) pediatric considerations.

RESULTS

Key recommendations and suggestions, listed by category, include: early quantitative resuscitation of the septic patient during the first 6 h after recognition (1C); blood cultures before antibiotic therapy (1C); imaging studies performed promptly to confirm a potential source of infection (UG); administration of broad-spectrum antimicrobials therapy within 1 h of the recognition of septic shock (1B) and severe sepsis without septic shock (1C) as the goal of therapy; reassessment of antimicrobial therapy daily for de-escalation, when appropriate (1B); infection source control with attention to the balance of risks and benefits of the chosen method within 12 h of diagnosis (1C); initial fluid resuscitation with crystalloid (1B) and consideration of the addition of albumin in patients who continue to require substantial amounts of crystalloid to maintain adequate mean arterial pressure (2C) and the avoidance of hetastarch formulations (1B); initial fluid challenge in patients with sepsis-induced tissue hypoperfusion and suspicion of hypovolemia to achieve a minimum of 30 mL/kg of crystalloids (more rapid administration and greater amounts of fluid may be needed in some patients (1C); fluid challenge technique continued as long as hemodynamic improvement is based on either dynamic or static variables (UG); norepinephrine as the first-choice vasopressor to maintain mean arterial pressure ≥65 mmHg (1B); epinephrine when an additional agent is needed to maintain adequate blood pressure (2B); vasopressin (0.03 U/min) can be added to norepinephrine to either raise mean arterial pressure to target or to decrease norepinephrine dose but should not be used as the initial vasopressor (UG); dopamine is not recommended except in highly selected circumstances (2C); dobutamine infusion administered or added to vasopressor in the presence of (a) myocardial dysfunction as suggested by elevated cardiac filling pressures and low cardiac output, or (b) ongoing signs of hypoperfusion despite achieving adequate intravascular volume and adequate mean arterial pressure (1C); avoiding use of intravenous hydrocortisone in adult septic shock patients if adequate fluid resuscitation and vasopressor therapy are able to restore hemodynamic stability (2C); hemoglobin target of 7-9 g/dL in the absence of tissue hypoperfusion, ischemic coronary artery disease, or acute hemorrhage (1B); low tidal volume (1A) and limitation of inspiratory plateau pressure (1B) for acute respiratory distress syndrome (ARDS); application of at least a minimal amount of positive end-expiratory pressure (PEEP) in ARDS (1B); higher rather than lower level of PEEP for patients with sepsis-induced moderate or severe ARDS (2C); recruitment maneuvers in sepsis patients with severe refractory hypoxemia due to ARDS (2C); prone positioning in sepsis-induced ARDS patients with a PaO (2)/FiO (2) ratio of ≤100 mm Hg in facilities that have experience with such practices (2C); head-of-bed elevation in mechanically ventilated patients unless contraindicated (1B); a conservative fluid strategy for patients with established ARDS who do not have evidence of tissue hypoperfusion (1C); protocols for weaning and sedation (1A); minimizing use of either intermittent bolus sedation or continuous infusion sedation targeting specific titration endpoints (1B); avoidance of neuromuscular blockers if possible in the septic patient without ARDS (1C); a short course of neuromuscular blocker (no longer than 48 h) for patients with early ARDS and a PaO (2)/FI O (2) <150 mm Hg (2C); a protocolized approach to blood glucose management commencing insulin dosing when two consecutive blood glucose levels are >180 mg/dL, targeting an upper blood glucose ≤180 mg/dL (1A); equivalency of continuous veno-venous hemofiltration or intermittent hemodialysis (2B); prophylaxis for deep vein thrombosis (1B); use of stress ulcer prophylaxis to prevent upper gastrointestinal bleeding in patients with bleeding risk factors (1B); oral or enteral (if necessary) feedings, as tolerated, rather than either complete fasting or provision of only intravenous glucose within the first 48 h after a diagnosis of severe sepsis/septic shock (2C); and addressing goals of care, including treatment plans and end-of-life planning (as appropriate) (1B), as early as feasible, but within 72 h of intensive care unit admission (2C). Recommendations specific to pediatric severe sepsis include: therapy with face mask oxygen, high flow nasal cannula oxygen, or nasopharyngeal continuous PEEP in the presence of respiratory distress and hypoxemia (2C), use of physical examination therapeutic endpoints such as capillary refill (2C); for septic shock associated with hypovolemia, the use of crystalloids or albumin to deliver a bolus of 20 mL/kg of crystalloids (or albumin equivalent) over 5-10 min (2C); more common use of inotropes and vasodilators for low cardiac output septic shock associated with elevated systemic vascular resistance (2C); and use of hydrocortisone only in children with suspected or proven "absolute"' adrenal insufficiency (2C).

CONCLUSIONS

Strong agreement existed among a large cohort of international experts regarding many level 1 recommendations for the best care of patients with severe sepsis. Although a significant number of aspects of care have relatively weak support, evidence-based recommendations regarding the acute management of sepsis and septic shock are the foundation of improved outcomes for this important group of critically ill patients.

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.

Risk of postoperative hypoglycemia in cardiovascular surgical patients receiving computer-based versus paper-based insulin therapy

OBJECTIVE

To evaluate the safety and efficacy of replacing a paper-based protocol with a computer-guided glucose management system (CGMS) for the treatment of postoperative hyperglycemia in the cardiovascular intensive care unit (CVICU).

METHODS

With use of a before-and-after analysis, adult patients (≥18 years) discharged from the CVICU and treated with the paper protocol were compared with patients discharged from the CVICU and treated with the CGMS. Of the 1,648 patients analyzed, 991 were in the CGMS group. Clinical end points were evaluated by using the Wilcoxon test. Unadjusted and adjusted hazard ratios (HRs) for each hypoglycemic end point were calculated from Cox models with use of the proportional hazards regression procedure, and clinical end points were adjusted for potential confounders.

RESULTS

Patients treated with the paper protocol were 6 times as likely to experience clinical hypoglycemia (blood glucose ≤70 mg/dL) as patients treated with the CGMS (adjusted HR = 6.06; P<.0001) and more than 7 times as likely to experience severe hypoglycemia (blood glucose ≤40 mg/dL) (adjusted HR = 7.59; P=.01). Despite the increased risk of hypoglycemia, no significant difference in length of stay or mortality was observed between the groups.

CONCLUSION

CGMS treatment of postoperative hyperglycemia in CVICU patients can successfully attain goal glucose levels with a significant reduction in hypoglycemia in comparison with a paper protocol. This association persists after controlling for covariates.

Intravenous insulin infusions: what nurses need to know

Glucose control in the acute setting has long been a debate among leaders in the fields of diabetes and acute care. Providers struggle to maintain euglycemia in critically ill patients and simultaneously prevent hypoglycemia. Intravenous insulin infusions are frequently preferred in the critical care setting to control glucose, because they can be titrated for changing insulin requirements and can be cleared from the system rapidly. This article discusses the basics of the insulin drip, common insulin drips, and the role of the nurse in managing an insulin drip.

Evaluating an insulin infusion protocol in an acute care setting

OBJECTIVE

The objective of the present study was to evaluate the adherence, safety, and effectiveness of a paper versus an electronic insulin infusion protocol.

DESIGN

This quasi-experimental implementation study compared experimental and control groups using a nonrandomized prospective cohort design.

SETTING

The study was performed at 2 surgical units within a federal tertiary care teaching hospital.

SAMPLE

Fifty-eight registered nurses volunteered.

METHODS

We compared time intervals using electronic time stamps from glucometers and insulin infusion devices to measure protocol adherence. We assessed perceived adherence using a nurse survey, and, to evaluate safety, we reviewed each paper protocol infusion calculation for correctness.

FINDINGS

Median times from blood glucose acquisition to infusion rate adjustment did not differ significantly between groups (P = .215). The majority of infusions (96.6%) had glucose acquisition times within the acceptable range. Median values of time to next "glucose due" did not differ significantly (P = .88), and relative variation in median glucose reporting times did not differ significantly between groups (P = .16). Evaluation of 877 paper protocol entries demonstrated a 10.7% (n = 94) calculation error rate. Registered nurses within the electronic group reported greater ease in balancing workload when compared with paper protocol use (P = .03). Attitudes did not differ significantly between groups in areas of determining infusion adjustment, bolus insulin dose, next glucose due, ease of access, understanding protocol, or overall satisfaction.

CONCLUSIONS

This study demonstrates that one can adhere to an insulin infusion protocol, regardless of the format (paper or electronic), in the medical-surgical setting. Our results suggest there are safety and nurse workload benefits when an electronic protocol was used.

IMPLICATIONS

Adherence, safety, and effectiveness can be achieved when using insulin infusion in the medical-surgical setting.

Duration of time on intensive insulin therapy predicts severe hypoglycemia in the surgically critically ill population

BACKGROUND

Hypoglycemia has emerged as a barrier to the practice of intensive insulin therapy. Current literature suggests that hypoglycemia occurs at variable rates and has different effects on outcomes in surgical and medical populations. We sought to determine the incidence, independent predictors, and effect on outcome of severe hypoglycemia (≤ 40 mg/dl) in a surgical population.

METHODS

A retrospective analysis was performed on all critically ill surgical patients treated with IIT from October 2004 to February 2007. Euglycemia (goal 80-110 mg/dl) was maintained using automated computerized titration of an insulin infusion. The primary outcome of interest was any episode of severe hypoglycemia (≤ 40 mg/dl). Multivariate logistic regression was used to determine the independent predictors of developing severe hypoglycemia.

RESULTS

A total of 60,298 data entries (1,118 patients) for glucose were analyzed. There were 64 severe hypoglycemic episodes in 52 patients (4.6% of the patients). There was a significant increase in deaths among patients who experienced at least one episode of hypoglycemia when compared with those who did not (26.9% vs. 15.3%, P = 0.03). Logistic regression revealed that the time spent on the protocol was the best predictor of developing a hypoglycemic event when controlling for other known risk factors of hypoglycemia.

CONCLUSIONS

Intensive insulin therapy can be implemented with a low percentage of patients (4.6%) experiencing severe hypoglycemia. Mortality rate was higher for patients experiencing hypoglycemia. The duration of the time spent on the protocol was the best predictor of hypoglycemia, suggesting that hypoglycemia is a mathematic probability of prolonged illness, not a reflection of illness severity or demographic features.

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.

A retrospective cohort study of a nurse-driven computerized insulin infusion program versus a paper-based protocol in critically ill patients

BACKGROUND

There is variability in the extent of outcome achievement between computerized insulin infusion programs (CIIPs) and paper-based protocols (PBPs). This reported variability may be improved by intensive CIIP training prior to implementation. The objective was to evaluate the impact of a CIIP following intensive nurse training versus a PBP in a critical care setting.

METHODS

A retrospective cohort study was performed on patients admitted to a mixed intensive care unit comparing glucose control between the CIIP following intensive training and a PBP. Consecutive patients on each protocol were assessed to obtain glucose concentrations and outcomes. The primary measure was the percentage of blood glucose values within target range (90-130 mg/dL). Patient glucose values were pooled and assessed using the χ(2) test for independence.

RESULTS

In total, 61 patients with 5,495 glucose tests were included in the PBP group, and 51 patients with 5,645 glucose tests in the CIIP group. A greater percentage of glucose tests was within target range in the CIIP group (68.4% vs. 36.5%, P<0.001). In the CIIP group, time-to-target (median [interquartile range] 5 [3-8] h vs. 7 [4-20] h, P=0.02) and severe hypoglycemic events were reduced (26 vs. 6, P<0.0001).

CONCLUSIONS

The nurse-driven CIIP led to a higher percentage of glucose values within target range, faster achievement of target glucose values, and a reduction in the number of severe hypoglycemic events. This improved outcome achievement compared with previous reports may be associated with intensive user training.

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.

Analyses of longitudinal, hospital clinical laboratory data with application to blood glucose concentrations

Electronic medical record (EMR) systems afford researchers with opportunities to investigate a broad range of scientific questions. In contrast to purposeful study designs, however, EMR data acquisition procedures typically do not align with any specific hypothesis. Subsequent investigations therefore require detailed characterization of clinical procedures and protocols that underlie EMR data, as well as careful consideration of model choice. For example, many intensive care units currently implement insulin infusion protocols to better control patients' blood glucose levels. The protocols use prior glucose levels to determine, in part, how to adjust the infusion rate. Such feedback loops introduce time-dependent confounding into longitudinal analyses even though they may not always be evident to the analyst. In this paper, we review commonly used longitudinal model specifications and interpretations and show how these are particularly important in the presence of hospital-based clinical protocols. We show that parameter relationships among various models can be used to identify and characterize the impact of time-dependent confounding and therefore help explain seemingly incongruous conclusions. We also review important estimation challenges in the presence of time-dependent confounding and show how certain model specifications may be more or less susceptible to bias. To illustrate these points, we present a detailed analysis of the relationship between blood glucose levels and insulin doses on the basis of data from an intensive care unit.

Provision of balanced nutrition protects against hypoglycemia in the critically ill surgical patient

BACKGROUND

Intensive insulin therapy lowers blood glucose and improves outcomes but increases the risk of hypoglycemia. Typically, insulin protocols require a dextrose solution to prevent hypoglycemia. The authors hypothesized that the provision of balanced nutrition (enteral nutrition [EN] or parenteral nutrition [PN]) would be more protective against hypoglycemia (≤50 mg/dL) than carbohydrate alone.

METHODS

A retrospective analysis was performed of patients treated with intensive insulin therapy and surviving ≥24 hours. The computer-based insulin protocol requires infusion of D10W at 30 mL/h if EN or PN is not provided. Nutrition provision was assessed in 2-hour increments, comparing periods of blood glucose control with and without balanced nutrition. The risk of hypoglycemia for each blood glucose measurement was estimated by multivariable regression.

RESULTS

In total, 66,592 glucose measurements were collected on 1392 patients. Hypoglycemic events occurred in 5.8/1000 glucose tests after 2 hours without balanced nutrition compared to 2.2/1000 tests when balanced nutrition was given in the preceding 2 hours. In multivariable regression models, balanced nutrition was the strongest protective factor against hypoglycemia. Patients who did not receive balanced nutrition in the preceding 2 hours had a 3 times increase in the odds of a hypoglycemic event at their next glucose check (odds ratio = 3.6, P < .001). Providing carbohydrate alone was not protective.

CONCLUSIONS

Balanced nutrition is associated with reduced risk of hypoglycemia. These results suggest that balanced nutrition should be given when insulin therapy is initiated. Future studies should evaluate the efficacy of EN vs PN in preventing hypoglycemia.

Computer decision support software safely improves glycemic control in the burn intensive care unit: a randomized controlled clinical study

The optimal method for glycemic control in the critically burned patient is unknown. The purpose of this randomized controlled study was to determine the safety and efficacy of computer decision support software (CDSS) to control serum glucose concentration in a burn intensive care unit. Eighteen adult burn/trauma patients receiving continuous insulin infusion were initially randomized to receive glucose management by a traditional paper protocol or a computer protocol (CP) for 72 hours and then crossed over to the alternate method for an additional 72 hours. Time in target glucose range (range: 80-110 mg/dl) was higher in the CP group (47 ± 17% vs 41 ± 16.6%; P ≤ .05); time over target range was not significantly reduced in the CP group (49 ± 17.8% vs 54 ± 17.1%; P = .08); and no difference was noted in time under target range of 80 mg/dl (CP: 4.5 ± 2.8%, paper protocol: 4.8 ± 3.3%; P = .8), less than 60 mg/dl (P = .7), and less than 40 mg/dl (P = 1.0). Severe hypoglycemic events (<40 mg/dl) did not differ from the CP group compared with historical controls for patients receiving no insulin (P = .6). More glucose measurements were performed in the CP group (P = .0003), and nursing staff compliance with CP recommendations was greater (P < .0001). Glycemic control using CDSS is safe and effective for the critically burned patient. Time in target range improved without increase in hypoglycemic events. CDSS enhanced consistency in practice, providing standardization among nursing staff.

The effect of a computer-assisted insulin protocol on glycemic control in a surgical intensive care unit

BACKGROUND

Computer-assisted insulin protocols (CAIPs) contain complex mathematical algorithms to assist with insulin dosing. This study compared the quality of glucose control utilizing a CAIP with a paper-based insulin protocol (PBIP).

METHODS

This before-after study identified consecutive patients who received continuous insulin therapy for at least 24 h. Patients were stratified into two groups (PBIP and CAIP). The target blood glucose range for both was 80-110 mg/dL. Hypoglycemia was defined as the percentage of patients with any glucose value <40 mg/dL. Variability was measured by reporting the SD for each patients mean glucose value.

RESULTS

There were 192 patients evaluated (PBIP, n = 145; CAIP, n = 47). More glucose readings were within target range using the CAIP protocol (49 ± 14% vs. 40 ± 12%, P < 0.001), but no difference in mean glucose was noted (113 ± 11 mg/dL with CAIP vs. 116 ± 11 mg/dL with PBIP, P = 0.067). The incidence of hypoglycemia was similar between the CAIP and PBIP groups, respectively (2.1% vs. 4.1%, P = 0.518). Glucose variability was lower with the CAIP (25 ± 9 mg/dL vs. 31 ± 11 mg/dL, P = 0.001). The CAIP required more frequent blood glucose assessments (16 ± 2 vs. 12 ± 2 per day, P < 0.001), more insulin dosing adjustments (14 ± 3 vs. 5 ± 2 per day, P < 0.001), and more time per day (84 ± 15 vs. 51 ± 8 min per patient, P < 0.001) compared with the PBIP.

CONCLUSIONS

A CAIP will lead to minor improvements in glucose control and decrease glucose variability but will not change the rate of hypoglycemia or response to insulin therapy. These differences could largely be due to more aggressive monitoring and titrations required by a CAIP.

Increasing blood glucose variability heralds hypoglycemia in the critically ill

BACKGROUND

Control of hyperglycemia improves outcomes, but increases the risk of hypoglycemia. Recent evidence suggests that blood glucose variability (BGV) is more closely associated with mortality than either isolated or mean BG. We hypothesized that differences in BGV over time are associated with hypoglycemia and can be utilized to estimate risk of hypoglycemia (<50 mg/dL).

MATERIALS AND METHODS

Patients treated with intravenous insulin in the Surgical Intensive Care Unit of a tertiary care center formed the retrospective cohort. Exclusion criteria included death within 24 h of admission. We describe BGV in patients over time and its temporal relationship to hypoglycemic events. The risk of hypoglycemia for each BG measurement was estimated in a multivariable regression model. Predictors were measures of BGV, infusions of dextrose and vasopressors, patient demographics, illness severity, and BG measurements.

RESULTS

A total of 66,592 BG measurements were collected on 1392 patients. Hypoglycemia occurred in 154 patients (11.1%). Patient BGV fluctuated over time, and increased in the 24 h preceding a hypoglycemic event. In crude and adjusted analyses, higher BGV was positively associated with a hypoglycemia (OR 1.41, P < 0.001). Previous hypoglycemic events and time since previous BG measurement were also positively associated with hypoglycemic events. Severity of illness, vasopressor use, and diabetes were not independently associated with hypoglycemia.

CONCLUSIONS

BGV increases in the 24 h preceding hypoglycemia, and patients are at increased risk during periods of elevated BG variability. Prospective measurement of variability may identify periods of increased risk for hypoglycemia, and provide an opportunity to mitigate this risk.

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.

Failure to achieve euglycemia despite aggressive insulin control signals abnormal physiologic response to trauma

PURPOSE

We hypothesize that a failure to normalize a patient's glucose on an automated euglycemia protocol signals an adverse response after trauma and that this response can identify patients with an increased mortality.

MATERIALS AND METHODS

There were 1246 ventilated, critically ill trauma patients who were placed on an automated euglycemia. All glucose values collected both by laboratory serum measurements and by bedside arterial samples were included in the analysis.

RESULTS

Forty six thousand two hundred eighteen data entries for glucose (mg/dL) were analyzed. Time to normalization, defined as the first value in the goal range of 80 to 110 mg/dL, was different between the 2 groups, survivors correcting significantly faster (396 vs 487 minutes; P = .003). Mortality in patients who normalized (80-110 mg/dL) in the first 6 hours of admission was 13.6% vs 18.3% in patients requiring greater than 6 hours (P = .02). Patients who never normalized also required significantly greater insulin doses despite there being no significant difference in demographic data between the 2 groups.

CONCLUSIONS

A posttraumatic patient's response to tight glycemic control revealed important prognostic information about the patients' physiologic status. Patients who failed to reach euglycemia in the first 6 hours of admission had an increased hospital mortality. The time to normalization is significantly longer in those patients who died. Patients who did not correct rapidly required significantly higher insulin doses, suggesting insulin resistance.

Glucose Metabolism, Not Obesity, Predicts Mortality in Critically Ill Surgical Patients

Our hypothesis was to determine if insulin resistance and hyperglycemia, rather than obesity, are predictive of mortality in the surgically critically ill. An observational study of an automated protocol in surgical and trauma intensive care units was performed. Two groups were created based on body mass index (BMI): Obese (OB) defined as BMI ≥ 30 (n = 338) and nonobese defined as BMI < 30 (n = 885). Euglycemia was maintained using an automated protocol using an adapting multiplier, which we used as our marker of stress insulin resistance. The primary outcome was hospital mortality. One thousand, two hundred and twenty-three patients met criteria with 73,225 glucose values. The OB group required more insulin (4.5 U/hr vs 3.2 U/hr, P ≤ 0.01) and had a higher mean multiplier (0.07 vs 0.06, P < 0.01) reflecting insulin resistance. There was no difference in mortality between OB and nonobese (11.6% vs 11.5%, P = 0.96). Logistic regression showed that insulin dose (odds ratio 0.864; 95% confidence interval 0.772-0.967, P = 0.01), and not BMI, was an independent predictor of survival in this population. Obesity is not an independent risk factor for mortality in the surgical critical care population. Insulin resistance and subsequent hyperglycemia are increased in obesity and are independent predictors of mortality.

Adverse drug events in intensive care units: risk factors, impact, and the role of team care

Advances in diagnostic tests, technological interventions, and pharmacotherapy have resulted in spectacular results for many intensive care unit (ICU) patients who, in earlier generations, would have succumbed to their critical illness. At the same time, the complexity and intensity of care required for ICU patients is also associated with greater risks for harm resulting from care. As in other inpatient areas, medications are the most common type of therapy in ICUs and are also associated with the most frequent type of ICU adverse events. Critically ill patients are at high risk for adverse drug events for many reasons, including the complexity of their disease that creates challenges in drug dosing, their vulnerability to rapid changes in pharmacotherapy, the intensive care environment providing ample distractions and opportunity for error, the administration of complex drug regimens, the numerous high-alert medications that they receive, and the mode of drug administration. The clinical outcomes of adverse drug events can result in end-organ damage and even death. The costs of an adverse drug event can be substantial to healthcare systems with an additional $6,000-$9,000 for each event. The multiprofessional patient care team is one approach to promoting patient safety in the ICU.