Tight glycaemic control: a prospective observational study of a computerised decision-supported intensive insulin therapy protocol

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.

Monitoring the Impact of Aggressive Glycemic Intervention during Critical Care after Cardiac Surgery with a Glycemic Expert System for Nurse-Implemented Euglycemia: The MAGIC GENIE Project

A novel, multi-dimensional protocol named GENIE has been in use for intensive insulin therapy (IIT, target glucose <140 mg/dL) in the surgical intensive care unit (SICU) after open heart surgery (OHS) at VA Pittsburgh since 2005. Despite concerns over increased mortality from IIT after the publication of the NICE-SUGAR Trial, it remains in use, with ongoing monitoring under the MAGIC GENIE Project showing that GENIE performance over 12 years (2005-2016) aligns with the current consensus that IIT with target blood glucose (BG) <140 mg/dL is advisable only if it does not provoke severe hypoglycemia (SH). Two studies have been conducted to monitor glucometrics and outcomes during GENIE use in the SICU. One compares GENIE (n = 382) with a traditional IIT protocol (FORMULA, n = 289) during four years of contemporaneous use (2005-2008). The other compares GENIE's impact overall (n = 1404) with a cohort of patients who maintained euglycemia after OHS (euglycemic no-insulin [ENo-I], n = 111) extending across 12 years (2005-2016). GENIE performed significantly better than FORMULA during contemporaneous use, maintaining lower time-averaged glucose, provoking less frequent, severe, prolonged, or repetitive hypoglycemia, and achieving 50% lower one-year mortality, with no deaths from mediastinitis (0 of 8 cases vs 4 of 9 on FORMULA). Those benefits were sustained over the subsequent eight years of exclusive use in OHS patients, with an overall one-year mortality rate (4.2%) equivalent to the ENo-I cohort (4.5%). The results of the MAGIC GENIE Project show that GENIE can maintain tight glycemic control without provoking SH in patients undergoing OHS, and may be associated with a durable survival benefit. The results, however, await confirmation in a randomized control trial.

Performance of Stochastic Targeted Blood Glucose Control Protocol by virtual trials in the Malaysian intensive care unit

BACKGROUND AND OBJECTIVE

Blood glucose variability is common in healthcare and it is not related or influenced by diabetes mellitus. To minimise the risk of high blood glucose in critically ill patients, Stochastic Targeted Blood Glucose Control Protocol is used in intensive care unit at hospitals worldwide. Thus, this study focuses on the performance of stochastic modelling protocol in comparison to the current blood glucose management protocols in the Malaysian intensive care unit. Also, this study is to assess the effectiveness of Stochastic Targeted Blood Glucose Control Protocol when it is applied to a cohort of diabetic patients.

METHODS

Retrospective data from 210 patients were obtained from a general hospital in Malaysia from May 2014 until June 2015, where 123 patients were having comorbid diabetes mellitus. The comparison of blood glucose control protocol performance between both protocol simulations was conducted through blood glucose fitted with physiological modelling on top of virtual trial simulations, mean calculation of simulation error and several graphical comparisons using stochastic modelling.

RESULTS

Stochastic Targeted Blood Glucose Control Protocol reduces hyperglycaemia by 16% in diabetic and 9% in nondiabetic cohorts. The protocol helps to control blood glucose level in the targeted range of 4.0-10.0 mmol/L for 71.8% in diabetic and 82.7% in nondiabetic cohorts, besides minimising the treatment hour up to 71 h for 123 diabetic patients and 39 h for 87 nondiabetic patients.

CONCLUSION

It is concluded that Stochastic Targeted Blood Glucose Control Protocol is good in reducing hyperglycaemia as compared to the current blood glucose management protocol in the Malaysian intensive care unit. Hence, the current Malaysian intensive care unit protocols need to be modified to enhance their performance, especially in the integration of insulin and nutrition intervention in decreasing the hyperglycaemia incidences. Improvement in Stochastic Targeted Blood Glucose Control Protocol in terms of u_en model is also a must to adapt with the diabetic cohort.

International multidisciplinary consensus conference on multimodality monitoring: ICU processes of care

There is an increased focus on evaluating processes of care, particularly in the high acuity and cost environment of intensive care. Evaluation of neurocritical-specific care and evidence-based protocol implementation are needed to effectively determine optimal processes of care and effect on patient outcomes. General quality measures to evaluate intensive care unit (ICU) processes of care have been proposed; however, applicability of these measures in neurocritical care populations has not been established. A comprehensive literature search was conducted for English language articles from 1990 to August 2013. A total of 1,061 articles were reviewed, with 145 meeting criteria for inclusion in this review. Care in specialized neurocritical care units or by neurocritical teams can have a positive impact on mortality, length of stay, and in some cases, functional outcome. Similarly, implementation of evidence-based protocol-directed care can enhance outcome in the neurocritical care population. There is significant evidence to support suggested quality indicators for the general ICU population, but limited research regarding specific use in neurocritical care. Quality indices for neurocritical care have been proposed; however, additional research is needed to further validate measures.

An in silico method to identify computer-based protocols worthy of clinical study: An insulin infusion protocol use case

OBJECTIVE

Develop an efficient non-clinical method for identifying promising computer-based protocols for clinical study. An in silico comparison can provide information that informs the decision to proceed to a clinical trial. The authors compared two existing computer-based insulin infusion protocols: eProtocol-insulin from Utah, USA, and Glucosafe from Denmark.

MATERIALS AND METHODS

The authors used eProtocol-insulin to manage intensive care unit (ICU) hyperglycemia with intravenous (IV) insulin from 2004 to 2010. Recommendations accepted by the bedside clinicians directly link the subsequent blood glucose values to eProtocol-insulin recommendations and provide a unique clinical database. The authors retrospectively compared in silico 18,984 eProtocol-insulin continuous IV insulin infusion rate recommendations from 408 ICU patients with those of Glucosafe, the candidate computer-based protocol. The subsequent blood glucose measurement value (low, on target, high) was used to identify if the insulin recommendation was too high, on target, or too low.

RESULTS

Glucosafe consistently provided more favorable continuous IV insulin infusion rate recommendations than eProtocol-insulin for on target (64% of comparisons), low (80% of comparisons), or high (70% of comparisons) blood glucose. Aggregated eProtocol-insulin and Glucosafe continuous IV insulin infusion rates were clinically similar though statistically significantly different (Wilcoxon signed rank test P = .01). In contrast, when stratified by low, on target, or high subsequent blood glucose measurement, insulin infusion rates from eProtocol-insulin and Glucosafe were statistically significantly different (Wilcoxon signed rank test, P < .001), and clinically different.

DISCUSSION

This in silico comparison appears to be an efficient nonclinical method for identifying promising computer-based protocols.

CONCLUSION

Preclinical in silico comparison analytical framework allows rapid and inexpensive identification of computer-based protocol care strategies that justify expensive and burdensome clinical trials.

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.

On the management of hyperglycaemia in critically ill patients undergoing surgery

Hyperglycaemia is a major health risk and a negative determinant of surgical outcome. Despite its increasing prevalence, the limited treatments for restoration of normoglycaemia make its effective management a highly complex individualized clinical art. In this context, we review the mechanisms leading to hyperglycaemic damage as the basis for effective management of surgical complications of diabetic and non diabetic critically ill patients.

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.

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

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

A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients

Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, S(I), the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to S(I) only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients (N=42,941 h in total) who received insulin while in the ICU and stayed for ≥ 72 h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care.

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

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

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

Computerized intensive insulin dosing can mitigate hypoglycemia and achieve tight glycemic control when glucose measurement is performed frequently and on time

Introduction

Control of blood glucose (BG) in critically ill patients is considered important, but is difficult to achieve, and often associated with increased risk of hypoglycemia. We examined the use of a computerized insulin dosing algorithm to manage hyperglycemia with particular attention to frequency and conditions surrounding hypoglycemic events.

Methods

This is a retrospective analysis of adult patients with hyperglycemia receiving intravenous (IV) insulin therapy from March 2006 to December 2007 in the intensive care units of 2 tertiary care teaching hospitals. Patients placed on a glycemic control protocol using the Clarian GlucoStabilizer™ IV insulin dosing calculator with a target range of 4.4-6.1 mmol/L were analyzed. Metrics included time to target, time in target, mean blood glucose ± standard deviation, % measures in hypoglycemic ranges <3.9 mmol/L, per-patient hypoglycemia, and BG testing interval.

Results

4,588 ICU patients were treated with the GlucoStabilizer to a BG target range of 4.4-6.1 mmol/L. We observed 254 severe hypoglycemia episodes (BG <2.2 mmol/L) in 195 patients, representing 0.1% of all measurements, and in 4.25% of patients or 0.6 episodes per 1000 hours on insulin infusion. The most common contributing cause for hypoglycemia was measurement delay (n = 170, 66.9%). The median (interquartile range) time to achieve the target range was 5.9 (3.8 - 8.9) hours. Nearly all (97.5%) of patients achieved target and remained in target 73.4% of the time. The mean BG (± SD) after achieving target was 5.4 (± 0.52) mmol/L. Targeted blood glucose levels were achieved at similar rates with low incidence of severe hypoglycemia in patients with and without diabetes, sepsis, renal, and cardiovascular disease.

Conclusions

Glycemic control to a lower glucose target range can be achieved using a computerized insulin dosing protocol. With particular attention to timely measurement and adjustment of insulin doses the risk of hypoglycemia experienced can be minimized.

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

INTRODUCTION

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

RESULTS

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

DISCUSSION

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

CONCLUSION

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

Towards closed-loop glycaemic control

Blood glucose control performed by intensive care unit (ICU) nurses is becoming standard practice for critically ill patients. New algorithms, ranging from basic protocols to elementary computerized protocols to advanced computerized protocols, have been presented during the last years aiming to reduce the workload of the medical team. This paper gives an overview of the different types of algorithms and their features. Performance comparisons between different algorithms are avoided as blood glucose sampling frequencies and protocol durations were not similar among different studies and even within studies. Particularly advanced computerized protocols can potentially be introduced as fully-automated blood glucose algorithms when accurate and reliable near-continuous glucose sensor devices are available. Furthermore, it is surprising to consider in some of the described protocols that the original blood glucose target ranges (80-110 mg/dl) were increased (due to fear of hypoglycaemia) and/or that glycaemia levels were determined in capillary blood samples.

Tight glycemic control and computerized decision-support systems: a systematic review

Objective

To identify and summarize characteristics of computerized decision-support systems (CDSS) for tight glycemic control (TGC) and to review their effects on the quality of the TGC process in critically ill patients.

Methods

We searched Medline (1950–2008) and included studies on critically ill adult patients that reported original data from a clinical trial or observational study with a main objective of evaluating a given TGC protocol with a CDSS.

Results

Seventeen articles met the inclusion criteria. Eleven out of seventeen studies evaluated the effect of a new TGC protocol that was introduced simultaneously with a CDSS implementation. Most of the reported CDSSs were stand-alone, were not integrated in any other clinical information systems and used the “passive” mode requiring the clinician to ask for advice. Different implementation sites, target users, and time of advice were used, depending on local circumstances. All controlled studies reported on at least one quality indicator of the blood glucose regulatory process that was improved by introducing the CDSS. Nine out of ten controlled studies either did not report on the number of hypoglycemia events (one study), or reported on no change (six studies) or even a reduction in this number (two studies).

Conclusions

While most studies evaluating the effect of CDSS on the quality of the TGC process found improvement when evaluated on the basis of the quality indicators used, it is impossible to define the exact success factors, because of simultaneous implementation of the CDSS with a new or modified TGC protocol and the hybrid solutions used to integrate the CDSS into the clinical workflow.

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

OBJECTIVE

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

DESIGN

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

SETTING

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

PATIENTS AND PARTICIPANTS

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

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

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

CONCLUSIONS

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

Comparison of three protocols for tight glycemic control in cardiac surgery patients

OBJECTIVE

We performed a randomized trial to compare three insulin-titration protocols for tight glycemic control (TGC) in a surgical intensive care unit: an absolute glucose (Matias) protocol, a relative glucose change (Bath) protocol, and an enhanced model predictive control (eMPC) algorithm.

RESEARCH DESIGN AND METHODS

A total of 120 consecutive patients after cardiac surgery were randomly assigned to the three protocols with a target glycemia range from 4.4 to 6.1 mmol/l. Intravenous insulin was administered continuously or in combination with insulin boluses (Matias protocol). Blood glucose was measured in 1- to 4-h intervals as requested by the protocols.

RESULTS

The eMPC algorithm gave the best performance as assessed by time to target (8.8 +/- 2.2 vs. 10.9 +/- 1.0 vs. 12.3 +/- 1.9 h; eMPC vs. Matias vs. Bath, respectively; P < 0.05), average blood glucose after reaching the target (5.2 +/- 0.1 vs. 6.2 +/- 0.1 vs. 5.8 +/- 0.1 mmol/l; P < 0.01), time in target (62.8 +/- 4.4 vs. 48.4 +/- 3.28 vs. 55.5 +/- 3.2%; P < 0.05), time in hyperglycemia >8.3 mmol/l (1.3 +/- 1.2 vs. 12.8 +/- 2.2 vs. 6.5 +/- 2.0%; P < 0.05), and sampling interval (2.3 +/- 0.1 vs. 2.1 +/- 0.1 vs. 1.8 +/- 0.1 h; P < 0.05). However, time in hypoglycemia risk range (2.9-4.3 mmol/l) in the eMPC group was the longest (22.2 +/- 1.9 vs. 10.9 +/- 1.5 vs. 13.1 +/- 1.6; P < 0.05). No severe hypoglycemic episode (<2.3 mmol/l) occurred in the eMPC group compared with one in the Matias group and two in the Bath group.

CONCLUSIONS

The eMPC algorithm provided the best TGC without increasing the risk of severe hypoglycemia while requiring the fewest glucose measurements. Overall, all protocols were safe and effective in the maintenance of TGC in cardiac surgery patients.

Intensive versus conventional glucose control in critically ill patients

BACKGROUND

The optimal target range for blood glucose in critically ill patients remains unclear.

METHODS

Within 24 hours after admission to an intensive care unit (ICU), adults who were expected to require treatment in the ICU on 3 or more consecutive days were randomly assigned to undergo either intensive glucose control, with a target blood glucose range of 81 to 108 mg per deciliter (4.5 to 6.0 mmol per liter), or conventional glucose control, with a target of 180 mg or less per deciliter (10.0 mmol or less per liter). We defined the primary end point as death from any cause within 90 days after randomization.

RESULTS

Of the 6104 patients who underwent randomization, 3054 were assigned to undergo intensive control and 3050 to undergo conventional control; data with regard to the primary outcome at day 90 were available for 3010 and 3012 patients, respectively. The two groups had similar characteristics at baseline. A total of 829 patients (27.5%) in the intensive-control group and 751 (24.9%) in the conventional-control group died (odds ratio for intensive control, 1.14; 95% confidence interval, 1.02 to 1.28; P=0.02). The treatment effect did not differ significantly between operative (surgical) patients and nonoperative (medical) patients (odds ratio for death in the intensive-control group, 1.31 and 1.07, respectively; P=0.10). Severe hypoglycemia (blood glucose level, < or = 40 mg per deciliter [2.2 mmol per liter]) was reported in 206 of 3016 patients (6.8%) in the intensive-control group and 15 of 3014 (0.5%) in the conventional-control group (P<0.001). There was no significant difference between the two treatment groups in the median number of days in the ICU (P=0.84) or hospital (P=0.86) or the median number of days of mechanical ventilation (P=0.56) or renal-replacement therapy (P=0.39).

CONCLUSIONS

In this large, international, randomized trial, we found that intensive glucose control increased mortality among adults in the ICU: a blood glucose target of 180 mg or less per deciliter resulted in lower mortality than did a target of 81 to 108 mg per deciliter. (ClinicalTrials.gov number, NCT00220987.)

Pre- and postoperative accuracy and safety of a real-time continuous glucose monitoring system in cardiac surgical patients: a randomized pilot study

BACKGROUND

Our objective was to evaluate the accuracy and safety of a real-time (RT) continuous glucose monitoring system (CGMS) in patients before and after cardiothoracic surgery and to investigate whether activation of the alarm function of the RT-CGMS had an effect on glucose control.

METHODS

Patients scheduled for elective cardiothoracic procedures, without a history of insulin-requiring diabetes, were perioperatively monitored with RT-CGMS for 72 h and were randomized into two groups: with or without the alarm function (set at 4 and 10 mmol/L) of the device activated. Sensor values were compared with capillary, arterial, and venous blood glucose values. Percentages of time spent in various glucose ranges were compared between groups.

RESULTS

There were no adverse effects of the RT-CGMS. Of the 1,001 sensor value comparisons with capillary or arterial measurements, 96.6% fell within Clarke Error Grid zones A and B, with relative absolute differences ranging from 15% (preoperative period) to 12% (intensive care unit period) to 14% (postoperative period on the ward). Seventeen (7.9%) arterial and 16 (2.0%) capillary comparisons fell within zone D or E. Whether or not the alarm function, as used in this pilot study, was activated did not affect time spent in different glucose ranges.

CONCLUSIONS

Although the RT-CGMS is safe and accurate according to accepted standards, there are still small aberrations, which in our opinion preclude unlimited use in its present form in a clinical setting. The effect of the alarm function at different glucose levels remains to be investigated.

A systematic review on quality indicators for tight glycaemic control in critically ill patients: need for an unambiguous indicator reference subset

Introduction

The objectives of this study were to systematically identify and summarize quality indicators of tight glycaemic control in critically ill patients, and to inspect the applicability of their definitions.

Methods

We searched in MEDLINE^® for all studies evaluating a tight glycaemic control protocol and/or quality of glucose control that reported original data from a clinical trial or observational study on critically ill adult patients.

Results

Forty-nine studies met the inclusion criteria; 30 different indicators were extracted and categorized into four nonorthogonal categories: blood glucose zones (for example, 'hypoglycaemia'); blood glucose levels (for example, 'mean blood glucose level'); time intervals (for example, 'time to occurrence of an event'); and protocol characteristics (for example, 'blood glucose sampling frequency'). Hypoglycaemia-related indicators were used in 43 out of 49 studies, acting as a proxy for safety, but they employed many different definitions. Blood glucose level summaries were used in 41 out of 49 studies, reported as means and/or medians during the study period or at a certain time point (for example, the morning blood glucose level or blood glucose level upon starting insulin therapy). Time spent in the predefined blood glucose level range, time needed to reach the defined blood glucose level target, hyperglycaemia-related indicators and protocol-related indicators were other frequently used indicators. Most indicators differ in their definitions even when they are meant to measure the same underlying concept. More importantly, many definitions are not precise, prohibiting their applicability and hence the reproducibility and comparability of research results.

Conclusions

An unambiguous indicator reference subset is necessary. The result of this systematic review can be used as a starting point from which to develop a standard list of well defined indicators that are associated with clinical outcomes or that concur with clinicians' subjective views on the quality of the regulatory process.

Evaluation of implementation of a fully automated algorithm (enhanced model predictive control) in an interacting infusion pump system for establishment of tight glycemic control in medical intensive care unit patients

BACKGROUND

The objective of this study was to investigate the performance of a newly developed decision support system for the establishment of tight glycemic control in medical intensive care unit (ICU) patients for a period of 72 hours.

METHODS

This was a single-center, open, non-controlled feasibility trial including 10 mechanically ventilated ICU patients. The CS-1 decision support system (interacting infusion pumps with integrated enhanced model predictive control algorithm and user interface) was used to adjust the infusion rate of administered insulin to normalize blood glucose. Efficacy and safety were assessed by calculating the percentage of values within the target range (80-110 mg/dl), hyperglycemic index, mean glucose, and hypoglycemic episodes (<40 mg/dl).

RESULTS

The percentage of values in time in target was 47.0% (+/-13.0). The average blood glucose concentration and hyperglycemic index were 109 mg/dl (+/-13) and 10 mg/dl (+/-9), respectively. No hypoglycemic episode (<40 mg/dl) was detected. Eleven times (1.5% of all given advice) the nurses did not follow and, thus, overruled the advice of the CS-1 system. Several technical malfunctions of the device (repetitive error messages and missing data in the data log) due to communication problems between the new hardware components are shortcomings of the present version of the device. As a consequence of these technical failures of system integration, treatment had to be stopped ahead of schedule in three patients.

CONCLUSIONS

Despite technical malfunctions, the performance of this prototype CS-1 decision support system was, from a clinical point of view, already effective in maintaining tight glycemic control. Accordingly, and with technical improvement required, the CS-1 system has the capacity to serve as a reliable tool for routine establishment of glycemic control in ICU patients.

Intensive care unit hypoglycemia predicts depression during early recovery from acute lung injury

OBJECTIVE

To evaluate the association between intensive care unit blood glucose levels and depression after acute lung injury.

DESIGN

Prospective cohort study.

SETTING

Twelve intensive care units in four hospitals in Baltimore, MD.

PATIENTS

Consecutive acute lung injury survivors (n = 104) monitored during 1717 intensive care unit patient-days and screened for depression at 3 months after acute lung injury.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The prevalence of a positive screening test for depression (Hospital Anxiety and Depression subscale score > or = 8) at follow-up was 28%. After adjustment for confounders, patients with a mean daily minimum intensive care unit glucose level < 100 mg/dL had significant increases in mean depression score (2.1 points, 95% confidence interval 0.6-3.7) and in the likelihood of a positive depression screening test (relative risk 2.6, 95% confidence interval 1.2-4.2). Patients with documented hypoglycemia < 60 mg/dL during their intensive care unit stay also had greater symptoms of depression (2.0 points, 95% confidence interval 0.5-3.5; relative risk 3.6, 95% confidence interval 1.8-5.1). Other factors independently associated with a positive depression screening test included body mass index > 40 kg/m2 (relative risk 3.3, 95% confidence interval 1.2-4.2), baseline depression/anxiety (relative risk 3.9, 95% confidence interval 1.5-6.5), and mean daily intensive care unit benzodiazepine dose > 100 mg of midazolam-equivalent agent (relative risk 2.4, 95% confidence interval 1.1-3.8).

CONCLUSIONS

Hypoglycemia in the intensive care unit is associated with an increased risk of positive screening for depression during early recovery from acute lung injury. Baseline depressive symptoms, morbid obesity, and intensive care unit benzodiazepine dose were also associated with postacute lung injury depressive symptoms. These findings warrant increased glucose monitoring for intensive care unit patients at risk for hypoglycemia and further research on how patient and intensive care unit management factors may contribute to postintensive care unit depression.

A benchmark data set for model-based glycemic control in critical care

BACKGROUND

Hyperglycemia is prevalent in critical care. That tight control saves lives is becoming more clear, but the "how" and "for whom" in repeating the initial results remain elusive. Model-based methods can provide tight, patient-specific control, as well as providing significant insight into the etiology and evolution of this condition. However, it is still often difficult to compare results due to lack of a common benchmark. This article puts forward a benchmark data set for critical care glycemic control in a medical intensive care unit (ICU). Based on clinical patient data from SPecialized Relative Insulin and Nutrition Tables (SPRINT) studies, it provides a benchmark for comparing and analyzing performance in model-based glycemic control.

METHODS

Data from 20 of the first 150 postpilot patients treated under SPRINT are presented. All patients had longer than a 5-day length of stay (LoS) in the Christchurch ICU. The benchmark data set matches overall patient data and glycemic control results for the entire cohort and this particular LoS >5-day group. The mortality outcome (n =3, 15%) also matches SPRINT results for this patient group.

RESULTS

Data cover 20 patients and 6372 total patient hours with an average of 339.4 hours per patient. It includes insulin and nutrition inputs along with 4182 blood glucose measurements at an average of 224.3 measurements per patient, averaging a measurement approximately every 1.5 hours (16 per day). Data are available via download in a Microsoft Excel format. A series of cumulative distribution functions and tables are used to summarize data in this article.

CONCLUSION

Model-based methods can provide tighter, more adaptable "one method fits all" solutions using methods that enable patient-specific modeling and control. A benchmark data set will enable easier model and protocol development for groups lacking clinical data, as well as providing a benchmark to compare results of different protocols on a single (virtual) cohort based on real clinical data.

Adsorption of insulin onto infusion sets used in adult intensive care unit and neonatal care settings

INTRODUCTION

Insulin adsorption onto infusion equipment may affect glycaemic control.

METHODS

The change in insulin concentration during delivery through tubing employed for adult ICU and neonatal patients was determined using continuous flow UV analysis.

RESULTS

Insulin adsorption depended on tubing composition, dimensions and flow rate, being highest for neonatal polyvinylchloride tubing at low flow rates.

CONCLUSION

In continuous insulin therapy, we should consider the nature of the infusion set and flow rate.

Model-based insulin sensitivity as a sepsis diagnostic in critical care

BACKGROUND

Timely diagnosis and treatment of sepsis in critical care require significant clinical effort, experience, and resources. Insulin sensitivity is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to identify insulin sensitivity in real time.

METHODS

Receiver operating characteristic curves and cutoff insulin sensitivity values for diagnosing sepsis were calculated for model-based insulin sensitivity (S(I)) and a simpler metric (SS(I)) that was estimated from glycemic control data of 30 patients with sepsis and can be calculated in real time without use of a computer. Results were compared to the insulin sensitivity profiles of a general intensive care unit population of 113 patients without sepsis and 30 patients with sepsis, comprising a total of 26,453 patient hours. Patients with sepsis were identified as having sepsis based on a sepsis score (ss) of 3 or higher (ss = 0 - 4 for increasing severity). Patients with type I or type II diabetes were excluded. Ethics approval for this study was granted by the South Island Regional Ethics Committee.

RESULTS

Receiver operating characteristic cutoff values of S(I) = 8 x 10-5 liter mU(-1) min(-1) and SS(I) = 2.8 x 10-4 liter mU(-1) min(-1) were determined for ss > or = 3. The model-based S(I) fell below this value in 15% of all patient hours. The S(I) test had a negative predictive value of 99.8%. The test sensitivity was 78% and specificity was 82%. However, the positive predictor value was 2.8%. Slightly lower sensitivity (68.8%) and specificity (81.7%), but equally good negative prediction (99.7%), were obtained for the estimated SS(I).

CONCLUSIONS

Insulin sensitivity provides a negative predictive diagnostic for sepsis. High insulin sensitivity rules out sepsis for the majority of patient hours and may be determined noninvasively in real time from glycemic control protocol data. Low insulin sensitivity is not an effective diagnostic, as it can equally mark the presence of sepsis or other conditions.

Improvement in glycemic control and outcome corresponding to intensive insulin therapy protocol development

BACKGROUND

Intensive insulin therapy (IIT) has been shown to reduce mortality and morbidity in longer stay, critically ill patients. However, this has been demonstrated in a single site, whereas two multicentric studies have been terminated prematurely mainly due to hypoglycemia. Other difficulties with IIT include efficacy of glycemic control. This report describes how IIT can be improved by protocol simplification and removal of glucose supplementation.

METHODS

A clinical information system established at each bedspace guided staff through the IIT algorithms. Time spent within predefined glycemic ranges was calculated assuming a linear trend between successive measurements. Three groups were investigated retrospectively: IIT1 protocol,(1) an updated IIT2 version, and intuitive nurse dosing of conventional insulin therapy (CIT).

RESULTS

Fifty consecutive, critically ill patients were included in each study group. Patient characteristics were similar in each group. The frequency of CIT and IIT2 blood glucose measurements were 11.6 and 11.5 measurements per day, respectively, while the IIT1 measurements were more frequent (14.5 measurements per day). The mean proportion of time spent in the target glycemic range (4.4-6.1 mmol/liter) was highest in the IIT2 group (34.9%), as compared to the IIT1 (22.9%) and CIT groups (20.3%) (p <.001). Survival at 28 days was 74.5% for IIT2 (highest), 68% for IIT1, and 48% for CIT (p = .02). There were a similar number of those experiencing a severe hypoglycemic event in each group.

CONCLUSIONS

IIT protocol optimization was associated with increased glycemic control and improved 28-day survival. The better optimized IIT2 protocol provided tighter control than either the IIT1 or CIT protocol, without increased sampling or incidence of hypoglycemia. The clinical effectiveness of the IIT algorithm appeared to be improved by simplifying the protocol to meet the needs of the critical care unit.

Impact of human factors on clinical protocol performance: a proposed assessment framework and case examples

BACKGROUND

Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and can often produce highly variable results. Thus, tight control remains elusive as there is not enough understanding of the relationship between control performance and protocol design, particularly with regard to how a given protocol is implemented.

METHODS

This article examines the role of human factors and how individuals relate to technological protocols in clinical settings. The study consists of an overall brief review that is used to create a first graphical representation of the impact of human factors in clinical medical protocol implementations. This initial framework is examined in the context of two similar, but different, case studies-the specialized relative insulin and nutrition tables glycemic control protocol and the TREAT system for antibiotic selection.

RESULTS

A graphical framework relating the human factors impact on medical protocol implementation is created. This framework describes the primary impacts on performance as resulting from clinical burden and protocol transparency. Their primary effect is on compliance with the protocol, which directly affects performance and outcome, particularly in long-term studies versus short pilot studies.

SUMMARY

Compliance is a key element in obtaining the best clinical outcome that a given protocol can provide. The issues that most affect compliance are quite often unrelated to the patient or treatment, but are a function of the protocol design and its ability to integrate (by its design) into a given clinical setting. A framework for examining these issues in design and in post-hoc assessment is therefore proposed and examined in two brief case studies.

Computer-assisted glucose control in critically ill patients

Objective

Intensive insulin therapy is associated with the risk of hypoglycemia and increased costs of material and personnel. We therefore evaluated the safety and efficiency of a computer-assisted glucose control protocol in a large population of critically ill patients.

Design and setting

Observational cohort study in three intensive care units (32 beds) in a 1,300-bed university teaching hospital.

Patients

All 2,800 patients admitted to the surgical, neurosurgical, and cardiothoracic units; the study period started at each ICU after implementation of Glucose Regulation for Intensive Care Patients (GRIP), a freely available computer-assisted glucose control protocol.

Measurements and results

We analysed compliance in relation to recommended insulin pump rates and glucose measurement frequency. Patients were on GRIP-ordered pump rates 97% of time. Median measurement time was 5 min late (IQR 20 min early to 34 min late). Hypoglycemia was uncommon (7% of patients for mild hypoglycemia, < 3.5 mmol/l; 0.86% for severe hypoglycemia, < 2.2 mmol/l). Our predefined target range (4.0–7.5 mmol/l) was reached after a median of 5.6 h (IQR 0.2–11.8) and maintained for 89% (70–100%) of the remaining stay at the ICU. The number of measurements needed was 5.9 (4.8–7.3) per patient per day. In-hospital mortality was 10.1%.

Conclusions

Our computer-assisted glucose control protocol provides safe and efficient glucose regulation in routine intensive care practice. A low rate of hypoglycemic episodes was achieved with a considerably lower number of glucose measurements than used in most other schemes.

Electronic supplementary material

The online version of this article (doi:10.1007/s00134-008-1091-y) contains supplementary material, which is available to authorized users.

Inpatient insulin therapy

PURPOSE OF REVIEW

In a 2001 report from a surgical intensive care unit in Leuven, Belgium, intravenous insulin infusion targeting blood glucose 80-110 mg/dl reduced patient mortality and morbidities. Subsequent research has failed to define glycemic targets necessary or sufficient for attainment of desired health outcomes in other inpatient settings, but a large body of evidence suggests hospital outcomes are related to hyperglycemia.

RECENT FINDINGS

Recent literature describes observational evidence for hypoglycemia as an independent predictor of mortality in a general medical intensive care unit; superiority of performance of computerized intravenous insulin algorithms in comparison to earlier manual algorithms; acceptability of early transition to scheduled basal prandial correction subcutaneous insulin analog therapy for maintenance of glycemic targets after induction of euglycemia by intravenous insulin infusion, among cardiothoracic surgery patients; inferiority of sliding scale insulin compared to basal prandial correction therapy; and feasibility of diabetes patient self-management in the hospital setting.

SUMMARY

With development of improved insulin administration strategies problems of hypoglycemia and variability of glycemic control are reduced. Investigators and care providers need to achieve glycemic targets to optimize patient outcomes.

Is there more to glycaemic control than meets the eye?

Tight glycaemic control has emerged as a major focus in critical care. However, the struggle to repeat, improve and standardize the results of the initial landmark studies is ongoing. The prospective computerized glycaemic control study by Shulman et al. highlights two emerging and often overlooked aspects of intensive insulin therapy protocols beyond simple glycaemic performance. First, the clinical ergonomics and ability to integrate into the critical care unit workflow must be considered as they may impact results and definitely affect uptake. Second, the real lessons of any protocol's performance are likely to be best realized by comparison with other results, a task that is very difficult without a consensus method of reporting that allows such comparisons across studies. Embracing these issues will take the field closer to accepted, repeatable approaches to tight glycaemic control.

Intensive insulin therapy and mortality in critically ill patients

INTRODUCTION

Intensive insulin therapy (IIT) with tight glycemic control may reduce mortality and morbidity in critically ill patients and has been widely adopted in practice throughout the world. However, there is only one randomized controlled trial showing unequivocal benefit to this approach and that study population was dominated by post-cardiac surgery patients. We aimed to determine the association between IIT and mortality in a mixed population of critically ill patients.

METHODS

We conducted a cohort study comparing three consecutive time periods before and after IIT protocol implementation in a Level 1 trauma center: period I (no protocol); period II, target glucose 80 to 130 mg/dL; and period III, target glucose 80 to 110 mg/dL. Subjects were 10,456 patients admitted to intensive care units (ICUs) between 1 March 2001 and 28 February 2005. The main study endpoints were ICU and hospital mortality, Sequential Organ Failure Assessment score, and occurrence of hypoglycemia. Multivariable regression analysis was used to evaluate mortality and organ dysfunction during periods II and III relative to period I.

RESULTS

Insulin administration increased over time (9% period I, 25% period II, and 42% period III). Nonetheless, patients in period III had a tendency toward higher adjusted hospital mortality (odds ratio [OR] 1.15, 95% confidence interval [CI] 0.98, 1.35) than patients in period I. Excess hospital mortality in period III was present primarily in patients with an ICU length of stay of 3 days or less (OR 1.47, 95% CI 1.11, 1.93 There was an approximately fourfold increase in the incidence of hypoglycemia from periods I to III.

CONCLUSION

A policy of IIT in a group of ICUs from a single institution was not associated with a decrease in hospital mortality. These results, combined with the findings from several recent randomized trials, suggest that further study is needed prior to widespread implementation of IIT in critically ill patients.

Nurse-led implementation of an insulin-infusion protocol in a general intensive care unit: improved glycaemic control with increased costs and risk of hypoglycaemia signals need for algorithm revision

Background

Strict glycaemic control (SGC) has become a contentious issue in modern intensive care. Physicians and nurses are concerned about the increased workload due to SGC as well as causing harm through hypoglycaemia. The objective of our study was to evaluate our existing degree of glycaemic control, and to implement SGC safely in our ICU through a nurse-led implementation of an algorithm for intensive insulin-therapy.

Methods

The study took place in the adult general intensive care unit (11 beds) of a 44-bed department of intensive care at a tertiary care university hospital. All patients admitted during the 32 months of the study were enrolled. We retrospectively analysed all arterial blood glucose (BG) results from samples that were obtained over a period of 20 months prior to the implementation of SGC. We then introduced an algorithm for intensive insulin therapy; aiming for arterial blood-glucose at 4.4 – 6.1 mmol/L. Doctors and nurses were trained in the principles and potential benefits and risks of SGC. Consecutive statistical analyses of blood samples over a period of 12 months were used to assess performance, provide feedback and uncover incidences of hypoglycaemia.

Results

Median BG level was 6.6 mmol/L (interquartile range 5.6 to 7.7 mmol/L) during the period prior to implementation of SGC (494 patients), and fell to 5.9 (IQR 5.1 to 7.0) mmol/L following introduction of the new algorithm (448 patients). The percentage of BG samples > 8 mmol/L was reduced from 19.2 % to 13.1 %. Before implementation of SGC, 33 % of samples were between 4.4 to 6.1 mmol/L and 12 patients (2.4 %) had one or more episodes of severe hypoglycaemia (< 2.2 mmol/L). Following implementation of SGC, 45.8 % of samples were between 4.4 to 6.1 mmol/L and 40 patients (8.9 %) had one or more episodes of severe hypoglycaemia. Of theses, ten patients died while still hospitalised (all causes).

Conclusion

The retrospective part of the study indicated ample room for improvement. Through the implementation of SGC the fraction of samples within the new target range increased from 33% to 45.8%. There was also a significant increase in severe hypoglycaemic episodes. There continues to be potential for improved glycaemic control within our ICU. This might be achieved through an improved algorithm and continued efforts to increase nurses' confidence and skills in achieving SGC.