Software-guided versus nurse-directed blood glucose control in critically ill patients: the LOGIC-2 multicenter randomized controlled clinical trial.

BACKGROUND: Blood glucose control in the intensive care unit (ICU) has the potential to save lives. However, maintaining blood glucose concentrations within a chosen target range is difficult in clinical practice and holds risk of potentially harmful hypoglycemia. Clinically validated computer algorithms to guide insulin dosing by nurses have been advocated for better and safer blood glucose control. METHODS: We conducted an international, multicenter, randomized controlled trial involving 1550 adult, medical and surgical critically ill patients, requiring blood glucose control. Patients were randomly assigned to algorithm-guided blood glucose control (LOGIC-C, n = 777) or blood glucose control by trained nurses (Nurse-C, n = 773) during ICU stay, according to the local target range (80-110 mg/dL or 90-145 mg/dL). The primary outcome measure was the quality of blood glucose control, assessed by the glycemic penalty index (GPI), a measure that penalizes hypoglycemic and hyperglycemic deviations from the chosen target range. Incidence of severe hypoglycemia (<40 mg/dL) was the main safety outcome measure. New infections in ICU, duration of hospital stay, landmark 90-day mortality and quality of life were clinical safety outcome measures. RESULTS: The median GPI was lower in the LOGIC-C (10.8 IQR 6.2-16.1) than in the Nurse-C group (17.1 IQR 10.6-26.2) (P < 0.001). Mean blood glucose was 111 mg/dL (SD 15) in LOCIC-C versus 119 mg/dL (SD 21) in Nurse-C, whereas the median time-in-target range was 67.0% (IQR 52.1-80.1) in LOGIC-C versus 47.1% (IQR 28.1-65.0) in the Nurse-C group (both P < 0.001). The fraction of patients with severe hypoglycemia did not differ between LOGIC-C (0.9%) and Nurse-C (1.2%) (P = 0.6). The clinical safety outcomes did not differ between groups. The sampling interval was 2.3 h (SD 0.5) in the LOGIC-C group versus 3.0 h (SD 0.8) in the Nurse-C group (P < 0.001). CONCLUSIONS: In a randomized controlled trial of a mixed critically ill patient population, the use of the LOGIC-Insulin blood glucose control algorithm, compared with blood glucose control by expert nurses, improved the quality of blood glucose control without increasing hypoglycemia. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02056353 . Registered on 4 February 2014.

Modeling of effect of glucose sensor errors on insulin dosage and glucose bolus computed by LOGIC-Insulin.

BACKGROUND: Effective and safe glycemic control in critically ill patients requires accurate glucose sensors and adequate insulin dosage calculators. The LOGIC-Insulin calculator for glycemic control has recently been validated in the LOGIC-1 randomized controlled trial. In this study, we aimed to determine the allowable error for intermittent and continuous glucose sensors, on the basis of the LOGIC-Insulin calculator. METHODS: A gaussian simulation model with a varying bias (0%-20%) and CV (-20% to +20%) simulated blood glucose values from the LOGIC-1 study (n = 149 patients) in 10 Monte Carlo steps. A clinical error grid system was developed to compare the simulated LOGIC-Insulin-directed intervention with the nominal intervention (0% bias, 0% CV). The severity of error measuring the clinical effect of the simulated LOGIC-Insulin intervention was graded as type B, C, and D errors. Type D errors were classified as acutely life-threatening (0% probability preferred). RESULTS: The probability of all types of errors was lower for continuous sensors compared with intermittent sensors. The maximum total error (TE), defined as the first TE introducing a type B/C/D error, was similar for both sensor types. To avoid type D errors, TEs <15.7% for intermittent sensors and <17.8% for continuous sensors were required. Mean absolute relative difference thresholds for type C errors were 7.1% for intermittent and 11.0% for continuous sensors. CONCLUSIONS: Continuous sensors had a lower probability for clinical errors than intermittent sensors at the same accuracy level. These simulations demonstrated the suitability of the LOGIC-Insulin control system for use with continuous, as well as intermittent, sensors.

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.

Clinical review: Consensus recommendations on measurement of blood glucose and reporting glycemic control in critically ill adults.

The management reporting and assessment of glycemic control lacks standardization. The use of different methods to measure the blood glucose concentration and to report the performance of insulin treatment yields major disparities and complicates the interpretation and comparison of clinical trials. We convened a meeting of 16 experts plus invited observers from industry to discuss and where possible reach consensus on the most appropriate methods to measure and monitor blood glucose in critically ill patients and on how glycemic control should be assessed and reported. Where consensus could not be reached, recommendations on further research and data needed to reach consensus in the future were suggested. Recognizing their clear conflict of interest, industry observers played no role in developing the consensus or recommendations from the meeting. Consensus recommendations were agreed for the measurement and reporting of glycemic control in clinical trials and for the measurement of blood glucose in clinical practice. Recommendations covered the following areas: How should we measure and report glucose control when intermittent blood glucose measurements are used? What are the appropriate performance standards for intermittent blood glucose monitors in the ICU? Continuous or automated intermittent glucose monitoring - methods and technology: can we use the same measures for assessment of glucose control with continuous and intermittent monitoring? What is acceptable performance for continuous glucose monitoring systems? If implemented, these recommendations have the potential to minimize the discrepancies in the conduct and reporting of clinical trials and to improve glucose control in clinical practice. Furthermore, to be fit for use, glucose meters and continuous monitoring systems must match their performance to fit the needs of patients and clinicians in the intensive care setting.

Ingredients for adequate evaluation of blood glucose algorithms as applied to the critically ill.

The article by Eslami and colleagues provides an overview of the indicators used to measure the quality of blood glucose control in patients admitted to the intensive care unit. Each indicator can be related to one or more of the following categories: blood glucose zones, blood glucose levels, time intervals, and features of the insulin titration algorithm. Some important issues (for instance those concerning the clarity of definitions used for glycaemic thresholds) are raised. This systematic review calls for a practical guide to advise the clinician how different blood glucose signals should (ideally) be evaluated and which steps should to be undertaken.

Glycemic penalty index for adequately assessing and comparing different blood glucose control algorithms.

INTRODUCTION: Blood glucose (BG) control performed by intensive care unit (ICU) nurses is becoming standard practice for critically ill patients. New (semi-automated) 'BG control' algorithms (or 'insulin titration' algorithms) are under development, but these require stringent validation before they can replace the currently used algorithms. Existing methods for objectively comparing different insulin titration algorithms show weaknesses. In the current study, a new approach for appropriately assessing the adequacy of different algorithms is proposed. METHODS: Two ICU patient populations (with different baseline characteristics) were studied, both treated with a similar 'nurse-driven' insulin titration algorithm targeting BG levels of 80 to 110 mg/dl. A new method for objectively evaluating BG deviations from normoglycemia was founded on a smooth penalty function. Next, the performance of this new evaluation tool was compared with the current standard assessment methods, on an individual as well as a population basis. Finally, the impact of four selected parameters (the average BG sampling frequency, the duration of algorithm application, the severity of disease, and the type of illness) on the performance of an insulin titration algorithm was determined by multiple regression analysis. RESULTS: The glycemic penalty index (GPI) was proposed as a tool for assessing the overall glycemic control behavior in ICU patients. The GPI of a patient is the average of all penalties that are individually assigned to each measured BG value based on the optimized smooth penalty function. The computation of this index returns a number between 0 (no penalty) and 100 (the highest penalty). For some patients, the assessment of the BG control behavior using the traditional standard evaluation methods was different from the evaluation with GPI. Two parameters were found to have a significant impact on GPI: the BG sampling frequency and the duration of algorithm application. A higher BG sampling frequency and a longer algorithm application duration resulted in an apparently better performance, as indicated by a lower GPI. CONCLUSION: The GPI is an alternative method for evaluating the performance of BG control algorithms. The blood glucose sampling frequency and the duration of algorithm application should be similar when comparing algorithms.

An Analysis of Reliability and Accuracy of Muscle Thickness Ultrasonography in Critically Ill Children and Adults.

BACKGROUND: Muscle wasting starts already within the first week in critically patients and is strongly related to poor outcome. Nevertheless, the early detection of muscle wasting is difficult. Therefore, we investigated the reliability and accuracy of ultrasonography to evaluate skeletal muscle wasting in critically ill children and adults. METHODS: This prospective observational study enrolled 30 sedated critically ill children and 14 critically ill adults. Two independent investigators made 210 ultrasonographical assessments of muscle thigh thickness. Inter- and intraobserver reliability and cutoff levels were calculated as a function of muscle thickness and the expected reduction in muscle size (predefined at 20% and 30%). RESULTS: Mean ± SD muscle thickness was 1.67 ± 0.55 cm in the pediatric and 2.10 ± 0.85 cm in the adult population. The median absolute interobserver variability was 0.07 cm (interquartile range [IQR], 0.04-0.20 cm) in the pediatric population and 0.05 cm (IQR, 0.03-0.09 cm) in the adult population. However, the absolute intraobserver accuracy had a 95% confidence interval of 0.43 cm in children and 0.22 cm in adults. Only a 30% decrease (0.50 cm) in muscle thickness can be detected in critically ill children. CONCLUSION: Although the interobserver variability is acceptable in the pediatric population, the intraobserver variability is too large with respect to the expected reduction in muscle thickness. In adults, ultrasonography may be a reliable tool for early detection of muscle mass wasting.

Glucose Control in the ICU: A Continuing Story.

In the present era of near-continuous glucose monitoring (CGM) and automated therapeutic closed-loop systems, measures of accuracy and of quality of glucose control need to be standardized for licensing authorities and to enable comparisons across studies and devices. Adequately powered, good quality, randomized, controlled studies are needed to assess the impact of different CGM devices on the quality of glucose control, workload, and costs. The additional effects of continuing glucose control on the general floor after the ICU stay also need to be investigated. Current algorithms need to be adapted and validated for CGM, including effects on glucose variability and workload. Improved collaboration within the industry needs to be encouraged because no single company produces all the necessary components for an automated closed-loop system. Combining glucose measurement with measurement of other variables in 1 sensor may help make this approach more financially viable.

LOGIC-insulin algorithm-guided versus nurse-directed blood glucose control during critical illness: the LOGIC-1 single-center, randomized, controlled clinical trial.

OBJECTIVE: Tight blood glucose control (TGC) in critically ill patients is difficult and labor intensive, resulting in poor efficacy of glycemic control and increased hypoglycemia rate. The LOGIC-Insulin computerized algorithm has been developed to assist nurses in titrating insulin to maintain blood glucose levels at 80-110 mg/dL (normoglycemia) and to avoid severe hypoglycemia (<40 mg/dL). The objective was to validate clinically LOGIC-Insulin relative to TGC by experienced nurses. RESEARCH DESIGN AND METHODS: The investigator-initiated LOGIC-1 study was a prospective, parallel-group, randomized, controlled clinical trial in a single tertiary referral center. A heterogeneous mix of 300 critically ill patients were randomized, by concealed computer allocation, to either nurse-directed glycemic control (Nurse-C) or algorithm-guided glycemic control (LOGIC-C). Glycemic penalty index (GPI), a measure that penalizes both hypoglycemic and hyperglycemic deviations from normoglycemia, was the efficacy outcome measure, and incidence of severe hypoglycemia (<40 mg/dL) was the safety outcome measure. RESULTS: Baseline characteristics of 151 Nurse-C patients and 149 LOGIC-C patients and study times did not differ. The GPI decreased from 12.4 (interquartile range 8.2-18.5) in Nurse-C to 9.8 (6.0-14.5) in LOGIC-C (P < 0.0001). The proportion of study time in target range was 68.6 ± 16.7% for LOGIC-C patients versus 60.1 ± 18.8% for Nurse-C patients (P = 0.00016). The proportion of severe hypoglycemic events was decreased in the LOGIC-C group (Nurse-C 0.13%, LOGIC-C 0%; P = 0.015) but not when considered as a proportion of patients (Nurse-C 3.3%, LOGIC-C 0%; P = 0.060). Sampling interval was 2.2 ± 0.4 h in the LOGIC-C group versus 2.5 ± 0.5 h in the Nurse-C group (P < 0.0001). CONCLUSIONS: Compared with expert nurses, LOGIC-Insulin improved efficacy of TGC without increasing rate of hypoglycemia.

Blood glucose measurements in critically ill patients.

Studies on tight glycemic control by intensive insulin therapy abruptly changed the climate of limited interest in the problem of hyperglycemia in critically ill patients and reopened the discussion on accuracy and reliability of glucose sensor devices. This article describes important components of blood glucose measurements and their interferences with the focus on the intensive care unit setting. Typical methodologies, organized from analytical accuracy to clinical accuracy, to assess imprecision and bias of a glucose sensor are also discussed. Finally, a list of recommendations and requirements to be considered when evaluating (time-discrete) glucose sensor devices is given.

Glycemic control in the pediatric intensive care unit of Leuven: two years of experience.

Stress hyperglycemia and hypoglycemia are associated with increased mortality and morbidity in critically ill patients. Three randomized controlled trials, in the surgical, medical, and pediatric intensive care unit (PICU) of the Leuven University in Belgium, demonstrated the beneficial response of tightly controlling blood glucose levels within age-adjusted narrow limits by applying intensive insulin therapy. Follow-up studies could not confirm the results obtained in the Leuven studies but revealed the complexity associated with tight glycemic control (TGC). This article gives an overview of the methodological aspects typical of the Leuven TGC concept, with the focus on the PICU. Differences between the adult and the PICU are described. This overview article might help other ICUs by addressing potential differences in clinical practice when implementing TGC.

Assessment of blood glucose control in the pediatric intensive care unit: extension of the glycemic penalty index toward children and infants.

BACKGROUND: The glycemic penalty index (GPI) is a measure to assess blood glucose (BG) control in critically ill adult patients but needs to be adapted for children and infants. METHOD: The squared differences between a clinical expertise penalty function and the corresponding polynomial function are minimized for optimization purposes. The average of all penalties (individually assigned to all BG readings) represents the patient-specific GPI. RESULTS: Penalization in the hypoglycemic range is more severe than in the hyperglycemic range as the developing brains of infants and children may be more vulnerable to hypoglycemia. Similarly, hypoglycemia is also more heavily penalized in infants than in children. CONCLUSIONS: Extending the adult GPI toward the age-specific GPI is an important methodological step. Long-term clinical studies are needed to determine the clinically acceptable GPI cut-off level.

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.

A minimal model for glycemia control in critically ill patients.

In this paper we propose a modified minimal model to be used for glycemia control in critically ill patients. For various reasons the Bergman minimal model is widely used to describe glucose and insulin dynamics. However, since this model is mostly valid in a rather restrictive setting, it might not be suitable to be used in a model predictive controller. Simulations show that the new model exhibits a similar glycemia behaviour but clinically more realistic insulin kinetics. Therefore it is potentially more suitable for glycemia control. The designed model is also estimated on a set of critically ill patients giving promising results.