Tom Van Herpe

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.See related commentary by Soto-Rivera and Agus, http://ccforum.com/content/17/3/155

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.

Glycemia Prediction in Critically Ill Patients Using an Adaptive Modeling Approach

Background: Strict blood glucose control by applying nurse-driven protocols is common nowadays in intensive care units (ICUs). Implementation of a predictive control system can potentially reduce the workload for medical staff but requires a model for accurately predicting the glycemia signal within a certain time horizon. Methods: GlucoDay (A. Menarini Diagnostics, Italy) data coming from 19 critically ill patients (from a surgical ICU) are used to estimate the initial ICU “minimal” model (based on data of the first 24 hours) and to reestimate the model as new measurements are obtained. The reestimation is performed every hour or every 4 hours. For both approaches the optimal size of the data set for each reestimation is determined. Results: The prediction error that is obtained when applying the 1-hour reestimation strategy is significantly smaller than when the model is reestimated only every 4 hours (p < 0.001). The optimal size of the data set to be considered in each reestimation process of the ICU minimal model is found to be 4 hours. The obtained average “mean percentage error” is 7.6% (SD 3.1%) and 14.6% (SD 7.0%) when the model is reestimated every hour and 4 hours, respectively. Conclusions: Implementation of the ICU minimal model in the appropriate reestimation process results in clinically acceptable prediction errors. Therefore, the model is able to predict glycemia trends of patients admitted to the surgical ICU and can potentially be used in a predictive control system.

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

Nonlinear model predictive control with moving horizon state and disturbance estimation - Application to the normalization of blood glucose in the critically ill

Abstract In this paper we present a nonlinear model predictive control (NMPC) strategy that can be used to tackle nonlinear control problems with changing model parameters, unknown disturbance factors and specifications on the rates of change of the inputs. The closed-loop performance of the proposed NMPC strategy is demonstrated by applying it to the problem of blood glucose normalization in critically ill patients. A nonlinear patient model, that is particularly developed for describing the glucose and the insulin dynamics of these patients, is used for online state and disturbance estimation and control under a realistic disturbance realization. The results are satisfactory both in terms of control behavior (set point tracking and the suppression of unknown disturbance factors) and clinical acceptability.

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.

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.

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.

DEVELOPMENT OF A CRITICALLY ILL PATIENT INPUT-OUTPUT MODEL

Abstract In this paper we apply system identification in order to build a model suitable for prediction of the glycemia levels of critically ill patients in the Intensive Care Unit. These patients typically show increased glycemia levels, and it has been shown that glycemia control by means of insulin therapy reduces morbidity and mortality. Based on a real-life dataset from 41 critically ill patients, an ARX model is estimated which captures the insulin effect on glycemia under different settings. The results are satisfactory both in terms of forecasting ability and in the clinical interpretation of the estimated coefficients.

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.