Prediction Quality of Glucose Trend Indicators in Two Continuous Tissue Glucose Monitoring Systems

A Proposal for the Clinical Characterization of Continuous Glucose Monitoring Trend Arrow Accuracy

The assessment and characterization of trend accuracy, that is, the ability of a continuous glucose monitoring (CGM) system to correctly indicate the direction and rate of change (RoC) of glucose levels, has received comparatively little attention in the overall evaluation of CGM performance. As such, only few approaches that examine the trend accuracy have been put forward. In this article, we review existing approaches and propose the clinical trend concurrence analysis (CTCA) which is an adaptation of the conventional trend concurrence analysis. The CTCA is intended to directly evaluate the trend arrows displayed by the CGM systems by characterizing their agreement to suitably categorized comparator RoCs. Here, we call on manufactures of CGM systems to provide the displayed trend arrows for retrospective analysis. The CTCA classifies any deviations between the CGM trend and comparator RoC according to their risk for an adverse clinical event arising from a possibly erroneous treatment decision. For that, the existing rate error grid analysis and a specific set of trend arrow-based insulin dosing recommendations were used. The results of the CTCA are presented in an accessible graphical display and exemplified on data from three CGM systems. With this article, we hope to increase the awareness for the importance and challenges of assessing the accuracy of trend information displayed by CGM systems.

Time in Specific Glucose Ranges, Glucose Management Indicator, and Glycemic Variability: Impact of Continuous Glucose Monitoring (CGM) System Model and Sensor on CGM Metrics

BACKGROUND

International consensus recommends a set of continuous glucose monitoring (CGM) metrics to assess quality of diabetes therapy. The impact of individual CGM sensors on these metrics has not been thoroughly studied yet. This post hoc analysis aimed at comparing time in specific glucose ranges, coefficient of variation (CV) of glucose concentrations, and glucose management indicator (GMI) between different CGM systems and different sensors of the same system.

METHOD

A total of 20 subjects each wore two Dexcom G5 (G5) sensors and two FreeStyle Libre (FL) sensors for 14 days in parallel. Times in ranges, GMI, and CV were calculated for each 14-day sensor experiment, with up to four sensor experiments per subject. Pairwise differences between different sensors of the same CGM system as well as between sensors of different CGM system were calculated for these metrics.

RESULTS

Pairwise differences between sensors of the same model showed larger differences and larger variability for FL than for G5, with some subjects showing considerable differences between the two sensors. When pairwise differences between sensors of different CGM models were calculated, substantial differences were found in some subjects (75th percentiles of differences of time spent <70 mg/dL: 5.0%, time spent >180 mg/dL: 9.2%, and GMI: 0.42%).

CONCLUSION

Relevant differences in CGM metrics between different models of CGM systems, and between different sensors of the same model, worn by the same study subjects were found. Such differences should be taken into consideration when these metrics are used in the treatment of diabetes.

Flash Continuous Glucose Monitoring: A Practical Guide and Call to Action for Pharmacists

Despite advances in diabetes technology, the proportion of patients with type 2 diabetes achieving recommended glycemic goals remains suboptimal. There is a growing interest in flash continuous glucose monitoring (CGM) among patients, pharmacists and providers. Pharmacists are well positioned to collaborate with patients and providers in ambulatory care or community-based settings to allow a greater number of patients with diabetes to harness the benefits of flash CGM. The purpose of this narrative review is to provide pharmacists with a background on flash CGM technology, review the data supporting pharmacist-driven flash CGM services, and address common questions that arise in pharmacy practice surrounding flash CGM.

Basics and use of continuous glucose monitoring (CGM) in diabetes therapy

Background

For a long time, self-monitoring of blood glucose (SMBG) was widely viewed as the essential glucose measurement procedure in the therapy of insulin-treated people with diabetes. With increasing accuracy and simplified handling of continuous glucose monitoring (CGM) systems, this evolving technology challenges and at least partly replaces SMBG systems.

Content

Sensors of all currently available CGM systems measure glucose levels in the subcutaneous interstitial fluid for 6–14 days. The only available implantable sensor facilitates a measurement span of up to 6 months. Depending on the used system, glucose levels are either shown in real time (rtCGM systems) or after scanning (iscCGM systems). Functions such as alerts, alarms and trend arrows and data presentation encourage independent self-management of diabetes therapy. The high frequency of glucose data and the multitude of existing functions require an extensive training of people with diabetes and their caregivers.

Summary

CGM systems provide a much more detailed picture of glycemia in people with diabetes. Educated patients can use these data to react adequately to their glucose levels and therefore avoid hypoglycemic and hyperglycemic events. Studies showed that glycated hemoglobin (HbA1c) levels and hypoglycemic events can be significantly reduced by frequent use of CGM systems.

Benefits and limitations of continuous glucose monitoring in type 1 diabetes

Introduction: Type 1 diabetes (T1D) is a chronic condition characterized by a complete deficiency in insulin production. Optimal management requires constant knowledge of glucose levels for safe and effective insulin administration. Self-monitoring of blood glucose (SMBG) using capillary blood glucose meters is cumbersome and provides limited information to guide management. Continuous glucose monitoring (CGM) technology addresses many of these gaps, but itself has limitations which have prevented people with diabetes and their clinicians from fully embracing this technology. This review covers the benefits and limitations of CGM use, and looks toward future application of this technology in the management of T1D.Areas covered: Impact of CGM on physical and psychosocial outcomes in people with T1D. Barriers to CGM uptake. Integration with insulin pumps and other technologies. Opportunities for future application.Expert opinion: CGM technology will be utilized by the majority of people with T1D and increasing numbers of people with type 2 diabetes due to improved insurance coverage and easier-to-use systems. Its use as part of artificial pancreas systems will add further utility, as it will help to protect from both hypoglycemia and hyperglycemia. People with diabetes will spend more time in range and experience fewer acute and chronic complications.

Benefits and Limitations of MARD as a Performance Parameter for Continuous Glucose Monitoring in the Interstitial Space

High-quality performance of medical devices for glucose monitoring is important for a safe and efficient usage of this diagnostic option by patients with diabetes. The mean absolute relative difference (MARD) parameter is used most often to characterize the measurement performance of systems for continuous glucose monitoring (CGM). Calculation of this parameter is relatively easy and comparison of the MARD numbers between different CGM systems appears to be straightforward on the first glance. However, a closer look reveals that a number of complex aspects make interpretation of the MARD numbers provided by the manufacturer for their CGM systems difficult. In this review, these aspects are discussed and considerations are made for a systematic and appropriate evaluation of the MARD in clinical trials. The MARD should not be used as the sole parameter to characterize CGM systems, especially when it comes to nonadjunctive usage of such systems.

Predictive Glucose Trends From Continuous Glucose Monitoring: Friend or Foe in Clinical Decision Making?

In this commentary, we briefly review the currently recommended approaches to interpretation and management of continuous glucose monitor (CGM) rate of change (ROC) trend arrows and discuss the inherent difficulty in incorporating practical recommendations for their application into routine clinical care. We have limited our review and discussion to the currently available Dexcom G5 and G6 CGM systems and Abbott's Freestyle Libre flash glucose monitor (FGM) system, as they are the most widely used and currently approved for nonadjunctive use in the United States.

Measures of Accuracy for Continuous Glucose Monitoring and Blood Glucose Monitoring Devices

Currently, patients with diabetes may choose between two major types of system for glucose measurement: blood glucose monitoring (BGM) systems measuring glucose within capillary blood and continuous glucose monitoring (CGM) systems measuring glucose within interstitial fluid. Although BGM and CGM systems offer different functionality, both types of system are intended to help users achieve improved glucose control. Another area in which BGM and CGM systems differ is measurement accuracy. In the literature, BGM system accuracy is assessed mainly according to ISO 15197:2013 accuracy requirements, whereas CGM accuracy has hitherto mainly been assessed by MARD, although often results from additional analyses such as bias analysis or error grid analysis are provided. The intention of this review is to provide a comparison of different approaches used to determine the accuracy of BGM and CGM systems and factors that should be considered when using these different measures of accuracy to make comparisons between the analytical performance (ie, accuracy) of BGM and CGM systems. In addition, real-world implications of accuracy and its relevance are discussed.