Performance Comparison of CGM Systems: MARD Values Are Not Always a Reliable Indicator of CGM System Accuracy

Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems

BACKGROUND

The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for "integrated" CGM systems.

METHODS

The statistical concept of tolerance intervals and data from two approved CGM systems was used to illustrate the CG-DIVA.

RESULTS

The CG-DIVA characterizes the expected range of deviations of the CGM system from a comparison method in different glucose concentration ranges and the variability of accuracy within and between sensors. The results of the CG-DIVA are visualized in an intuitive and straightforward graphical presentation. Compared with conventional accuracy characterizations, the CG-DIVA infers the expected accuracy of a CGM system and highlights important differences between CGM systems. Furthermore, it provides information on the incidence of large errors which are of particular clinical relevance. A software implementation of the CG-DIVA is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment).

CONCLUSIONS

We argue that the CG-DIVA can simplify the discussion and comparison of CGM accuracy and could replace the high number of conventional approaches. Future adaptations of the approach could thus become a putative standard for the accuracy characterization of CGM systems and serve as the basis for the definition of future CGM performance requirements.

Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

Continuous glucose monitoring for inpatient diabetes management: an update on current evidence and practice

Over the last few years, several exciting changes in continuous glucose monitoring (CGM) technology have expanded its use and made CGM the standard of care for patients with type 1 and type 2 diabetes using insulin therapy. Consequently, hospitals started to notice increased use of these devices in their hospitalized patients. Furthermore during the coronavirus disease 2019 (COVID) pandemic, there was a critical need for innovative approaches to glycemic monitoring, and several hospitals started to implement CGM protocols in their daily practice. Subsequently, a plethora of studies have demonstrated the efficacy and safety of CGM use in the hospital, leading to clinical practice guideline recommendations. Several studies have also suggested that CGM has the potential to become the standard of care for some hospitalized patients, overcoming the limitations of current capillary glucose testing. Albeit, there is a need for more studies and particularly regulatory approval. In this review, we provide a historical overview of the evolution of glycemic monitoring in the hospital and review the current evidence, implementation protocols, and guidance for the use of CGM in hospitalized patients.

Variation of Mean Absolute Relative Differences of Continuous Glucose Monitoring Systems Throughout the Day

BACKGROUND

There is an increasing use of continuous glucose monitoring (CGM) by people with diabetes. Measurement performance is often characterized by the mean absolute relative difference (MARD). However, MARD is influenced by a number of factors and little is known about whether MARD is stable throughout the day.

MATERIAL AND METHODS

A total of 24 participants with type 1 diabetes were enrolled in the study. The study was performed for seven in-patient days. Participants wore two CGM systems in parallel and performed additional frequent blood glucose (BG) measurements. On two days, glucose excursions were induced.MARD was calculated between pairs of CGM and BG values, with BG values serving as reference values. ARD values calculated from CGM-BG pairs were grouped by hour of the day. Results were analyzed separately for glucose excursion days and for regular days.

RESULTS

Total MARDs for the complete study duration were 12.5% ± 3.6% and 13.2% ± 2.4% (n = 24). Throughout the day marked variability of MARD was observed (8.0% ± 1.3%-16.3% ± 2.9% (G5); 9.1% ± 1.4%-16.3% ± 5.3% (FL), up to n = 157 each). Low(est) MARD values were observed before breakfast and dinner, when subjects were in or near a fasting state. Especially after breakfast and lunch, MARD values were higher than average.

CONCLUSIONS

Analytical performance of the two CGM systems, assessed by MARD, was found to vary markedly throughout the day. Activities of daily life likely triggered these variations. An increasing number of CGM users base therapeutic decisions on CGM values, and they should be aware of these variations of performance throughout the day.

Clinical Use of a 180-Day Implantable Glucose Monitoring System in Dogs with Diabetes Mellitus: A Case Series

Simple Summary

A novel continuous glucose monitoring system (CGMS) equipped with a long-term sensor has recently been developed for humans with diabetes mellitus. The sensor is inserted under the skin and continuously measures the glucose in the interstitial fluid over a period of up to 180 days. The aim of this study was to describe, for the first time, the clinical use of this novel CGMS in three diabetic dogs (DD). The insertion and use of the device were straightforward and well tolerated by the dogs. Some device-related issues, such as sensor dislocation and trouble with daily calibrations, were reported. A good correlation between the glucose values measured by this CGMS and those obtained with a flash glucose monitoring system and a portable-blood glucose meter, previously validated for use in DD, was found (rs = 0.85 and rs = 0.81, respectively). The functional life of the sensor was 180 days in two of the three dogs, and the use of the device provided high satisfaction to the owners. This innovative device might be considered a future alternative for continuous glucose monitoring in dogs with diabetes mellitus.

Abstract

The novel Eversense XL continuous glucose monitoring system (Senseonics, Inc., Germantown, Maryland) has recently been developed for monitoring diabetes in humans. The sensor is fully implanted and has a functional life of up to 180 days. The present study describes the use of Eversense XL in three diabetic dogs (DD) with good glycemic control managed by motivated owners. The insertion and use of the device were straightforward and well tolerated by the dogs. During the wearing period, some device-related drawbacks, such as sensor dislocation and daily calibrations, were reported. A good correlation between the glucose values measured by the Eversense XL and those obtained with two commercially available devices, previously validated for use in DD, was found (r_s = 0.85 and r_s = 0.81, respectively). The life of the sensor was 180 days in two of the DD and provided high satisfaction. This innovative device might be considered a future alternative for home glucose monitoring in DD.

Benefits of a Switch from Intermittently Scanned Continuous Glucose Monitoring (isCGM) to Real-Time (rt) CGM in Diabetes Type 1 Suboptimal Controlled Patients in Real-Life: A One-Year Prospective Study §

The switch from intermittently scanned continuous glucose monitoring (isCGM) to real-time (rt) CGM could improve glycemic management in suboptimal controlled type 1 diabetes patients, but long-term study is lacking. We evaluated retrospectively the ambulatory glucose profile (AGP) in such patients after switching from Free Style Libre 1 (FSL1) to Dexcom G4 (DG4) biosensors over 1 year. Patients (n = 21, 43 ± 15 years, BMI 25 ± 5, HbA1c 8.1 ± 1.0%) had severe hypoglycemia and/or HbA1c ≥ 8%. AGP metrics (time-in-range (TIR) 70–180 mg/dL, time-below-range (TBR) <70 mg/dL or <54 mg/dL, glucose coefficient of variation (%CV), time-above-range (TAR) >180 mg/dL or >250 mg/dL, glucose management indicator (GMI), average glucose) were collected the last 3 months of FSL1 use (M0) and of DG4 for 3, 6 (M6) and 12 (M12) months of use. Values were means ± standard deviation or medians [Q1;Q3]. At M12 versus M0, the higher TIR (50 ± 17 vs. 45 ± 16, p = 0.036), and lower TBR < 70 mg/dL (2.5 [1.6;5.5] vs. 7.0 [4.5;12.5], p = 0.0007), TBR < 54 mg/dL (0.7 [0.4;0.8] vs. 2.3 [0.8;7.0], p = 0.007) and %CV (39 ± 5 vs. 45 ± 8, p = 0.0009), evidenced a long-term effectiveness of the switch. Compared to M6, TBR < 70 mg/dL decreased, %CV remained stable, while the improvement on hyperglycemia exposure decreased (higher GMI, TAR and average glucose). This switch was a relevant therapeutic option, though a loss of benefit on hyperglycemia stressed the need for optimized management of threshold alarms. Nevertheless, few patients attained the recommended values for AGP metrics, and the reasons why some patients are “responders” vs. “non-responders” warrant to be investigated.

Tracking metabolic responses based on macronutrient consumption: A comprehensive study to continuously monitor and quantify dual markers (cortisol and glucose) in human sweat using WATCH sensor

Wearable Awareness Through Continuous Hidrosis (WATCH) sensor is a sweat based monitoring platform that tracks cortisol and glucose for the purpose of understanding metabolic responses related to macronutrient consumption. In this research article, we have demonstrated the ability of tracking these two biomarkers in passive human sweat over a workday period (8 h) for 10 human subjects in conjunction with their macronutrient consumption. The validation of the WATCH sensor performance was carried out via standard reference methods such as Luminex and ELISA This is a first demonstration of a passive sweat sensing technology that can detect interrelated dual metabolites, cortisol, and glucose, on a single sensing platform. The significance of detecting the two biomarkers simultaneously is that capturing the body's metabolic and endocrinal responses to dietary triggers can lead to improved lifestyle management. For sweat cortisol, we achieved a detection limit of 1 ng/ml (range ∼1-12.5 ng/ml) with Pearson's "r" of 0.897 in reference studies and 0.868 in WATCH studies. Similarly, for sweat glucose, we achieved a detection limit of 1 mg/dl (range ∼ 1-11 mg/dl) with Pearson's "r" of 0.968 in reference studies and 0.947 in WATCH studies, respectively. The statistical robustness of the WATCH sensor was established through the Bland-Altman analysis, whereby the sweat cortisol and sweat glucose levels are comparable to the standard reference method. The probability distribution (t-test), power analysis (power 0.82-0.87), α = 0.05. Mean absolute relative difference (MARD) outcome of ˷5.10-5.15% further confirmed the statistical robustness of the sweat sensing WATCH device output.

Comparative Accuracy Analysis of a Real-time and an Intermittent-Scanning Continuous Glucose Monitoring System

BACKGROUND

Currently, two different types of continuous glucose monitoring (CGM) systems are available: real time (rt) CGM systems that continuously provide glucose values and intermittent-scanning (is) CGM systems. This study compared accuracy of an rtCGM and an isCGM system when worn in parallel.

METHODS

Dexcom G5 Mobile (DG5) and FreeStyle Libre (FL) were worn in parallel by 27 subjects for 14 days including two clinic sessions with induced glucose excursions. The percentage of CGM values within ±20% or ±20 mg/dL of the laboratory comparison method results (YSI 2300 STAT Plus, YSI Inc., Yellow Springs, OH, United States; glucose oxidase based) or blood glucose meter values and mean absolute relative difference (MARD) were calculated. Consensus error grid and continuous glucose error grid analyses were performed to assess clinical accuracy.

RESULTS

Both systems displayed clinically accurate readings. Compared to laboratory comparison method results during clinic sessions, DG5 had 91.5% of values within ±20%/20 mg/dL and a MARD of 9.5%; FL had 82.5% of scanned values within ±20%/20 mg/dL and an MARD of 13.6%. Both systems showed a lower level of performance during the home phase and when using the blood glucose meter as reference.

CONCLUSION

The two systems tested in this study represent two different principles of CGM. DG5 generally provided higher accordance with laboratory comparison method results than FL.

Infrared measurements of glucose in peritoneal fluid with a tuneable quantum cascade laser

Fast and accurate continuous glucose monitoring is needed in future systems for control of blood glucose levels in type 1 diabetes patients. Direct spectroscopic measurement of glucose in the peritoneal cavity is an attractive alternative to conventional electrochemical sensors placed subcutaneously. We demonstrate the feasibility of fast glucose measurements in peritoneal fluid using a fibre-coupled tuneable mid-infrared quantum cascade laser. Mid-infrared spectra (1200-925 cm^-1) of peritoneal fluid samples from pigs with physiological glucose levels (32-426 mg/dL, or 1.8-23.7 mmol/L) were acquired with a tuneable quantum cascade laser employing both transmission and attenuated total reflection (ATR) spectroscopy. Using partial least-squares regression, glucose concentrations were predicted with mean absolute percentage errors (MAPEs) of 8.7% and 12.2% in the transmission and ATR configurations, respectively. These results show that highly accurate concentration predictions are possible with mid-infrared spectroscopy of peritoneal fluid, and represent a first step towards a miniaturised optical sensor for intraperitoneal continuous glucose monitoring.

Assessment of factors that determine the mean absolute relative difference in flash glucose monitoring with reference to plasma glucose levels in Japanese subjects without diabetes

The Abbott FreeStyle Libre flash glucose monitoring system (FGM) is a recently introduced, but widespread continuous glucose monitoring system. While its mean absolute relative difference (MARD) value indicating its accuracy is acceptable with reference to the self-monitoring of blood glucose (SMBG) levels, few reports have examined the MARD in sensor glucose values of FGM (FGM-SG) with reference to plasma glucose (PG) levels and the factors determining it. We performed oral glucose tolerance tests (OGTTs) in 25 Japanese subjects without diabetes. Parkes error grid analyses showed that FGM-SG with either SMBG or PG levels as a reference met International Organization for Standardization criteria. The MARD in FGM-SG with reference to SMBG levels was 10.9 ± 4.1% during OGTTs. Surprisingly, the MARD in FGM-SG with reference to PG levels was 20.3 ± 10.3% during OGTTs, revealing a discrepancy in the accuracy of FGM-SG compared with that of PG levels; moreover, the MARD showed negative correlations with fasting blood sugar level, homeostasis model assessment insulin resistance index, and body mass index (BMI). Multiple regression analyses revealed that BMI contributed the most to the MARD when FGM-SG and PG level were compared, as lean individuals have a greater MARD regardless of glucose levels. Inaccurate FGM data could potentially increase the risk of inappropriate treatment; consideration of such factors is critical to ensure reliable FGM values.

Adaptive Boosting Based Personalized Glucose Monitoring System (PGMS) for Non-Invasive Blood Glucose Prediction with Improved Accuracy

In this paper, we present an architecture of a personalized glucose monitoring system (PGMS). PGMS consists of both invasive and non-invasive sensors on a single device. Initially, blood glucose is measured invasively and non-invasively, to train the machine learning models. Then, paired data and corresponding errors are divided scientifically into six different clusters based on blood glucose ranges as per the patient’s diabetic conditions. Each cluster is trained to build the unique error prediction model using an adaptive boosting (AdaBoost) algorithm. Later, these error prediction models undergo personalized calibration based on the patient’s characteristics. Once, the errors in predicted non-invasive values are within the acceptable error range, the device gets personalized for a patient to measure the blood glucose non-invasively. We verify PGMS on two different datasets. Performance analysis shows that the mean absolute relative difference (MARD) is reduced exceptionally to 7.3% and 7.1% for predicted values as compared to 25.4% and 18.4% for measured non-invasive glucose values. The Clarke error grid analysis (CEGA) plot for non-invasive predicted values shows 97% data in Zone A and 3% data in Zone B for dataset 1. Moreover, for dataset 2 results echoed with 98% and 2% in Zones A and B, respectively.

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 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.

Monitoring of Pediatric Type 1 Diabetes

Regular self-monitoring of blood glucose levels, and ketones when indicated, is an essential component of type 1 diabetes (T1D) management. Although fingerstick blood glucose monitoring has been the standard of care for decades, ongoing rapid technological developments have resulted in increasingly widespread use of continuous glucose monitoring (CGM). This article reviews recommendations for self-monitoring of glucose and ketones in pediatric T1D with particular emphasis on CGM and factors that impact the accuracy and real-world use of this technology.

Performance and Usability of Three Systems for Continuous Glucose Monitoring in Direct Comparison

BACKGROUND

To be able to compare continuous glucose monitoring (CGM) systems, they have to be worn in parallel by the same subjects. This study evaluated the performance and usability of three different CGM systems in direct comparison.

METHOD

In this open, prospective study at two sites, 54 patients with diabetes wore three CGM systems each (Dexcom G5™ Mobile CGM system [DG5], Guardian™ Connect system [GC], and a Roche CGM system [RCGM]) in parallel for 6 or 7 days in a mixed inpatient and outpatient setting. Capillary comparison measurements were performed using a self-monitoring of blood glucose (SMBG) system. During study site visits, glucose excursions were induced. Performance of the systems was evaluated by calculating mean absolute relative differences (MARD, calculated as absolute differences for glucose concentrations <100 mg/dL and as relative differences for glucose concentrations ≥100 mg/dL), and mean relative differences (MRD, bias) between CGM and SMBG results. In addition, usability of the systems was assessed.

RESULTS

Overall MARD was 10.1 ± 2.1 for DG5, 11.5 ± 4.2 for GC, and 11.9 ± 5.6 for RCGM. Performance improved in all systems after the first day of use. All systems showed >99% of values within zones A and B of the consensus error grid. Overall, all CGM systems showed a small negative bias compared to SMBG. Usability of the systems differed regarding patch adhesion rate, failure rate, and patient rating. Most patients preferred GC, but in general all systems were rated positively.

CONCLUSION

All three CGM systems showed similar overall accuracy in this direct comparison, but small differences were observed with regard to specific glucose ranges and usability aspects.

Time Lag and Accuracy of Continuous Glucose Monitoring During High Intensity Interval Training in Adults with Type 1 Diabetes

Background: This study investigated the accuracy of real-time continuous glucose monitoring (rtCGM) during high intensity interval training (HIIT) in patients with type 1 diabetes (T1D). Methods: Seventeen participants with T1D, using multiple daily injections (MDI) with basal insulin glargine 300 U/mL (Gla-300), completed four fasted HIIT sessions over 4 weeks while wearing a Dexcom rtCGM G4 Platinum system. Each exercise consisted of high intensity interval cycling and multimodal training over 25 min. Reference venous plasma glucose (PG) was measured at 60- and 10-min before exercise (Stage 1), every 10 min during exercise and then every 15 min until 180 min after the end of exercise (Stage 2: during exercise and 45-min early recovery; Stage 3: 45 min to 3 h after the end of exercise); and at 6-, 10-, and 13-h postexercise (Stage 4). Results: In the 64 HIIT sessions that resulted in hyperglycemia, PG increased 90.0 ± 32.4 mg/dL (mean ± standard deviation), peaking at 68.0 ± 18.4 min from the start of HIIT. Mean absolute relative difference was highest during exercise and early recovery (Stage 2) at 17.8%, versus Stage 1 (10.4%), Stage 3 (10.6%), and Stage 4 (11.5%) (P < 0.001). During Stage 2, rtCGM showed a significant negative bias of 35.3 mg/dL (P < 0.001) compared to reference glucose. Lag time to reach the half-maximal glucose rise was 35 min in rtCGM versus PG. The Surveillance Error Grid found that in Stage 2, only 65.5% of paired values were in the no-risk zone and the %15/15 was 50%, significantly lower than the other stages (P < 0.001). Conclusions: During HIIT and early recovery, there is an increase in lag time and a related decline in accuracy of Dexcom rtCGM G4, compared to pre-exercise and later recovery, in patients with T1D using MDI.

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.

Analysis of "Performance of a Factory-Calibrated, Real-Time Continuous Glucose Monitoring System in Pediatric Participants With Type 1 Diabetes"

Accuracy of sensors play an important role in the acceptance and long-term use of CGM which is related to improved glycemic outcome. This has been lower in children and adolescents in the past for reasons such as size of the device, pain, and necessity to calibrate sensors but also inaccuracy and perceived nonreliability on alarms among others. In the study of Welsh et al, performance of a new, factory-calibrated sensor was assessed. The accuracy of the sensor, as measured in MARD, paired values within 20/20% or 15/15% and surveillance error grid analysis showed very good results, although less so in low value range <70 mg/dL, falling rate of change, and on first day of use. Accurate glucose measurements especially in these incidences are of utmost importance to people with diabetes as their treatment decisions are based on these, also in the beginning of sensor use.

Self-measurement of Blood Glucose and Continuous Glucose Monitoring - Is There Only One Future?

Monitoring glycaemic control in patients with diabetes has evolved dramatically over the past decades. The introduction of easy-to-use systems for self-monitoring of blood glucose (SMBG) utilising capillary blood samples has resulted in the availability of a wide range of systems, providing different measurement quality. Systems for continuous glucose monitoring (CGM) – used mainly in patients with type 1 diabetes (T1D) – were made possible by the development of glucose sensors that measure glucose levels in the interstitial fluid (ISF) in the subcutaneous tissue of the skin. CGM readings might not correspond exactly to SMBG measurement results taken at the same time, especially during rapid changes in either blood glucose or ISF glucose levels. The mean absolute relative difference is the most popular method used for characterising the measurement performance of CGM systems. Unlike the International Organization for Standardization 15197:2013 criteria for SMBG systems, no accuracy standards for CGM systems exist. Measurement quality of CGM systems can vary based on several factors, limiting their safety and effective use in managing diabetes. Patients have to be trained adequately to make safe and efficient use of CGM systems (like with SMBG systems). Also, systems for CGM must be evaluated in terms of patient safety and the ability to provide accurate measurements regardless of the fluctuation of glucose levels. As new technological advancements in glucose monitoring are essential for improved management options of diabetes, such as automated insulin dosing systems, there is a need for a critical view of all such developments. It is likely that both, SMBG and CGM systems, will play important future roles in the treatment of diabetes.

Modeling of Diabetes and Its Clinical Impact

Understanding all aspects of diabetes treatment is hindered by the complexity of this chronic disease and its multifaceted complications and comorbidities, including social and financial impacts. In vivo studies as well as clinical trials provided invaluable information for unraveling not only metabolic processes but also risk estimations of, for example, complications. These approaches are often time- and cost-consuming and have frequently been supported by simulation models. Simulation models provide the opportunity to investigate diabetes treatment from additional viewpoints and with alternative objectives. This review presents selected models focusing either on metabolic processes or risk estimations and financial outcomes to provide a basic insight into this complex subject. It also discusses opportunities and challenges of modeling diabetes.

Measurement Performance of Two Continuous Tissue Glucose Monitoring Systems Intended for Replacement of Blood Glucose Monitoring

BACKGROUND

Currently, two systems for continuous tissue glucose monitoring (CGM) (Dexcom^® G5 [DG5] and FreeStyle Libre [FL]) are intended to replace blood glucose monitoring (BGM) and, according to manufacturer labeling, are distributed as such in some jurisdictions, including the United States and the European Union.

METHODS

The measurement performance of these two systems in comparison with a BGM system was analyzed in a 14-day study with 20 participants comprising study site visits, which included phases of induced rapid glucose changes, and home use phases. Performance analysis was mainly based on deviations between CGM readings and BGM results. Sensor-to-sensor precision was also analyzed.

RESULTS

Approximately 25% of DG5 and FL results showed differences from BGM results exceeding 15 mg/dL or 15% (at glucose concentration below or above 100 mg/dL, respectively) at times of therapeutic decisions, and ∼5% of differences exceeded 30 mg/dL or 30%. Performance was different depending on the setting (study site visits, home use phases, and phases of induced rapid glucose changes). In consensus error grid (CEG) analysis, both systems showed >99.5% of results within the clinically acceptable zones A and B.

CONCLUSIONS

In this study, both systems showed deviations from blood glucose (BG) measurements, the current standard approach in diabetes therapy. Although a large percentage of results was found in CEG zones A and B, for approximately one in four therapeutic decisions, CGM and BG readings differed by at least 15 mg/dL or 15%. Such deviations should be taken into account when using CGM systems.

Limits to the Evaluation of the Accuracy of Continuous Glucose Monitoring Systems by Clinical Trials

Systems for continuous glucose monitoring (CGM) are evolving quickly, and the data obtained are expected to become the basis for clinical decisions for many patients with diabetes in the near future. However, this requires that their analytical accuracy is sufficient. This accuracy is usually determined with clinical studies by comparing the data obtained by the given CGM system with blood glucose (BG) point measurements made with a so-called reference method. The latter is assumed to indicate the correct value of the target quantity. Unfortunately, due to the nature of the clinical trials and the approach used, such a comparison is subject to several effects which may lead to misleading results. While some reasons for the differences between the values obtained with CGM and BG point measurements are relatively well-known (e.g., measurement in different body compartments), others related to the clinical study protocols are less visible, but also quite important. In this review, we present a general picture of the topic as well as tools which allow to correct or at least to estimate the uncertainty of measures of CGM system performance.

Practical guidance for treatment of patients with diabetes using flash glucose monitoring: a pilot study

BACKGROUND

Flash glucose monitoring (FGM) is a factory-calibrated, blood glucose measuring sensor system for patients with diabetes. We aimed to investigate the correlation between the sensor glucose (SG) value obtained using an FGM device and the traditional self-monitoring of blood glucose (SMBG) value.

METHODS

In 30 patients with diabetes under insulin treatment, SG and SMBG values were measured for 2 weeks, and the correlation between the values was analyzed.

RESULTS

The mean number of accumulated measurements of SG values was 1223.2 ± 193.0, whereas that of the SMBG values was 49.2 ± 21.3. Although SG and SMBG values showed a favorable correlation (R²  = 0.8413), SG values were lower than SMBG values by an average of 7.9 ± 29.8 mg/dL. The correlation patterns fell into four types: low type (SG values lower than SMBG values; n = 12), high type (SG values higher than SMBG values; n = 3), cross type (the slope of the two regression lines crossed at a certain measurement value; n = 14), and matching type (the values overlapped; n = 1).

CONCLUSIONS

Recognition of the characteristic correlation patterns between SG and SMBG values is indispensable for certified diabetes educators to provide appropriate treatment guidance to patients with diabetes.

Clinical Implications of Accuracy Measurements of Continuous Glucose Sensors

The accuracy of a continuous glucose monitor (CGM) now supports its use by persons with diabetes and clinicians caring for them. This article reviews measures of CGM accuracy, factors contributing to accuracy, comparative accuracy assessment, clinical implications of CGM sensor accuracy, and recent clinical trials that have demonstrated the utility of CGMs.

Significance and Reliability of MARD for the Accuracy of CGM Systems

BACKGROUND

There is a need to assess the accuracy of continuous glucose monitoring (CGM) systems for several uses. Mean absolute relative difference (MARD) is the measure of choice for this. Unfortunately, it is frequently overlooked that MARD values computed with data acquired during clinical studies do not reflect the accuracy of the CGM system only, but are strongly influenced by the design of the study. Thus, published MARD values must be understood not as precise values but as indications with some uncertainty.

DATA AND METHODS

Data from a recent clinical trial, Monte Carlo simulations, and assumptions about the error distribution of the reference measurements have been used to determine the confidence region of MARD as a function of the number and the accuracy of the reference measurements.

RESULTS

The uncertainty of the computed MARD values can be quantified by a newly introduced MARD reliability index (MRI), which independently mirrors the reliability of the evaluation. Thus MARD conveys information on the accuracy of the CGM system, while MRI conveys information on the uncertainty of the computed MARD values.

CONCLUSIONS

MARD values from clinical studies should not be used blindly but the reliability of the evaluation should be considered as well. Furthermore, it should not be ignored that MARD does not take into account the key feature of CGM sensors, the frequency of the measurements. Additional metrics, such as precision absolute relative difference (PARD) should be used as well to obtain a better evaluation of the CGM performance for specific uses, for example, for artificial pancreas.