A Review of Current Evidence with Continuous Glucose Monitoring in Patients with Diabetes

Devices that measure glucose on a near-continuous basis may provide a better insight into glycemic profiles, allowing patients with diabetes to make therapeutic adjustments to improve metabolic control, thereby reducing the risk of diabetic complications. Motivated and technologically adept patients with brittle diabetes, hypoglycemia unawareness, diabetic pregnancy, or who use pumps might benefit.Current evidence of continuous glucose monitoring (CGM) on health outcome in patients with diabetes is critically reviewed. No data are available on chronic complications or mortality. Therefore, surrogate endpoints need to be investigated, particularly HbA1c, number of hypo- and hyperglycemic episodes, time within normal, high, or low glucose concentrations, glycemic variability, and quality of life.Randomized controlled trials (RCTs) using CGM in a retrospective way did not show metabolic improvement. In contrast, most RCTs applying real-time CGM showed a decrease in HbA1c, reduced glycemic variability, and a diminished number and length of hypo- and hyperglycemic events. Using accurate, real-time CGM devices improves quality of life by reducing the fear of unexpected hypoglycemic events. These beneficial effects were observed despite the fact that in most studies no clear treatment algorithm based on CGM results was provided to the patients. However, most trials were too short in duration, with a variable use of CGM, and were performed in small study samples.In conclusion, real-time CGM systems can improve metabolic control, reduce hypoglycemic episodes, and improve quality of life. Whether this holds true for longer time periods and in the majority of patients remains to be proven. In the long term, CGM might help to reduce chronic diabetes complications and perhaps also mortality, thereby reducing health care costs.     

Intermittent Use of Continuous Glucose Monitoring: Expanding the Clinical Value of CGM

In addition to the continuous use, the intermittent use of continuous glucose monitoring (CGM) is an application of CGM, expanding the typical medical use cases. There are a variety of reasons and occasions that speak in favor of using CGM only for a limited time. To date, these circumstances have not been sufficiently discussed. In this article, we define discontinuous or intermittent CGM use, provide reasons for using it, and expand on the benefits and possibilities of using CGM on a temporary basis. We aim to draw attention to this important topic in the discussion of CGM use and give examples for a different method of CGM use. As well, we would like to foster the allocation of CGM to the right patient groups and indications, especially in cases of limited resources. From a global point of view, intermittent CGM use is more likely to occur than continuous use, primarily for economic reasons.     

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.     

Clinical Overview of Continuous Glucose Monitoring

Continuous glucose monitoring (CGM) is now available from several companies in the United States for purchase or research studies. This article provides an overview of these devices and reviews the use of sensors for managing diabetes in “real time,” as well as the use of retrospective analysis of CGM results.     

Establishing a Continuous Glucose Monitoring Program

Real-time continuous glucose monitoring (RT-CGM) devices provide detailed information on glucose patterns and trends, and alarms that alert the patient to both hyper- and hypoglycemia. This technology can dramatically improve the day-to-day management of patients with diabetes and promises to be a major advance in diabetes care. The safe and effective use of RT-CGM in diabetes management rests on an understanding of several physiological as well as technological issues. This article outlines the key issues that should be addressed in the training curriculum for patients starting on RT-CGM: (1) physiologic lag between interstitial and blood glucose levels and the implications for device calibration, and interpretation and use of data in diabetes management; (2) practical considerations with the use of sensor alarms and caveats in the setting of alarm thresholds; and (3) potential risk for hypoglycemia related to excessive postprandial bolusing by RT-CGM users, and the practical implications for patient training.     

Current Trends in Continuous Glucose Monitoring

The market introduction of systems for continuous glucose monitoring (CGM) some 15 years ago did not immediately revolutionize the treatment of diabetes; however, for a given group of patients, it would almost be inconceivable nowadays to imagine life without CGM. One day the development of insulin pumps together with CGM could culminate in an artificial pancreas system. The performance of the glucose sensors used for glucose measurement in the interstitial fluid in the subcutaneous tissue and the algorithms employed to analyze these data have improved so much over the past decade that current CGM systems by far outperform those of the first generations. This commentary discusses a number of aspects about what we have learned since CGM systems entered the market and what current trends exist in their usage. Some of these are major hurdles facing a more widespread usage of CGM.     

Performance Evaluations of a Novel Continuous Glucose Monitor: Overview of a Novel Sensor for Continuous Glucose Monitoring

The core element of a continuous glucose monitoring (CGM) system is the glucose sensor, which should enable reliable CGM readings in the interstitial fluid in subcutaneous tissue for a period of several days. The aim of this article is to describe the layout and constituents of a novel glucose sensor and the rationale behind the measures that were used to optimize its performance. In order to achieve a stable glucose sensor signal, special attention was paid to the sensor materials and architecture, i.e., biocompatible coating of the sensor, limitation of glucose flux into the working electrode, low oxidation potential by use of manganese dioxide, and a tissue-averaging sensor design. A series of in vitro and in vivo evaluations showed that the sensor enables stable and accurate glucose sensing in the subcutaneous tissue for up to 7 days. Parallel measurements with four sensors in a single patient showed a close agreement between these sensors. In summary, this high-performance needle-type glucose sensor is well suited for CGM in patients with diabetes.     

Multicenter Evaluation Study Comparing a New Factory-Calibrated Real-Time Continuous Glucose Monitoring System to Existing Flash Glucose Monitoring System

This study reports a clinical evaluation of AiDEX CGM system featuring a 14-day sensor, real-time glucose monitoring and factory-calibration. A multicenter, prospective, masked clinical study was conducted at with a total of 120 participants. Each participant wore 4 studied sensors and had one in-clinic visit for venous blood reference tests. 40 out of the 120 participants wore additional Abbott Libre sensors and performed at least 7 capillary BG tests daily for additional reference and comparison. Continuous glucose error grid analysis (CG-EGA) showed that AiDEX and Abbott Libre had good agreement with venous blood glucose, with 98.69% and 98.96% accurate readings, respectively. Overall MARD of AiDEX CGM systems was 9.08% when compared to venous blood reference and 10.1% when compared to finger capillary BG reference.     

Sensors & Algorithms for Continuous Glucose Monitoring: Continuous Glucose Sensors: Continuing Questions about Clinical Accuracy

Continuous glucose sensors (CGS) offer the potential to greatly change the lives of people with diabetes. Even though two of these systems (Guardian RT, Medtronic, Northridge, CA, and DexCom STS, DexCom, San Diego, CA) have been approved by the Food and Drug Administration for use as adjuncts to self-blood glucose monitoring (SBGM), questions remain concerning the accuracy of these devices. When considering accuracy, two distinct approaches should be emphasized: (1) numerical and (2) clinical. Because CGS data are a process in time, each of these two approaches includes two subtypes of accuracy: point and rate. Conventional statistics such as correlation coefficients, mean and median relative absolute differences, and International Standards Organization criteria are measures of numerical point accuracy. A new measure, the R deviation, is introduced to quantify numerical rate accuracy. Error-grid analysis (Clarke EGA) measures clinical point accuracy. The only measure of both clinical point accuracy and rate accuracy is continuous glucose error-grid analysis. This analysis is a combination of two components, P-EGA measuring point accuracy and R-EGA measuring rate accuracy, which are designed to assess the information that distinguishes continuous glucose measurements from intermittent SBGM determinations. Further, a better understanding of the source of the error associated with time lag and its effect on CGS readings may improve sensor output. Finally, the reliability of the CGS sensors, in terms of initial calibration and long-term application, needs to be assessed carefully if current CGS systems are to be used as hypoglycemia monitors or incorporated in the future design of closed loop (artificial pancreas) systems.     

Continuous Subcutaneous Glucose Monitoring Shows a Close Correlation between Mean Glucose and Time Spent in Hyperglycemia and Hemoglobin A1c

Background

The Diabetes Control and Complications Trial and United Kingdom Prospective Diabetes Study highlighted hemoglobin A1c (HbA1c) as the main predictor of diabetic complications. Currently, diabetes is managed by frequent capillary spot glucose measurements, but continuous monitoring systems may have the capacity of improving diabetic control. The SCGM 1 system is microdialysis based and allows for monitoring of changes in interstitial fluid glucose levels every minute. The aim of this study was to evaluate the correlation between HbA1c and short-term glucose excursions in patients with type 1 diabetes.

Material and Methods

We investigated 91 patients with type 1 diabetes (mean ± standard deviation (SD); age 34 ± 10 years, body mass index 24.2 ± 4.1 kg/m2) with a duration of diabetes of 17 ± 11 years for 4.8 ± 0.4 days. The average HbA1c was 7.9 ± 1.4%. From the monitoring profiles we determined individual mean glucose, the SD of glucose, and the relative time spent in hyperglycemia and hypoglycemia calculated as the area under the curve (AUC) with arbitrary cutoffs of 180 and 70 mg/dl, respectively.

Results

Mean glucose, SD glucose, and hyperglycemic and hypoglycemic AUC all correlated with HbA1c, but with decreasing statistical power. In multiple linear regression analysis, mean glucose was the sole independent variable (r = 0.626, p < 0.0001). A close correlation between HbA1c and various measures of short-term hyperglycemic values was observed. There was a close correlation between mean glucose and SD glucose, pointing toward increased variability with increasing mean glucose.

Conclusions

Mean glucose generated after short-term continuous monitoring is the main predictor of HbA1c and reveals increased lability of glucose with increasing mean glucose and HbA1c.     

Continuous Glucose Monitoring and Clinical Trials

The use of glucose sensors during clinical trials seems like a great idea at first glance. Continuous glucose monitoring (CGM) should allow the gathering of more detailed information about metabolic control, without requiring much additional effort. In principle, CGM can reduce the duration of such studies and the number of participants required. The aim of this commentary is to highlight some of the reasons why, in practice, at least some of these hopes have not been realized. It is not only that a new technology requires extensive training of the study personnel; the practical handling of the devices and the time and effort required to download and analyze the data are often grossly underestimated initially. In addition, one must select the best endpoints for describing the level of metabolic control in view of the overwhelming amount of information provided by CGM. Several measures and endpoints were proposed as (potential) parameters that would be more meaningful than the standard parameters currently used to describe glucose profiles. Unfortunately, most of these proposed parameters have not, as yet, been proven to be more meaningful. Calibration is another critical aspect of using CGM that must be addressed. How this procedure is handled in practice has a profound impact on the quality of the glucose recordings. Finally, shall the current measurement results be displayed to the study participant or not? CGM can help prevent severe hypoglycemic episodes, but this can profoundly affect the study outcome in a manner that is unrelated to basic aim of the study (e.g., comparing medications that are designed to control glycemia). Therefore, the use of CGM in clinical trials requires much more careful consideration than was initially thought.     

Performance Evaluations of a Novel Continuous Glucose Monitor: Performance Evaluations of Continuous Glucose Monitoring Systems: Precision Absolute Relative Deviation Is Part of the Assessment

Background

Even though a Clinical and Laboratory Standards Institute proposal exists on the design of studies and performance criteria for continuous glucose monitoring (CGM) systems, it has not yet led to a consistent evaluation of different systems, as no consensus has been reached on the reference method to evaluate them or on acceptance levels. As a consequence, performance assessment of CGM systems tends to be inconclusive, and a comparison of the outcome of different studies is difficult.

Materials and Methods

Published information and available data (as presented in this issue of Journal of Diabetes Science and Technology by Freckmann and coauthors) are used to assess the suitability of several frequently used methods [International Organization for Standardization, continuous glucose error grid analysis, mean absolute relative deviation (MARD), precision absolute relative deviation (PARD)] when assessing performance of CGM systems in terms of accuracy and precision.

Results

The combined use of MARD and PARD seems to allow for better characterization of sensor performance. The use of different quantities for calibration and evaluation, e.g., capillary blood using a blood glucose (BG) meter versus venous blood using a laboratory measurement, introduces an additional error source. Using BG values measured in more or less large intervals as the only reference leads to a significant loss of information in comparison with the continuous sensor signal and possibly to an erroneous estimation of sensor performance during swings. Both can be improved using data from two identical CGM sensors worn by the same patient in parallel.

Conclusions

Evaluation of CGM performance studies should follow an identical study design, including sufficient swings in glycemia. At least a part of the study participants should wear two identical CGM sensors in parallel. All data available should be used for evaluation, both by MARD and PARD, a good PARD value being a precondition to trust a good MARD value. Results should be analyzed and presented separately for clinically different categories, e.g., hypoglycemia, exercise, or night and day.     

Continuous Glucose Monitoring: Evidence and Consensus Statement for Clinical Use

Continuous glucose monitoring (CGM) is an essential tool for modern diabetes therapy. Randomized controlled studies have provided evidence that hemoglobin A1c (HbA1c) results can be improved in patients with type 1 diabetes with elevated baseline HbA1c when using CGM frequently enough and that the frequency and duration of hypoglycemic events can be reduced in patients with satisfactory baseline HbA1c. The CGM group within the Working Group Diabetes Technology (AGDT) of the German Diabetes Association (DDG) has defined evidence-based indications for the practical use of CGM in this consensus statement related to hypoglycemia (frequent, severe, or nocturnal) or hypoglycemia unawareness, insufficient metabolic control despite use of all possible therapeutic options and patient compliance, pregnancy associated with inadequate blood glucose results, and the need for more than 10 blood glucose measurements per day. Contraindications and defined preconditions for the successful use of CGM should be considered.     

The Accuracy of a New Real-Time Continuous Glucose Monitoring Algorithm: An Analysis

In this issue of Journal of Diabetes Science and Technology, Keenan and colleagues used archival data from the STAR 1 clinical trial (Medtronic Diabetes) to support the claim that the new Veo™ calibration algorithm improves the accuracy of continuous glucose monitoring, particularly in the critical hypoglycemic range. Extensive data analyses are presented to support this claim; the results are convincing, and the estimated improvement in hypoglycemic detection from 55% for the standard calibration to 82% for the Veo is particularly impressive. We can therefore conclude that the Veo algorithm has the potential to improve the accuracy of hypoglycemia alarms and ultimately contribute to closed-loop control. However, the presented results should be interpreted cautiously because they are based on retrospective analysis and are heavily dependent on the distribution of blood glucose levels observed in a particular data set.     

Numerical Simulation of the Effect of Rate of Change of Glucose on Measurement Error of Continuous Glucose Monitors

Background

A 5-day in-patient study designed to assess the accuracy of the FreeStyle Navigator® Continuous Glucose Monitoring System revealed that the level of accuracy of the continuous sensor measurements was dependent on the rate of glucose change. When the absolute rate of change was less than 1 mg•dl⁻¹•min⁻¹ (75% of the time), the median absolute relative difference (ARD) was 8.5%, with 85% of all points falling within the A zone of the Clarke error grid. When the absolute rate of change was greater than 2 mg•dl⁻¹•min⁻¹ (8% of the time), the median ARD was 17.5%, with 59% of all points falling within the Clarke A zone.

Method

Numerical simulations were performed to investigate effects of the rate of change of glucose on sensor measurement error. This approach enabled physiologically relevant distributions of glucose values to be reordered to explore the effect of different glucose rate-of-change distributions on apparent sensor accuracy.

Results

The physiological lag between blood and interstitial fluid glucose levels is sufficient to account for the observed difference in sensor accuracy between periods of stable glucose and periods of rapidly changing glucose.

Conclusions

The role of physiological lag on the apparent decrease in sensor accuracy at high glucose rates of change has implications for clinical study design, regulatory review of continuous glucose sensors, and development of performance standards for this new technology. This work demonstrates the difficulty in comparing accuracy measures between different clinical studies and highlights the need for studies to include both relevant glucose distributions and relevant glucose rate-of-change distributions.     

A Look at the Current Reimbursement Environment for Continuous Glucose Monitoring (CGM): Understanding the Fundamentals

As reimbursement continues to decline for diabetes management technologies and practice overhead continues to rise, it is becoming increasingly important to understand the current reimbursement environment for new technological innovations in diabetes care. The current environment demands a strong partnership among providers, key stakeholder groups, and industry to advocate for favorable coding, coverage, and payment of these new advancements, in order to improve accessibility to the patient. This article describes trends in the current reimbursement environment for continuous glucose monitoring and provides recommendations on how to maneuver through the intricacies of coding and coverage related to this emerging category of glucose monitoring.     

Treatment Recommendations Following 3-Day Masked Continuous Glucose Monitoring (CGM) in Youth With Type 1 Diabetes

Glycemic control remains suboptimal in youth with type 1 diabetes. Retrospective continuous glucose monitoring (CGM) has demonstrated utility in fine-tuning diabetes management by detecting postprandial hyperglycemia and hypoglycemia. In this study, we explored the process of 3-day masked CGM use, subsequent treatment recommendations, and impact on A1c in a clinic-based sample of youth with type 1 diabetes. Over 2 years, 122 youth were referred for masked CGM. Patients/families completed a diary of blood glucose levels, insulin doses, food intake, and exercise during CGM use. A1c was assessed pre- and 2-3 months post-CGM. Treatment recommendations were formulated using data from CGM reports and diaries. Mean age was 14.3 ± 3.9 years, diabetes duration was 7.5 ± 4.7 years, and A1c was 8.5 ± 1.1% (69 ± 12 mmol/mol); 61% were pump-treated. Patients received an average of 3.1 ± 1.1 treatment recommendations following review of the CGM report. Most (80%) received reinforcement of the importance of preprandial bolusing; 37% received a recommendation regarding advanced insulin management (use of combination boluses/attend to active insulin). Receipt of the latter recommendation was related to A1c improvement ≥0.5% (OR: 4.0, P < .001). Masked CGM offers opportunities to guide advanced insulin management (by injection or pump), which may yield A1c improvements in youth with type 1 diabetes.     

Continuous Glucose Monitoring Awaits Its ��Killer App��

Continuous glucose monitoring (CGM) could drive a paradigm shift in diabetes care, but realization of this promise awaits a complementary shift in the way CGM data is used. The most exciting use for CGM is as the input for automated, closed-loop glucose control. Although first generation CGM devices leave much room for improvement, closed-loop control does not have to wait. Algorithms should target blood glucose levels above the normal range for safety in the setting of imperfect CGM measurements. If the mean glucose under closed-loop control is sufficiently close to the chosen target, hemoglobin A1c goals could be met while minimizing risk of hypoglycemia. CGM may also improve the care of intensive care unit patients treated with intensive insulin therapy and the large numbers of diabetic patients in general hospital wards.     

Sensors & Algorithms for Continuous Glucose Monitoring: Reconstruction of Glucose in Plasma from Interstitial Fluid Continuous Glucose Monitoring Data: Role of Sensor Calibration

Background

Continuous glucose monitoring (CGM) sensors measure glucose concentration in the interstitial fluid (ISF). Equilibration between plasma and ISF glucose is not instantaneous. Therefore, ISF and plasma glucose concentrations exhibit different dynamic patterns, particularly during rapid changes. The purpose of this work was to investigate how well plasma glucose can be reconstructed from ISF CGM data.

Methods

Six diabetic volunteers were monitored for 2 days using the TheraSense FreeStyle Navigator (Abbott Diabetes Care, Alameda, CA), a minimally invasive device that, on the basis of an initial calibration procedure (hereafter referred to as standard calibration), returns ISF glucose concentration. Simultaneously, plasma glucose concentration was also measured every 15 minutes. First we identified, in each subject, the linear time-invariant (LTI) two-compartment model of plasma-interstitium kinetics. Then, a nonparametric regularization deconvolution method was used to reconstruct plasma from ISF glucose.

Results

Deconvoluted profiles were always closer to plasma glucose than ISF ones. However, the quality of the reconstruction is unsatisfactory. Some visible discrepancies between average plasma and ISF time series suggest problems either in the applicability of the LTI model of plasma-interstitium kinetics to normal life conditions or in the standard calibration with which ISF glucose is determined from the sensor internal readings. Assuming that the LTI model of plasma-interstitium kinetics is correct, we focused on the influence of calibration and we employed a recently proposed method to recalibrate ISF data.

Conclusions

After the recalibration step, the relative error in reconstructing plasma glucose was reduced significantly. Results also demonstrate that further margins of improvement of plasma glucose reconstruction are possible by developing more sophisticated recalibration procedures.     

Continuous Glucose Monitoring and Trend Accuracy: News About a Trend Compass

Continuous glucose monitoring (CGM) devices are being increasingly used to monitor glycemia in people with diabetes. One advantage with CGM is the ability to monitor the trend of sensor glucose (SG) over time. However, there are few metrics available for assessing the trend accuracy of CGM devices. The aim of this study was to develop an easy to interpret tool for assessing trend accuracy of CGM data. SG data from CGM were compared to hourly blood glucose (BG) measurements and trend accuracy was quantified using the dot product. Trend accuracy results are displayed on the Trend Compass, which depicts trend accuracy as a function of BG. A trend performance table and Trend Index (TI) metric are also proposed. The Trend Compass was tested using simulated CGM data with varying levels of error and variability, as well as real clinical CGM data. The results show that the Trend Compass is an effective tool for differentiating good trend accuracy from poor trend accuracy, independent of glycemic variability. Furthermore, the real clinical data show that the Trend Compass assesses trend accuracy independent of point bias error. Finally, the importance of assessing trend accuracy as a function of BG level is highlighted in a case example of low and falling BG data, with corresponding rising SG data. This study developed a simple to use tool for quantifying trend accuracy. The resulting trend accuracy is easily interpreted on the Trend Compass plot, and if required, performance table and TI metric.