Toward Calibration-Free Continuous Glucose Monitoring Sensors: Bayesian Calibration Approach Applied to Next-Generation Dexcom Technology

Calibration algorithms for continuous glucose monitoring systems based on interstitial fluid sensing

Continuous glucose monitoring (CGM) is of great importance to the treatment and prevention of diabetes. As a proven commercial technology, electrochemical glucose sensor based on interstitial fluid (ISF) sensing has high sensitivity and wide detection range. Therefore, it has good promotion prospects in noninvasive or minimally-invasive CGM system. However, since there are concentration differences and time lag between glucose in plasma and ISF, the accuracy of this type of sensors are still limited. Typical calibration algorithms rely on simple linear regression which do not account for the variability of the sensitivity of sensors. To enhance the accuracy and stability of CGM based on ISF, optimization of calibration algorithm for sensors is indispensable. While there have been considerable researches on improving calibration algorithms for CGM, they have still received less attention. This article reviews the problem of typical calibration and presents the outstanding calibration algorithms in recent years. Finally, combined with existing research and emerging sensing technologies, this paper makes an outlook on the future calibration algorithms for CGM sensors.

Minimally invasive electrochemical continuous glucose monitoring sensors: Recent progress and perspective

Diabetes and its complications are seriously threatening the health and well-being of hundreds of millions of people. Glucose levels are essential indicators of the health conditions of diabetics. Over the past decade, concerted efforts in various fields have led to significant advances in glucose monitoring technology. In particular, the rapid development of continuous glucose monitoring (CGM) based on electrochemical sensing principles has great potential to overcome the limitations of self-monitoring blood glucose (SMBG) in continuously tracking glucose trends, evaluating diabetes treatment options, and improving the quality of life of diabetics. However, the applications of minimally invasive electrochemical CGM sensors are still limited owing to the following aspects: i) invasiveness, ii) short lifespan, iii) biocompatibility, and iv) calibration and prediction. In recent years, the performance of minimally invasive electrochemical CGM systems (CGMSs) has been significantly improved owing to breakthrough developments in new materials and key technologies. In this review, we summarize the history of commercial CGMSs, the development of sensing principles, and the research progress of minimally invasive electrochemical CGM sensors in reducing the invasiveness of implanted probes, maintaining enzyme activity, and improving the biocompatibility of the sensor interface. In addition, this review also introduces calibration algorithms and prediction algorithms applied to CGMSs and describes the application of machine learning algorithms for glucose prediction.

Technology in the management of type 2 diabetes: Present status and future prospects

The growing incidence of type 2 diabetes (T2D) is a significant health concern, representing 90% of diabetes cases worldwide. As the disease progresses, resultant insulin deficiency and hyperglycaemia necessitates insulin therapy in many cases. It has been recognized that a significant number of people who have a clinical requirement for insulin therapy, as well as their healthcare professionals, are reluctant to intensify treatment with insulin due to fear of hypoglycaemia, poor understanding of treatment regimens or lack of engagement, and are therefore at higher risk of developing complications from poor glycaemic control. Over the past decade, the rise of diabetes technologies, including dosing advisors, continuous glucose monitoring systems, insulin pumps and automated insulin delivery systems, has led to great improvements in the therapies available, particularly to those requiring insulin. Although the focus has largely been on delivering these therapies to the type 1 diabetes population, it is becoming increasingly recognized that people with T2D face similar challenges to achieve recommended glycaemic standards and also have the potential to benefit from these advances. In this review, we discuss diabetes technologies that are currently available for people with T2D and the evidence supporting their use, as well as future prospects. We conclude that there is a clinical need to extend the use of these technologies to the T2D population to curb the consequences of suboptimal disease management in this group.

Temperature-Alternated Electrochemical Aptamer-Based Biosensor for Calibration-Free and Sensitive Molecular Measurements in an Unprocessed Actual Sample

Frequently calibrating electrochemical biosensors (ECBs) to obtain acceptable accuracy can be cumbersome for the users. Thus, the achievement of calibration-free operation would effectively lead to commercial applications for ECBs in the real world. Herein, we fabricated a temperature-alternated electrochemical aptamer-based (TAEAB) sensor, producing a cycle of "enhanced-responsive and ∼nonresponsive" state at rapidly alternated interface temperatures (5 and 30 °C, respectively). The ratio of peak currents collected at two temperatures overcomes sensor-to-sensor fabrication variations, obviating sensor calibration prior to use due to its good reproducibility. We then demonstrated the capability of TAEAB sensors for improved, sensitive, and calibration-free measurement of different targets within 7 min, which respectively achieved a detection limit of 0.5 μM procaine in undiluted urine and 1.0 μM adenosine triphosphate in undiluted serum. This generalizable approach ameliorates sensitivity without the complicated amplification step, thus simplifying the operation procedure and reducing the detection time, which will effectively improve the clinical utility of biosensors.

Continuous glucose monitoring for hypoglycaemia in children: Perspectives in 2020

Hypoglycaemia in children is a major risk factor for adverse neurodevelopment with rates as high as 50% in hyperinsulinaemic hypoglycaemia (HH). A key part of management relies upon timely identification and treatment of hypoglycaemia. The current standard of care for glucose monitoring is by infrequent fingerprick plasma glucose testing but this carries a high risk of missed hypoglycaemia identification. High-frequency Continuous Glucose Monitoring (CGM) offers an attractive alternative for glucose trend monitoring and glycaemic phenotyping but its utility remains largely unestablished in disorders of hypoglycaemia. Attempts to determine accuracy through correlation with plasma glucose measurements using conventional methods such as Mean Absolute Relative Difference (MARD) overestimate accuracy at hypoglycaemia. The inaccuracy of CGM in true hypoglycaemia is amplified by calibration algorithms that prioritize hyperglycaemia over hypoglycaemia with minimal objective evidence of efficacy in HH. Conversely, alternative algorithm design has significant potential for predicting hypoglycaemia to prevent neuroglycopaenia and consequent brain dysfunction in childhood disorders. Delays in the detection of hypoglycaemia, alarm fatigue, device calibration and current high cost are all barriers to the wider adoption of CGM in disorders of hypoglycaemia. However, machine learning, artificial intelligence and other computer-generated algorithms now offer significant potential for further improvement in CGM device technology and widespread application in childhood hypoglycaemia.

Comparison of Continuous Glucose Monitoring Accuracy Between Abdominal and Upper Arm Insertion Sites

Background: The aim was to compare the accuracy of the Dexcom^® G4 Platinum continuous glucose monitor (CGM) sensor inserted on the upper arm and the abdomen in adults. Methods: Fourteen adults with type 1 diabetes wore two CGMs, one placed on the upper arm and one placed on the abdomen. Three in-clinic visits of 5 h with YSI (2300 STAT, Yellow Springs Instrument) measurements as comparator were performed. Each visit was followed by 4 days with seven-point self-monitoring of blood glucose (SMBG) in free-living conditions. Accuracy analyses on the paired CGM-YSI and CGM-SMBG measurements of the two CGM sensors were performed. Results: Using YSI as comparator, the overall Mean Absolute Relative Difference (MARD) for the CGM_abd was 12.3% and CGM_arm was 12.0%. The percentage of the CGM measurements in zone A of Clarke error grid analysis for the CGM_abd was 85.6% and CGM_arm was 86.0%. The hypoglycemia sensitivity for the CGM_abd and CGM_arm was 69.3%. Using SMBG as comparator, the overall MARD for the CGM_abd was 12.5% and CGM_arm was 12.0%. The percentage of the CGM measurements in zone A for the CGM_abd was 84.1% and the CGM_arm was 85.0%. The hypoglycemia sensitivity for the CGM_abd was 60.0% and the CGM_arm was 71.1%. All the P-values from the comparisons between the accuracy of CGM_abd and CGM_arm were >0.05. Conclusion: The accuracy of a Dexcom G4 Platinum CGM sensor placed on the upper arm was not different from the accuracy of the sensor placed on the abdomen in adults with type 1 diabetes.

Factory-Calibrated Continuous Glucose Monitoring: How and Why It Works, and the Dangers of Reuse Beyond Approved Duration of Wear

Continuous glucose monitors (CGM) display real-time glucose values enabling greater glycemic awareness with reduced management burden. Factory-calibrated CGM systems allow for glycemic assessment without the pain and inconvenience of fingerstick glucose testing. Advances in sensor chemistry and CGM algorithms have enabled factory-calibrated systems to have greater accuracy than previous generations of CGM technology. Despite these advances many patients and providers are hesitant about the idea of removing fingerstick testing from their diabetes care. In this commentary, we aim to review the clinical trials on factory-calibrated CGM systems, present the algorithms which facilitate factory-calibrated CGMs to improve accuracy, discuss clinical use of factory-calibrated CGMs, and finally present two cases demonstrating the dangers of utilizing exploits in commercial systems to prolong sensor life.

Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications

The recent announcement of the production of new low-cost continuous glucose monitoring (CGM) sensors, the approval of marketed CGM sensors for making treatment decisions, and new reimbursement criteria have the potential to revolutionize CGM use. After briefly summarizing current CGM applications, we discuss how, in our opinion, these changes are expected to extend CGM utilization beyond diabetes patients, for example, to subjects with prediabetes or even healthy individuals. We also elaborate on how the integration of CGM data with other relevant information, for example, health records and other medical device/wearable sensor data, will contribute to creating a digital data ecosystem that will improve our understanding of the etiology and complications of diabetes and will facilitate the development of data analytics for personalized diabetes management and prevention.

Calibration of Minimally Invasive Continuous Glucose Monitoring Sensors: State-of-The-Art and Current Perspectives

Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide real-time measurement of subcutaneous glucose concentration. This can be of great help in the daily management of diabetes. Most of the commercially available CGM devices have a wire-based sensor, usually placed in the subcutaneous tissue, which measures a "raw" current signal via a glucose-oxidase electrochemical reaction. This electrical signal needs to be translated in real-time to glucose concentration through a calibration process. For such a scope, the first commercialized CGM sensors implemented simple linear regression techniques to fit reference glucose concentration measurements periodically collected by fingerprick. On the one hand, these simple linear techniques required several calibrations per day, with the consequent patient's discomfort. On the other, only a limited accuracy was achieved. This stimulated researchers to propose, over the last decade, more sophisticated algorithms to calibrate CGM sensors, resorting to suitable signal processing, modelling, and machine-learning techniques. This review paper will first contextualize and describe the calibration problem and its implementation in the first generation of CGM sensors, and then present the most recently-proposed calibration algorithms, with a perspective on how these new techniques can influence future CGM products in terms of accuracy improvement and calibration reduction.