Hypoglycemia Reduction and Accuracy of Continuous Glucose Monitoring

Kovatchev, Boris

doi:10.1089/dia.2015.0144

Cited by 21 publications

(17 citation statements)

References 32 publications

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“…In addition, errors from calibration, loss of sensitivity, and random noise confound CGM data Garg et al 2009;Christiansen et al 2013) and has reached a point where CGM could be used as a replacement to traditional BG measurement without calibration with capillary blood several times a day . For example, a few years ago the Dexcom G4 Platinum and G5 CGMs used algorithmic signal processing to improve its accuracy and obtain replacement clearance from the Food and Drug Administration (FDA) (Facchinetti et al 2013;Peyser et al 2015;Kovatchev 2015). Most recently, the new version of these devices -Dexcom G6was approved by the FDA for use without fingerstick calibration.…”

Section: Continuous Glucose Monitoringmentioning

confidence: 99%

Automated closed-loop control of diabetes: the artificial pancreas

Kovatchev

2018

Bioelectron Med

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The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closedloop AP is well on its way to become the digital-age treatment of diabetes.

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Section: Continuous Glucose Monitoringmentioning

confidence: 99%

Automated closed-loop control of diabetes: the artificial pancreas

Kovatchev

2018

Bioelectron Med

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“…49 They concluded that a combination of MARD below 10% and a rate of large errors (beyond 20% of the reference) below approximately 12% should be sufficient for use of CGM systems as replacement for BG measurements; this result was further depicted by a curve linking MARD and the rate of large errors, later used to show system improvement by optimal signal treatment. 50 In Figure 4, we have represented the published curve with published CGM accuracy data from several articles 38,[50][51][52] as well as the data analyzed in this project. This figure clearly shows that the RCGM system's predicted accuracy is well beneath the theoretical (obtained in simulation) threshold for nonadjunctive use in the treatment of T1D.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator

Breton

Hinzmann²,

Campos-Náñez

et al. 2016

J Diabetes Sci Technol

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Background: Computer simulation has been shown over the past decade to be a powerful tool to study the impact of medical devices characteristics on clinical outcomes. Specifically, in type 1 diabetes (T1D), computer simulation platforms have all but replaced preclinical studies and are commonly used to study the impact of measurement errors on glycemia. Method:We use complex mathematical models to represent the characteristics of 3 continuous glucose monitoring systems using previously acquired data. Leveraging these models within the framework of the UVa/Padova T1D simulator, we study the impact of CGM errors in 6 simulation scenarios designed to generate a wide variety of glycemic conditions. Assessment of the simulated accuracy of each different CGM systems is performed using mean absolute relative deviation (MARD) and precision absolute relative deviation (PARD). We also quantify the capacity of each system to detect hypoglycemic events. Results:The simulated Roche CGM sensor prototype (RCGM) outperformed the 2 alternate systems (CGM-1 & CGM-2) in accuracy (MARD = 8% vs 11.4% vs 18%) and precision (PARD = 6.4% vs 9.4% vs 14.1%). These results held for all studied glucose and rate of change ranges. Moreover, it detected more than 90% of hypoglycemia, with a mean time lag less than 4 minutes (CGM-1: 86%/15 min, CGM-2: 57%/24 min). Conclusion:The RCGM system model led to strong performances in these simulation studies, with higher accuracy and precision than alternate systems. Its characteristics placed it firmly as a strong candidate for CGM based therapy, and should be confirmed in large clinical studies.

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“…From in silico data, it seems that within the sweet spot further improvement of sensor accuracy leads to minimal gains in terms of clinical outcome improvement. 20,21 We can, therefore, conclude that within these sensor accuracy limits, a CGM system should perform safely and efficiently, provided that CGM reliability and signal stability requirements are in place.…”

Section: -6mentioning

confidence: 97%

“…To date, through all these, overall accuracy of CGM systems has improved toward the proposed mark based on modeling to be accurate enough for making insulin dosing decisions in a MARD of less than 10%. 20,21 Point accuracy is closely related to the problem of egregious errors, prolonged periods of substantial inaccuracy, which would lead to substantial insulin dosing errors in the setting of a closed-loop system. Indeed, this was a problem with previous CGM generations.…”

Section: -6mentioning

confidence: 99%

Future of Automated Insulin Delivery Systems

Castle

DeVries

Kovatchev

2017

Diabetes Technology & Therapeutics

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Advances in continuous glucose monitoring (CGM) have brought on a paradigm shift in the management of type 1 diabetes. These advances have enabled the automation of insulin delivery, where an algorithm determines the insulin delivery rate in response to the CGM values. There are multiple automated insulin delivery (AID) systems in development. A system that automates basal insulin delivery has already received Food and Drug Administration approval, and more systems are likely to follow. As the field of AID matures, future systems may incorporate additional hormones and/or multiple inputs, such as activity level. All AID systems are impacted by CGM accuracy and future CGM devices must be shown to be sufficiently accurate to be safely incorporated into AID. In this article, we summarize recent achievements in AID development, with a special emphasis on CGM sensor performance, and discuss the future of AID systems from the point of view of their input-output characteristics, form factor, and adaptability.

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Hypoglycemia Reduction and Accuracy of Continuous Glucose Monitoring

Cited by 21 publications

References 32 publications

Automated closed-loop control of diabetes: the artificial pancreas

Automated closed-loop control of diabetes: the artificial pancreas

Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator

Future of Automated Insulin Delivery Systems

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