Background: High levels of glycemic variability are still observed in most patients with diabetes with severe insulin deficiency. Glycemic variability may be an important risk factor for acute and chronic complications. Despite its clinical importance, there is no consensus on the optimum method for characterizing glycemic variability.Method: We developed a simple new metric, the glycemic variability percentage (GVP), to assess glycemic variability by analyzing the length of the continuous glucose monitoring (CGM) temporal trace normalized to the duration under evaluation. The GVP is similar to other recently proposed glycemic variability metrics, the distance traveled, and the mean absolute glucose (MAG) change. We compared results from distance traveled, MAG, GVP, standard deviation (SD), and coefficient of variation (CV) applied to simulated CGM traces accentuating the difference between amplitude and frequency of oscillations. The GVP metric was also applied to data from clinical studies for the Dexcom G4 Platinum CGM in subjects without diabetes, with type 2 diabetes, and with type 1 diabetes (adults, adolescents, and children).Results: In contrast to other metrics, such as CV and SD, the distance traveled, MAG, and GVP all captured both the amplitude and frequency of glucose oscillations. The GVP metric was also able to differentiate between diabetic and nondiabetic subjects and between subjects with diabetes with low, moderate, and high glycemic variability based on interquartile analysis.Conclusion: A new metric for the assessment of glycemic variability has been shown to capture glycemic variability due to fluctuations in both the amplitude and frequency of glucose given by CGM data.
Input from continuous glucose monitors (CGMs) is a critical component of artificial pancreas (AP) systems, but CGM performance issues continue to limit progress in AP research. While G4 PLATINUM has been integrated into AP systems around the world and used in many successful AP controller feasibility studies, this system was designed to address the needs of ambulatory CGM users as an adjunctive use system. Dexcom and the University of Padova have developed an advanced CGM, called G4AP, to specifically address the heightened performance requirements for future AP studies. The G4AP employs the same sensor and transmitter as the G4 PLATINUM but contains updated denoising and calibration algorithms for improved accuracy and reliability. These algorithms were applied to raw data from an existing G4 PLATINUM clinical study using a simulated prospective procedure. The results show that mean absolute relative difference (MARD) compared with venous plasma glucose was improved from 13.2% with the G4 PLATINUM to 11.7% with the G4AP. Accuracy improvements were seen over all days of sensor wear and across the plasma glucose range (40-400 mg/dl). The greatest improvements occurred in the low glucose range (40-80 mg/dl), in euglycemia (80-120 mg/dl), and on the first day of sensor use. The percentage of sensors with a MARD <15% increased from 69% to 80%. Metrics proposed by the AP research community for addressing specific AP requirements were also computed. The G4AP consistently exhibited improved sensor performance compared with the G4 PLATINUM. These improvements are expected to enable further advances in AP research.
Background: The potential clinical benefits of continuous glucose monitoring (CGM) have been recognized for many years, but CGM is used by a small fraction of patients with diabetes. One obstacle to greater use of the technology is the lack of simplified tools for assessing glycemic control from CGM data without complicated visual displays of data. Methods: We developed a simple new metric, the personal glycemic state (PGS), to assess glycemic control solely from continuous glucose monitoring data. PGS is a composite index that assesses four domains of glycemic control: mean glucose, glycemic variability, time in range and frequency and severity of hypoglycemia. The metric was applied to data from six clinical studies for the G4 Platinum continuous glucose monitoring system (Dexcom, San Diego, CA). The PGS was also applied to data from a study of artificial pancreas comparing results from open loop and closed loop in adolescents and in adults. Results: The new metric for glycemic control, PGS, was able to characterize the quality of glycemic control in a wide range of study subjects with various mean glucose, minimal, moderate, and excessive glycemic variability and subjects on open loop versus closed loop control. Conclusion: A new composite metric for the assessment of glycemic control based on CGM data has been defined for use in assessing glycemic control in clinical practice and research settings. The new metric may help rapidly identify problems in glycemic control and may assist with optimizing diabetes therapy during timeconstrained physician office visits.
The GVP may be a clinically useful tool in characterizing the change in glycemic variability in subjects using CGM devices. Compared with metrics, such as the standard deviation, that focus solely on the amplitude of oscillations, the GVP, which measures both frequency and amplitude, may also be a more useful tool in assessing the true level of glycemic variability in artificial pancreas studies.
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.
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