Comparisons of Fifth-, Sixth-, and Seventh-Generation Continuous Glucose Monitoring Systems.

Background: Between-system differences for continuous glucose monitoring (CGM) devices have important clinical consequences.

Purpose: Here we review attributes of Dexcom's fifth-, sixth-, and seventh-generation (G5, G6, and G7) CGM systems.

Methods: Accuracy metrics were derived from preapproval trials of the three systems and compared after propensity score adjustments were used to balance baseline demographic characteristics.

Accuracy and Safety of Dexcom G7 Continuous Glucose Monitoring in Adults with Diabetes.

We evaluated the accuracy and safety of a seventh generation (G7) Dexcom continuous glucose monitor (CGM) during 10.5 days of use in adults with diabetes. Adults with either type 1 or type 2 diabetes (on intensive insulin therapy or not) participated at 12 investigational sites in the United States.

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

Background: The perceived value and consistent use of continuous glucose monitoring (CGM) systems depends in part on their accuracy. We assessed the performance of a sixth-generation CGM system (Dexcom G6) in children and adolescents.

Methods: Forty-nine participants (6-17 years of age, mean ± SD of 13.

Resistance to Acetaminophen Interference in a Novel Continuous Glucose Monitoring System.

Acetaminophen (APAP) can cause erroneously high readings in real-time continuous glucose monitoring (rtCGM) systems. APAP-associated bias in an investigational rtCGM system (G6) was evaluated by taking the difference in glucose measurements between rtCGM and YSI from 1 hour before to 6 hours after a 1-g oral APAP dose in 66 subjects with type 1 or type 2 diabetes. The interference effect was defined as the average post-dose (30-90 minutes) bias minus the average baseline bias for each subject.

Application of Glycemic Variability Percentage: Implications for Continuous Glucose Monitor Utilization and Analysis of Artificial Pancreas Data.

Background: The problem of glycemic variability has been widely acknowledged in patients with diabetes with severe insulin deficiency. In a companion article, we proposed a novel metric, the glycemic variability percentage (GVP), for assessing glycemic variability that accounts for both the amplitude and frequency of glycemic fluctuations.

Method: We applied the new metric, the GVP, to a previously reported case of a subject using an earlier generation continuous glucose monitoring (CGM) device, in which successive periods of use were associated with an apparent decrease in glycemic variability.

Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data.

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.

Stability, Accuracy, and Risk Assessment of a Novel Subcutaneous Glucose Sensor.

Background: Users of continuous glucose monitoring (CGM) systems are concerned with the frequency of inserting and calibrating new sensors, with sensor accuracy and reliability throughout the sensor's functional life, and with the risks associated with inaccurate sensor readings.

Methods: A sensor for our next-generation CGM system was tested for accuracy by comparison with self-monitored blood glucose (SMBG) values throughout 10 days of wear. Fifty subjects (49 with type 1 diabetes, 1 with type 2 diabetes, 20 male, mean ± standard deviation [SD] age 32.

A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas.

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.