scholarly journals Response to Comment on Bergenstal et al. Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care 2018;41:2275–2280

Diabetes Care ◽  
2018 ◽  
Vol 42 (2) ◽  
pp. e29-e30
Author(s):  
Richard M. Bergenstal ◽  
Roy W. Beck ◽  
Kelly L. Close ◽  
George Grunberger ◽  
David B. Sacks ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Diabetes Care ◽  
Glucose Monitoring ◽  
Glucose Management

scholarly journals Accuracy of flash glucose monitoring and continuous glucose monitoring technologies: Implications for clinical practice

2018 ◽  
Vol 15 (3) ◽  
pp. 175-184 ◽  
Author(s):  
Ramzi A Ajjan ◽  
Michael H Cummings ◽  
Peter Jennings ◽  
Lalantha Leelarathna ◽  
Gerry Rayman ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Diabetes Care ◽  
Interstitial Fluid ◽  
Glucose Monitoring ◽  
Relative Difference ◽  
Glucose Sensing ◽  
Sensor Data ◽  
Flash Glucose Monitoring ◽  
Fluid Sensor ◽  
Monitoring Technologies

Continuous glucose monitoring and flash glucose monitoring technologies measure glucose in the interstitial fluid and are increasingly used in diabetes care. Their accuracy, key to effective glycaemic management, is usually measured using the mean absolute relative difference of the interstitial fluid sensor compared to reference blood glucose readings. However, mean absolute relative difference is not standardised and has limitations. This review aims to provide a consensus opinion on assessing accuracy of interstitial fluid glucose sensing technologies. Mean absolute relative difference is influenced by glucose distribution and rate of change; hence, we express caution on the reliability of comparing mean absolute relative difference data from different study systems and conditions. We also review the pitfalls associated with mean absolute relative difference at different glucose levels and explore additional ways of assessing accuracy of interstitial fluid devices. Importantly, much data indicate that current practice of assessing accuracy of different systems based on individualised mean absolute relative difference results has limitations, which have potential clinical implications. Healthcare professionals must understand the factors that influence mean absolute relative difference as a metric for accuracy and look at additional assessments, such as consensus error grid analysis, when evaluating continuous glucose monitoring and flash glucose monitoring systems in diabetes care. This in turn will ensure that management decisions based on interstitial fluid sensor data are both effective and safe.

scholarly journals Clinical Need for Continuous Glucose Monitoring in the Hospital

2009 ◽  
Vol 3 (6) ◽  
pp. 1309-1318 ◽  
Author(s):  
Jeffrey I Joseph ◽  
Brian Hipszer ◽  
Boris Mraovic ◽  
Inna Chervoneva ◽  
Mark Joseph ◽  
...  
Keyword(s):  
Critical Care ◽  
Glycemic Control ◽  
Clinical Outcome ◽  
Continuous Glucose Monitoring ◽  
Patient Population ◽  
Glucose Monitoring ◽  
Glucose Measurement ◽  
Hospital Patient ◽  
Critical Care Units ◽  
Glucose Management

Automation and standardization of the glucose measurement process have the potential to greatly improve glycemic control, clinical outcome, and safety while reducing cost. The resources required to monitor glycemia in hospitalized patients have thus far limited the implementation of intensive glucose management to patients in critical care units. Numerous available and up-and-coming technologies are targeted for the hospital patient population. Advantages and limitations of these devices are discussed herewith in.

scholarly journals Continuous Glucose Monitoring Use in Clinical Trials for On-Market Diabetes Drugs

2021 ◽  
Author(s):  
Bradley Q. Fox ◽  
Peninah F. Benjamin ◽  
Ammara Aqeel ◽  
Emily Fitts ◽  
Spencer Flynn ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Clinical Practice ◽  
Continuous Glucose Monitoring ◽  
Diabetes Care ◽  
Glucose Monitoring ◽  
Published Data ◽  
Start Date ◽  
Treatment Of Diabetes ◽  
Pharmacologic Agents ◽  
Over Time

Despite the growing momentum behind a movement to augment adoption of continuous glucose monitoring (CGM) in clinical practice and investigation, to the best of our knowledge, there are no published data on the historical and recent use of CGM in clinical trials of pharmacologic agents used in the treatment of diabetes. We analyzed 2,032 clinical trials of 40 diabetes therapies currently on the market with a study start date between 1 January 2000 and 31 December 2019. According to ClinicalTrials.gov listings, 119 (5.9%) of these trials used CGM. CGM usage in clinical trials has increased over time, rising from <5% before 2005 to 12.5% in 2019. However, it is still low given its inclusion in the American Diabetes’s Association’s latest guidelines and known limitations of A1C for assessing ongoing diabetes care.

scholarly journals Diabetes Healthcare Professionals Use Multiple Continuous Glucose Monitoring Data Indicators to Assess Glucose Management

2019 ◽  
Vol 14 (2) ◽  
pp. 271-276 ◽  
Author(s):  
Tong Sheng ◽  
Reid Offringa ◽  
David Kerr ◽  
Mark Clements ◽  
Jerome Fischer ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Healthcare Professionals ◽  
Rank Order ◽  
Glucose Monitoring ◽  
Absolute Error ◽  
Multiple Data ◽  
Time In Range ◽  
Glucose Management ◽  
Diabetes Educators ◽  
Glycemic Management

Background: Continuous glucose monitoring (CGM) offers multiple data features that can be leveraged to assess glucose management. However, how diabetes healthcare professionals (HCPs) actually assess CGM data and the extent to which they agree in assessing glycemic management are not well understood. Methods: We asked HCPs to assess ten de-identified CGM datasets (each spanning seven days) and rank order each day by relative glycemic management (from “best” to “worst”). We also asked HCPs to endorse features of CGM data that were important in making such assessments. Results: In the study, 57 HCPs (29 endocrinologists; 28 diabetes educators) participated. Hypoglycemia and glycemic variance were endorsed by nearly all HCPs to be important (91% and 88%, respectively). Time in range and daily lows and highs were endorsed more frequently by educators (all Ps < .05). On average, HCPs endorsed 3.7 of eight data features. Overall, HCPs demonstrated agreement in ranking days by relative glycemic control (Kendall’s W = .52, P < .001). Rankings were similar between endocrinologists and educators ( R2 = .90, Cohen’s kappa = .95, mean absolute error = .4 [all Ps < .05]; Mann-Whitney U = 41, P = .53). Conclusions: Consensus in the endorsement of certain data features and agreement in assessing glycemic management were observed. While some practice-specific differences in feature endorsement were found, no differences between educators and endocrinologists were observed in assessing glycemic management. Overall, HCPs tended to consider CGM data holistically, in alignment with published recommendations, and made converging assessments regardless of practice.

scholarly journals Time in Specific Glucose Ranges, Glucose Management Indicator, and Glycemic Variability: Impact of Continuous Glucose Monitoring (CGM) System Model and Sensor on CGM Metrics

2020 ◽  
pp. 193229682093182
Author(s):  
Stefan Pleus ◽  
Ulrike Kamecke ◽  
Delia Waldenmaier ◽  
Manuela Link ◽  
Eva Zschornack ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
International Consensus ◽  
Freestyle Libre ◽  
Pairwise Differences ◽  
Assess Quality ◽  
Glucose Management ◽  
Post Hoc ◽  
The Impact

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.

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