Choice of Continuous Glucose Monitoring Systems May Affect Metrics: Clinically Relevant Differences in Times in Ranges

Author(s):  
Guido Freckmann ◽  
Stefan Pleus ◽  
Sebastian Schauer ◽  
Manuela Link ◽  
Nina Jendrike ◽  
...  

Abstract Background Continuous glucose monitoring-derived parameters are becoming increasingly important in the treatment of people with diabetes. The aim of this study was to assess whether these parameters, as calculated from different continuous glucose monitoring systems worn in parallel, are comparable. In addition, clinical relevance of differences was investigated. Methods A total of 24 subjects wore a FreeStyle Libre (A) and a Dexcom G5 (B) sensor in parallel for 7 days. Mean glucose, coefficient of variation, glucose management indicator and time spent in different glucose ranges were calculated for each system. Pairwise differences between the two different continuous glucose monitoring systems were computed for these metrics. Results On average, the two CGM systems indicated an identical time in range (67.9±10.2 vs. 67.9±11.5%) and a similar coefficient of variation; both categorized as unstable (38.1±5.9 vs. 36.0±4.8%). In contrast, the mean time spent below and above range, as well as the individual times spent below, in and above range differed substantially. System A indicated about twice the time spent below range than system B (7.7±7.2 vs. 3.8±2.7%, p=0.003). This could have led to different therapy recommendations in approximately half of the subjects. Discussion The differences in metrics found between the two continuous glucose monitoring systems may result in different therapy recommendations. In order to make adequate clinical decisions, measurement performance of CGM systems should be standardized and all available information, including the HbA1c, should be utilized.

2020 ◽  
pp. 193229682093182
Author(s):  
Stefan Pleus ◽  
Ulrike Kamecke ◽  
Delia Waldenmaier ◽  
Manuela Link ◽  
Eva Zschornack ◽  
...  

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.


Author(s):  
Bando Hiroshi

As to the development of treatment for diabetes, Continuous Glucose Monitoring (CGM) has been recently prevalent rapidly. By the analysis of real-time CGM, Ambulatory Glucose Profile (AGP) has been used. It includes time in range (TIR, 70-180 mg/dL), time above range (TAR, >181mg/dL), time below range (TBR, <69 mg/dL), Glycemic Variability (GV), Glucose Management Indicator (GMI), Glycemic variability, Coefficient Of Variation (CV%) and so on. TIR value indicating approximately 70% seems to correlate closely with the HbA1c level of 6.77.0%. Marked discordance of HbA1c values has been found between laboratory HbA1c and estimated HbA1c (eA1c) using GMI from CGM.


2020 ◽  
pp. 193229682097582
Author(s):  
Klavs Würgler Hansen ◽  
Bo Martin Bibby

Background: Glucose data from intermittently scanned continuous glucose monitoring (isCGM) is a combination of scanned and imported glucose values. The present knowledge of glycemic metrics originate mostly from glucose data from real-time CGM sampled every five minutes with a lack of information derived from isCGM. Methods: Glucose data obtained with isCGM and hemoglobin A1c (HbA1c) were obtained from 169 patients with type 1 diabetes. Sixty-one patients had two observations with an interval of more than three months. Results: The best regression line of HbA1c against mean glucose was observed from 60 days prior to HbA1c measurement as compared to 14, 30, and 90 days. The difference between HbA1c and estimated HbA1c (=glucose management indicator [GMI]) first observed correlated with the second observation (R2 0.61, P < .001). Time in range (TIR, glucose between 3.9 and 10 mmol/L) was significantly related to GMI (R2 0.87, P < .001). A TIR of 70% corresponded to a GMI of 6.8% (95% confidence interval, 6.3-7.4). The fraction of patients with the optimal combination of TIR >70% and time below range (TBR) <4% was 3.6%. The fraction of patients with TBR>4% was four times higher for those with high glycemic variability (coefficient of variation [CV] >36%) than for those with lower CV. Conclusion: The individual difference between HbA1c and GMI was reproducible. High glycemic variability was related to increased TBR. A combination of TIR and TBR is suggested as a new composite quality indicator.


Author(s):  
Melike Şahinol ◽  
Gülşah Başkavak

AbstractThe conventional treatment of Type 1 Diabetes (T1D) is especially demanding for children, both physically and psychologically (Iversen et al. Int J Qual Stud Health Well-being,13(1), 1487758, 2018). Continuous Glucose Monitoring Systems (CGM) are an important aid for children and their families in dealing with the disease. In their work, however, Şahinol and Başkavak (2020) point out that CGM carry the risk of viewing T1D as a technologically solvable problem instead of considering the disease as a whole. This is mainly creating confidence in technology due to CGM experiences while neglecting significant dietary measures and exercises needed to be integrated into daily routines. During the current pandemic, this problem seems to take on a whole new level. Based on two periods of in-depth interviews and observations conducted with 8 families with T1D children aged 6 to 14 living in Istanbul and Ankara (Turkey) from May to November 2019 and again from May to June 2020, we compare and focus on the experiences prior to and during the pandemic time. We argue that despite the possibility of technological regulation of the disease, the vulnerability of children is increased and, more than ever, depends on socio-bio-technical entanglements.


Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 68-LB
Author(s):  
IRL B. HIRSCH ◽  
GREGORY J. ROBERTS ◽  
JENNIFER JOSEPH ◽  
YELENA NABUTOVSKY ◽  
NAUNIHAL VIRDI ◽  
...  

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