Discrepancies between methods of continuous glucose monitoring in key metrics of glucose control in children with type 1 diabetes

Michalak, Arkadiusz; Pagacz, Konrad; Młynarski, Wojciech; Szadkowska, Agnieszka; Fendler, Wojciech

doi:10.1111/pedi.12854

Cited by 12 publications

(12 citation statements)

References 29 publications

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“…Third, patient characteristics, such as education level and socioeconomic status, dietary habits, and physical exercise, that may affect glycemic control were not assessed. Fourth, there may be significant difference in the CGM-derived metrics, including TIR and TBR, between the assessment with real-time CGM and that with isCGM [36]. Therefore, such glycemic markers cannot be directly compared in the discussion.…”

Section: Discussion/conclusionmentioning

confidence: 99%

Significance of “Time below Range” as a Glycemic Marker Derived from Continuous Glucose Monitoring in Japanese Children and Adolescents with Type 1 Diabetes

Urakami¹,

Yoshida²,

Kuwabara³

et al. 2020

Horm Res Paediatr

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Introduction: We evaluated the frequencies of various glycemic markers derived from continuous glucose monitoring in Japanese children and adolescents with type 1 diabetes and assessed the significance of hypoglycemia duration. Methods: We enrolled 85 children and adolescents (36 boys and 49 girls) with type 1 diabetes who used FreeStyle® Libre in the present study. Frequencies of blood glucose levels as time within target range (TIR; 70–180 mg/dL), time below target range (TBR; <70 mg/dL), time below extreme hypoglycemia range (TBER; <54 mg/dL), and time above range (TAR; >180 mg/dL) were assessed during a 3-month study period. Furthermore, we evaluated the intraday frequencies of TBR and TBER. Results: The mean frequencies of TIR, TBR, and TAR were 52.7 ± 11.3%, 10.8 ± 5.4%, and 36.5 ± 10.8%, respectively, whereas the mean frequency of TBER was 1.1 ± 0.9% (0–3.0%); there was no clinical episode of severe hypoglycemia. The mean frequency of TBR was significantly greater in 0–6 h (16.9 ± 5.2%) than in 6–12 h (7.8 ± 2.9%) and 18–24 h (6.8 ± 4.8%; p < 0.01) time zones, respectively. Discussion/Conclusion: We found similar TIR and comparatively higher TBR frequencies, particularly during sleep, than those that were previously reported. Possible reasons for the higher frequency of TBR include differences in the quality of insulin treatment and diabetes care between the present study and the European studies. The utilization of advanced technologies, such as a predictive low-glucose suspend-function pump or closed-loop therapy, can reduce the frequency of TBR, with a consequent increase in TIR frequency and comprehensive improvement in glycemic control.

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Section: Discussion/conclusionmentioning

confidence: 99%

Significance of “Time below Range” as a Glycemic Marker Derived from Continuous Glucose Monitoring in Japanese Children and Adolescents with Type 1 Diabetes

Urakami¹,

Yoshida²,

Kuwabara³

et al. 2020

Horm Res Paediatr

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“…Therefore it is important to be able to rely on these parameters. However, it has already been shown that the time in glycemic ranges differs between CGM systems from different manufacturers worn in parallel and even between different sensors of the same CGM system [15,16].…”

Section: Discussionmentioning

confidence: 99%

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

Freckmann

Pleus

Schauer

et al. 2021

Exp Clin Endocrinol Diabetes

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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.

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“…Considering those devices had a greater data resolution of data transfer (5 minutes), it is encouraging to note that mean errors were comparable. Particularly given that flash glucose monitoring may obtain different estimates of important glycaemic variability compared to more traditional continuous monitoring devices (Michalak et al 2019).…”

Section: Main Findingsmentioning

confidence: 99%

Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis

Kingsnorth

Whelan

Orme

et al. 2021

Appl. Physiol. Nutr. Metab.

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Like many wearables, flash glucose monitoring relies on user compliance and is subject to missing data. As recent research is beginning to utilise glucose technologies as behaviour change tools, it is important to understand whether missing data is tolerable. Complete Freestyle Libre data files were amputed to remove 1-6 hours of data both at random and over mealtimes (breakfast, lunch and dinner). Absolute percent errors (MAPE) and intraclass correlation coefficients (ICC) were calculated to evaluate agreement and reliability. Thirty-two (91%) participants provided at least one complete day (24-hours) of data (age: 44.8±8.6 years, female: 18 (56%); mean fasting glucose: 5.0±0.6 mmol/L). Mean and CONGA (60 minutes) were robust to data loss (MAPE ≤3%). Larger errors were calculated for standard deviation, coefficient of variation (CV) and MAGE at increasing missingness (MAPE 2-10%, 2-9% and 4-18%, respectively). ICC decreased as missing data increased, with most indicating excellent reliability (>0.9) apart from certain MAGE ICC, which indicated good reliability (0.84-0.9). Researchers and clinicians should be aware of the potential for larger errors when reporting standard deviation, CV and MAGE at higher rates of data loss in nondiabetic populations. But where mean and CONGA are of interest, data loss is less of a concern. Novelty:  As research now utilises flash glucose monitoring as behavioural change tools in nondiabetic populations, it is important to consider the influence of missing data.  Glycaemic variability indices of mean and CONGA are robust to data loss, but standard deviation, CV and MAGE are influenced at higher rates of missingness.

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Discrepancies between methods of continuous glucose monitoring in key metrics of glucose control in children with type 1 diabetes

Cited by 12 publications

References 29 publications

Significance of “Time below Range” as a Glycemic Marker Derived from Continuous Glucose Monitoring in Japanese Children and Adolescents with Type 1 Diabetes

Significance of “Time below Range” as a Glycemic Marker Derived from Continuous Glucose Monitoring in Japanese Children and Adolescents with Type 1 Diabetes

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

Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis

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