Optimal Sampling Duration for Continuous Glucose Monitoring to Determine Long-Term Glycemic Control

Riddlesworth, Tonya D; Beck, Roy W.; Gal, Robin L.; Connor, Crystal G.; Bergenstal, Richard M.; Lee, Sooji; Willi, Steven M.

doi:10.1089/dia.2017.0455

Cited by 197 publications

(180 citation statements)

References 6 publications

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“…While GMI is generally suggested to be calculated from at least 10 days of sensor use, 27 a recent study indicates that 5 days of sensor data can provide an estimation of mean glucose concentration that is reasonably wellcorrelated with that calculated from a 3-month period of use. 39 The GMI was an average of 6.9%, 7.2%, and 7.5% based on ST data for adults, adolescents, and children, respectively, compared to an average of 6.9%, 6.8%, and 7.0% based on HCL data. While long-term studies are needed, these results, in combination with the decreased percentage of time in hypoglycemia and hyperglycemia and decreased coefficient of variation during HCL compared to ST, are encouraging and indicate that the Omnipod personalized MPC algorithm has the potential to improve overall glycemic control.…”

Section: Discussionmentioning

confidence: 87%

Safety and Performance of the Omnipod Hybrid Closed-Loop System in Adults, Adolescents, and Children with Type 1 Diabetes Over 5 Days Under Free-Living Conditions

Sherr

Buckingham

Forlenza

et al. 2020

Diabetes Technology & Therapeutics

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Background: The objective of this study was to assess the safety and performance of the Omnipod Ò personalized model predictive control (MPC) algorithm in adults, adolescents, and children aged ‡6 years with type 1 diabetes (T1D) under free-living conditions using an investigational device. Materials and Methods: A 96-h hybrid closed-loop (HCL) study was conducted in a supervised hotel/rental home setting following a 7-day outpatient standard therapy (ST) phase. Eligible participants were aged 6-65 years with A1C <10.0% using insulin pump therapy or multiple daily injections. Meals during HCL were unrestricted, with boluses administered per usual routine. There was daily physical activity. The primary endpoints were percentage of time with sensor glucose <70 and ‡250 mg/dL. Results: Participants were 11 adults, 10 adolescents, and 15 children aged (meanstandard deviation) 28.8 -7.9, 14.3 -1.3, and 9.9 -1.0 years, respectively. Percentage time ‡250 mg/dL during HCL was 4.5% -4.2%, 3.5% -5.0%, and 8.6% -8.8% per respective age group, a 1.6-, 3.4-, and 2.0-fold reduction compared to ST (P = 0.1, P = 0.02, and P = 0.03). Percentage time <70 mg/dL during HCL was 1.9% -1.3%, 2.5% -2.0%, and 2.2% -1.9%, a statistically significant decrease in adults when compared to ST (P = 0.005, P = 0.3, and P = 0.3). Percentage time 70-180 mg/dL increased during HCL compared to ST, reaching significance for adolescents and children: HCL 73.7% -7.5% vs. ST 68.0% -15.6% for adults (P = 0.08), HCL 79.0% -12.6% vs. ST 60.6% -13.4% for adolescents (P = 0.01), and HCL 69.2% -13.5% vs. ST 54.9% -12.9% for children (P = 0.003). Conclusions: The Omnipod personalized MPC algorithm was safe and performed well over 5 days and 4 nights of use by a cohort of participants ranging from youth aged ‡6 years to adults with T1D under supervised freeliving conditions with challenges, including daily physical activity and unrestricted meals.

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Section: Discussionmentioning

confidence: 87%

Safety and Performance of the Omnipod Hybrid Closed-Loop System in Adults, Adolescents, and Children with Type 1 Diabetes Over 5 Days Under Free-Living Conditions

Sherr

Buckingham

Forlenza

et al. 2020

Diabetes Technology & Therapeutics

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“…The comparability of CGM systems and the central laboratory measurements of HbA 1c levels were additional strengths of the present study. The choice to analyse participants with at least 14 days of CGM data was justified by separate studies showing that this amount of data provides a good estimate of glucose metrics for a 3‐month period; however, most participants evaluated in the present study provided more than twice this amount.…”

Section: Discussionmentioning

confidence: 99%

Associations between HbA_1c and continuous glucose monitoring‐derived glycaemic variables

et al. 2019

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Aims To identify clinically useful associations between HbA1c levels and various continuous glucose monitoring‐derived metrics. Methods We retrospectively analysed end‐of‐study HbA1c levels and >2 weeks of continuous glucose monitoring data collected from 530 adults with Type 1 diabetes or insulin‐requiring Type 2 diabetes during four randomized trials. Each trial lasted ≥24 weeks and provided central laboratory end‐of‐study HbA1c levels and continuous glucose monitoring data from the preceding 3 months. Participants were assigned to groups based on either HbA1c levels or continuous glucose monitoring‐derived glucose values. Results HbA1c was strongly correlated with mean glucose value (r=0.80), time spent with glucose values in the 3.9–10.0 mmol/l range (time in range; r=–0.75) and percentage of glucose values >13.9 mmol/l (r=0.72), but was weakly correlated with the percentage of glucose values <3.9 mmol/l (r=–0.39) or <3.0 mmol/l (r=–0.21). The median percentage of glucose values <3.0 mmol/l was <1.2% (<20 min/day) for all HbA1c‐based groups, but the median percentage of values >13.9 mmol/l varied from 2.5% (0.6 h/day) to 27.8% (6.7 h/day) in the lowest and highest HbA1c groups, respectively. More than 90% of participants with either <2% of glucose values >13.9 mmol/l, mean glucose <7.8 mmol/l, or time in range >80% had HbA1c levels ≤53 mmol/mol (≤7.0%). For participants with HbA1c ≥64 mmol/mol (≥8.0%), the median time in range was 44%, with 90% of participants having a time in range of <59%. Conclusions The associations shown in the present study suggest that continuous glucose monitoring‐derived metrics may help guide diabetes therapy intensification efforts in an HbA1c‐independent manner.

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“…8,9 Two studies showed that glucose patterns observed during a 14-day period of CGM are already representative of interstitial glucose patterns seen over a longer period of time (3 months). 10,11 Thus, short-term CGM use is relevant to uncover otherwise missed glycaemic excursion when assessed via self-monitoring of blood glucose (SMBG) and HbA1c only. Previous measures of diabetes management (HbA1c, glucometer data) are being challenged, as various CGMderived metrics become available (e.g.…”

Section: Introductionmentioning

confidence: 99%

A head‐to‐head comparison of personal and professional continuous glucose monitoring systems in people with type 1 diabetes: Hypoglycaemia remains the weak spot

Moser

Pandis

Aberer

et al. 2018

Diabetes Obesity Metabolism

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To compare the performance of a professional continuous glucose monitoring (proCGM) and a personal continuous glucose monitoring (persCGM) system worn in parallel under standardized conditions in individuals with type 1 diabetes (T1D), two CGM systems (iPro2 – proCGM; Minimed 640G – persCGM) worn in parallel using the same sensor (Enlite 2) were compared. Ten people with T1D were included in this single‐centre, open‐label study in which CGM performance was evaluated. The study consisted of a 24‐hours inpatient phase (meals, exercise, glycaemic challenges) and a 4‐day home phase. Analyses included fulfilment of ISO 15197:2013 criteria, mean absolute relative difference (MARD), Parkes Error Grid and Bland–Altman plots. During the inpatient stay, ISO 15197:2013 criteria fulfilment was 58.4% (proCGM) and 57.8% (persCGM). At home, the systems met ISO 15197:2013 criteria by 66.5% (proCGM) and 65.3% (persCGM). No difference of MARD in inpatient phase (19.1 ± 16.7% vs. 19.0 ± 19.6; P = 0.83) and home phase (18.6 ± 26.8% vs. 17.4 ± 21.3%, P = 0.87) was observed. All sensors performed less accurately during hypoglycaemia. ProCGM and persCGM showed similar performance during daytime and night‐time for the inpatient and the home phase. However, sensor performance was reduced during hypoglycaemia for both systems.

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Optimal Sampling Duration for Continuous Glucose Monitoring to Determine Long-Term Glycemic Control

Abstract: Fourteen days of CGM data provide a good estimation of glucose metrics for a 3-month period.

Cited by 197 publications

References 6 publications

Safety and Performance of the Omnipod Hybrid Closed-Loop System in Adults, Adolescents, and Children with Type 1 Diabetes Over 5 Days Under Free-Living Conditions

Safety and Performance of the Omnipod Hybrid Closed-Loop System in Adults, Adolescents, and Children with Type 1 Diabetes Over 5 Days Under Free-Living Conditions

Associations between HbA_1c and continuous glucose monitoring‐derived glycaemic variables

A head‐to‐head comparison of personal and professional continuous glucose monitoring systems in people with type 1 diabetes: Hypoglycaemia remains the weak spot

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Optimal Sampling Duration for Continuous Glucose Monitoring to Determine Long-Term Glycemic Control

Abstract: Fourteen days of CGM data provide a good estimation of glucose metrics for a 3-month period.

Cited by 197 publications

References 6 publications

Safety and Performance of the Omnipod Hybrid Closed-Loop System in Adults, Adolescents, and Children with Type 1 Diabetes Over 5 Days Under Free-Living Conditions

Safety and Performance of the Omnipod Hybrid Closed-Loop System in Adults, Adolescents, and Children with Type 1 Diabetes Over 5 Days Under Free-Living Conditions

Associations between HbA1c and continuous glucose monitoring‐derived glycaemic variables

A head‐to‐head comparison of personal and professional continuous glucose monitoring systems in people with type 1 diabetes: Hypoglycaemia remains the weak spot

Contact Info

Product

Resources

About

Associations between HbA_1c and continuous glucose monitoring‐derived glycaemic variables