Clinical accuracy of a continuous glucose monitoring system with an advanced algorithm

Bailey, Timothy S.; Chang, Anna; Christiansen, Mark

doi:10.1177/1932296814559746

Cited by 168Diabetes Management and ResearchDiabetes Management and EducationDiabetes Treatment and Management

Clinical accuracy of a continuous glucose monitoring system with an advanced algorithm

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Bailey, Timothy S., Chang, Anna, Christiansen, Mark·2015·Journal of Diabetes Science and Technology

DOI 10.1177/1932296814559746PMC PMC4604574

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Abstract

Background: We assessed the performance of a modified Dexcom G4 Platinum system with an advanced algorithm, in comparison with frequent venous samples measured on a laboratory reference (YSI) during a clinic session and in comparison to self-monitored blood glucose (SMBG) during home use. Methods: Fifty-one subjects with diabetes were enrolled in a prospective multicenter study. Subjects wore 1 sensor for 7-day use and participated in one 12-hour in-clinic session on day 1, 4, or 7 to collect YSI reference venous glucose every 15 minutes and capillary SMBG test every 30 minutes. Carbohydrate consumption and insulin dosing and timing were manipulated to obtain data in low and high glucose ranges. Results: In comparison with the laboratory reference method (n = 2,263) the system provided a mean and median absolute relative differences (ARD) of 9.0% and 7.0%, respectively. The mean absolute difference for CGM was 6.4 mg/dL when the YSIs were within hypoglycemia ranges (≤ 70 mg/dL). The percentage in the clinically accurate Clarke error grid A zone was 92.4% and in the benign error B zone was 7.1%. Majority of the sensors (73%) had an aggregated MARD in reference to YSI ≤ 10%. The MARD of CGM-SMBG for home use was 11.3%. Conclusions: The study showed that the point and rate accuracy, clinical accuracy, reliability, and consistency over the duration of wear and across glycemic ranges were superior to current commercial real-time CGM systems. The performance of this CGM is reaching that of a self-monitoring blood glucose meter in real use environment.

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CGM utilizes subcutaneous sensors for continuous monitoring of glucose levels with a high degree of accuracy.
“CGM utilizes subcutaneous sensors for continuous monitoring of glucose levels with a high degree of accuracy. 17 , 18”
From Insulin pump therapy is associated with higher rates of mild diabetic ketoacidosis compared to injection therapy: A 2‐year Swedish national survey of children and adolescents with type 1 diabetes (2022) · cites this paper as [17]
CGM and intermittently viewed CGM devices for at-home use have accuracy similar to capillary blood glucose monitors.
“continuous glucose monitoring (CGM) and intermittently viewed CGM devices for at-home patient use with minimally invasive devices have become available, which have similar accuracy to capillary blood glucose monitors.91”
From Type 1 diabetes (2018) · cites this paper as [91]
Errors in CGM increase the difficulty of predicting BG values (approximately 9% of mean absolute relative difference for best sensors).
“Errors in CGM also increase the difficulty of predicting BG values (approximately 9% of the mean absolute relative difference for the best sensors70).”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [70]

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Self-citation
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Authors: Bailey, T. S., Chang, A., & Christiansen, M.
Affiliations: AMCR Institute (US)
John Muir Health (US)
Diablo Clinical Research (US)
Corresponding author: Bailey, Timothy S.
ORCID: Bailey, Timothy S.
Venue: Journal of Diabetes Science and Technology
Publisher: SAGE Publications
Published: 2014-11-03
Volume: 9
Issue: 2
Pages: 209-214
ISSN: 1932-2968 (ISSN-L)
DOI: 10.1177/1932296814559746
PMC: PMC4604574
OpenAlex: W2165138941
Cited by: 168
References: 24
Citations by year: 2026: 7 · 2025: 7 · 2024: 9 · 2023: 7 · 2022: 7 · 2021: 14 · 2020: 15 · 2019: 23 · 2018: 21 · 2017: 30 · 2016: 18 · 2015: 10
FWCI: 15.54
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Journal of Diabetes Science and Technology (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: Dexcom
Topics: Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
Keywords: continuous glucose monitoring, diabetes mellitus, clinical accuracy, hypoglycemia, self-monitoring of blood glucose, Clarke error grid, mean absolute relative difference, sensor reliability, insulin dose
MeSH terms: Adult, Aged, Aged, 80 and over, Algorithms, Blood Glucose, Diabetes Mellitus, Female, Humans, Male, Middle Aged, Blood Glucose Self-Monitoring, Young Adult

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