The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Clinical Accuracy of a Continuous Glucose Monitoring System With an Advanced Algorithm
Open accessAbstract
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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“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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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In the citing paper
“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]
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Cites
Evaluating clinical accuracy of systems for self-monitoring of blood glucose
Clarke, W. L., Cox, D., Gonder-Frederick, L. A., Carter, W., & Pohl, S. L. (1987) · Diabetes Care · doi:10.2337/diacare.10.5.622 · Cited by 1,435
Freckmann, G., Schmid, C., Baumstark, A., Pleus, S., Link, M., & Haug, C. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600510 · Cited by 237
“Turn it Off!”: Diabetes Device Alarm Fatigue Considerations for the Present and the Future
Shivers, J. P., Mackowiak, L., Anhalt, H., & Zisser, H. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700324 · Cited by 159
Christiansen, M. P., Bailey, T. S., Watkins, E., Liljenquist, D. R., Price, D. A., Nakamura, K., Boock, R. J., & Peyser, T. A. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0077 · Cited by 140
Self-citationA Comparative Effectiveness Analysis of Three Continuous Glucose Monitors
Damiano, E. R., McKeon, K., El-Khatib, F. H., Zheng, H., Nathan, D. M., & Russell, S. J. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814532203 · Cited by 139
Kropff, J., Bruttomesso, D., Doll, W., Farret, A., Galasso, S., Luijf, Y. M., Mader, J. K., Place, J., Boscari, F., Pieber, T. R., et al. (2014) · Diabetes Obesity and Metabolism · doi:10.1111/dom.12378 · Cited by 132
Šoupal, J., Škrha, J., Fajmon, M., Horová, E., Mráz, M., Škrha, J., & Prázný, M. (2014) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0205 · Cited by 122
Mazze, R. S., Strock, E. S., Borgman, S. K., Wesley, D., Stout, P. J., & Racchini, J. R. (2009) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0041 · Cited by 116
Accuracy and Acceptability of the 6-Day Enlite Continuous Subcutaneous Glucose Sensor
Bailey, T. S., Ahmann, A., Brazg, R., Christiansen, M. P., Garg, S. K., Watkins, E., Welsh, J. B., & Lee, S. W. (2014) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0222 · Cited by 106
Self-citationTack, C. J., Pohlmeier, H., Behnke, T., Schmid, V., Grenningloh, M., Forst, T. M., & Pfützner, A. (2011) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0170 · Cited by 91
Research works printed but not linked: 5
- Christiansen M, 2013, Diabetes Technol Ther, 5, 1
- Kropff J, 2014, Diabetes Obes Metab
- Brauker JH, 2007, Review of Endocrinology
- Pettus J, 2014, Diabetes Technol Ther, 16, 198
- Hessler DM, Paper presented at: 71st American Diabetes Association meeting
Cited by
Sorted by citations
DiMeglio, L. A., Evans-Molina, C., & Oram, R. A. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31320-5 · Cited by 1,561
Beck, R. W., Riddlesworth, T., Ruedy, K., Ahmann, A., Bergenstal, R., Haller, S., Kollman, C., Kruger, D., McGill, J. B., Polonsky, W., et al. (2017) · JAMA · doi:10.1001/jama.2016.19975 · Cited by 1,185
Electrochemical glucose sensors in diabetes management: an updated review (2010–2020)
Teymourian, H., Barfidokht, A., & Wang, J. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00304b · Cited by 915
Artificial intelligence for diabetes management and decision support: Literature review
Contreras, I., & Vehi, J. (2018) · Journal of Medical Internet Research · doi:10.2196/10775 · Cited by 560
DiMeglio, L. A., Acerini, C. L., Codner, E., Craig, M. E., Hofer, S. E., Pillay, K., & Maahs, D. M. (2018) · Pediatric Diabetes · doi:10.1111/pedi.12737 · Cited by 604
Metadata
- 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)
- 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
- 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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