Improved Real-World Glycemic Control With Continuous Glucose Monitoring System Predictive Alerts
Open accessAbstract
Background: Most standalone real-time continuous glucose monitoring (RT-CGM) systems provide predictive low and high sensor glucose (SG) threshold alerts. The durations and risk of low and high SG excursions following Guardian™ Connect CGM system predictive threshold alerts were evaluated. Methods: Continuous glucose monitoring system data uploaded between January 2, 2017 and May 22, 2018 by 3133 individuals using multiple daily injections (MDIs) or continuous subcutaneous insulin infusion (CSII) therapy were deidentified and retrospectively analyzed. Glucose excursions were defined as SG values that went beyond a preset low or high SG threshold for ≥15 minutes. For a control group, thresholds were based on the median of the low SG threshold limit (70 mg/dL) and the high SG threshold limit (210 mg/dL) preset by all system users. During periods when alerts were not enabled, timestamps were identified when a predictive alert would have been triggered. The time before low horizon was 17.5 minutes and the time before high horizon was 15 minutes, of all users who enabled alerts. Excursions occurring after a low SG or high SG predictive alert were segmented into prevented, ≤20, 20-60, and >60 minutes. Results: Excursions were prevented after 59% and 39% of low and high SG predictive alerts, respectively. The risk of a low or high excursion occurring was 1.9 ( P < 0.001, 95% CI, 1.88-1.93) and 3.3 ( P < 0.001, 95% CI, 3.20-3.30) times greater, respectively, when alerts were not enabled. Conclusions: The predictive alerts of the RT-CGM system under study can help individuals living with diabetes prevent some real-world low and high SG excursions. This can be especially important for those unable to reach or maintain glycemic control with basic RT-CGM or CSII therapy.
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The most recent CGM sensors adopt simple prediction algorithms, such as linear extrapolation, to predict future interstitial glucose concentrations 15-30 min in advance in real-time and generate predictive alerts when a hypo/hyperglycemic event is foreseen.
“The most recent CGM sensors adopt very simple prediction algorithms, e.g., linear extrapolation, to predict future interstitial glucose concentrations (e.g., 15–30 min in advance) in real-time and generate predictive alerts when a hypo/hyperglycemic event is foreseen [49].”
From Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors (2020) · cites this paper as [49]
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- Authors
- Abraham, S. B., Arunachalam, S., Zhong, A., Agrawal, P., Cohen, O., & McMahon, C. M.
- Affiliations
- Medtronic (United States) (US)
- Medtronic (Switzerland) (CH)
- Corresponding author
- McMahon, Chantal M.
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2019-07-04
- Volume
- 15
- Issue
- 1
- Pages
- 91-97
- ISSN
- 1932-2968 (ISSN-L)
- PMC
- PMC7783013
- OpenAlex
- W2954444460
- Cited by
- 63
- References
- 49
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 9 · 2023: 6 · 2022: 9 · 2021: 10 · 2020: 5 · 2019: 1
- FWCI
- 5.07
- 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
- Medtronic
- 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, hypoglycemia, hyperglycemia, glycemic excursions, glycemic control, multiple daily insulin injections, insulin pump therapy, diabetes management
- MeSH terms
- Glycemic Control, Blood Glucose, Diabetes Mellitus, Type 1, Glucose, Humans, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Retrospective Studies, Blood Glucose Self-Monitoring
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