ArticleJournal of diabetes science and technology2014
Evaluation of the utility of a glycemic pattern identification system.
Article in Journal of diabetes science and technology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Type I Diabetes Hypoglycemia Prevention Pilot Study
A Pilot Study of Nocturnal Hypoglycemia Prevention in Type 1 Diabetes Using the Vigilant Diabetes Management Companion
Who cites it
3 citing papers in PubMed.
- Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.Cell reports. Medicine · 2023Review
- Profiles of Intraday Glucose in Type 2 Diabetes and Their Association with Complications: An Analysis of Continuous Glucose Monitoring Data.Diabetes technology & therapeutics · 2021Article
- Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.Journal of medical Internet research · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the increasing prevalence of systems allowing automated, real-time transmission of blood glucose data there is a need for pattern recognition techniques that can inform of deleterious patterns in glycemic control when people test. We evaluated the utility of pattern identification with a novel pattern identification system named Vigilant™ and compared it to standard pattern identification methods in diabetes.
methodTo characterize the importance of an identified pattern we evaluated the relative risk of future hypoglycemic and hyperglycemic events in diurnal periods following identification of a pattern in a data set of 536 patients with diabetes. We evaluated events 2 days, 7 days, 30 days, and 61-90 days from pattern identification, across diabetes types and cohorts of glycemic control, and also compared the system to 6 pattern identification methods consisting of deleterious event counts and percentages over 5-, 14-, and 30-day windows.
resultsEpisodes of hypoglycemia, hyperglycemia, severe hypoglycemia, and severe hyperglycemia were 120%, 46%, 123%, and 76% more likely after pattern identification, respectively, compared to periods when no pattern was identified. The system was also significantly more predictive of deleterious events than other pattern identification methods evaluated, and was persistently predictive up to 3 months after pattern identification.
conclusionsThe system identified patterns that are significantly predictive of deleterious glycemic events, and more so relative to many pattern identification methods used in diabetes management today. Further study will inform how improved pattern identification can lead to improved glycemic control.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.