ArticleDiabetes technology & therapeutics2017
Evaluation of a New Digital Automated Glycemic Pattern Detection Tool.
Article in Diabetes technology & therapeutics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes.Nature medicine · 2020Trial
- Digital microbiology.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020Review
- A Review of Emerging Technologies in Diabetes Management for Multiple-Dose Insulin-Injecting Patients With Type 2 Diabetes Who Self-monitor Blood Glucose.The Journal of pharmacy technology : jPT : official publication of the Association of Pharmacy Technicians · 2019Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBlood glucose meters are reliable devices for data collection, providing electronic logs of historical data easier to interpret than handwritten logbooks. Automated tools to analyze these data are necessary to facilitate glucose pattern detection and support treatment adjustment. These tools emerge in a broad variety in a more or less nonevaluated manner. The aim of this study was to compare eDetecta, a new automated pattern detection tool, to nonautomated pattern analysis in terms of time investment, data interpretation, and clinical utility, with the overarching goal to identify early in development and implementation of tool areas of improvement and potential safety risks.
methodsMulticenter web-based evaluation in which 37 endocrinologists were asked to assess glycemic patterns of 4 real reports (2 continuous subcutaneous insulin infusion [CSII] and 2 multiple daily injection [MDI]). Endocrinologist and eDetecta analyses were compared on time spent to analyze each report and agreement on the presence or absence of defined patterns.
resultseDetecta module markedly reduced the time taken to analyze each case on the basis of the emminens eConecta reports (CSII: 18 min; MDI: 12.5), compared to the automatic eDetecta analysis. Agreement between endocrinologists and eDetecta varied depending on the patterns, with high level of agreement in patterns of glycemic variability. Further analysis of low level of agreement led to identifying areas where algorithms used could be improved to optimize trend pattern identification.
conclusioneDetecta was a useful tool for glycemic pattern detection, helping clinicians to reduce time required to review emminens eConecta glycemic reports. No safety risks were identified during the study.
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Registered trials
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