Evaluation of a New Digital Automated Glycemic Pattern Detection Tool.
Diabetes Technol Ther
; 19(11): 633-640, 2017 11.
Article
em En
| MEDLINE
| ID: mdl-29091477
ABSTRACT
BACKGROUND:
Blood 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.METHODS:
Multicenter 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.RESULTS:
eDetecta 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.CONCLUSION:
eDetecta 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.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Glicemia
/
Diabetes Mellitus Tipo 1
Tipo de estudo:
Clinical_trials
/
Diagnostic_studies
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Evaluation_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article