Evaluation of a New Digital Automated Glycemic Pattern Detection Tool
Comellas, María José (Roche Diabetes Care Spain SL)
Albiñana, Emma (Vithas Hospital Internacional Medimar)
Artes, Maite (Adelphi Spain)
Corcoy i Pla, Rosa (Institut d'Investigació Biomèdica Sant Pau)
Fernández-García, Diego (Hospital Universitario Virgen de la Victoria (Màlaga, Andalusia))
García-Alemán, Jorge (Hospital Universitario Virgen de la Victoria (Màlaga, Andalusia))
García-Cuartero, Beatriz (Hospital Universitario Ramón y Cajal (Madrid))
González, Cintia (Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina)
Rivero, María Teresa (Complejo Hospitalario Universitario de Ourense)
Casamira, Núria (Roche Diabetes Care Spain SL)
Weissmann, Jörg (Roche Diabetes Care Deutschland GmbH)
Universitat Autònoma de Barcelona

Data:	2017
Resum:	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.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Blood glucose ; Pattern analysis ; Diabetes ; Pattern management ; Automated tool
Publicat a:	Diabetes Technology and Therapeutics, Vol. 19 (november 2017) , p. 633-640, ISSN 1557-8593

DOI: 10.1089/dia.2017.0180
PMID: 29091477

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