Improved individual and population-level HbA1c estimation using CGM data and patient characteristics

Machine learning and linear regression models using CGM and participant data reduced HbA1c estimation error by up to 26% compared to the GMI formula, and exhibit superior performance in estimating the median of HbA1c at the cohort level, potentially of value for remote clinical trials interrupted by...

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Detalles Bibliográficos
Autores principales: Grossman, Joshua, Ward, Andrew, Crandell, Jamie L., Prahalad, Priya, Maahs, David M., Scheinker, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316291/
https://www.ncbi.nlm.nih.gov/pubmed/34127370
http://dx.doi.org/10.1016/j.jdiacomp.2021.107950
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author Grossman, Joshua
Ward, Andrew
Crandell, Jamie L.
Prahalad, Priya
Maahs, David M.
Scheinker, David
author_facet Grossman, Joshua
Ward, Andrew
Crandell, Jamie L.
Prahalad, Priya
Maahs, David M.
Scheinker, David
author_sort Grossman, Joshua
collection PubMed
description Machine learning and linear regression models using CGM and participant data reduced HbA1c estimation error by up to 26% compared to the GMI formula, and exhibit superior performance in estimating the median of HbA1c at the cohort level, potentially of value for remote clinical trials interrupted by COVID-19.
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spelling pubmed-83162912022-06-13 Improved individual and population-level HbA1c estimation using CGM data and patient characteristics Grossman, Joshua Ward, Andrew Crandell, Jamie L. Prahalad, Priya Maahs, David M. Scheinker, David J Diabetes Complications Article Machine learning and linear regression models using CGM and participant data reduced HbA1c estimation error by up to 26% compared to the GMI formula, and exhibit superior performance in estimating the median of HbA1c at the cohort level, potentially of value for remote clinical trials interrupted by COVID-19. Elsevier Inc. 2021-08 2021-05-17 /pmc/articles/PMC8316291/ /pubmed/34127370 http://dx.doi.org/10.1016/j.jdiacomp.2021.107950 Text en © 2021 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Grossman, Joshua
Ward, Andrew
Crandell, Jamie L.
Prahalad, Priya
Maahs, David M.
Scheinker, David
Improved individual and population-level HbA1c estimation using CGM data and patient characteristics
title Improved individual and population-level HbA1c estimation using CGM data and patient characteristics
title_full Improved individual and population-level HbA1c estimation using CGM data and patient characteristics
title_fullStr Improved individual and population-level HbA1c estimation using CGM data and patient characteristics
title_full_unstemmed Improved individual and population-level HbA1c estimation using CGM data and patient characteristics
title_short Improved individual and population-level HbA1c estimation using CGM data and patient characteristics
title_sort improved individual and population-level hba1c estimation using cgm data and patient characteristics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316291/
https://www.ncbi.nlm.nih.gov/pubmed/34127370
http://dx.doi.org/10.1016/j.jdiacomp.2021.107950
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