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Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor

Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLU...

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Autores principales: Biagi, Lyvia, Ramkissoon, Charrise M., Facchinetti, Andrea, Leal, Yenny, Vehi, Josep
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492301/
https://www.ncbi.nlm.nih.gov/pubmed/28604634
http://dx.doi.org/10.3390/s17061361
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author Biagi, Lyvia
Ramkissoon, Charrise M.
Facchinetti, Andrea
Leal, Yenny
Vehi, Josep
author_facet Biagi, Lyvia
Ramkissoon, Charrise M.
Facchinetti, Andrea
Leal, Yenny
Vehi, Josep
author_sort Biagi, Lyvia
collection PubMed
description Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLUS (7P), G4 PLATINUM (G4P), and advanced G4 for Artificial Pancreas studies (G4AP) systems. An enhanced methodology to a previously employed technique was utilized to dissect the sensor error into several components. The dataset used included 37 inpatient sessions in 10 subjects with type 1 diabetes (T1D), in which CGMs were worn in parallel and blood glucose (BG) samples were analyzed every 15 ± 5 min Calibration error and sensor drift of the ENL sensor was best described by a linear relationship related to the gain and offset. The mean time lag estimated by the model is 9.4 ± 6.5 min. The overall average mean absolute relative difference (MARD) of the ENL sensor was 11.68 ± 5.07% Calibration error had the highest contribution to total error in the ENL sensor. This was also reported in the 7P, G4P, and G4AP. The model of the ENL sensor error will be useful to test the in silico performance of CGM-based applications, i.e., the artificial pancreas, employing this kind of sensor.
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spelling pubmed-54923012017-07-03 Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor Biagi, Lyvia Ramkissoon, Charrise M. Facchinetti, Andrea Leal, Yenny Vehi, Josep Sensors (Basel) Article Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLUS (7P), G4 PLATINUM (G4P), and advanced G4 for Artificial Pancreas studies (G4AP) systems. An enhanced methodology to a previously employed technique was utilized to dissect the sensor error into several components. The dataset used included 37 inpatient sessions in 10 subjects with type 1 diabetes (T1D), in which CGMs were worn in parallel and blood glucose (BG) samples were analyzed every 15 ± 5 min Calibration error and sensor drift of the ENL sensor was best described by a linear relationship related to the gain and offset. The mean time lag estimated by the model is 9.4 ± 6.5 min. The overall average mean absolute relative difference (MARD) of the ENL sensor was 11.68 ± 5.07% Calibration error had the highest contribution to total error in the ENL sensor. This was also reported in the 7P, G4P, and G4AP. The model of the ENL sensor error will be useful to test the in silico performance of CGM-based applications, i.e., the artificial pancreas, employing this kind of sensor. MDPI 2017-06-12 /pmc/articles/PMC5492301/ /pubmed/28604634 http://dx.doi.org/10.3390/s17061361 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Biagi, Lyvia
Ramkissoon, Charrise M.
Facchinetti, Andrea
Leal, Yenny
Vehi, Josep
Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor
title Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor
title_full Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor
title_fullStr Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor
title_full_unstemmed Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor
title_short Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor
title_sort modeling the error of the medtronic paradigm veo enlite glucose sensor
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5492301/
https://www.ncbi.nlm.nih.gov/pubmed/28604634
http://dx.doi.org/10.3390/s17061361
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