Literature DB >> 24918271

Accuracy evaluation of a new real-time continuous glucose monitoring algorithm in hypoglycemia.

Zeinab Mahmoudi1, Morten Hasselstrøm Jensen, Mette Dencker Johansen, Toke Folke Christensen, Lise Tarnow, Jens Sandahl Christiansen, Ole Hejlesen.   

Abstract

BACKGROUND: The purpose of this study was to evaluate the performance of a new continuous glucose monitoring (CGM) calibration algorithm and to compare it with the Guardian(®) REAL-Time (RT) (Medtronic Diabetes, Northridge, CA) calibration algorithm in hypoglycemia. SUBJECTS AND METHODS: CGM data were obtained from 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. Data were obtained in two separate sessions using the Guardian RT CGM device. Data from the same CGM sensor were calibrated by two different algorithms: the Guardian RT algorithm and a new calibration algorithm. The accuracy of the two algorithms was compared using four performance metrics.
RESULTS: The median (mean) of absolute relative deviation in the whole range of plasma glucose was 20.2% (32.1%) for the Guardian RT calibration and 17.4% (25.9%) for the new calibration algorithm. The mean (SD) sample-based sensitivity for the hypoglycemic threshold of 70 mg/dL was 31% (33%) for the Guardian RT algorithm and 70% (33%) for the new algorithm. The mean (SD) sample-based specificity at the same hypoglycemic threshold was 95% (8%) for the Guardian RT algorithm and 90% (16%) for the new calibration algorithm. The sensitivity of the event-based hypoglycemia detection for the hypoglycemic threshold of 70 mg/dL was 61% for the Guardian RT calibration and 89% for the new calibration algorithm. Application of the new calibration caused one false-positive instance for the event-based hypoglycemia detection, whereas the Guardian RT caused no false-positive instances. The overestimation of plasma glucose by CGM was corrected from 33.2 mg/dL in the Guardian RT algorithm to 21.9 mg/dL in the new calibration algorithm.
CONCLUSIONS: The results suggest that the new algorithm may reduce the inaccuracy of Guardian RT CGM system within the hypoglycemic range; however, data from a larger number of patients are required to compare the clinical reliability of the two algorithms.

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Year:  2014        PMID: 24918271     DOI: 10.1089/dia.2014.0043

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  6 in total

1.  Benefits and Limitations of MARD as a Performance Parameter for Continuous Glucose Monitoring in the Interstitial Space.

Authors:  Lutz Heinemann; Michael Schoemaker; Günther Schmelzeisen-Redecker; Rolf Hinzmann; Adham Kassab; Guido Freckmann; Florian Reiterer; Luigi Del Re
Journal:  J Diabetes Sci Technol       Date:  2019-06-19

Review 2.  Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications.

Authors:  Simon Lebech Cichosz; Mette Dencker Johansen; Ole Hejlesen
Journal:  J Diabetes Sci Technol       Date:  2015-10-14

3.  Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction.

Authors:  Darpit Dave; Daniel J DeSalvo; Balakrishna Haridas; Siripoom McKay; Akhil Shenoy; Chester J Koh; Mark Lawley; Madhav Erraguntla
Journal:  J Diabetes Sci Technol       Date:  2020-06-01

4.  Effect of Continuous Glucose Monitoring Accuracy on Clinicians' Retrospective Decision Making in Diabetes: A Pilot Study.

Authors:  Zeinab Mahmoudi; Mette Dencker Johansen; Hanne Holdflod Nørgaard; Steen Andersen; Ulrik Pedersen-Bjergaard; Lise Tarnow; Jens Sandahl Christiansen; Ole Hejlesen
Journal:  J Diabetes Sci Technol       Date:  2015-06-08

5.  A Single-Use, Self-Powered, Paper-Based Sensor Patch for Detection of Exercise-Induced Hypoglycemia.

Authors:  Eunyoung Cho; Maedeh Mohammadifar; Seokheun Choi
Journal:  Micromachines (Basel)       Date:  2017-08-31       Impact factor: 2.891

Review 6.  Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments.

Authors:  Omar Diouri; Monika Cigler; Martina Vettoretti; Julia K Mader; Pratik Choudhary; Eric Renard
Journal:  Diabetes Metab Res Rev       Date:  2021-03-24       Impact factor: 4.876

  6 in total

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