Literature DB >> 24876582

"Glucose-at-a-Glance": New Method to Visualize the Dynamics of Continuous Glucose Monitoring Data.

Teresa Henriques1, Medha N Munshi2, Alissa R Segal3, Madalena D Costa4, Ary L Goldberger5.   

Abstract

The standard continuous glucose monitoring (CGM) output provides multiple graphical and numerical summaries. A useful adjunct would be a visualization tool that facilitates immediate assessment of both long- and short-term variability. We developed an algorithm based on the mathematical method of delay maps to display CGM signals in which the glucose value at time ti is plotted against its value at time ti+1. The data points are then color-coded based on their frequency of occurrence (density). Examples of this new visualization tool, along with the accompanying time series, are presented for selected patients with type 2 diabetes and non-diabetic controls over the age of 70 years. The method reveals differences in the structure of the glucose variability between subjects with a similar range of glucose values. We also observe that patients with comparable hemoglobin A1c (HbA1c) values may have very different delay maps, consistent with marked differences in the dynamics of glucose control. These differences are not accounted by the amplitude of the fluctuations. Furthermore, the delay maps allow for rapid recognition of hypo- and hyperglycemic periods over the full duration of monitoring or any subinterval. The glucose-at-a-glance visualization tool, based on colorized delay maps, provides a way to quickly assess the complex data acquired by CGM systems. This method yields dynamical information not contained in single summary statistics, such as HbA1c values, and may also serve as the basis for developing novel metrics of glycemic control.
© 2014 Diabetes Technology Society.

Entities:  

Keywords:  Poincaré map; continuous glucose monitor; density delay maps; glucose; hemoglobin A1c

Year:  2014        PMID: 24876582      PMCID: PMC4455408          DOI: 10.1177/1932296814524095

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  7 in total

1.  Statistical tools to analyze continuous glucose monitor data.

Authors:  William Clarke; Boris Kovatchev
Journal:  Diabetes Technol Ther       Date:  2009-06       Impact factor: 6.118

2.  Poincaré plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans.

Authors:  P W Kamen; H Krum; A M Tonkin
Journal:  Clin Sci (Lond)       Date:  1996-08       Impact factor: 6.124

3.  Application of the Poincaré plot to heart rate variability: a new measure of functional status in heart failure.

Authors:  P W Kamen; A M Tonkin
Journal:  Aust N Z J Med       Date:  1995-02

4.  Frequent hypoglycemia among elderly patients with poor glycemic control.

Authors:  Medha N Munshi; Alissa R Segal; Emmy Suhl; Elizabeth Staum; Laura Desrochers; Adrianne Sternthal; Judy Giusti; Richard McCartney; Yishan Lee; Patricia Bonsignore; Katie Weinger
Journal:  Arch Intern Med       Date:  2011-02-28

5.  Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application.

Authors:  Boris P Kovatchev; William L Clarke; Marc Breton; Kenneth Brayman; Anthony McCall
Journal:  Diabetes Technol Ther       Date:  2005-12       Impact factor: 6.118

6.  Patterns of beat-to-beat heart rate variability in advanced heart failure.

Authors:  M A Woo; W G Stevenson; D K Moser; R B Trelease; R M Harper
Journal:  Am Heart J       Date:  1992-03       Impact factor: 4.749

7.  Assessment of barriers to improve diabetes management in older adults: a randomized controlled study.

Authors:  Medha N Munshi; Alissa R Segal; Emmy Suhl; Courtney Ryan; Adrianne Sternthal; Judy Giusti; Yishan Lee; Shane Fitzgerald; Elizabeth Staum; Patricia Bonsignor; Laura DesRochers; Richard McCartney; Katie Weinger
Journal:  Diabetes Care       Date:  2012-11-27       Impact factor: 19.112

  7 in total
  5 in total

1.  A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas.

Authors:  Irl B Hirsch; Andrew K Balo; Kevin Sayer; Arturo Garcia; Bruce A Buckingham; Thomas A Peyser
Journal:  Diabetes Technol Ther       Date:  2017-06       Impact factor: 6.118

2.  Remembrance of time series past: simple chromatic method for visualizing trends in biomedical signals.

Authors:  Anton Burykin; Sara Mariani; Teresa Henriques; Tiago F Silva; William T Schnettler; Madalena D Costa; Ary L Goldberger
Journal:  Physiol Meas       Date:  2015-05-27       Impact factor: 2.833

3.  Multiscale Poincaré plots for visualizing the structure of heartbeat time series.

Authors:  Teresa S Henriques; Sara Mariani; Anton Burykin; Filipa Rodrigues; Tiago F Silva; Ary L Goldberger
Journal:  BMC Med Inform Decis Mak       Date:  2016-02-09       Impact factor: 2.796

4.  Detrended Fluctuation Analysis in the prediction of type 2 diabetes mellitus in patients at risk: Model optimization and comparison with other metrics.

Authors:  Ana Colás; Luis Vigil; Borja Vargas; David Cuesta-Frau; Manuel Varela
Journal:  PLoS One       Date:  2019-12-18       Impact factor: 3.240

Review 5.  Introducing Patterns of Variability for Overcoming Compensatory Adaptation of the Immune System to Immunomodulatory Agents: A Novel Method for Improving Clinical Response to Anti-TNF Therapies.

Authors:  Tawfik Khoury; Yaron Ilan
Journal:  Front Immunol       Date:  2019-11-20       Impact factor: 7.561

  5 in total

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