"Glucose-at-a-Glance": New Method to Visualize the Dynamics of Continuous Glucose Monitoring Data. (March 2014)

• Record Type: Journal Article
• Authors: Henriques, Teresa; Munshi, Medha N.; Segal, Alissa R.; Costa, Madalena D.; Goldberger, Ary L.
• Is Part Of: Journal of diabetes science and technology. Volume 8:Number 2(2014:Mar.)
• Page Start: 299
• Page End: 306
• Publication Date: 2014-03
• Subjects: continuous glucose monitor -- density delay maps -- glucose -- hemoglobin A1c -- Poincaré map; Diabetes -- Periodicals; Medical technology -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462005
• DOI: 10.1177/1932296814524095
• Languages: English
• ISSNs: 1932-2968

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.