Updated Software for Automated Assessment of Glucose Variability and Quality of Glycemic Control in Diabetes | JoVE Visualize

Updated Software for Automated Assessment of Glucose Variability and Quality of Glycemic Control in Diabetes

Vanessa Moscardó¹, Marga Giménez², Nick Oliver³

¹Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain.

Diabetes Technology & Therapeutics|March 21, 2020

View abstract on PubMed

Summary

EasyGV software now offers validated tools for assessing glycemic variability in diabetes management. The updated version includes new metrics and robust calculations for continuous glucose monitoring data analysis.

Area of Science:

Endocrinology
Medical Informatics
Biomedical Engineering

Background:

Glycemic variability is a critical factor in diabetes management, requiring accurate assessment using continuous glucose monitoring (CGM) data.
Existing tools like EasyGV software automate the analysis of glycemic variability metrics and quality of control indices.
Robust and repeatable calculations are essential for reliable diabetes management strategies.

Purpose of the Study:

To implement and validate novel glycemic variability assessment methods within the EasyGV software.
To enhance EasyGV's capabilities by adding new metrics for comprehensive diabetes control analysis.
To ensure the accuracy and reliability of EasyGV's calculations against established reference implementations.

Main Methods:

The study utilized validation data from the JDRF CGM study for testing.
New methods including personal glycemic state, index of glycemic control, times in ranges, and glycemic variability percentage were implemented and validated.
Reference calculations were derived from MATLAB implementations, serving as the gold standard for comparison.

Main Results:

The updated EasyGV software (v10) demonstrated high accuracy, with Pearson correlation coefficients above 0.98 for most metrics.
A correlation coefficient of r=0.87 was observed for mean amplitude of glycemic excursion due to EasyGV's fuzzy logic approach.
Bland-Altman plots confirmed the successful validation of the software's new implementations.

Conclusions:

The freely available EasyGV software v10 is a validated and robust tool for analyzing diverse glycemic variability and control metrics.
The enhancements provide clinicians and researchers with improved capabilities for diabetes management and continuous glucose monitoring data interpretation.
EasyGV v10 supports more comprehensive and accurate assessment of glycemic control, aiding in personalized diabetes care.