Evaluation of an open access software for calculating glucose variability parameters of a continuous glucose monitoring system applied at pediatric intensive care unit

Abstract

BackgroundContinuous Glucose Monitoring (CGM) has become an increasingly investigated tool, especially with regards to monitoring of diabetic and critical care patients. The continuous glucose data allows the calculation of several glucose variability parameters, however, without specific application the interpretation of the results is time-consuming, utilizing extreme efforts. Our aim was to create an open access software [Glycemic Variability Analyzer Program (GVAP)], readily available to calculate the most common parameters of the glucose variability and to test its usability.MethodsThe GVAP was developed in MATLAB® 2010b environment. The calculated parameters were the following: average area above/below the target range (Avg. AUC-H/L); Percentage Spent Above/Below the Target Range (PATR/PBTR); Continuous Overall Net Glycemic Action (CONGA); Mean of Daily Differences (MODD); Mean Amplitude of Glycemic Excursions (MAGE). For verification purposes we selected 14 CGM curves of pediatric critical care patients. Medtronic® Guardian® Real-Time with Enlite® sensor was used. The reference values were obtained from Medtronic®’s own software for Avg. AUC-H/L and PATR/PBTR, from GlyCulator for MODD and CONGA, and using manual calculation for MAGE.ResultsThe Pearson and Spearman correlation coefficients were above 0.99 for all parameters. The initial execution took 30 minutes, for further analysis with the Windows® Standalone Application approximately 1 minute was needed.ConclusionsThe GVAP is a reliable open access program for analyzing different glycemic variability parameters, hence it could be a useful tool for the study of glycemic control among critically ill patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-015-0035-3) contains supplementary material, which is available to authorized users.

Effect of short‐term treatment with sitagliptin or glibenclamide on daily glucose fluctuation in drug‐naïve Japanese patients with type 2 diabetes mellitus

“…In order to eliminate such subjective factors in this study, MAGE calculations were conducted with a computer program. Several programs/algorithms to calculate MAGE are currently available, and we selected GVAP which discloses the algorithm for identification of peaks/nadirs greater than 1 SD of mean glucose . Further validation of such programmes/algorithms, as well as of the duration of CGM appropriate to capture a sufficient number of peaks/nadirs will lead to the development of more reliable and objective methods of calculating MAGE values.…”

“…During the 48 hours of glucose monitoring, data from 0:00 to 24:00 of the second day of hospitalization were used for investigating Mean Amplitude of Glycaemic Excursion (MAGE), Standard Deviation (SD) and the coefficient of variation (CV) of 24‐hour glucose level, incremental peak postprandial glucose level for each meal and 24‐hour mean glucose level. MAGE was calculated using open access software, the Glycaemic Variability Analyzer Program (GVAP), with permission of the author. The calculation was performed by a contract research organization (CRO), Mediscience Planning Inc. (Tokyo, Japan).…”

Intensive insulin therapy combined with metformin is associated with reduction in both glucose variability and nocturnal hypoglycaemia in patients with type 2 diabetes

“…Parameters representing glucose variability including mean blood glucose level (MBG), standard deviation (SD), mean amplitude of glycaemic excursions (MAGE), continuous overlapping net glycaemic action (CONGA), parameters representing hypoglycaemia frequency and amplitude, and the low blood glucose index (LBGI) were further calculated by using data obtained from CGM according to previously published methods …”

“…Parameters representing glucose variability including mean blood glucose level (MBG), standard deviation (SD), mean amplitude of glycaemic excursions (MAGE), continuous overlapping net glycaemic action (CONGA), parameters representing hypoglycaemia frequency and amplitude, and the low blood glucose index (LBGI) were further calculated by using data obtained from CGM according to previously published methods. [18][19][20] Frequency analyses of the nocturnal hypoglycaemia (defined as number of sensor monitored hypoglycaemia episode of all the patients/number of total sensor monitored glucose of all the patients) at time interval from 22:00 to 24:00 to 0:00 to 6:00 the next day were calculated, and nocturnal glucose data at the continuous 2 nights were used for final analysis. The diurnal hypoglycaemia at time interval from 6:00 to 22:00 was analysed separately.…”

Discrete Mean Amplitude of Glycemic Excursion (MAGE) Measurement on Diabetics with Spline Interpolation Method

“…Therefore, this research proposes a model of blood glucose observation discretely. Since MAGE can only be measured with continuous signals [5] [6] [7], the observed discrete data must be converted into continuous form. The measurement in this research is conducted to monitor the discrete level of blood glucose 7 times a day during 3 days of observation.…”