ArticleBiomedical engineering online2015
Evaluation of an open access software for calculating glucose variability parameters of a continuous glucose monitoring system applied at pediatric intensive care unit.
Article in Biomedical engineering online, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 29 citations in OpenAlex.
- Early Glucose Variability Is Associated with Mortality in Critically Ill Children: A Retrospective Pediatric Intensive Care Study.Nutrients · 2026Observational
- Glucostats: an efficient Python library for glucose time series feature extraction and visual analysis.BMC bioinformatics · 2025Article
- AGATA: A Toolbox for Automated Glucose Data Analysis.Journal of diabetes science and technology · 2024Article
- State of the Art in Pediatric Anesthesia: A Narrative Review about the Use of Preoperative Time.Journal of personalized medicine · 2024Review
- Glucose Fluctuation and Severe Internal Carotid Artery Siphon Stenosis in Type 2 Diabetes Patients.Nutrients · 2021Observational
- A Prospective Longitudinal Study on the Relationship Between Glucose Fluctuation and Cognitive Function in Type 2 Diabetes: PROPOSAL Study Protocol.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2020Article
- Updated Software for Automated Assessment of Glucose Variability and Quality of Glycemic Control in Diabetes.Diabetes technology & therapeutics · 2020Article
- GLU: a software package for analysing continuously measured glucose levels in epidemiology.International journal of epidemiology · 2020Article
- Hypoglycemia in blood glucose level in type 2 diabetic Japanese patients by continuous glucose monitoring.Diabetology & metabolic syndrome · 2019Article
- Twenty-four-hour variations in blood glucose level in Japanese type 2 diabetes patients based on continuous glucose monitoring.Journal of diabetes investigation · 2018Article
- Glycemic Variability Percentage: A Novel Method for Assessing Glycemic Variability from Continuous Glucose Monitor Data.Diabetes technology & therapeutics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.