Glucose Variability Grid Analysis for Diabetes Monitoring
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Solution Overview
Problem
Current methods for tracking blood glucose variability in diabetes, such as HbA1c, are inadequate as they do not fully capture postprandial glycemic excursions and are biased towards hyperglycemia, leading to poor prediction of severe hypoglycemia and hyperglycemia events.
Innovation Solution
A method and system using Variability Grid Analysis (VGA) that tracks blood glucose variability from self-monitoring and continuous glucose monitoring data, providing visual and quantitative analysis to identify risk zones for hypoglycemia and hyperglycemia, enabling early detection of extreme glycemic events and informing treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HbA1c is used to measure glycemic control, then average glycemic control is captured, but postprandial glycemic excursions and blood glucose variability are not reflected
Solution Approach 1:
The patent segments the continuous glucose profile into distinct components: average glycemic control (HbA1c) and glucose variability (MAGE, SD, CV). By dividing the glycemic information into separate measurable components, the system captures both average control and postprandial excursions independently, resolving the limitation of HbA1c alone
Solution Approach 2:
The patent adds a new dimension of measurement by introducing glucose variability metrics (MAGE, SD, CV) alongside the traditional HbA1c measure. This transforms the single-dimension HbA1c assessment into a multi-dimensional glycemic profile that includes both magnitude and variability of glucose fluctuations
2Measurement precision
If traditional variability measures (SD, MAGE) are used, then glycemic variability is quantified, but prediction of severe hypoglycemia and hyperglycemia events is poor
Solution Approach 1:
The patent changes the parameters used to assess glycemic variability from traditional statistical measures alone to a composite set including MAGE, SD, CV, and percent time in range. This parameter transformation improves the reliability of predicting extreme events by capturing both the magnitude and frequency of glucose excursions
3Measurement precision
If comprehensive glucose monitoring data is collected, then blood glucose variability is accurately tracked, but data complexity and analysis difficulty increase
Solution Approach 1:
The patent extracts key variability metrics (MAGE, SD, CV) from the comprehensive glucose monitoring data, separating the essential variability information from the raw data stream. This extraction simplifies the analysis by focusing on specific calculated parameters rather than analyzing all raw glucose readings
Data Source
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AI summary
An embodiment may be in the field of glycemic analysis and control. More specifically, an embodiment or approach may provide a novel method, system, and computer program for the visual and quantitative tracking of blood glucose variability in diabetes from self-monitoring blood glucose (SMBG) data and/or continuous glucose monitoring (CGM) data. More particularly, an embodiment or aspects thereof may use glucose measurements obtained from self-monitoring data and/or CGM data of an individual or a group of individuals to track and analyze blood glucose variability.