Glycemic Health Metric Using Log-Square Cost Function
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Solution Overview
Problem
Current methods for determining glycemic health in diabetic patients rely on contemporaneous blood glucose measurements, which do not adequately capture long-term glycemic control or the balance between short-term and long-term risks of hypoglycemia and hyperglycemia, limiting their effectiveness in guiding insulin therapy.
Innovation Solution
A method that computes a unidimensional metric representing glycemic health by applying a log-square cost or loss function to the mean and statistical dispersion of blood glucose concentration profiles, allowing for improved insulin infusion control and balancing short-term and long-term risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If contemporaneous blood glucose measurements are used to determine glycemic health, then immediate therapy decisions can be made, but long-term glycemic control assessment is inadequate
Solution Approach 1:
The patent transforms the single-dimension contemporaneous blood glucose measurement into a multi-dimensional assessment by computing a profile that includes mean blood glucose concentration and statistical dispersion (standard deviation). This dimensional expansion allows simultaneous evaluation of both immediate glucose levels and long-term glycemic control patterns, resolving the contradiction between rapid response capability and comprehensive assessment accuracy.
2Measurement precision
If hemoglobin A1c measurement is used to assess long-term glycemic health, then comprehensive glycemic control is captured, but immediate therapy adjustment capability is lost
Solution Approach 1:
The patent segments the glycemic health assessment into distinct components: mean blood glucose concentration representing long-term control (similar to A1c) and statistical dispersion representing variability and risk. This segmentation allows the system to provide comprehensive long-term assessment while maintaining the capability for immediate therapy adjustment by analyzing different aspects of glucose control separately and independently.
3Measurement precision
If only mean blood glucose concentration is used, then long-term glycemic control is assessed, but short-term variability and hypoglycemia risk are not captured
Solution Approach 1:
The patent merges multiple glucose control metrics into a unified profile: mean blood glucose concentration, standard deviation (statistical dispersion), and potentially other parameters. This combination preserves both long-term control information and short-term variability data, ensuring that neither aspect is lost and both can inform therapy decisions simultaneously.
4Loss of information
If only standard deviation is used to assess glycemic variability, then short-term risk is captured, but long-term glycemic control context is lost
Solution Approach 1:
The patent adds the dimension of mean blood glucose concentration to the standard deviation metric, creating a two-dimensional profile that contextualizes variability within the overall glycemic control picture. This dimensional approach ensures that short-term risk assessment through standard deviation is informed by long-term control context from the mean, preventing loss of either information type.
Data Source
AI summary
Disclosed are methods, apparatuses, etc. for determination and application of a metric for assessing a patient's glycemic health. In one particular implementation, a computed metric may be used to balance short-term and long-term risks associated with a particular therapy.