Circadian Profile Modeling for Glucose Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional diabetes management is largely retrospective, relying on historical blood glucose data, which limits caregivers' ability to make timely and effective adjustments in medication dosing, often leading to unsatisfactory glycemic control and increased risk of hypoglycemia.
Innovation Solution
A portable glucometer implements a circadian profile database that organizes recent SMBG data to model expected blood glucose values and predict errors, allowing caregivers to make informed adjustments in medication dosing, including insulin and oral agents, to achieve target glycemic ranges while minimizing hypoglycemic risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional retrospective diabetes management using historical blood glucose data is used, then caregivers can evaluate patient condition, but timely and effective adjustments in medication dosing cannot be made
Solution Approach 1:
The system performs preliminary analysis of blood glucose patterns and predicts future glycemic trends before medication dosing decisions are needed. By modeling circadian profiles and forecasting future blood glucose levels, the system prepares dosing recommendations in advance, enabling timely medication adjustments that proactively maintain glycemic control rather than reactively responding to historical data.
2Reliability
If frequent SMBG testing is performed to improve glycemic control, then more data is available for management decisions, but patient burden and testing frequency requirements increase
Solution Approach 1:
The system enables self-service diabetes management by empowering patients to perform fewer SMBG tests while the automated circadian profile modeling and prediction algorithms continuously analyze the available data. The intelligent system compensates for reduced input frequency by extracting maximum insights from limited measurements, maintaining high glycemic control accuracy without requiring intensive patient testing routines.
3Reliability
If medication dosing is increased to achieve target glycemic ranges, then glycemic control improves, but risk of hypoglycemia increases
Solution Approach 1:
The system implements continuous feedback through circadian profile modeling that monitors blood glucose patterns and predicts future levels. By comparing predicted values against target ranges and hypoglycemia thresholds, the system dynamically adjusts medication dosing recommendations to achieve glycemic targets while preventing hypoglycemia. The feedback loop ensures dosing decisions account for both upward pressure to reach targets and downward pressure to avoid dangerous lows.
4Productivity
If caregivers make medication adjustments based on historical data, then treatment decisions can be made, but confidence in achieving desired glycemic control without hypoglycemia is reduced
Solution Approach 1:
The system replaces manual, experience-based medication adjustment decisions with an automated intelligent system that uses circadian profile modeling and predictive algorithms. This substitution transforms subjective caregiver judgment into objective, data-driven predictions with quantified confidence levels, enabling faster and more reliable dosing decisions that consistently achieve glycemic targets while minimizing hypoglycemia risk.
Data Source
AI summary
A blood glucose meter and computer-implemented method for improving glucose management through modeling of circadian profiles is provided. For each daily meal period, two sets of pre- and post-meal period data are collected into a circadian profile and stored on a glucose meter, including a level of blood glucose of a diabetic patient and a dosage of diabetes medication. A model of predicted blood glucose for the patient is created from the blood glucose levels in each record as expected blood glucose values and predicted errors and visualized in a log-normal distribution. Target ranges for blood glucose at each meal period are determined and superimposed over the expected blood glucose values. Pharmacodynamics of the medication are obtained. An incremental change in dosing of the medication is propagated over a model day and the expected blood glucose values and their predicted errors are adjusted in response to the incremental dosing change.