Infusion Device Meal Bolus Adjustment via Predictive Glucose Modeling
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Solution Overview
Problem
Current insulin infusion systems face challenges in effectively managing postprandial glucose excursions due to manual bolus initiation, which can lead to risks of hypoglycemic events, as they fail to account for preceding automated insulin deliveries and variations in carbohydrate consumption and insulin response.
Innovation Solution
A processor-implemented method that uses physiological modeling to predict future glucose levels, adjusting the initial bolus amount using a golden ratio-based search to ensure the predicted glucose level remains above a threshold, thereby minimizing the risk of postprandial hypoglycemia by accounting for both manual and automated insulin deliveries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual bolus initiation is used to prevent postprandial glucose excursions, then glucose control is improved, but the risk of hypoglycemic events increases due to failure to account for preceding automated insulin deliveries
Solution Approach 1:
The system performs preliminary action by predicting future glucose levels and determining optimal bolus amounts before the actual bolus is administered. The processor calculates predicted glucose trajectories accounting for preceding automated insulin deliveries, then adjusts the manual bolus amount proactively to prevent both hyperglycemia and hypoglycemia, rather than reacting after glucose levels deviate
Solution Approach 2:
The system implements feedback by continuously monitoring glucose levels and using this information to adjust bolus recommendations. The processor compares predicted glucose levels against target ranges and modifies the suggested bolus amount accordingly, creating a closed-loop control system that adapts to the patient's actual physiological response and preceding insulin deliveries
2Measurement precision
If physiological modeling with golden ratio-based search is implemented to optimize bolus adjustments, then prediction accuracy for glucose levels is improved, but device complexity increases
Solution Approach 1:
The system replaces complex manual calculations and iterative adjustments with a processor-based physiological modeling approach. The processor automatically performs golden ratio-based search algorithms to determine optimal bolus amounts, substituting what would otherwise require complex mechanical or manual computational systems with electronic processing that achieves higher precision
Solution Approach 2:
The system changes parameters dynamically by adjusting bolus amounts based on predicted glucose trajectories. The golden ratio-based search method systematically varies the bolus parameter to find the optimal value that keeps predicted glucose levels within target ranges, allowing precise control without requiring the user to understand or manually adjust multiple complex parameters
Data Source
AI summary
Techniques disclosed herein relate to infusion devices and related meal bolus adjustment methods. In some embodiments, the techniques may involve determining an initial bolus amount. The techniques may further involve predicting a value for a first physiological condition based at least in part on the initial bolus amount. The techniques may further involve when the predicted value for the first physiological condition violates a threshold: identifying an adjusted bolus amount that results in the predicted value for the first physiological condition satisfying the threshold, and causing delivery of the adjusted bolus amount.