Correction Bolus Control Using Predicted Glucose Thresholds
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Solution Overview
Problem
Existing insulin infusion pumps face challenges in accurately delivering correction boluses to maintain blood glucose levels within a safe range, particularly due to variations in patient activity and insulin response, risking postprandial glucose excursions.
Innovation Solution
A predictive model is used to forecast future glucose levels based on current measurements, preceding insulin deliveries, and carbohydrate intake, adjusting correction bolus amounts to maintain glucose levels above a hypoglycemic threshold, thereby reducing the risk of hypoglycemic events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If correction bolus is delivered to lower high blood glucose, then blood glucose control is improved, but risk of hypoglycemia increases
Solution Approach 1:
The system performs preliminary actions by predicting future glucose levels before delivering the correction bolus. The predictive model forecasts glucose levels at future time points based on current glucose level, insulin sensitivity factor, and projected basal insulin deliveries, allowing the system to prevent hypoglycemia before it occurs by adjusting or withholding the correction bolus accordingly
Solution Approach 2:
The system implements feedback by using the predictive model to continuously evaluate the expected outcome of delivering a correction bolus. The predicted future glucose levels are fed back into the decision-making process to determine whether to deliver, withhold, or adjust the correction bolus amount, creating a closed-loop control system that balances glucose lowering with hypoglycemia prevention
2Extent of automation
If automatic correction bolus is delivered during automatic basal insulin delivery, then glucose regulation is improved, but complexity of control increases
Solution Approach 1:
The system merges the correction bolus control function with the existing automatic basal insulin delivery system. By integrating the predictive model and correction bolus logic into the same control algorithm that manages basal insulin, the system achieves higher automation without proportionally increasing complexity, as both functions share common computational resources and decision-making frameworks
Data Source
AI summary
Techniques disclosed herein relate to safe correction boluses. In some embodiments, the techniques involve predicting a future glucose level that would result from delivery of a correction bolus. The techniques may also involve comparing the future glucose level to a threshold level for hypoglycemia. The techniques may further involve causing delivery of the correction bolus when the future glucose level is above the threshold level for hypoglycemia.


