Open-Loop Insulin Delivery Adjustment Using Closed-Loop History
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Solution Overview
Problem
Conventional medicament delivery devices operating in an open loop manner often result in sub-optimal dosage selection by users, leading to less than optimal glucose control and increased risk of hypoglycemia due to underestimated insulin needs.
Innovation Solution
A medicament delivery device that adjusts medicament delivery amounts in open loop mode based on an average delivery history from closed loop mode, using a processor to calculate and display suggested changes or automatically adjust dosages to align with closed loop averages, and optionally adjust insulin-to-carbohydrate ratio (ICR) and correction factor (CF) to improve glucose control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select medicament delivery dosages in open loop mode, then the device operates with user control and simplicity, but the dosages tend to be sub-optimal and underestimate insulin needs
Solution Approach 1:
The system uses feedback from closed loop mode performance to automatically adjust open loop mode dosages. The processor compares actual medicament delivery amounts from closed loop mode with programmed open loop mode amounts, calculates differences, and generates suggestions or automatic adjustments to improve dosage accuracy while maintaining user control options
Solution Approach 2:
The system performs self-adjustment by automatically calculating optimal dosage amounts based on historical closed loop data. The processor programmatically adjusts the open loop mode medicament delivery amounts without requiring manual user recalculation, allowing the device to serve itself in optimizing delivery parameters
2Reliability
If users increase medicament delivery dosages to compensate for underestimation, then glucose control may improve, but the risk of hypoglycemia increases
Solution Approach 1:
The feedback mechanism analyzes actual delivery amounts from closed loop mode to determine precise adjustment needs. By comparing historical data and calculating specific differences, the system provides targeted dosage adjustments that improve glucose control without excessive increases that would cause hypoglycemia
Solution Approach 2:
The system dynamically adjusts medicament delivery parameters based on analyzed data. The processor modifies delivery amounts, insulin-to-carbohydrate ratios, and correction factors to optimize glucose control while maintaining safety margins against hypoglycemia through data-driven parameter optimization
3Reliability
If the device operates in closed loop mode with feedback, then optimal glucose control is achieved, but the device complexity and automation increase
Solution Approach 1:
The system provides multi-functionality by enabling both open loop and closed loop modes within a single device architecture. The processor can operate in either mode or transition between them, allowing the device to serve multiple purposes and reducing the need for separate systems for different control approaches
Data Source
AI summary
The exemplary embodiments provide an automated approach for adjusting the medicament delivery rate to the user when operating in an open loop manner (“open mode”). The approach relies upon an insulin delivery history to the user make adjustments to the medicament delivery rate in the open mode. In particular, the exemplary embodiments may look at the medicament delivery history while the medicament delivery device is operating in a closed loop manner (“closed mode”) to determine how to adjust the open mode medicament delivery rate. It is presumed that in closed mode, the control system of the medicament device has gained knowledge over time about how to control the medicament delivery rate to produce good treatment outcomes for the user. The exemplary embodiments leverage this knowledge to adjust the open mode medicament delivery rates. Medicament bolus deliveries in open mode may also be adjusted in like fashion.