Closed-Loop Insulin Algorithm Meal Response
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Solution Overview
Problem
Current closed-loop insulin delivery systems experience undesirable glucose level oscillations when increasing the basal rate in response to meal announcements, as the timing and amount of carbohydrates consumed are unknown.
Innovation Solution
Modifying the closed-loop algorithm to increase the insulin on board (IOB) tolerance during eating periods by activating a pre-meal scale, which adjusts the IOB set point based on continuous glucose monitor values, rather than simply increasing the basal rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the basal rate is increased in response to a meal announcement, then the system anticipates increased blood glucose levels, but this causes undesirable oscillations in glucose levels because the timing and amount of carbohydrates are unknown
Solution Approach 1:
The system dynamically adjusts the insulin delivery profile by modifying the pre-meal insulin delivery rate based on the meal carbohydrate amount and insulin-to-carb ratio, rather than using a fixed basal rate increase. This dynamic adjustment allows the system to adapt to varying meal compositions and timing, preventing glucose oscillations while maintaining effective meal coverage.
Solution Approach 2:
The system changes the insulin delivery parameters by calculating a specific pre-meal insulin delivery rate based on the anticipated meal carbohydrate content and the user's insulin-to-carb ratio. This parameter change enables precise control of insulin delivery in response to meal announcements, avoiding the oscillations caused by generic basal rate increases.
2Adaptability or versatility
If the closed-loop algorithm increases basal rate in response to meal announcements, then it prepares for carbohydrate consumption, but this simple approach cannot account for unknown timing and amount of carbohydrates
Solution Approach 1:
The system performs preliminary action by delivering insulin at a modified pre-meal rate before the actual meal consumption occurs. This preliminary insulin delivery is calculated based on the anticipated meal size and composition, allowing the system to proactively prepare for glucose elevation without waiting for actual carbohydrate intake data.
Solution Approach 2:
The system uses feedback from continuous glucose monitoring to adjust and refine the pre-meal insulin delivery. By monitoring actual glucose responses and comparing them with predicted responses, the system can learn and adapt to individual user patterns, improving the precision of carbohydrate intake prediction over time.
Data Source
AI summary
Disclosed herein are apparatuses and methods that account for meal announcements in closed loop insulin delivery systems. Rather than simply increasing an insulin delivery rate in response to the meal announcement, the closed loop algorithm can be modified to increase the insulin on board tolerance during eating periods. This approach utilizes the stability of the cascaded loop in the closed loop algorithm to prevent the oscillations in glucose levels that can occur by simply increasing the basal rate.