Automated Insulin Delivery Accounting for Meal Bolus Stacking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automated insulin delivery systems risk insulin stacking when they automatically increase insulin delivery in response to rising glucose levels due to a meal, followed by a user manually programming a meal bolus, leading to dangerously low blood glucose levels.
Innovation Solution
The system accounts for recent automated insulin increases when a meal bolus is programmed by adjusting the meal bolus amount or delivery timing to prevent insulin stacking, using a closed-loop algorithm that considers insulin-on-board and glucose levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system automatically increases insulin delivery based on CGM readings to counteract rising blood glucose, then blood glucose control is improved, but the risk of insulin stacking increases when users subsequently program meal boluses
Solution Approach 1:
The system implements feedback by monitoring CGM readings and automatically adjusting insulin delivery based on detected glucose trends. The processor continuously receives glucose level data, analyzes whether glucose is rising above target, and automatically increases basal insulin or delivers correction boluses accordingly. This closed-loop feedback mechanism improves blood glucose control while the system tracks these automatic deliveries to prevent subsequent stacking with manual meal boluses.
Solution Approach 2:
The system performs preliminary action by automatically delivering correction boluses or increasing basal insulin rates before the user has a chance to program a manual meal bolus. When the system detects rising glucose levels, it proactively increases insulin delivery in advance, which may occur before the user programs their intended meal bolus, thereby preventing the stacking scenario.
2Adaptability or versatility
If the system enables both automatic correction boluses and manual meal bolus programming, then user control and flexibility are improved, but the complexity of managing insulin deliveries increases
Solution Approach 1:
The system applies self-service by automatically managing correction insulin deliveries based on CGM feedback, freeing the user from manually calculating and programming correction boluses. The processor autonomously monitors glucose levels and delivers appropriate correction doses, while still allowing users to program meal boluses according to their dietary needs. This division of labor maintains user control over meal-related insulin while automating the correction function.
Solution Approach 2:
The system implements dynamics by allowing the insulin delivery regime to adapt automatically based on real-time glucose conditions. The processor dynamically adjusts basal insulin rates and correction bolus sizes according to current glucose levels and trends, while meal bolus parameters remain configurable by the user. This dynamic adaptation enables the system to handle both automatic and manual insulin delivery scenarios flexibly.
3Extent of automation
If the system delivers correction boluses in response to CGM readings, then automatic glucose management is improved, but the possibility of double dosing with delayed meal boluses increases
Solution Approach 1:
The system uses feedback to track the timing and amount of automatic correction boluses delivered, and uses this information when processing subsequent manual meal bolus requests. The processor maintains a record of recent automatic deliveries and uses this feedback to determine whether a user-programmed meal bolus would result in stacking, thereby preventing double dosing while preserving automatic glucose management.
Solution Approach 2:
The system performs preliminary action by delivering automatic correction boluses before the user programs delayed meal boluses. When glucose rises and triggers an automatic correction, the system acts proactively to lower glucose levels before the user has a chance to program their intended meal bolus, thereby preventing the double dosing scenario that would occur if the meal bolus were programmed first.
Data Source
AI summary
Disclosed herein are systems and methods for mitigating the risk of insulin stacking in automated insulin delivery systems. In AID systems configured to both automatically calculate insulin delivery based on glucose levels and receive manual programming of meal boluses configured to counteract carbohydrates in a meal, insulin stacking can result if the system automatically increases insulin delivery based on a rise in glucose levels in response to consumption of a meal and the user later programs a meal bolus for the meal. The risk of such double dosing is mitigated by the systems and methods disclosed herein by enabling the system to account for recent automated insulin increases when a meal bolus is programmed.