Time Averaged Basal Rate Optimizer for Insulin Delivery
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Solution Overview
Problem
Current open-loop continuous subcutaneous insulin infusion (CSII) therapy faces challenges in establishing the correct pattern of basal rates over a day, as rates suitable for one day with high physical activity may be inadequate on another day with lower activity, and vice versa.
Innovation Solution
The method involves time averaging of optimized basal rates to initialize real-time basal rate optimization with a starting basal rate profile. This includes providing a programmed basal rate profile, periodically updating it based on retrospective analysis of continuous glucose sensor data, and optionally adjusting it in response to real-time glucose data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed basal rate profile is programmed for insulin therapy, then the insulin delivery schedule is simple to implement, but it cannot adapt to changing patient activity levels and health conditions
Solution Approach 1:
The system automatically adjusts basal insulin rates by analyzing continuous glucose sensor data and patient activity patterns without requiring manual reprogramming. The processor module autonomously optimizes the basal rate profile based on retrospective glucose data analysis, eliminating the need for patients to manually adjust settings while adapting to changing conditions.
Solution Approach 2:
The system uses continuous glucose sensor feedback to dynamically adjust basal insulin delivery. By continuously monitoring glucose levels and comparing them against target ranges, the system automatically modifies basal rates to maintain euglycemia, creating a closed-loop control mechanism that adapts to changing physiological states.
2Manufacturing precision
If basal rates are manually programmed by patients, then the system remains simple to operate, but patients struggle to establish correct basal rate patterns
Solution Approach 1:
The processor module automatically generates optimized basal rate patterns by analyzing retrospective continuous glucose sensor data and identifying patterns related to patient activity levels and physiological responses. This eliminates the need for patients to manually program complex basal rate schedules while achieving precise, personalized insulin delivery patterns.
Solution Approach 2:
The system performs preliminary analysis of glucose data patterns before implementing basal rate adjustments. By retrospectively analyzing glucose sensor data to identify optimal basal rates for different activity levels and time periods, the system prepares optimized profiles in advance, ensuring precise insulin delivery without requiring patients to perform complex programming tasks.
3Reliability
If real-time glucose monitoring is implemented, then glycemic control can be improved, but the system requires continuous data processing and adjustment
Solution Approach 1:
The system performs retrospective analysis of continuous glucose sensor data at predetermined intervals to update the basal rate profile. By periodically processing glucose data rather than continuously adjusting in real-time, the system maintains reliable glycemic control while reducing the computational burden and complexity of continuous data processing.
Data Source
AI summary
Systems and methods for integrating a continuous glucose sensor, including a receiver, a medicament delivery device, a controller module, and optionally a single point glucose monitor are provided. Integration may be manual, semi-automated and/or fully automated.