Closed-loop glucose control with maximal insulin limits
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Solution Overview
Problem
Current model-based artificial pancreas systems for blood glucose control face challenges in accurately predicting insulin requirements, leading to risks of hyperglycemia or hypoglycemia due to limitations in physiological model accuracy.
Innovation Solution
An automated closed-loop blood glucose control system that includes a continuous glucose-monitoring sensor, a subcutaneous insulin delivery device, and a controller using a physiological model to determine insulin doses, with features such as computing a maximal allowable insulin injection amount based on patient sensitivity and heart rate, and incorporating simulated experiments to refine predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If model-based predictive control is used to determine insulin doses, then automation extent is improved, but measurement precision of glucose level prediction deteriorates
Solution Approach 1:
The system implements a closed-loop feedback mechanism where actual glucose measurements are continuously compared with predicted glucose levels. The prediction errors are fed back to update and refine the physiological model parameters, thereby improving prediction accuracy over time while maintaining full automation. This resolves the contradiction by enabling the automated system to learn and adapt from actual patient responses.
Solution Approach 2:
The system dynamically adjusts model parameters based on individual patient characteristics, insulin sensitivity variations, and real-time glucose responses. By changing and optimizing model parameters rather than using fixed values, the system improves prediction precision while maintaining automated operation. This allows the same automated framework to adapt to different patients and conditions.
2Productivity
If physiological model predictions are used to estimate insulin requirements, then productivity of glucose control is improved, but reliability of glucose level management deteriorates
Solution Approach 1:
The system incorporates safety margins and constraints in the predictive control algorithm to prevent overly aggressive insulin dosing. By built-in cushioning mechanisms that account for prediction uncertainties and individual patient variability, the system maintains high productivity while ensuring reliability. The controller deliberately under-doses relative to model predictions to create a safety buffer against hypoglycemia.
Solution Approach 2:
Continuous monitoring of actual glucose responses provides feedback that validates or corrects model predictions. This feedback loop enhances reliability by detecting when predictions diverge from actual patient responses, allowing the system to adjust and maintain safe operation while preserving the efficiency benefits of model-based control.
3Manufacturing precision
If maximal allowable insulin injection amount is computed based on patient sensitivity, then manufacturing precision of insulin dosing is improved, but device complexity increases
Solution Approach 1:
The system computes maximal allowable insulin amounts by dynamically changing parameters such as insulin sensitivity factors, glucose targets, and correction factors based on patient-specific data. These parameter adjustments enable precise dosing without requiring complex hardware modifications. The complexity is managed through software-based parameter optimization rather than mechanical complexity.
Solution Approach 2:
The system performs preliminary computations of maximal allowable insulin doses based on patient characteristics and current glucose state before actual insulin delivery. This advance calculation of dosing limits simplifies the real-time control decision-making process, achieving precision dosing while managing complexity through pre-computed safety boundaries.
Data Source
AI summary
An automated closed-loop blood glucose control system comprises a continuous glucose-monitoring sensor (101), a subcutaneous insulin delivery device (103); and a controller (105) which determines a maximal allowable insulin injection amount and determines an insulin delivery control signal on the basis of the maximal allowable insulin injection amount and the quantity of insulin to inject.