Dual Hormone Delivery Control for Predicted Glucose Excursions
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Solution Overview
Problem
Conventional drug delivery systems for insulin fail to account for individual insulin sensitivity variations, leading to unintended hypoglycemic or hyperglycemic episodes.
Innovation Solution
A drug delivery system with a communication interface, storage for insulin and glucagon history, and processing logic to predict and prevent hypoglycemia/hyperglycemia by adjusting insulin/glucagon delivery, including alerts and automated dosages based on glucose levels, insulin history, and carbohydrate ingestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional drug delivery systems deliver insulin at periodic intervals or use closed loop control, then insulin delivery is automated and adjusted, but the systems inadvertently drive blood glucose levels to hypoglycemia or hyperglycemia due to ignoring individual insulin sensitivity variations
Solution Approach 1:
The system dynamically changes the parameter of insulin delivery dosage based on predicted future glucose levels and individual insulin sensitivity factors. Instead of fixed periodic delivery, the system adjusts delivery parameters (dosage, timing) according to predicted glucose trajectories and patient-specific sensitivity variations, preventing both hypoglycemia andhyperglycemia while maintaining automated control
Solution Approach 2:
The system performs preliminary prediction of future blood glucose levels before insulin delivery occurs. By using glucose monitoring data and insulin sensitivity factors to predict upcoming glucose excursions, the system can pre-adjust or prevent insulin delivery that would otherwise cause hypoglycemia, acting in advance rather than reactively
2Device complexity
If drug delivery systems assume uniform insulin sensitivity for all patients, then the system design is simplified, but the system fails to account for individual variations leading to unintended hypoglycemic orhyperglycemic episodes
Solution Approach 1:
The system applies local quality by incorporating patient-specific insulin sensitivity factors into the control algorithm. Instead of a universal one-size-fits-all approach, each patient's unique sensitivity characteristics are integrated into their personalized delivery profile, allowing the system to adapt to individual metabolic responses while maintaining overall system architecture
Solution Approach 2:
The system uses feedback from glucose monitoring data to continuously refine and adjust insulin delivery predictions. By monitoring actual glucose levels and comparing them to predicted levels, the system learns and adapts to individual patient sensitivity variations over time, improving accuracy without requiring complete redesign of the delivery mechanism
Data Source
AI summary
The exemplary embodiments attempt to identify impending hypoglycemia and/or hyperglycemia and take measures to prevent the hypoglycemia or hyperglycemia. Exemplary embodiments may provide a drug delivery system for delivering insulin and glucagon as needed by a user of the drug delivery system. The drug delivery system may deploy a control system that controls the automated delivery of insulin and glucagon to a patient by the drug delivery system. The control system seeks among other goals to avoid the user experiencing hypoglycemia or hyperglycemia. The control system may employ a clinical decision support algorithm as is described below to control delivery of insulin and glucagon to reduce the risk of hypoglycemia or hyperglycemia and to provide alerts to the user when needed. The control system assesses whether the drug delivery system can respond enough to avoid hypoglycemia or hyperglycemia and generates alerts when manual action is needed to avoid hypoglycemia or hyperglycemia.