Glucose Sensor Accuracy Correction Using Insulin Delivery Feedback
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Solution Overview
Problem
Continuous glucose sensors (CGS) suffer from inaccuracies, particularly during hypoglycemic events, due to delays in blood-to-interstitial glucose transport and other factors, and existing accuracy-enhancing methods rely solely on blood glucose data without incorporating insulin delivery data.
Innovation Solution
Incorporating insulin pump data into the CGS system using filtering/state estimation procedures like Kalman Filters, and applying weighting schemes based on metabolic state estimates to improve glucose measurement accuracy, particularly during hypoglycemic events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If continuous glucose sensors are used to monitor glucose levels, then real-time glucose information is available, but measurement accuracy deteriorates during hypoglycemic events due to delays in blood-to-interstitial glucose transport
Solution Approach 1:
The system uses insulin delivery data as feedback to adjust and correct glucose measurements. By continuously monitoring insulin delivery information and comparing it with sensor readings, the system can identify discrepancies caused by transport delays and apply corrections to improve measurement accuracy during hypoglycemic events.
Solution Approach 2:
Insulin delivery information serves as an intermediary variable to indirectly assess glucose levels. Instead of relying solely on direct glucose sensor measurements that are affected by transport delays, the system uses insulin delivery data as a mediator to infer actual glucose levels, particularly during hypoglycemic events where the relationship between insulin delivery and glucose change is well-established.
2Device complexity
If blood glucose data only is used for accuracy enhancement, then processing complexity is reduced, but measurement accuracy improvement is limited
Solution Approach 1:
The system merges multiple data sources - continuous glucose sensor readings and insulin delivery information - into a unified analysis framework. By combining these complementary data streams, the system achieves superior measurement accuracy compared to using either data source alone, as the insulin delivery data provides contextual information that compensates for sensor limitations during hypoglycemic events.
Data Source
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AI summary
Method and System for providing a signal from an insulin pump, artificial pancreas, or another insulin delivery device as a source of information for improving the accuracy of a continuous glucose sensor (CGS). The effect of using insulin information to enhance sensor accuracy is most prominent at low blood glucose levels, i.e. in the hypoglycemic range, which is critical for any treatment. A system for providing a filtering/state estimation methodology that may be used to determine a glucose state estimate at time t-τ. The estimation may be extrapolated to some future time t and then the extrapolated value is used to extract the blood glucose component. The blood glucose component of the extrapolation and the output of the CGS are weighted and used to estimate the blood glucose level of a subject.