Dynamic Calibration for Continuous Glucose Sensor Accuracy
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Solution Overview
Problem
Current continuous glucose sensors (CGS) face challenges in accuracy and reliability due to physiological time lag and calibration issues when measuring blood glucose levels by sampling interstitial glucose, leading to errors in blood glucose estimation.
Innovation Solution
The method involves dynamic calibration of CGS based on monitored values and time derivatives, using a calibration module to improve accuracy by adjusting calibration settings and incorporating a time lag correction module to address the physiological time lag between blood and interstitial glucose levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If CGS samples interstitial glucose to estimate blood glucose, then continuous monitoring is achieved, but accuracy deteriorates due to physiological time lag and gradient between blood and interstitial glucose
Solution Approach 1:
The system performs preliminary calibration by measuring both blood glucose and interstitial glucose levels at calibration time points to establish a subject-specific transfer function before continuous monitoring begins. This advance preparation allows the system to account for individual physiological characteristics and time lag variations, improving subsequent measurement accuracy without compromising continuous monitoring capability
Solution Approach 2:
The system dynamically adjusts calibration parameters and transfer function coefficients based on monitored glucose levels, rate of change, and time derivatives. By changing calibration parameters adaptively rather than using fixed values, the system compensates for physiological variations and maintains accuracy despite the inherent time lag between blood and interstitial glucose measurements
2Device complexity
If CGS uses fixed calibration, then device complexity is reduced, but accuracy deteriorates due to dynamic physiological changes over time
Solution Approach 1:
The calibration system transitions from static fixed calibration to dynamic adaptive calibration. The system continuously monitors glucose levels, rate of change, and time derivatives, then adjusts calibration parameters in real-time based on physiological conditions. This dynamic approach maintains measurement accuracy throughout the monitoring period while managing complexity through algorithmic rather than hardware complexity
Solution Approach 2:
The system performs self-calibration by automatically adjusting calibration parameters based on its own monitored data and predefined physiological models. The calibration module uses the sensor's own output signals to detect when recalibration is needed and automatically updates calibration factors without requiring external intervention or complex manual calibration procedures
3Measurement precision
If CGS recalibrates frequently, then measurement accuracy is improved, but loss of time increases due to calibration interruptions
Solution Approach 1:
The system implements feedback-based calibration scheduling by continuously monitoring glucose levels, rate of change, and the difference between measured and expected values. When the feedback indicates calibration drift beyond acceptable thresholds, the system automatically triggers recalibration. This feedback mechanism ensures calibration occurs only when necessary, maintaining accuracy while minimizing time loss compared to fixed frequent recalibration schedules
Solution Approach 2:
The system performs partial recalibration by updating only the specific calibration parameters that have drifted, rather than performing a complete recalibration of all parameters. This partial action approach maintains measurement accuracy for the affected parameters while minimizing the time and computational resources required, avoiding excessive full recalibration cycles
Data Source
AI summary
A method, apparatus, and a kit are capable of improving accuracy of CGS devices using dynamic outputs of continuous glucose sensors.