Continuous Analyte Sensor End of Life Detection
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Solution Overview
Problem
Conventional continuous analyte sensors require frequent and inconvenient calibration, often involving painful finger stick measurements, and lack effective methods to determine the end of life, leading to delayed detection of hyperglycemic or hypoglycemic conditions in diabetic patients.
Innovation Solution
The system processes analyte sensor data to determine sensor sensitivity and detect end of life by using a sensitivity profile and risk factor analysis, allowing for self-calibration and reducing the need for reference measurements, and intelligently identifying outliers and end of life indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If continuous analyte sensors are used for extended periods, then the ability to continuously monitor glucose levels is improved, but sensor performance degrades and end of life detection becomes necessary
Solution Approach 1:
The system implements feedback mechanisms by continuously analyzing sensor data quality metrics and comparing them against established thresholds to detect end of life conditions. The processor module monitors sensor responses and provides feedback signals when performance degradation is detected, enabling timely sensor replacement while maintaining reliable glucose monitoring throughout the extended operational period.
Solution Approach 2:
The patent acknowledges that continuous analyte sensors have limited operational lifetimes and are ultimately disposable. By implementing end of life detection algorithms, the system optimizes the utilization of these short-lived sensors, extracting maximum value from each sensor before replacement is necessary, thereby reducing waste while maintaining monitoring continuity.
2Measurement precision
If frequent calibration is performed to maintain accuracy, then measurement precision is improved, but user convenience deteriorates due to frequent finger stick measurements
Solution Approach 1:
The system implements self-service calibration by using the sensor's own operational data and responses to determine calibration needs. The processor module analyzes sensor performance metrics and automatically identifies when calibration is necessary, eliminating the need for frequent manual finger stick measurements while maintaining measurement accuracy through intelligent, data-driven calibration scheduling.
Solution Approach 2:
The patent employs parameter changes by monitoring multiple sensor characteristics (response time, signal stability, sensitivity) to dynamically adjust calibration requirements. When sensor parameters indicate stable performance, calibration frequency is reduced; when parameters suggest degradation, calibration is triggered. This adaptive approach maintains precision while minimizing user burden.
3Loss of time
If sensor end of life is not detected, then device complexity is reduced, but time intervals between measurements increase leading to delayed detection of glycemic events
Solution Approach 1:
The patent replaces complex mechanical or manual monitoring systems with computational algorithms that analyze sensor data patterns. The processor module uses software-based end of life detection through mathematical modeling and pattern recognition, substituting physical complexity with intelligent data processing to maintain rapid detection of glycemic events without requiring cumbersome additional hardware.
Data Source
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AI summary
Systems and methods for processing sensor data and end of life detection are provided. In some embodiments, a method for determining the end of life of a continuous analyte sensor includes evaluating a plurality of risk factors using an end of life function to determine an end of life status of the sensor and providing an output related to the end of life status of the sensor. The plurality of risk factors may be selected from the list including the number of days the sensor has been in usc, whether there has been a decrease in signal sensitivity, whether there is a predetermined noise pattern, whether there is a predetermined oxygen concentration pattern, and error between reference BG values and EGV sensor values. An estimative algorithm function 120 can be used to define the relationship between time during the sensor session and sensor sensitivity 110.