Implantable CGM Sensor Degradation Patents 2026
Implantable CGM Sensor Degradation Patents 2026
Implantable CGM systems pushing toward 90-day and 180-day lifespans face progressive sensitivity decline, biofouling, and oxygen depletion. This dataset spans 2007–2025 filings across US, EP, WO, AU, and CN jurisdictions.
How Implantable CGM Sensors Degrade Over Time
Implantable CGM sensor degradation encompasses three primary phenomena: progressive sensitivity decline driven by enzyme inactivation and membrane biofouling; signal noise escalation characterized as non-symmetrical, nonstationary noise of increasing amplitude and duration; and oxygen depletion effects that compromise amperometric signal generation. These processes are interrelated and accelerate over extended in vivo operation.
The dominant technical platform in this dataset is the electrochemical glucose oxidase (GOx) biosensor, in which glucose oxidase is immobilized within a multi-membrane working electrode stack. Biofouling of the outer membrane restricts glucose and oxygen diffusion simultaneously, while foreign body capsule formation around the implanted sensor reduces local tissue perfusion over weeks—compounding sensitivity loss.
For optical architectures, the Eversense system employs a fluorescent boronic-acid-based glucose-indicating polymer, decoupling sensing from enzymatic oxygen dependence but introducing photostability and fluorophore bleaching as distinct degradation vectors. The PROMISE study (2022) evaluated a sacrificial boronic acid (SBA) modification achieving 92.9% of readings within 20%/20% of reference values over 180 days.
In this dataset, Dexcom, Inc. holds at least 25 distinct patent records across US, EP, AU, and WO jurisdictions, representing the densest IP concentration in retrieved records. Abbott Diabetes Care and Ascensia Diabetes Care Holdings each contribute 3 retrieved records, while Roche Diabetes Care and Chinese academic assignees each contribute 1 record in this dataset.
Filing Trends and Technology Cluster Distribution
Retrieved filings span 2007–2025 across four primary technology clusters: multi-risk-factor EOL detection algorithms, progressive sensor decline (PSD) detection, impedance-based degradation sensing, and long-term drift compensation. Activity in this dataset accelerated markedly from 2016 onward.
Patent Records by Technology Cluster (Dataset Snapshot)
Multi-risk-factor EOL detection and PSD detection together account for the majority of retrieved records in this dataset, with Dexcom holding all identified filings in those clusters.
↗ Click bars to exploreFiling Activity by Phase: Foundational, Development, and Maturity (Dataset Snapshot)
Filing activity in this dataset increased sharply in the Development and Maturity phases (2016–2025), with the Maturity phase (2022–2025) alone contributing the majority of next-generation drift compensation and multi-modal degradation detection filings in retrieved records.
↗ Click bars to exploreKey Clinical and Commercial Contexts for CGM Sensor Degradation Technology
Implantable CGM sensor degradation technology has been validated and deployed across four primary clinical contexts, each with distinct accuracy requirements and degradation risk profiles. Named trials and real-world registries anchor the evidence base for each domain.
PRECISE II & PROMISE Trials
The PRECISE II trial (2018) demonstrated 90-day implantable sensor accuracy with MARD 8.8% across 90 participants at multicenter US and EU sites. The PROMISE study (2022) extended this to 180-day use with a sacrificial boronic acid (SBA) modification, achieving 92.9% of readings within 20%/20% of reference values across 181 subjects at 8 US sites. Both studies used calibration stability and sensor survival as primary degradation management endpoints.
Diabetes Clinical TrialsArtificial Pancreas Closed-Loop Systems
The “Sensor Life and Overnight Closed Loop” randomized clinical trial (2016) confirmed that sensor accuracy on day 1 of insertion was significantly inferior to days 3–4, establishing that the degradation and stabilization curve is safety-critical in closed-loop dosing contexts. Control-IQ real-world data (2021) covering 9,451 users provides the longitudinal operational context in which CGM degradation management affects algorithmic insulin dosing decisions.
Closed-Loop Insulin DeliveryICU and Post-Surgical Monitoring
The EIRUS microdialysis-based CGM (2015) and Symphony CGM non-invasive system (Echo Therapeutics, 2014) represent ICU-specific platforms where sensor degradation over multi-day use in critically ill patients introduces particular dosing risks. The Dexcom G6 was evaluated in pediatric ICU patients post-total pancreatectomy with islet autotransplantation (2021), characterizing sensor performance over the first seven post-operative days in a high-stakes degradation context.
Intensive Care MonitoringSensor Reuse and Lifecycle Management
Dexcom holds multiple active US patents (2015, 2017) covering sensor reuse detection based on EOL risk factor profiles, addressing patient behavior that extends sensors beyond their designed operational lifetime. Ascensia Diabetes Care Holdings AG holds US patents (2022, 2025) covering insertion and removal time tracking with a maximum removal time limit enforcer—recognizing that degradation state depends on both cumulative in vivo time and ex vivo storage conditions between cycles.
Sensor Lifecycle ManagementLeading Patent Assignees in Implantable CGM Sensor Degradation — Dataset Snapshot
In this dataset, Dexcom, Inc. holds at least 25 retrieved patent records across US, EP, AU, and WO jurisdictions—the dominant position in retrieved records by a substantial margin. Abbott Diabetes Care and Ascensia Diabetes Care Holdings each hold 3 retrieved records, representing parallel but narrower IP positions in specific sub-domains of sensor degradation detection.
Assignee Filing Counts in Retrieved Records (Dataset Snapshot)
↗ Click bars to exploreDexcom, Inc.
Dexcom holds at least 25 retrieved patent records spanning US, EP, AU, and WO jurisdictions, with filings from 2014 through 2025. The portfolio covers multi-risk-factor EOL detection (introduced in a 2014 US filing), progressive sensor decline (PSD) detection with noise asymmetry and duration components (active US grants through 2023–2025), and long-term drift compensation via population-level calibration models (WO, December 2025). Key active grants include EP, US, and AU jurisdictions across the EOL and PSD families.
United StatesAbbott Diabetes Care Inc.
Abbott Diabetes Care holds 3 retrieved US patent records (2021–2024) covering real-time detection of sensitivity decline in analyte sensors, representing a parallel but narrower IP position to Dexcom’s multi-risk-factor EOL framework. The filings focus on signal processing methods for detecting downward sensitivity drift in real time, with applications to continuous glucose monitoring. All 3 records are in the US jurisdiction.
United StatesNext-Generation Degradation Management: 2024–2025 Signals
The most recent filings in this dataset (2024–2025) signal a shift from reactive EOL shutdown toward prospective, model-based degradation management—combining population-level predictive models, multi-modal electrochemical signatures, and probabilistic recovery assessment.
Population-Level Predictive Drift Modeling
Dexcom’s ‘Analyte Sensor System Long Term Drift Compensation’ (WO, December 2025) trains a model on factory calibration data from a first sensor cohort to predict sensitivity trajectories for new sensors. This approach enables pre-emptive drift correction without frequent in vivo recalibration, potentially supporting factory-calibrated sensors with extended lifespans. It represents a step-change from reactive EOL detection toward prospective degradation management.
Combined Multi-Modal Degradation Signatures
Roche Diabetes Care’s WO filing (December 2025) integrates sensitivity loss indicators derived from continuous monitoring data with impedance-based defect detection, combining electrochemical and signal-processing modalities in a single detection framework. This multi-modal approach addresses the limitation of single-channel degradation monitoring that can miss specific failure modes such as membrane rupture or biofouling without a corresponding amperometric signal change.
EOL Detection Algorithms vs. Long-Term Drift Compensation: Approaches Compared
Click any row to explore further.
| Dimension | EOL / PSD Detection (Dexcom) | Long-Term Drift Compensation (Dexcom / Abbott) |
|---|---|---|
| Primary Goal | Detect sensor end-of-life and disable data display to prevent unsafe readings | Compensate for predictable sensitivity drift to extend useful sensor lifetime |
| Core Mechanism | Weighted multi-risk-factor fusion (days-in-use, noise asymmetry, oxygen concentration, sensitivity rate-of-change) via fuzzy logic or probabilistic function | Time-varying linear model applied to CGM time series using sparse blood glucose reference values (retrofitting); or population-level factory calibration model (predictive) |
| Key Signal Inputs | Days-in-use, EOL noise characteristics (amplitude, asymmetry, stationarity, duration), oxygen concentration, glucose pattern plausibility, reference-sensor error | Sparse fingerstick BG reference values (retrofitting) or factory cohort calibration data (predictive drift model) |
| Output | Continuous EOL confidence score (0–1 scale); recovery likelihood score; display disable trigger when EOL threshold exceeded and recovery likelihood low | Corrected CGM time series with reduced drift; predicted initial and final sensitivity for pre-emptive correction |
| Failure Mode Addressed | Abrupt or progressive sensitivity decline, noise escalation, biofouling-related signal loss, oxygen depletion | Systematic monotonic sensitivity drift; gradual calibration offset accumulation over weeks |
| Clinical Evidence | Foundational patents filed 2014 (US); PSD-specific active US grants through 2023–2025; EOL detection named as APS safety motivation in 2016 Dexcom patent | Retrofitting algorithm: University of Padova WO 2014, incorporated into Dexcom EP 2016; Predictive drift model: Dexcom WO December 2025 |
| Sensor Lifespan Target | Up to 90-day implantable sensor (Eversense platform); detects EOL within that window | Targets extended lifetime beyond standard calibration interval; 180-day use supported by PROMISE study (2022) with SBA modification |
| IP Concentration | Dominated by Dexcom in this dataset (EOL + PSD families, 2014–2025, US/EP/AU/WO) | Dexcom (drift compensation WO 2025), Abbott Diabetes Care (sensitivity decline detection, 3 US records 2021–2024), University of Padova (retrofitting WO 2014) |
Frequently Asked Questions: Implantable CGM Sensor Degradation
According to this dataset, the three primary degradation phenomena are: (1) progressive sensitivity decline driven by enzyme inactivation, membrane biofouling, and foreign body response; (2) signal noise escalation characterized as non-symmetrical, nonstationary noise of increasing amplitude and duration; and (3) oxygen depletion effects that compromise amperometric signal generation. These are interrelated—biofouling of the outer membrane restricts glucose and oxygen diffusion simultaneously.
The PROMISE study (2022), which evaluated a next-generation Eversense system with a sacrificial boronic acid (SBA) modification, achieved 92.9% of readings within 20%/20% of reference values over up to 180-day use across 181 subjects at 8 US sites.
The Dexcom EOL algorithm evaluates a weighted combination of risk factors in real time—including days-in-use, rate of change of sensor sensitivity, noise characteristics (amplitude, asymmetry, stationarity, duration), oxygen concentration, glucose pattern plausibility, and error between reference and sensor values. These are fused via a fuzzy logic or probabilistic function to produce a continuous EOL confidence score. When EOL likelihood exceeds a threshold and recovery likelihood is low (sensor not tracking glucose for more than 12 hours), the system disables data display and prompts sensor replacement.
EIS is used as an orthogonal degradation signal independent of the amperometric glucose channel. Changes in impedance over time reflect membrane biofouling, membrane rupture, and changes in the electrode-tissue interface. The Ascensia platform (US, 2011) describes periodic in vivo EIS measurements with comparison to reference values to identify biological fouling, membrane pistoning, and electrode drift, with automatic calibration profile adjustment. Dexcom’s implantable sensor device patent (US, 2020) uses impedance changes between two time points to trigger sensor deactivation.
In this dataset, Dexcom, Inc. holds at least 25 distinct patent records across US, EP, AU, and WO jurisdictions (2014–2025). Abbott Diabetes Care Inc. and Ascensia Diabetes Care Holdings AG each hold 3 retrieved US patent records. Roche Diabetes Care GmbH holds 1 WO filing (2025). Shanghai Second Polytechnic University and University of Padova each hold 1 record in this dataset.
PSD refers to sensors that do not fail abruptly but exhibit gradual, monotonic sensitivity decline—identified in this dataset as the most clinically hazardous degradation mode. Because it may produce systematically biased glucose readings without triggering noise-based EOL flags, PSD can go undetected and lead to incorrect insulin dosing. Dexcom’s PSD-specific patents (2021–2025) detect downward drift in sensor sensitivity via moving averages of raw count signals and non-symmetrical nonstationary noise of increasing duration, combined with a probabilistic recovery assessment.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.