Systems, devices, and methods for improved analyte sensor accuracy and fault detection

Patent metadata

Field	Value
Publication number	US-12551105-B2
Application number	US-202117928794-A
Country	US
Kind code	B2
Filing date	Jun 7, 2021
Priority date	Jun 8, 2020
Publication date	Feb 17, 2026
Grant date	Feb 17, 2026

Abstract

Various embodiments of systems, devices and methods for improving the accuracy of an analyte sensor and for detecting sensor fault conditions are disclosed. According to some embodiments, these systems, devices, and methods can utilize a first data collected by a glucose sensor and a second data collected by a secondary sensing element. In some embodiments, the secondary sensing element can be one of a lactate sensing element, a ketone sensing element, or a heart rate monitor, among others.

First claim

What is claimed is:

1 . An analyte monitoring system, comprising:
a sensor control device including an analyte sensor, first processing circuitry, and a first non-transitory memory, wherein the analyte sensor includes at least a portion configured to be inserted into a user's body and collect a first data indicative of a glucose level; a secondary sensing element configured to collect a second data indicative of a secondary physiological measurement; and a reader device comprising second processing circuitry and a second non-transitory memory, wherein at least one of the first or the second non-transitory memory includes instructions that, when executed, cause at least one of the first or the second processing circuitry to:
determine, based on the first data, if a suspected false glucose condition is absent, determine, based on the second data, if a correlative physiological condition is present, and
perform a first corrective action if the suspected false glucose condition is absent and the correlative physiological condition is present, wherein the first corrective action is an aggressive lag correction.

2 . The analyte monitoring system of claim 1 , wherein the secondary sensing element comprises one or more of a heart rate monitor, an insertable cardiac monitor, an implantable electrocardiogram (ECG) device, or an implantable electroencephalogram (EEG) device.

3 . The analyte monitoring system of claim 1 , wherein the secondary sensing element comprises one or more of a ketone sensor, a continuous ketone monitor, or a ketone strip reader.

4 . The analyte monitoring system of claim 3 , wherein the secondary physiological measurement comprises a ketone level.

5 . The analyte monitoring system of claim 1 , wherein the suspected false glucose condition is a suspected false high glucose condition or a suspected false low glucose condition.

6 . The analyte monitoring system of claim 1 , wherein the suspected false glucose condition is a suspected false low glucose condition, and wherein the instructions to determine the suspected false glucose condition comprise one or more of the following instructions to:
determine if one or more glucose sensor data quality checks indicate the suspected false low glucose condition; determine if the glucose level, based on the first data, is below a first predetermined low glucose threshold; determine if an Area Under the Curve (AUC) calculation, based on the first data and a second predetermined low glucose threshold, exceeds a predetermined low glucose AUC threshold; determine if a glucose percentile metric exceeds a predetermined low glucose percentile threshold; or determine if an average glucose level in a predetermined recent time window exceeds a third predetermined low glucose threshold.

7 . The analyte monitoring system of claim 6 , wherein the AUC calculation is based on the first data in a first recent predetermined time window, and wherein the glucose percentile metric is based on the first data in a second recent predetermined time window.

8 . The analyte monitoring system of claim 1 , wherein the suspected false glucose condition is a suspected false high glucose condition, and wherein the instructions to determine the suspected false glucose condition comprise instructions to:
determine if one or more glucose sensor data quality checks indicate the suspected false high glucose condition; determine if the glucose level, based on the first data, is above a first predetermined high glucose threshold; determine if an Area Under the Curve (AUC) calculation, based on the first data and a second predetermined high glucose threshold, exceeds a predetermined high glucose AUC threshold; determine if a glucose percentile metric exceeds a predetermined high glucose percentile threshold; or determine if an average glucose in a predetermined recent time window exceeds a third predetermined high glucose threshold.

9 . The analyte monitoring system of claim 8 , wherein the AUC calculation is based on the first data in a first recent predetermined time window, and wherein the glucose percentile metric is based on the first data in a second recent predetermined time window.

10 . The analyte monitoring system of claim 1 , wherein the correlative physiological condition comprises one of: an inferred absence of high glucose, an inferred presence of high glucose, an inferred absence of low glucose, or an inferred presence of low glucose.

11 . The analyte monitoring system of claim 1 , wherein the instructions to determine the correlative physiological condition comprise instructions to compare a ketone level to a predetermined ketone threshold.

12 . The analyte monitoring system of claim 1 , wherein the instructions to determine the correlative physiological condition comprise instructions to compare a heart rate measurement to a predetermined heart rate threshold.

13 . The analyte monitoring system of claim 1 , wherein the suspected false glucose condition is a suspected false low glucose condition and wherein the correlative physiological condition is an inferred absence of low glucose.

14 . The analyte monitoring system of claim 1 , wherein the suspected false glucose condition is a suspected false high glucose condition and wherein the correlative physiological condition is an inferred absence of high glucose.

15 . The analyte monitoring system of claim 1 , wherein the instructions, when executed, further cause the at least one of the first or the second processing circuitry to perform a second corrective action if the suspected false glucose is absent and the correlative physiological condition is absent, wherein the second corrective action comprises one or more of a moderate lag correction or an increased glucose sensor signal smoothing.

16 . The analyte monitoring system of claim 1 , wherein the instructions to determine if a correlative physiological condition is present further comprise instructions to determine a degree of correlation between the correlative physiological condition and the suspected false glucose condition.

17 . The analyte monitoring system of claim 1 , wherein the instructions are stored in the second non-transitory memory of the reader device,
wherein the instructions further comprise a first mobile app configured to receive the first data and a second mobile app configured to receive the second data, and wherein one of the first mobile app or the second mobile is configured to determine the absence or the presence of the suspected false glucose condition and the correlative physiological condition and to perform the first corrective action.

18 . The analyte monitoring system of claim 1 , wherein the secondary sensing element comprises a lactate sensing element.

19 . The analyte monitoring system of claim 18 , wherein the secondary physiological measurement comprises a lactate level.

20 . An analyte monitoring system, comprising:
a sensor control device including an analyte sensor, first processing circuitry, and a first non-transitory memory, wherein the analyte sensor includes at least a portion configured to be inserted into a user's body and collect a first data indicative of a glucose level; a secondary sensing element configured to collect a second data indicative of a secondary physiological measurement; and a reader device comprising second processing circuitry and a second non-transitory memory, wherein at least one of the first or the second non-transitory memory includes instructions that, w

Assignees

• Abbott Diabetes Care Inc

Inventors

• Budiman Erwin S
• Wang Yi
• Feldman Benjamin Jay
• Cho Hyun
• Chen Kuan-Chou
• Tran Lam N
• Oja Stephen
• Ouyang Tianmei

Classifications

• A61B5/6833 Primary

  Adhesive patches · CPC title

• A61B5/145

  Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value {; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue} (non-radiation detecting or locating of foreign bodies in blood A61B5/06) · CPC title

• A61B5/0022 Primary

  Monitoring a patient using a global network, e.g. telephone networks, internet · CPC title

• A61B5/7203

  for noise prevention, reduction or removal · CPC title

• A61B5/369

  Electroencephalography [EEG] · CPC title