Systems and methods for detecting glucose level data patterns

Patent metadata

Field	Value
Publication number	US-10575762-B2
Application number	US-201213566844-A
Country	US
Kind code	B2
Filing date	Aug 3, 2012
Priority date	Aug 5, 2011
Publication date	Mar 3, 2020
Grant date	Mar 3, 2020

Abstract

Systems and methods for detecting and reporting patterns in analyte concentration data are provided. According to some implementations, an implantable device for continuous measurement of an analyte concentration is disclosed. The implantable device includes a sensor configured to generate a signal indicative of a concentration of an analyte in a host, a memory configured to store data corresponding at least one of the generated signal and user information, a processor configured to receive data from at least one of the memory and the sensor, wherein the processor is configured to generate pattern data based on the received information, and an output module configured to output the generated pattern data. The pattern data can be based on detecting frequency and severity of analyte data in clinically risky ranges.

First claim

What is claimed is:

1. A method implemented using improved continuous glucose monitoring technology for providing hypoglycemic-related alerts, the continuous glucose monitoring technology including a continuous analyte sensor, a graphical user interface, and one or more processors configured by machine-readable instructions, the method comprising:
automatically generating, using the continuous analyte sensor, sensor data including glucose concentration measurements of a user and time information associated with the glucose concentration measurements;
automatically determining, using the one or more processors, a hypoglycemic event based on comparison of at least one of the glucose concentration measurements and a glucose level threshold, a time of the hypoglycemic event based on the time information, and a period of hypoglycemic reoccurrence risk that lasts a predefined amount of time following the time of the hypoglycemic event;
automatically generating, using the one or more processors, a view for display on the graphical user interface, wherein the view includes both a trend graph of the glucose concentration measurements and a visual indication of the hypoglycemic reoccurrence risk during the predefined amount of time following the hypoglycemic event, wherein the trend graph is over a time window including at least a portion of the time information and at least a portion of the period of the hypoglycemic reoccurrence risk; and
displaying, using the graphical user interface, the automatically-generated view.

2. The method of claim 1 , wherein automatically determining the hypoglycemic event comprises automatically determining an average difference of the glucose concentration measurements spanning a segment of time with the glucose level threshold.

3. The method of claim 2 , wherein the average difference is determined by:
calculating a sum of the difference between the glucose level threshold and a glucose concentration measurement for a number of corresponding time points of the segment of time, and
calculating a quotient of the calculated sum by the number of corresponding time points.

4. The method of claim 2 , wherein the segment of time includes a start time point and an end time point spanning a period that includes time points corresponding to glucose concentration measurements that are below the glucose level threshold.

5. The method of claim 2 , wherein the segment of time includes a start time point and an end time point spanning a period that includes time points corresponding to glucose concentration measurements that are below the glucose level threshold and time points corresponding to glucose concentration measurements that are above the glucose level threshold within a predetermined tolerance.

6. The method of claim 5 , wherein the predetermined tolerance includes a predetermined time duration of when the glucose concentration measurements are above the glucose level threshold.

7. The method of claim 2 , herein detecting the hypoglycemic event further comprises:
comparing more than one of the glucose concentration measurements with a second glucose level threshold that is different from the glucose level threshold.

8. The method of claim 1 , wherein the hypoglycemic event occurred at a specific point in time, and wherein the trend graph includes an indication of the specific point in time.

9. The method of claim 1 , wherein the predefined amount of time is 48 hours.

10. The method of claim 1 , further comprising triggering an audible or visual alert using the user interface responsive to detecting the hypoglycemic event and detecting that a current rate of change of the glucose concentration exceeds a predetermined threshold.

11. The method of claim 1 , wherein automatically determining the hypoglycemic event is based on comparisons of more than one of the glucose concentration measurements with more than one glucose level thresholds.

12. The method of claim 1 , wherein the visual indication of the period of the hypoglycemic reoccurrence risk includes a highlighted portion of the trend graph on the graphical user interface.

13. The method of claim 1 , wherein the glucose level threshold is 66 mg/dL.

14. The method of claim 1 , further comprising triggering an audible or visual alert responsive to determining the period of hypoglycemic reoccurrence risk.

15. The method of claim 14 , wherein the audible or visual alert is triggered on a device that is remote and external from the continuous analyte sensor.

16. A system for providing hypoglycemic-related alerts, the system comprising:
a continuous glucose sensor configured to generate output signals that convey glucose concentration measurements of a user, wherein the glucose concentration measurements are associated with time information; and
one or more processors configured by machine-readable instructions to:
receive the generated output signals that convey the glucose concentration measurements from the continuous glucose sensor;
automatically determine a hypoglycemic event based on comparison of at least one of the glucose concentration measurements and a glucose level threshold, and a time of the hypoglycemic event based on the time information and a period of hypoglycemic reoccurrence risk that lasts a predefined amount of time following the time of the hypoglycemic event;
automatically generate a view for display on a graphical user interface, wherein the view includes both a trend graph of the glucose concentration measurements a visual indication of the hypoglycemic reoccurrence risk during the predefined amount of time following the hypoglycemic event, wherein the trend graph is over a time window including at least a portion of the time information and at least a portion of the period of the hypoglycemic reoccurrence risk; and
a graphical user interface configured to display the automatically-generated view.

17. The system of claim 16 , wherein the one or more processors are further configured to adjust the trend graph in real-time to reflect passage of the period of hypoglycemic reoccurrence risk as time passes.

18. The system of claim 16 , wherein the one or more processors are configured to automatically determine the hypoglycemic event by automatically determining an average difference of the glucose concentration measurements spanning a segment of time with the glucose level threshold.

19. The system of claim 18 , wherein the segment of time includes a start time point and an end time point spanning a period that includes time points corresponding to glucose concentration measurements that are below the glucose level threshold.

20. The system of claim 18 , wherein the segment of time includes a start time point and an end time point spanning a period that includes time points corresponding to glucose concentration measurements that are below the glucose level threshold and time points corresponding to glucose concentration measurements that are above the glucose level threshold within a predetermined tolerance.

Assignees

• Mayou Phil
• Hampapuram Hari
• Price David
• Weindel Keri
• Snisarenko Kostyantyn
• Mensinger Michael Robert
• Bowman Leif N
• Boock Robert J
• Kamath Apurv Ullas
• Reihman Eli
• Simpson Peter C
• Dexcom Inc

Inventors

• Mayou Phil
• Hampapuram Hari
• Price David
• Weindel Keri
• Snisarenko Kostyantyn
• Mensinger Michael Robert
• Bowman Leif N
• Boock Robert J
• Kamath Apurv Ullas
• Reihman Eli
• Simpson Peter C

Classifications

• G16H15/00

  ICT specially adapted for medical reports, e.g. generation or transmission thereof · CPC title

• G16H50/70

  for mining of medical data, e.g. analysing previous cases of other patients · CPC title

• A61B5/14532 Primary

  for measuring glucose, e.g. by tissue impedance measurement · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• Y02A90/26

  Cross-Sectional Technologies · mapped topic