SYSTEMS AND METHODS FOR DETECTING GLUCOSE LEVEL DATA PATTERNS

• US 20130035575A1
• Filed: 08/03/2012
• Published: 02/07/2013
• Est. Priority Date: 08/05/2011
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• 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.
• Granted Patent: US 10,349,871 B2
• Time in Patent Office: Days
• US Class Current: 600/365
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

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.

Claims

1. An analyte concentration monitoring system, comprising:

a display screen;

one or more processors;

an input module configured to receive sensor data from an analyte sensor configured to generate sensor data points indicative of a measured an analyte concentration;

memory; and

one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the one or more programs including;

instructions to apply a weighted value to each sensor data point falling within a timeframe of sensor data, the weighted value depending upon a concentration value of the sensor data;

instructions to aggregate the weighted sensor data according to time of day; and

instructions to display the aggregated, weighted sensor data on a chart.

2. The system of claim 1, wherein aggregating the sensor data according to time of day includes aggregating each sensor data point that falls within the same five minute interval of a day.

3. The system of claim 1, further comprising instructions to highlight significant time ranges of the displayed sensor data, wherein significant time ranges of displayed data are determined based on exceeding thresholds for both frequency and severity.

4. The system of claim 3, wherein the determination of significant time ranges has an increased sensitivity during a predefined nighttime range of time, wherein increased sensitivity corresponds to lower frequency and severity thresholds.

5. The system of claim 1, wherein displaying the aggregated, weighted data includes displaying a high pattern chart and a low pattern chart, wherein sensor data associated with the high pattern chart are weighted differently than sensor data displayed in the low pattern chart.

6. The system of claim 1, wherein the chart includes an x-axis indicative of the timeframe and a y-axis indicative of a magnitude, wherein the y-axis scale is a predetermined percentage that less than a 100% of the maximum possible sum of weighted values.

7. The system of claim 1, further comprising instructions to display a pattern summary table on the display, the pattern summary table indicating a total number of significant pattern matches and a description of one or more of the most significant matches, wherein the pattern matches are grouped into a plurality of categories corresponding to time of day and glucose level, wherein the most significant pattern match falling within in each of the four groups can is determined by based on a total sum of all contributed values within the pattern match interval.

8. The system of claim 1, further comprising a user interface incorporating the display screen, wherein the user interface includes a timeframe selection control that allows a user to select of modify the timeframe.

9. The system of claim 8, wherein the timeframe selection control includes a user selectable drop down menu configured to allow a user to select or modify a number of days of the timeframe.

10. The system of claim 8, wherein the timeframe selection control includes a slider bar that a user can drag horizontally to modify the start and end dates of the timeframe.

11. The system of claim 8, wherein the chart is automatically updated based on modification of the timeframe using the timeframe selection control.

12. A computer-implemented method for identifying patterns in continuous analyte data, the method comprising:

obtaining analyte data points from computer memory falling within a designated date range;

applying one of more filters to the analyte data to generate contributor data points;

weighting each the contributor data point based on the contributor data point'"'"'s analyte value;

assigning each weighted contributor data point to a matching epoch;

identifying if an epoch is a match based on whether the contributor or contributors assigned to the epoch meet at least one pattern threshold;

determining one or more patterns by scanning the epochs for flags; and

outputting information representative of the determined one or more patterns.

13. The method of claim 12, wherein the designated date range is at least three days.

14. The method of claim 12, wherein the one or more filters comprises filters selected from the group consisting of:

an analyte value filter that filters out analyte data points that have an analyte concentration value falling outside of a predetermined analyte level or range of analyte values;

(ii) a time range filter that filters out analyte data points measurements that falls outside of a time of day range;

(iii) a day of the week filter that filters out analyte data points measured one or more predetermined days of the week; and

(iv) an event filter that filters out data points that do not fall within a predetermined amount of time from an occurrence of an event.

15. The method of claim 14, wherein the event is an event selected from the group consisting of exercise, a meal, sleep and administration of a medication.

16. The method of claim 15, further comprising receiving user input indicative of the event using a user interface.

17. The method of claim 12, wherein weighting each contributor data point is based on a predetermined weight assignment map or mathematical assignment function.

18. The method of claim 12, wherein weighting each contributor comprises assigning a weighted value to each contributor, wherein the assigned weighted value is smaller if the analyte value is less clinically significant than if the analyte value is more clinically significant.

19. The method of claim 18, wherein the assignment of weighted values is non-linear based on the analyte value.

20. The method of claim 12, wherein each epoch spans a determined time of day and wherein each contributor is added to the epoch that spans the corresponding time of day.

21. The method of claim 12, wherein the pattern thresholds comprise thresholds selected from the group consisting of:

a threshold minimum number of contributors in an epoch;

a threshold average weighted value of the contributors in an epoch;

a threshold medium weighted value of the contributors in the epoch;

a threshold sum of the weighted values of the contributors in the epoch;

a threshold average difference of the weighted values of the contributors in the epoch;

a threshold standard deviation value of the weighted values of the contributors in the epoch; and

a threshold correlation value.

22. The method of claim 21, wherein at least one of the one or more pattern thresholds is defined in terms of a percentage.

23. The method of claim 12, wherein an epoch is flagged when at least some of the one or more thresholds are satisfied.

24. The method of claim 12, wherein determining the one or more patterns comprises determining a start and an end of the pattern.

25. The method of claim 24, wherein determining the start and the end of the pattern identifying a predetermined minimum number of contiguous matching epochs or a predetermined ratio of contiguous matching to non-matching epochs.

26. The method of claim 24, wherein the start of the pattern is determined based on identifying a first threshold number of contiguous matching epochs and the end of the pattern is determined based on identifying a second threshold number non-matching epochs.

27. The method of claim 24, wherein the outputted information comprises the start time and the end time.

28. The method of claim 12, wherein determining the one or more patterns is based on the frequency of matching epochs and the weighted values of the matching epochs.

29. The method of claim 12, wherein the outputted information comprises a triggering alert notifying a user of the one or more detected patterns.

30. The method of claim 12, wherein the outputted information is processed further to form or modify a medication administration routine.

31. A analyte concentration pattern detection system configured to perform the method of claim 12, the system comprising a continuous analyte sensor to generate the analyte concentration data points, computer memory to store the generated analyte data points, a processor module configured to perform the applying, the weighting, the assigning, the identifying, and the determining.

32. The system of claim 31, further comprising instructions stored in the memory, wherein the instructions, when executed by the processor module, cause the processor module to perform the applying, the weighting, the assigning, the identifying, and the determining.