MEDICAL DEVICES AND RELATED EVENT PATTERN PRESENTATION METHODS

• US 20200135311A1
• Filed: 10/30/2018
• Published: 04/30/2020
• Est. Priority Date: 10/30/2018
• Status: Active Application

• Current Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Inventors: Mairs, Nicholas S.
• Application Number:
  US16/175,774
• Publication Number:
  US 20200135311A1
• Time in Patent Office: Days
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Medical devices and related patient management systems and methods are provided. A method of presenting information pertaining to operation of a medical device involves obtaining historical glucose measurement data for a patient from a database, identifying, based on the historical glucose measurement data, a first plurality of event patterns within respective ones of a plurality of monitoring periods, determining a respective value for a confidence metric for each respective event pattern of the first plurality of event patterns based at least in part on a detection criterion associated with the respective event pattern, a respective subset of the historical glucose measurement data corresponding to the respective monitoring period of the plurality of monitoring periods associated with the respective event pattern, and an interval estimation metric associated with the historical glucose measurement data, and providing one or more graphical indicia influenced by the confidence metric.

Claims

1. A method of presenting information pertaining to operation of a medical device, the method comprising:

obtaining, by a computing device, historical glucose measurement data for a patient from a database;

identifying, by the computing device based on the historical glucose measurement data, a first plurality of event patterns within respective ones of a plurality of monitoring periods, wherein each monitoring period of the plurality of monitoring periods corresponds to a different time of day corresponding to a different subset of the historical glucose measurement data;

determining, by the computing device, a respective value for a confidence metric for each respective event pattern of the first plurality of event patterns based at least in part on a detection criterion associated with the respective event pattern, a respective subset of the historical glucose measurement data corresponding to the respective monitoring period of the plurality of monitoring periods associated with the respective event pattern, and an interval estimation metric associated with the historical glucose measurement data; and

providing, by the computing device, one or more graphical indicia influenced by the confidence metric.

2. The method of claim 1, further comprising prioritizing, by the computing device, the first plurality of event patterns based at least in part on the confidence metric, resulting in a prioritized list of event patterns, wherein the one or more graphical indicia are influenced by the prioritized list.

3. The method of claim 2, wherein providing the one or more graphical indicia comprises generating, by the computing device, a graphical user interface display comprising an event detection region including a respective pattern guidance display for each respective event pattern of the prioritized list ordered in accordance with the prioritization.

4. The method of claim 3, wherein prioritizing the first plurality of event patterns comprises ordering the plurality of event patterns according to the confidence metric and the respective pattern guidance displays are ordered according to the confidence metric.

5. The method of claim 1, wherein determining the respective value for the confidence metric comprises, for each respective event pattern:

identifying one or more measurement data samples of the respective subset of the historical glucose measurement data corresponding to the respective monitoring period of the plurality of monitoring periods associated with the respective event pattern that satisfy the detection criterion;

determining, for each respective measurement data sample of the one or more measurement data samples, a respective confidence value based on a difference between the respective measurement data sample and the detection criterion using the interval estimation metric, resulting in one or more confidence values; and

calculating the respective value for the confidence metric for the respective event pattern based on the one or more confidence values.

6. The method of claim 5, wherein calculating the respective value for the confidence metric comprises combining the one or more confidence values using Bayes'"'"' theorem.

7. The method of claim 6, the historical glucose measurement data for the patient being provided by a sensing arrangement, wherein the interval estimation metric comprises a confidence interval associated with measurements by the sensing arrangement.

8. The method of claim 1, the historical glucose measurement data for the patient being provided by a sensing arrangement, wherein the interval estimation metric comprises a confidence interval associated with measurements by the sensing arrangement.

9. A computer-readable medium having computer-executable instructions stored thereon that, when executed by a processing system of the computing device, cause the processing system to perform the method of claim 1.

10. A system comprising:

a database to maintain measurement values for a physiological condition in a body of a patient obtained by a medical device; and

a computing device coupled to the database to identify a first plurality of event patterns within a plurality of monitoring periods based on the measurement values, determine a respective value for a confidence metric for each respective event pattern of the first plurality of event patterns based at least in part on a detection criterion associated with the respective event pattern and an interval estimation metric associated with the measurement values, and provide one or more graphical indicia influenced by the respective values for the confidence metric, wherein each monitoring period of the plurality of monitoring periods corresponds to a different time of day corresponding to a different subset of the historical glucose measurement data.

11. The system of claim 10, wherein the one or more graphical indicia comprise a graphical user interface display comprising an event detection region including a respective pattern guidance display for each respective event pattern.

12. The system of claim 11, wherein the respective pattern guidance displays are ordered in accordance with the respective values for the confidence metric.

13. The system of claim 10, wherein the computing device provides a snapshot graphical user interface display including the event detection region to a client computing device communicatively coupled to the computing device, the client computing device displaying the snapshot graphical user interface display on a display device associated therewith.

14. The system of claim 10, wherein the medical device comprises a sensing arrangement and the interval estimation metric comprises a confidence interval associated with measurements by the sensing arrangement.

15. The system of claim 14, further comprising an infusion device communicatively coupled to the sensing arrangement and operable to deliver fluid to the body of the patient based on the measurement values, wherein and the fluid influences the physiological condition.

16. The system of claim 10, wherein the database stores the interval estimation metric in association with the medical device.

17. The system of claim 10, wherein the medical device comprises a continuous glucose monitoring (CGM) device.

18. The system of claim 10, wherein for each respective event pattern of the first plurality of event patterns, the computing device identifies one or more measurement data samples of a respective subset of the measurement values corresponding to a respective monitoring period of the plurality of monitoring periods associated with the respective event pattern that satisfy the detection criterion associated with the respective event pattern, determines, for each respective measurement data sample of the one or more measurement data samples, a respective confidence value based on a difference between the respective measurement data sample and the detection criterion using the interval estimation metric, and calculates the respective value for the confidence metric for the respective event pattern based on the one or more confidence values.

19. The system of claim 18, wherein:

the medical device comprises a sensing arrangement;

the interval estimation metric comprises a confidence interval associated with measurements by the sensing arrangement; and

the respective value for the confidence metric for the respective event pattern is calculated by combining the one or more confidence values using Bayes'"'"' theorem.

20. A system comprising a display device having rendered thereon a snapshot graphical user interface display comprising a graph overlay region and an event pattern detection region, wherein:

the graph overlay region comprises a graphical representation of historical measurement data for a physiological condition of a patient with respect to a time of day;

the event pattern detection region comprises a plurality of pattern guidance displays corresponding to a plurality of event patterns detected within a time period corresponding to the snapshot graphical user interface display based on the historical measurement data;

each respective event pattern of the plurality of event patterns corresponds to a respective one of a plurality of monitoring periods, wherein each monitoring period of the plurality of monitoring periods corresponds to a different time of day corresponding to a different subset of the historical glucose measurement data; and

the plurality of pattern guidance displays corresponding to the plurality of event patterns are prioritized in accordance with respective values for a confidence metric associated with the respective event patterns.