Formulation and manipulation of databases of analyte and associated values

• US 7,024,236 B2
• Filed: 04/08/2004
• Issued: 04/04/2006
• Est. Priority Date: 08/18/2000
• Status: Active Grant

• Current Assignee: LifeScan IP Holdings, LLC
• Original Assignee: Animas Technologies LLC (Johnson & Johnson)
• Inventors: Potts, Russell O., Tierney, Michael J., Lesho, Matthew J., Wei, Charles W., Ford, Russell
• Primary Examiner(s): Nasser, Robert L.
• APPLICATION: US 20050010093A1
• Time in patent office: 726 Days
• Field of Search: 600345-350, 600/365, 707/104.1
• US Class Current: 600/345
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 70/00 ICT specially adapted for the handling or processing of medical references
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing
• CPC Class Codes: Y10S 707/99948 Application of database or data structure, e.g. distributed, multimedia, or image

Abstract

The present invention relates to methods of formulating analyte data databases, the databases themselves, and methods of manipulating the same. In one aspect the present invention includes the formulation of analyte data points, derived data, and data attributes databases comprising data points collected using an analyte monitoring device capable of frequent monitoring of analyte concentrations or amounts. Such data points may comprise acquired data (e.g., values corresponding to analyte concentrations or amounts as measured by said analyte monitoring device). These data points are then associated with one or more relevant data attributes. The resulting databases may be manipulated to determine relationships among the components of the database.

Claims

1. A method of formulating one or more analyte data databases, said method comprising:

collecting analyte measurement values from one or more subject using an analyte monitoring device, comprising a sensing device, for each subject, and said analyte monitoring device providing (i) frequent analyte measurement values, wherein said analyte measurement values comprise acquired data points that are specifically related to analyte amount or concentration in she subject, (ii) one or more data attributes, and (iii) one or more error messages related to skipped analyte measurement values; and

formulating said one or more analyte data databases by associating each of said data points and each of said one or more error messages related to skipped analyte measurement values with one or more data attributes.

2. The method of claim 1, wherein said data points further comprise derived data determined from one or more acquired data points and the derived data are associated with the data points from which they are derived.

3. The method of claim 2, wherein each of said derived data are associated with one or more data attributes.

4. The method of claim 2, wherein said analyte is glucose and said derived data comprises glucose amount or concentration.

5. The method of claim 4, wherein said analyte monitoring device is a glucose monitoring device, said glucose monitoring device comprising a sensing device, a display, and means to provide an audible alert when glucose levels in a subject being monitored are outside of a predetermined range.

6. The method of claim 1, wherein said analyte measurement values are collected from a single individual.

7. The method of claim 1, wherein said analyte measurement values are collected from more than one individual.

8. The method of claim 7, wherein said formulating further comprises compiling multiple databases from each database where the data points are collected from a single individual and the data points for each single individual are associated with one or more relevant data attributes.

9. The method of claim 1, wherein said analyte is a biological analyte.

10. The method of claim 9, wherein said biological analyte is glucose.

11. The method of claim 1, wherein said acquired data points comprise electrochemical signals.

12. The method of claim 11, wherein said data attributes are selected from the group consisting of:

chronological information, user perspiration levels, device operating temperature, missed measurements;

skipped measurements, user body temperature, user skin conductance, environmental variables, alarm events, activity codes, total excursion, mean value, statistical function, subject code, demographic information, physical characteristics, and disease-associated characteristics.

13. The method of claim 1, wherein said analyte monitoring device is capable of measuring more than one analyte.

14. The method of claim 13, wherein one of said analytes is glucose.

15. The method of claim 1, wherein one or more of said analyte measurement values, one or more of said error messages, and one or more of said data attributes are transferred to a server via a network.

16. The method of claim 15, wherein said formulating is carried out on said server.

17. The method of claim 15, wherein said server communicates with said analyte monitoring device.

18. The method of claim 1, wherein said one or more data attributes associated with said data point or said skipped measurement value is one or more data attribute provided by the analyte monitoring device.

19. The method of claim 18, wherein said one or more data attributes are selected from the group consisting of chronological information, user perspiration level, device operating temperature, user body temperature, user skin conductance, environmental variable, number of alarm events, and type of alarm events.

20. The method of claim 1, wherein said one or more data attributes associated with said data point or said skipped measurement value is one or more data attribute comprising a user input.

21. The method of claim 20, wherein said one or more data attributes are selected from the group consisting of activity codes, sleep and administration of medications, dose of medications, and times of medications.

22. The method of claim 1, wherein said one or more data attributes associated with said data point or said skipped measurement value is one or more data identifier.

23. The method of claim 22, wherein said one or more data identifiers is selected from the group consisting of maximum analyte values, minimum analyte values, hypoglycemic analyte values, and hyperglycemic analyte values.

24. The method of claim 1, wherein said one or more data attributes associated with said data point or said skipped measurement value is one or more subject identifier.

25. The method of claim 24, wherein said one or more subject identifiers is selected from the group consisting of a subject code, demographic information, physical characteristic, selected aspects of the subject'"'"'s medical history, and disease-associated characteristics.

26. One or more analyte data databases on a computer readable medium, comprising

data points collected using an analyte monitoring device, comprising a sensing device, wherein said analyte monitoring device provides (i) frequent analyte measurement values and said analyte measurement values comprise data points that are specifically related to analyte amount or concentration, and (ii) one or more data attributes;

one or more data attributes; and

one or more error messages related to skipped analyte measurement values, wherein the data points and each of said one or more error messages related to skipped analyte measurement values are associated with one or more relevant data attributes.

27. The one or more databases of claim 26, wherein said data points further comprise derived data determined from one or more acquired data points and the derived data are associated with the data points from which they are derived.

28. The one or more databases of claim 27, wherein each of said derived data are associated with one or more data attributes.

29. The one or more databases of claim 27, wherein said analyte is glucose and said derived data comprises glucose amount or concentration.

30. The one or more databases of claim 26, wherein said analyte measurement values are collected from a single individual.

31. The one or more databases of claim 26, wherein said analyte measurement values are collected from more than one individual.

32. The one or more databases of claim 26, wherein said analyte is a biological analyte.

33. The one or more databases of claim 32, wherein said biological analyte is glucose.

34. The one or more databases of claim 26, wherein said acquired data points comprise electrochemical signals.

35. The one or more databases of claim 34, wherein said data attributes are selected from the group consisting of:

chronological information, user perspiration levels, device operating temperature, missed measurements;

skipped measurements, user body temperature, user skin conductance, environmental variables, alarm events, activity codes, total excursion, mean value, statistical function, subject code, demographic information, physical characteristics, and disease-associated characteristics.

36. The one or more databases of claim 26, wherein said analyte measurement values comprise analyte measurement values for more than one analyte.

37. The one or more databases of claim 36, wherein one of said analytes is glucose.

38. A method of manipulating one or more analyte data databases, comprising

providing the one or more analyte data databases of claim 26;

and

manipulating said data points via said attributes associated with said data points to determine relationships between said data points and said attributes.

39. The method of claim 38, wherein said one or more analyte databases are located on a network database server.

40. The method of claim 39, wherein said manipulating is carried out on said network database server.

41. A method of manipulating one or more analyte data databases, comprising

providing the one or more analyte data databases of claim 26;

and

manipulating said attributes via said data points associated with said attributes to determine relationships between said attributes and said data points.

42. The method of claim 41, wherein said one or more analyte databases are located on a network database server.

43. The method of claim 42, wherein said manipulating is carried out on said network database server.

44. The one or more databases of claim 26, wherein said one or more data attributes associated with said data point or said skipped measurement value is one or more data attribute provided by the analyte monitoring device.

45. The one or more databases of claim 44, wherein said one or more data attributes are selected from the group consisting of chronological information, user perspiration level, device operating temperature, user body temperature, user skin conductance, environmental variable, number of alarm events, and type of alarm events.

46. The one or more databases of claim 26, wherein said one or more data attributes associated with said data point or said skipped measurement value is one or more data attribute comprising a user input.

47. The one or more databases of claim 46, wherein said one or more data attributes are selected from the group consisting of activity codes, sleep and administration of medications, dose of medications, and times of medications.

48. The one or more databases of claim 26, wherein said one or more data attributes associated with said data point or said skipped measurement value is one or more data identifier.

49. The one or more databases of claim 48, wherein said one or more data identifiers is selected from the group consisting of maximum analyte values, minimum analyte values, hypoglycemic analyte values, and hyperglycemic analyte values.

50. The one or more databases of claim 26, wherein said one or more data attributes associated with said data point or said skipped measurement value is one or more subject identifier.

51. The one or more databases of claim 50, wherein said one or more subject identifiers is selected from the group consisting of a subject code, demographic information, physical characteristic, selected aspects of the subject'"'"'s medical history, and disease-associated characteristics.