Unified Platform for Monitoring and Control of Blood Glucose Levels in Diabetic Patients

• US 20150018633A1
• Filed: 06/23/2012
• Published: 01/15/2015
• Est. Priority Date: 06/23/2011
• Status: Active Grant

• Current Assignee: University of Virginia Patent Foundation (University of Virginia)
• Original Assignee: University of Virginia Patent Foundation (University of Virginia)
• Inventors: Keith-Hynes, Patrick T., Breton, Marc D., Patek, Stephen D., Kovachev, Boris P.
• Granted Patent: US 10,610,154 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/33 specially adapted for cooperation with other devices
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 5/747 in case of emergency, i.e. alerting emergency services
• CPC Class Codes: A61K 38/28 Insulins
• CPC Class Codes: A61M 5/16831 Monitoring, detecting, signalling or eliminating infusion flow anomalies low-level float-valves causing cut-off A61M5/40; indicating or recording presence, absence or direction of flow in general G01P13/0066
• CPC Class Codes: A61M 5/1723 using feedback of body parameters, e.g. blood-sugar, pressure measurement of body parameters A61B5/00
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment

Abstract

A flexible system capable of utilizing data from different monitoring techniques and capable of providing assistance to patients with diabetes at several scalable levels, ranging from advice about long-term trends and prognosis to real-time automated closed-loop control (artificial pancreas). These scalable monitoring and treatment strategies are delivered by a unified system called the Diabetes Assistant (DiAs) platform. The system provides a foundation for implementation of various monitoring, advisory, and automated diabetes treatment algorithms or methods. The DiAs recommendations are tailored to the specifics of an individual patient, and to the patient risk assessment at any given moment.

Claims

1. A system for managing glycemic control of a patient, comprising:

an input module configured to accept input data from one or more of a plurality of diverse blood glucose measurement devices and one or more of a plurality of diverse insulin delivery devices;

a data classifier module configured to classify data accepted by said input module and to determine appropriate processing of said input data according to its classification;

a patient state estimation module configured to process input data in accordance with at least one data processing algorithm corresponding to the classification of the input data as determined by the data classifier module;

a patient risk status module configured to determine a level of risk of said patient with respect to abnormal glycemic states using processed data from said patient state estimation module; and

an output module configured to output advisory messages, patient alerts, and control signals for said blood glucose measurement devices and said insulin delivery devices based on the level of risk determined by said patient risk status module.

2. A system as set forth in claim 1, wherein said diverse blood glucose measurement devices comprise a self-monitoring blood glucose (SMBG) device and a continuous glucose monitoring (CGM) device.

3. A system as set forth in claim 1, wherein said diverse insulin delivery devices comprise a multiple daily insulin injection pump and a continuous subcutaneous insulin injection pump.

4. A system as set forth in claim 1, wherein said input data is classified as one of SMBG data, SMBG plus insulin pump data, CGM data, or CGM plus insulin pump data.

5. A system as set forth in claim 1, wherein said input module is further configured to receive data from a heart rate sensor.

6. A system as set forth in claim 1, wherein said input module is further configured to receive data from a blood pressure sensor.

7. A system as set forth in claim 1, wherein said input module is further configured to receive data from an accelerometer sensor.

8. A system as set forth in claim 1, wherein said input module is further configured to receive data from an ECG/EKG sensor.

9. A system as set forth in claim 1, wherein said input module is further configured to receive data from a heart rate sensor.

10. A system as set forth in claim 1, wherein said input module is further configured to receive data from a plurality of different physiological sensors for said patient, said plurality of sensors forming a body sensor network.

11. A system as set forth in claim 1, further comprising a telecommunications module configured to communicate patient status data to a remote health care provider.

12. A system as set forth in claim 1, further comprising a telecommunications module configured to communicate patient status data to an emergency responder.

13. A system as set forth in claim 1, further comprising a telecommunications module configured to communicate patient status data to a public health care organization.

14. A system as set forth in claim 1, further comprising a diabetes management or control application module downloaded from a third party source.

15. A system as set forth in claim 3, further comprising a safety service module configured to analyze data from at least one of said insulin injection pumps and to determine an operational state of said pump from said data analysis.

16. A system as set forth in claim 15, wherein said safety service module is further configured to screen insulin bolus commands for safety prior to said commands being delivered to an insulin injection pump.

17. A system as set forth in claim 1, wherein said system is implemented on a cell phone.

18. A system as set forth in claim 1, wherein said system is implemented on a tablet computer.

19. A system as set forth in claim 1, wherein said control signals are configured to cause monitoring of said patient to be modified from a current level of monitoring to a higher level of monitoring in dependence on a determined risk status of said patient and a determination of data quality and/or density.

20. A system as set forth in claim 1, wherein said control signals are configured to carry out closed-loop control of insulin delivery to said patient in dependence on a determined risk status of said patient.

21. A non-transitory computer-readable storage medium having stored thereon computer-executable instructions for managing glycemic control of a patient, comprising instructions for:

accepting input data from one or more of a plurality of diverse blood glucose measurement devices and one or more of a plurality of diverse insulin delivery devices;

classifying data accepted by said input module and to determine appropriate processing of said input data according to its classification;

processing input data in accordance with at least one data processing algorithm corresponding to the classification of the input data as determined by the data classifier module;

determining a level of risk of said patient with respect to abnormal glycemic states using processed data from said patient state estimation module; and

outputting advisory messages, patient alerts, and control signals for said blood glucose measurement devices and said insulin delivery devices based on the level of risk determined by said patient risk status module.

22. A non-transitory computer-readable storage medium as set forth in claim 21, wherein said diverse blood glucose measurement devices comprise a self-monitoring blood glucose (SMBG) device and a continuous glucose monitoring (CGM) device.

23. A non-transitory computer-readable storage medium as set forth in claim 21, wherein said diverse insulin delivery devices comprise a multiple daily insulin injection pump and a continuous subcutaneous insulin injection pump.

24. A non-transitory computer-readable storage medium as set forth in claim 21, wherein said input data is classified as one of SMBG data, SMBG plus insulin pump data, CGM data, or CGM plus insulin pump data.

25. A non-transitory computer-readable storage medium as set forth in claim 21, further comprising instructions for receiving data from a heart rate sensor.

26. A non-transitory computer-readable storage medium as set forth in claim 21, further comprising instructions for receiving data from a blood pressure sensor.

27. A non-transitory computer-readable storage medium as set forth in claim 21, further comprising instructions for receiving data from an accelerometer sensor.

28. A non-transitory computer-readable storage medium as set forth in claim 21, further comprising instructions for receiving data from an ECG/EKG sensor.

29. A non-transitory computer-readable storage medium as set forth in claim 21, further comprising instructions for receiving data from a heart rate sensor.

30. A non-transitory computer-readable storage medium as set forth in claim 21, further comprising instructions for receiving data from a plurality of different physiological sensors for said patient, said plurality of sensors forming a body sensor network.

31. A non-transitory computer-readable storage medium as set forth in claim 21, further comprising instructions for communicating patient status data to a remote health care provider over a telecommunications network.

32. A non-transitory computer-readable storage medium as set forth in claim 21, further comprising instructions for communicating patient status data to an emergency responder over a telecommunications network.

33. A non-transitory computer-readable storage medium as set forth in claim 21, further comprising instructions for communicating patient status data to a public health care organization over a telecommunications network.

34. A non-transitory computer-readable storage medium as set forth in claim 21, further comprising diabetes management or control application instructions provided by a third party source.

35. A non-transitory computer-readable storage medium as set forth in claim 23, further comprising instructions for analyzing data from at least one of said insulin injection pumps and to determine an operational state of said pump from said data analysis.

36. A non-transitory computer-readable storage medium as set forth in claim 35, further comprising instructions for screening insulin bolus commands for safety prior to said commands being delivered to an insulin injection pump.

37. A non-transitory computer-readable storage medium as set forth in claim 21, wherein said medium is installed on a cell phone.

38. A non-transitory computer-readable storage medium as set forth in claim 21, wherein said medium is installed on a tablet computer.

39. A non-transitory computer-readable storage medium as set forth in claim 21, wherein said control signals are configured to cause monitoring of said patient to be modified from a current level of monitoring to a higher level of monitoring in dependence on a determined risk status of said patient and a determination of data quality and/or density.

40. A non-transitory computer-readable storage medium as set forth in claim 21, wherein said control signals are configured to carry out closed-loop control of insulin delivery to said patient in dependence on a determined risk status of said patient.