Multivariable artificial pancreas method and system

• US 10,646,650 B2
• Filed: 06/02/2016
• Issued: 05/12/2020
• Est. Priority Date: 06/02/2015
• Status: Active Grant

• Current Assignee: Illinois Institute of Technology
• Original Assignee: Illinois Institute of Technology
• Inventors: Cinar, Ali, Turksoy, Kamuran, Hajizadeh, Iman
• Primary Examiner(s): Lin, Jerry
• Application Number:
  US15/171,355
• Publication Number:
  US 20160354543A1
• Time in Patent Office: 1,440 Days
• Field of Search: 702 19
• CPC Class Codes: A61B 2560/0276 Determining malfunction
• CPC Class Codes: A61B 5/14503 invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors A61B5/1459, A61B5/1464, A61B5/1473, A61B5/1482, A61B5/14865 take precedence
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• 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: A61M 2005/14208 with a programmable infusion control system, characterised by the infusion program
• CPC Class Codes: A61M 2205/3523 using telemetric means
• CPC Class Codes: A61M 2205/50 with microprocessors or computers
• CPC Class Codes: A61M 2230/201 Glucose concentration
• CPC Class Codes: A61M 5/142 Pressure infusion, e.g. using pumps
• 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 40/63 for local operation
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Methods and modules for using physiological (biometric) variables to advance the state of the artificial pancreas. The method and system includes one or more modules for recursive model identification, hypoglycemia early alert and alarm, adaptive control, hyperglycemia early alert and alarm, plasma insulin concentration estimation, assessment of physical activity (e.g., presence, type, duration, expected effects on insulin sensitivity and GC), detection of acute stress and assessment of its impact on insulin sensitivity, detection of sleep and its stages and assessment of sleep stages on GC, sensor fault detection and diagnosis, software and controller performance evaluation and adjustment and/or pump fault detection and diagnosis.

Claims

1. A method for monitoring and/or treating glucose levels of a patient with diabetes, the method comprising:

automatically monitoring a glucose concentration level in the patient with a continuous glucose monitor worn by the patient to obtain a measured glucose level;

automatically monitoring physiological variables of the patient using at least one physiological sensor worn by the patient to obtain measured physiological variables;

automatically receiving the glucose concentration level and the physiological variables at a controller in communication combination with the continuous glucose monitor and the at least one physiological sensor;

the controller automatically determining a physiological state of the patient from the measured physiological variables by automatically matching the measured physiological variables to one of a plurality of predetermined classified physiological states stored in the controller;

the controller automatically estimating a future glucose level of the patient as a function of the measured glucose level, at least one of the measured physiological variables, and the determined physiological state of the patient;

the controller automatically computing the insulin dose to be infused and delivering instruction to an insulin pump to deliver the insulin dose to the patient; and

automatically updating the controller using further glucose measurements from the continuous glucose monitor and further physiological variables from the at least one physiological sensor.

2. The method according to claim 1, further comprising:

the controller automatically determining a change between at least two measured glucose levels that exceeds a predetermined value; and

the controller automatically comparing the change in the measured glucose level to any change in physiological state to determine a cause of the change in the measured glucose level.

3. The method according to claim 1, wherein the physiological state is determined using real time streaming data from the at least one physiological sensor and is selected from sleep, physical activity, stress, or combinations thereof.

4. The method according to claim 3, wherein the at least one physiological sensor worn by the patient measures physiological variables selected from a combination of movement, skin temperature, dissipated heat from the body, galvanic skin response, heart rate, ECG, respiration rate, posture, or body position.

5. The method according to claim 1, further comprising automatically predicting and alarming the patient of a predicted future low blood glucose concentration and the controller automatically determining and suggesting responsive measures including carbohydrate consumption to prevent low blood glucose concentration.

6. The method according to claim 1, further comprising automatically detecting food consumption and/or a rapid glucose level increase without manual input by the patient.

7. The method according to claim 1, wherein the physiological state is exercise and further comprising automatically determining an intensity, type, and/or duration of the exercise, wherein the predetermined classified physiological states includes predetermined aerobic and anaerobic intensity exercise classifications.

8. The method according to claim 1, wherein the physiological state is acute stress.

9. The method according to claim 1, wherein the physiological state is sleep and further comprising automatically determining a sleep pattern and stage of sleep for the patient.

10. The method according to claim 1, further comprising the controller automatically monitoring for and determining a faulty or missing measurement information transmitted by the continuous glucose monitor or the physiological sensors and estimating measurements of any malfunctioning sensor for the predicting.

11. The method of claim 1, wherein the predetermined classified physiological states include a predetermined acute stress classification and predetermined sleep stage or sleep pattern classification.

12. A device for monitoring and/or treating patient glucose levels, the device comprising:

a continuous glucose monitor adapted to measure a glucose level of a patient;

a physiological status monitoring system worn by the patient and adapted to measure physiological variables of the patient; and

an automatic controller in communication with the glucose monitor and the physiological status monitoring system, the controller including a recordable medium comprising a plurality of stored predetermined classified physiological states selected from physical activity states, stress states, and sleep states, wherein the controller is configured to compare the measured physiological variables to the stored predetermined classified physiological states to predict a future glucose measurement.

13. The device according to claim 12, wherein the controller comprises a recursively updated stable multivariate time-series model.

14. The device according to claim 12, further comprising a hypoglycemia detection alarm.

15. The device according to claim 12, wherein the controller is configured to detect food consumption and to compute a bolus insulin amount without manual input by the patient.

16. The device according to claim 12, further comprising an estimator adapted to estimate a future plasma glucose concentration and/or a future plasma insulin concentration.

17. The device according to claim 12, wherein the controller detects and classifies the physiological state as a function of activity intensity and duration, body position, heart rate, respiration pattern, or a combination thereof.

18. The device according to claim 12, further comprising an insulin pump in combination with the controller, wherein the controller controls an insulin infusion as a function of the predicted future glucose level.

19. A method for monitoring and/or treating glucose levels of a patient with diabetes, the method comprising:

automatically monitoring a glucose concentration level in the patient with a continuous glucose monitor worn by the patient to obtain a measured glucose level;

automatically measuring physiological variables of the patient using at least one physiological sensor worn by the patient to obtain measured physiological variables;

automatically receiving the glucose concentration level and the physiological variables at a controller in communication combination with the continuous glucose monitor and the at least one physiological sensor;

the controller automatically interpreting the measured physiological variables to determine a physiological state of the patient, the physiological state selected from a group consisting of exercise, stress, sleep, or combinations thereof;

the controller automatically estimating a future glucose level of the patient as a function of the measured glucose level and the determined physiological state of the patient;

the controller automatically computing the insulin dose to be infused and delivering instruction to an insulin pump to deliver the insulin dose to the patient; and

automatically updating the controller using further glucose measurements from the continuous glucose monitor and further physiological variables from the at least one physiological sensor.

20. The method of claim 19, wherein automatically interpreting the measured physiological variables comprises automatically matching the measured physiological variables to one of a plurality of predetermined classified exercise states, stress states, and sleep states stored in the controller.

21. The method of claim 20, wherein the predetermined classified exercise states include predetermined aerobic and anaerobic intensity classifications, the predetermined stress states include predetermined acute stress classifications, and the predetermined sleep states include predetermined sleep stage classification.