INFUSION SYSTEMS AND METHODS FOR PATTERN-BASED THERAPY ADJUSTMENTS

• US 20180169333A1
• Filed: 12/19/2017
• Published: 06/21/2018
• Est. Priority Date: 12/21/2016
• Status: Active Grant

• Current Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Minimed Incorporated (Medtronic Plc)
• Inventors: Grosman, Benyamin, Legray, David, Rankers, Ulrich H., Roy, Anirban, Vargas, Steven J., Sadeghzadeh, Mahta
• Granted Patent: US 10,861,591 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• 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 5/7465 Arrangements for interactive communication between patient and care services, e.g. by using a telephone network telemetry of measured physiological signal A61B5/0002
• CPC Class Codes: A61K 38/28 Insulins
• CPC Class Codes: A61M 2005/14208 with a programmable infusion control system, characterised by the infusion program
• CPC Class Codes: A61M 2205/18 with alarm
• CPC Class Codes: A61M 2205/3303 Using a biosensor
• CPC Class Codes: A61M 2205/35 Communication
• CPC Class Codes: A61M 2205/3569 sublocal, e.g. between console and disposable
• CPC Class Codes: A61M 2205/3584 using modem, internet or bluetooth
• CPC Class Codes: A61M 2205/3592 using telemetric means, e.g. radio or optical transmission
• CPC Class Codes: A61M 2205/50 with microprocessors or computers
• CPC Class Codes: A61M 2205/502 User interfaces, e.g. screens or keyboards
• CPC Class Codes: A61M 2205/52 with memories providing a history of measured variating parameters of apparatus or patient
• CPC Class Codes: A61M 2205/8206 battery-operated
• CPC Class Codes: A61M 2230/201 Glucose concentration
• CPC Class Codes: A61M 2230/63 Motion, e.g. physical activity
• CPC Class Codes: A61M 5/142 Pressure infusion, e.g. using pumps
• CPC Class Codes: A61M 5/14244 adapted to be carried by the patient, e.g. portable on the body
• CPC Class Codes: A61M 5/145 : using pressurised reservoirs, e.g. pressurised by means of pistons
• CPC Class Codes: A61M 5/16804 : Flow controllers
• 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 5/16877 : Adjusting flow; Devices for setting a flow rate
• 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 10/20 : for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 : for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/17 : delivered via infusion or injection
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/60 : relating to nutrition control, e.g. diets
• 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
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders

Abstract

Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device capable of delivering fluid to a patient involves obtaining, by a control system associated with the infusion device, measurement values indicative of a condition of the patient provided by a sensing device, identifying an event pattern based at least in part on the measurement values for the condition and historical data associated with the patient, generating a notification indicative of the event pattern in response to identifying the event pattern, and adjusting operation of the infusion device to deliver the fluid to the patient in a manner that is influenced by the event pattern in response to receiving user input confirming the event pattern.

Claims

1. A method of operating an infusion device capable of delivering fluid to a patient, the fluid influencing a physiological condition of the patient, the method comprising:

obtaining, by a control system associated with the infusion device, measurement values indicative of a condition of the patient provided by a sensing device;

identifying an event pattern based at least in part on the measurement values for the condition and historical data associated with the patient;

in response to identifying the event pattern, generating, by the control system, a notification indicative of the event pattern; and

in response to receiving user input confirming the event pattern, adjusting, by the control system, operation of the infusion device to deliver the fluid to the patient in a manner that is influenced by the event pattern.

2. The method of claim 1, the measurement values being indicative of the physiological condition of the patient and the historical data including historical meal event data and historical measurement data for the physiological condition of the patient corresponding to the historical meal event data, wherein identifying the event pattern comprises detecting a probable meal as a function of the measurement values for the physiological condition based at least in part on a correlation between the historical meal event data and the historical measurement data.

3. The method of claim 2, wherein the probable meal comprises a probable meal size, wherein adjusting the operation comprises:

determining, by the control system, a meal bolus dosage based at least in part on the probable meal size; and

automatically operating, by the control system, an actuation arrangement of the infusion device to deliver the meal bolus dosage of the fluid to the patient.

4. The method of claim 3, wherein:

the probable meal comprises a probable nutritional content; and

adjusting the operation of the infusion device comprises automatically adjusting the meal bolus dosage based at least in part on the probable nutritional content.

5. The method of claim 2, wherein the probable meal comprises a probable meal size, wherein adjusting the operation of the infusion device comprises automatically adjusting a closed-loop control parameter based at least in part on the probable meal size.

6. The method of claim 2, wherein the probable meal comprises a probable nutritional content, wherein adjusting the operation of the infusion device comprises automatically adjusting a closed-loop control parameter based at least in part on the probable nutritional content.

7. The method of claim 1, the historical data including historical exercise event data and historical measurement data for the patient corresponding to the historical exercise event data, wherein identifying the event pattern comprises detecting a probable exercise event as a function of the measurement values for the condition based at least in part on a correlation between the historical exercise event data and the historical measurement data.

8. The method of claim 7, wherein adjusting the operation of the infusion device comprises temporarily adjusting a closed-loop control parameter in response to the probable exercise event.

9. The method of claim 8, further comprising determining an expected duration of the probable exercise event based at least in part on the historical exercise event data, wherein a duration associated with the temporarily adjusting of the closed-loop control parameter is influenced by the expected duration.

10. The method of claim 1, wherein identifying the event pattern comprises:

determining a model for calculating a metric indicative of the event pattern as a function of the condition of the patient based on the historical data;

calculating a current value for the metric as a function of the measurement values using the model; and

detecting the event pattern based on a relationship between the current value and a threshold.

11. The method of claim 10, wherein:

determining the model comprises;

identifying a subset of predictive variables for the event pattern from among the historical data based on a correlation between the subset of predictive variables and previous instances of the event pattern; and

determining the model for calculating a probability of occurrence of the event pattern as a function of the subset of predictive variables;

calculating the current value comprises calculating a current probability of the event pattern a function of the measurement values using the model; and

detecting the event pattern comprises detecting the event pattern when the current probability is greater than a threshold probability.

12. A computer-readable medium having instructions stored thereon that are executable by the control system associated with the infusion device to perform the method of claim 1.

13. A method of operating an infusion device capable of delivering insulin to a patient, the method comprising:

obtaining, by a control system associated with the infusion device, sensor glucose measurement values for the patient provided by a glucose sensing arrangement;

identifying, by the control system, an event pattern indicative of an event based at least in part on the sensor glucose measurement values and a correlation between historical sensor glucose measurement values associated with the patient and previous instances of the event;

determining a probable characteristic of the event based at least in part on the previous instances of the event; and

after identifying the event pattern, operating the infusion device to deliver the insulin to the patient in a manner that is influenced by the probable characteristic of the event.

14. The method of claim 13, the event comprising a meal, wherein:

determining the probable characteristic comprises determining a probable meal size based at least in part on a current operational context and a correlation between historical operational contexts and historical meal data associated with the patient;

operating the infusion device to deliver the insulin to the patient in a manner that is influenced by the probable characteristic of the event comprises;

determining a meal bolus dosage of the insulin based on the probable characteristic; and

operating an actuation arrangement of the infusion device to deliver the meal bolus dosage.

15. The method of claim 13, the event comprising a meal, wherein:

determining the probable characteristic comprises determining a probable meal content based at least in part on a current operational context and a correlation between historical operational contexts and historical meal data associated with the patient;

operating the infusion device to deliver the insulin to the patient in a manner that is influenced by the probable characteristic of the event comprises;

determining a meal bolus dosage of the insulin based on the probable meal content; and

operating an actuation arrangement of the infusion device to deliver the meal bolus dosage.

16. The method of claim 13, wherein operating the infusion device to deliver the insulin to the patient in a manner that is influenced by the probable characteristic of the event comprises temporarily adjusting a closed-loop control parameter in a manner that is influenced by the probable characteristic.

17. The method of claim 13, further comprising:

determining a model for calculating a probability of occurrence of the event as a function of a glucose level of the patient and a time of day based at least in part on correlations between the historical sensor glucose measurement values and timestamps associated with the previous instances of the event; and

calculating a current probability of occurrence of the event as a function of the sensor glucose measurement values and a current time of day using the model, wherein identifying the event pattern comprises identifying the event pattern when the current probability is greater than a threshold.

18. The method of claim 13, the event comprising exercise, wherein:

determining the probable characteristic comprises determining a probable exercise duration based at least in part on a current operational context and a correlation between historical operational contexts and historical exercise durations associated with previous exercise events for the patient; and

operating the infusion device to deliver the insulin to the patient in a manner that is influenced by the probable characteristic of the event comprises adjusting a closed-loop control parameter for a temporary duration of time influenced by the probable exercise duration.

19. The method of claim 13, the event comprising exercise, wherein:

determining the probable characteristic comprises determining a probable exercise intensity based at least in part on a current operational context and a correlation between historical operational contexts and historical exercise intensities associated with previous exercise events for the patient; and

operating the infusion device to deliver the insulin to the patient in a manner that is influenced by the probable characteristic of the event comprises temporarily adjusting a closed-loop control parameter by an amount influenced by the probable exercise intensity.

20. An infusion system comprising:

an actuation arrangement operable to deliver fluid to a patient, the fluid influencing a physiological condition of the patient;

a sensing arrangement to provide measurement values indicative of the physiological condition of the patient;

a data storage element to maintain historical measurement data corresponding to previous instances of an event associated with the patient; and

a control system coupled to the actuation arrangement, the sensing arrangement, and the data storage element to identify an event pattern indicative of the event based at least in part on a correlation between the measurement values and the historical measurement data corresponding to previous instances of the event and thereafter adjust operation of the actuation arrangement to modify delivery of the fluid to the patient in a manner that is influenced by the historical measurement data corresponding to previous instances of the event.