Integrated delivery device for continuous glucose sensor

• US 9,937,293 B2
• Filed: 08/19/2015
• Issued: 04/10/2018
• Est. Priority Date: 02/26/2004
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Brauker, James H., Tapsak, Mark A., Saint, Sean T., Kamath, Apurv Ullas, Neale, Paul V., Simpson, Peter C., Mensinger, Michael Robert, Markovic, Dubravka
• Primary Examiner(s): Eisenberg, Rebecca E
• Application Number:
  US14/830,568
• Publication Number:
  US 20150351683A1
• Time in Patent Office: 965 Days
• CPC Class Codes: A61B 2560/0443 Modular apparatus
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1486 using enzyme electrodes, e.g. with immobilised oxidase
• CPC Class Codes: A61B 5/14865 invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors
• CPC Class Codes: A61B 5/4839 combined with drug delivery
• CPC Class Codes: A61M 11/00 Sprayers or atomisers specially adapted for therapeutic purposes in general B05B; aerosol containers B65D83/14
• CPC Class Codes: A61M 15/0001 Details of inhalators; Constructional features thereof
• CPC Class Codes: A61M 2005/1726 the body parameters being measured at, or proximate to, the infusion site
• CPC Class Codes: A61M 2205/18 with alarm
• CPC Class Codes: A61M 2205/3303 Using a biosensor
• 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 2230/201 Glucose concentration
• 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: A61M 5/31525 Dosing burettes, pipettes B01L3/02
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 40/63 for local operation

Abstract

Systems and methods for integrating a continuous glucose sensor, including a receiver, a medicament delivery device, and optionally a single point glucose monitor are provided. Manual integrations provide for a physical association between the devices wherein a user (for example, patient or doctor) manually selects the amount, type, and/or time of delivery. Semi-automated integration of the devices includes integrations wherein an operable connection between the integrated components aids the user (for example, patient or doctor) in selecting, inputting, calculating, or validating the amount, type, or time of medicament delivery of glucose values, for example, by transmitting data to another component and thereby reducing the amount of user input required. Automated integration between the devices includes integrations wherein an operable connection between the integrated components provides for full control of the system without required user interaction.

Claims

1. A method for monitoring and treating diabetes, the method using a device configured to receive glucose concentration data from a glucose sensor, the glucose sensor configured to continuously monitor glucose concentration in a user over time, the device further configured for outputting data including personalized therapy recommendations, comprising:

receiving data from a glucose sensor;

evaluating the received sensor data to determine one or more signal parameters, the signal parameters including one or more of a glucose concentration, a glucose concentration rate of change, or a glucose concentration acceleration;

evaluating the received sensor data to adaptively learn one or more patterns occurring in the received sensor data over time;

determining an output including at least one of an optimum time, amount, or type of medicament based on one or more of the determined signal parameters and at least one adaptively learned pattern; and

repeating, over time, the evaluating the received sensor data to adaptively learn one or more patterns,

wherein the determined output provides a personalized therapy recommendation for the user that improves over time based on the adaptive learning,

wherein the device is in RF communication with, and is configured for semi-automated integration with, a medicament delivery device, wherein the medicament delivery device is a pen or a pump, for delivery of insulin or glucagon or both, and

wherein the device is configured to implement a fail-safe mode, wherein if abnormal conditions are detected, the device reverts from a semi-automated mode to a manual mode.

2. The method of claim 1, further comprising displaying the output on a user interface associated with the device or in signal communication with the device.

3. The method of claim 1, further comprising prompting the user to enter a validation of the personalized therapy recommendation prior to providing the output to a pen or a pump, thereby enabling a degree of user or doctor control over therapy decisions.

4. The method of claim 3, wherein the entered validation of the personalized therapy recommendation includes data about at least one of exercise, food, medicament intake, and rest, and wherein the evaluating to adaptively learn one or more patterns further comprises basing the evaluating at least in part on the entered validation data.

5. The method of claim 1, wherein the pen or pump is configured to send data including at least one of an amount, type, or time of medicament delivery to the device.

6. The method of claim 5, wherein the evaluating to adaptively learn one or more patterns further comprises evaluating the medicament delivery data from the pen or pump, and wherein the evaluating is further based on a determined optimum time, amount, or type of medicament delivery to the user.

7. The method of claim 1, wherein the abnormal conditions include receiving a reference glucose value that indicates a discrepancy from the glucose concentration value that was measured by the sensor that could cause risk to the user if incorrect therapy is administered.

8. The method of claim 1, wherein the evaluating the received sensor data to adaptively learn one or more patterns, and the repeating the evaluating, is configured to adaptively learn a user response to at least one of a time of medicament dispensing, an amount of medicament dispensed, and a type of medicament dispensed.

9. The method of claim 8, wherein the adaptively learning a user response is performed in a startup or learning period.

10. The method of claim 8, wherein the adaptively learning a user response is performed iteratively over a time period, and wherein the determined output adaptively adjusts over time based on iterations of the adaptive learning to newly-learned user responses.

11. The method of claim 10, wherein the learned user response includes insulin sensitivity.

12. The method of claim 1, wherein the evaluating and the determining steps are performed in a startup or learning period.

13. The method of claim 1, wherein the evaluating steps and the determining are performed iteratively, and wherein the improving personalized therapy recommendations over time is based on iterations of the adaptively learning steps.

14. A method for monitoring and treating diabetes, the method using a device configured to receive glucose concentration data from a glucose sensor, the glucose sensor configured to continuously monitor glucose concentration in a user over time, the device further configured for outputting data including personalized therapy recommendations, comprising:

receiving data from a glucose sensor;

evaluating the received sensor data to determine one or more signal parameters, the signal parameters including one or more of a glucose concentration, a glucose concentration rate of change, or a glucose concentration acceleration;

evaluating the received sensor data to adaptively learn one or more patterns occurring in the received sensor data over time;

determining an output including at least one of an optimum time, amount, or type of medicament based on one or more of the determined signal parameters and at least one adaptively learned pattern; and

repeating, over time, the evaluating the received sensor data to adaptively learn one or more patterns,

wherein the determined output provides a personalized therapy recommendation for the user, and wherein the personalized therapy recommendation improves over time based on the adaptive learning,

wherein the evaluating the received sensor data to adaptively learn one or more patterns includes learning at least one pattern corresponding to glucose level, rate of change, or acceleration, and

wherein the determining an output includes determining an optimized amount of slow or fast acting insulin, or both, such that the personalized therapy recommendation allows the user'"'"'s glucose levels to be more proactively treated, keeping a diabetic user within a safe glucose range substantially all the time.