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US-12620470-B2 - Dynamic equivalent on board estimator

US12620470B2US 12620470 B2US12620470 B2US 12620470B2US-12620470-B2

Abstract

Adaptive on board estimation of exogenous pharmacon responsive to transient (i.e., impermanent) physiological effects is provided. Dynamically estimating an equivalent amount of an exogenous pharmacon on board (XOB), such as insulin and/or carbohydrates, left in the subject, is based on predictions of glucose time-series data. These estimated values, such as insulin on board (IOB), are useful for diabetes management software, including decision support and/or artificial pancreas (AP) algorithms, for example.

Inventors

  • Stephen D. Patek
  • Enrique Campos-Nañez

Assignees

  • DEXCOM, INC.

Dates

Publication Date
20260505
Application Date
20240401

Claims (11)

  1. 1 . A method for managing diabetes comprising: receiving an estimate of a glucose concentration of a patient; estimating an exogenous pharmacon on board (XOB) that is used in diabetes treatment, wherein the XOB estimate estimates an amount of the exogenous pharmacon that will produce a glucose response that is closest to a predicted glucose response due to historical insulin administrations, wherein estimating the XOB includes: receiving exogenous pharmacon data for a first time and continuous glucose monitoring (CGM) data for a time period preceding a second time of an on-board estimation of the exogenous pharmacon; estimating baseline glucose time-series data from the second time at a future third time based on the exogenous pharmacon data for the first time and the CGM data for a time period preceding the second time; iteratively estimating a plurality of glucose time-series data from the second time to the third time based on a range of possible values for the exogenous pharmacon data at the second time and the CGM data for the time period preceding the second time; comparing the estimated baseline glucose time-series data with the plurality of glucose time-series data from the second time to the third time to determine a best match from which the XOB estimate is selected; computing a desired exogenous pharmacon dose using the estimate of the glucose concentration of the patient; subtracting the XOB estimate from the desired exogenous pharmacon dose to compute a bolus recommendation for use in treatment of diabetes; and injecting or infusing the bolus recommendation into the patient for use in treatment of diabetes.
  2. 2 . The method of claim 1 , wherein the exogenous pharmacon is insulin or carbohydrates.
  3. 3 . The method of claim 1 , wherein the exogenous pharmacon is insulin and injecting or infusing the bolus recommendation to the patient includes injecting or infusing insulin to the patient in accordance with the bolus recommendation for use in treatment of diabetes.
  4. 4 . The method of claim 1 , further comprising receiving an updated estimate of the glucose concentration of the patient at predetermined intervals of time.
  5. 5 . The method of claim 1 , wherein the estimate of the glucose concentration of the patient is received as a continuous glucose monitoring (CGM) reading from a CGM sensor.
  6. 6 . The method of claim 1 , wherein the artificial pancreas system includes a semi closed-loop algorithm or a closed-loop algorithm.
  7. 7 . The method of claim 1 , wherein the artificial pancreas system is further configured to receive an updated estimate of the glucose concentration of the patient at predetermined intervals of time.
  8. 8 . A diabetes management system, comprising: at least one processor; and a computer readable storage medium storing instructions that when executed by the at least one processor cause the at least one processor to: receive an estimate of a glucose concentration of a patient; estimate an exogenous pharmacon on board (XOB) that is used in diabetes treatment, wherein the XOB estimate estimates an amount of the exogenous pharmacon that will produce a glucose response that is closest to a predicted glucose response due to historical insulin administrations, wherein the instructions, when executed by the at least one processor, further causes the at least one processor to estimate the XOB by: receiving exogenous pharmacon data for a first time and continuous glucose monitoring (CGM) data for a time period preceding a second time of an on-board estimation of the exogenous pharmacon; estimating baseline glucose time-series data from the second time at a future third time based on the exogenous pharmacon data for the first time and the CGM data for a time period preceding the second time; iteratively estimating a plurality of glucose time-series data from the second time to the third time based on a range of possible values for the exogenous pharmacon data at the second time and the CGM data for the time period preceding the second time; comparing the estimated baseline glucose time-series data with the plurality of glucose time-series data from the second time to the third time to determine a best match from which the XOB estimate is selected; compute a desired exogenous pharmacon dose using the estimate of the glucose concentration of the patient; subtract the XOB estimate from the desired exogenous pharmacon dose to compute a bolus recommendation for use in treatment of diabetes; and a delivery device configured to inject or infuse the bolus recommendation into the patient for use in treatment of diabetes.
  9. 9 . The system of claim 8 , wherein the exogenous pharmacon is insulin or carbohydrates.
  10. 10 . The system of claim 8 , wherein the exogenous pharmacon is insulin and the delivery device is configured to inject or infuse insulin to the patient in accordance with the bolus recommendation for use in treatment of diabetes.
  11. 11 . The system of claim 8 , wherein the bolus recommendation is provided to an artificial pancreas system that provides treatment to the patient.

Description

INCORPORATION BY REFERENCE TO RELATED APPLICATIONS Any and all priority claims identified in the Application Data Sheet, or any correction thereto, are hereby incorporated by reference under 37 CFR 1.57. This application is a continuation of U.S. application Ser. No. 16/907,062, filed Jun. 19, 2020, which is a continuation of U.S. application Ser. No. 16/906,812, filed Jun. 19, 2020, which claims the benefit of priority to U.S. Provisional Application No. 62/863,648, filed on Jun. 19, 2019. Each of the aforementioned applications is incorporated by reference herein in its entirety, and each is hereby expressly made a part of this specification. BACKGROUND Within diabetes management systems, known on board amounts of various exogenous pharmacons are important for improving diabetes management software. Unfortunately, the concentration or mass of these pharmacons are difficult to accurately determine in real-time, at least partly because measuring the state of absorption and distribution of exogenous pharmacons is impractical in vivo (as opposed to continuous glucose monitoring (CGM)). To address this problem, prior art techniques rely on reporting of ingestion and/or injection of the pharmacon along with an assumed and fixed time-action curve to extrapolate the quantity of the pharmacon present in the metabolism at a later time (exogenous pharmacon on board (XOB)). However, the human metabolism is highly complex, varying from subject to subject and from day to day, and often not following predefined action curves. SUMMARY Adaptive on board estimation of exogenous pharmacon responsive to transient (impermanent) physiological effects is provided. Dynamically estimating an equivalent amount of an exogenous pharmacon on board (XOB), such as insulin and/or carbohydrates, left in the subject, is based on predictions of glucose time-series data. These estimated values, such as insulin on board (IOB), are useful for diabetes management software, including decision support and/or artificial pancreas (AP) algorithms, for example. In an embodiment, a system comprises an input compiler configured to receive and process input data; an exogenous pharmacon on board (XOB) estimator; and an output compiler. In some embodiments, the input data corresponds to glucose concentration readings provided by a continuous glucose monitoring (CGM) system, insulin injection/infusion data, data on ingested meals, glucagon or other pharmacon dose and/or injection data, exercise data, or stress data. Alternatively or additionally, the input data comprises data pertaining to insulin, and/or the input data comprises data pertaining to carbohydrates. Some embodiments further comprise a continuous glucose monitoring (CGM) system in communication with the input compiler and configured to provide the input data to the input compiler. Alternatively or additionally, a closed-loop delivery system is provided in communication with the input compiler and configured to provide the input data to the input compiler. In some embodiments, the input data comprises a sequence of insulin dose recommendations or glucagon dose recommendations. In some embodiments, the input compiler is configured to compile at least one of (1) data related to a pharmacon whose glucose-effect equivalent is being compiled or (2) data related to the relevant history of continuous glucose monitoring (CGM) system readings. In some embodiments, the output compiler is an XOB output compiler configured to render an XOB estimation. The XOB estimation may comprise an insulin on board (IOB) estimation and/or a carbohydrates on board (COB) estimation. In some embodiments, the XOB estimator is configured to estimate the glucose equivalent effect of the pharmacon at a predetermined time. Alternatively or additionally, the XOB estimator comprises a baseline time-series estimator, an iterative time-series comparator, and a time-series comparator. The baseline time-series estimator may be a stateless machine that receives historical XOB amounts and continuous glucose monitoring (CGM) system history and produces a time-series that approximates future values of glucose. The iterative time-series comparator is configured to combine a procedure that generates candidate XOB amounts with a modification of the time-series estimator in which historical XOB amounts are substituted with the XOB amount under consideration. The time-series comparator is configured to match a pair of time-series that can be any measure of distance or similarity between the series. In an embodiment, a method comprises receiving exogenous pharmacon data for a time T1, and glucose time-series data for a time period preceding a time T2 of on board estimation of the pharmacon; estimating baseline glucose time-series data from the time T2 of XOB estimation into a time T3 based on the data for time T1 and continuous glucose monitoring (CGM) system data for the time period preceding T2; iteratively estimating a plurality of glucos