System and method for adjusting insulin delivery

Patent metadata

Field	Value
Publication number	US-2025256027-A1
Application number	US-202519196513-A
Country	US
Kind code	A1
Filing date	May 1, 2025
Priority date	Jan 13, 2017
Publication date	Aug 14, 2025
Grant date	—

Abstract

The embodiments described herein may relate to methods and systems for adjusting insulin delivery. Some methods and systems may be configured to adjust insulin delivery to personalize automated insulin delivery for a person with diabetes. Such personalization may include receiving and/or determining one or more user specific dosage parameters.

First claim

What is claimed is:

1 . A system comprising:
at least one processor; and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the system to:
receive a daily basal rate (BR) for a user;
based at least partially on the received daily BR for the user, determine a carbohydrate-to-insulin ratio (CR); and
calculate a bolus dose of insulin to account for food to be ingested by the user based on the CR; and
cause an electronic insulin delivery device to deliver the bolus dose of insulin.

2 . The system of claim 1 , wherein the system further comprises instructions that, when executed by the at least one processor, cause the system to receive the daily BR via an input from the user made by way of an interface of the system.

3 . The system of claim 2 , wherein the input from the user comprises a modification of a previously stored daily BR.

4 . The system of claim 1 , wherein the system further comprises instructions that, when executed by the at least one processor, cause the system to receive the daily BR from a remote device.

5 . The system of claim 1 , wherein causing the electronic insulin delivery device to deliver the bolus dose of insulin comprises generating a message to the electronic insulin delivery device to deliver the bolus dose of insulin.

6 . The system of claim 5 , wherein determining the CR is performed according to CR=a*BR−b where a is a number between approximately 114 and 126, and b is a number between approximately 0.785 and 0.815.

7 . The system of claim 1 , wherein the system further comprises instructions that, when executed by the at least one processor, cause the system to:
determine a first relationship between the BR and the CR; and based at least partially on the determined first relationship between the BR and the CR, determine a personalized model that represents the user's physiological response to insulin therapy.

8 . The system of claim 7 , wherein the system further comprises instructions that, when executed by the at least one processor, cause the system to:
predict, via an event model, a probability of an occurrence of an undesirable event responsive to a difference between the personalized model and a generalized model that represents a generalized physiological response by a general population to an insulin therapy; and generate an indication that a probability of occurrence of the undesirable event exceeds a threshold responsive to a prediction by an event model.

9 . The system of claim 8 , wherein predicting, via the event model, the probability of the occurrence of the undesirable event responsive to the difference between the personalized model and the generalized model comprises:
compare the first relationship with a probability distribution of a second relationship of the generalized model between a general BR and a general CR; and determine that the first relationship exceeds a threshold distance from the probability distribution.

10 . A system comprising:
at least one processor; and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the system to:
receive a daily basal rate (BR) for a user;
based at least partially on the received daily basal rate (BR) for the user, determine an insulin sensitivity factor (ISF);
calculate a bolus dose of insulin based at least partially on the ISF, the bolus dose of insulin to account for a blood glucose reading beyond a threshold variation from a target blood glucose level; and
cause an electronic insulin delivery device to deliver the bolus dose of insulin.

11 . The system of claim 10 , wherein causing the electronic insulin delivery device to deliver the bolus dose of insulin comprises generating a message to the electronic insulin delivery device to deliver the bolus dose of insulin.

12 . The system of claim 10 , wherein determining the ISF is performed according to ISF=x*BR−y where x is a number between approximately 1115 and 1140, and y is a number between approximately 1.00 and 1.06.

13 . The system of claim 10 , wherein the system further comprises instructions that, when executed by the at least one processor, cause the system to:
determine a first relationship between the BR and the ISF; and based at least partially on the determined first relationship between the BR and the ISF, determine a personalized model that represents the user's physiological response to insulin therapy.

14 . The system of claim 13 , wherein the system further comprises instructions that, when executed by the at least one processor, cause the system to:
predict, via an event model, a probability of an occurrence of an undesirable event responsive to a difference between the personalized model and a generalized model that represents a generalized physiological response by a general population to an insulin therapy; and generate an indication that a probability of occurrence of the undesirable event exceeds a threshold responsive to a prediction by an event model.

15 . The system of claim 14 , wherein predicting, via the event model, the probability of the occurrence of the undesirable event responsive to the difference between the personalized model and the generalized model comprises:
comparing the first relationship with a probability distribution of a second relationship of the generalized model between a general BR and a general ISF; and determining that the first relationship exceeds a threshold distance from the probability distribution.

16 . A system comprising:
at least one processor; and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the system to:
receive a daily basal rate (BR) for a user;
determine a first relationship between the BR and at least one of a carbohydrate-to-insulin ratio (CR) or an insulin sensitivity factor (ISF);
based at least partially on the determined first relationship, calculate a bolus dose of insulin; and
cause an electronic insulin delivery device to deliver the bolus dose of insulin.

17 . The system of claim 16 , wherein the system further comprises instructions that, when executed by the at least one processor, cause the system to:
receive one of the CR or the ISF; and determine the other of the CR and the ISF based at least partially on the received BR.

18 . The system of claim 17 , wherein the system further comprises instructions that, when executed by the at least one processor, cause the system to, based at least partially on the determined first relationship between the BR and at least one of the CR or ISF, determine a personalized model that represents the user's physiological response to insulin therapy.

19 . The system of claim 18 , wherein the system further comprises instructions that, when executed by the at least one processor, cause the system to:
predict, via an event model, a probability of an occurrence of an undesirable event responsive to a difference between the personalized model and a generalized model that represents a generalized physiological response to an insulin therapy; and generate an indication that a probability of occurrence of the undesirable event exceeds a threshold responsive to a prediction by an event model.

20 . The system of claim 19 , wherein predicting, via the event model, the probability of the occurrenc

Assignees

• Insulet Corp

Inventors

• Mazlish Bryan
• Desborough Lane
• Naylor Ross

Classifications

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title

• G16H40/63

  for local operation · CPC title

• A61M2005/14208

  with a programmable infusion control system, characterised by the infusion program · CPC title

• A61M5/1452

  pressurised by means of pistons · CPC title

• A61M2205/502

  User interfaces, e.g. screens or keyboards · CPC title