Determination of adjustments to fluid delivery settings

1. A processor-implemented method of automatically adjusting at least one parameter of an insulin delivery controller of an insulin delivery device to regulate delivery of insulin to a user, the processor-implemented method comprising:

obtaining therapy-related data associated with operation of the insulin delivery device for a number of days in the past, the therapy-related data including sensor glucose data for the user and meal data for the user;

determining, from the therapy-related data, an expected value of a fasting blood glucose that corresponds to fasting periods, and an expected value of estimated plasma insulin concentration at the expected value of the fasting blood glucose;

determining at least one adjusted parameter for the insulin delivery controller based on (1) the therapy-related data, and (2) at least one of the expected value of the fasting blood glucose or the expected value of estimated plasma insulin concentration at the expected value of the fasting blood glucose; and

causing the insulin delivery controller to control insulin delivery by the insulin delivery device in accordance with the determined at least one adjusted parameter.

2. The processor-implemented method of claim 1 , further comprising:

determining, from the obtained therapy-related data, an expected value of total daily dose of insulin delivered to the user during the number of days in the past.

3. The processor-implemented method of claim 2 , further comprising:

estimating absorption rates of each meal identified in the meal data by using at least one of: the determined expected value of total daily dose of insulin, the determined expected value of the fasting blood glucose, or the determined expected value of the estimated plasma insulin concentration; and

generating a pharmacokinetic/pharmacodynamic (PK/PD) model of the user, by adjusting and determining parameters of the PK/PD model to fit the sensor glucose data for well-fit days, wherein the sensor glucose data for well-fit days corresponds to each day of days represented by the obtained therapy-related data, in which the PK/PD model of the user can be fitted to the sensor glucose data using the absorption rates of each meal identified in the meal data.

4. The processor-implemented method of claim 3 , wherein the determining of the at least one adjusted parameter of the insulin delivery controller is based on using the PK/PD model and based on additional therapy-related data associated with further operation of the insulin delivery device.

5. The processor-implemented method of claim 4 , further comprising:

adjusting settings of the insulin delivery controller in accordance with the determined at least one adjusted parameter.

6. The processor-implemented method of claim 4 , wherein the obtained therapy-related data further comprises:

insulin delivery data, including basal insulin and insulin bolus amounts, and associated time data;

announced mealtime data;

carbohydrate intake estimates for announced meals;

an insulin sensitivity factor (ISF) associated with operation in a manual insulin delivery mode;

carbohydrate ratio (CR) values; and

user-entered blood glucose meter measurements.

7. The processor-implemented method of claim 4 , wherein the expected value of total daily dose of insulin is a median value.

8. The processor-implemented method of claim 4 , wherein the estimating of the absorption rates of each meal identified in the meal data includes using a known PK/PD model with fixed parameters to estimate the absorption rates based on the expected value of total daily dose of insulin, the expected value of the fasting blood glucose, and the expected value of the estimated plasma insulin concentration.

9. The processor-implemented method of claim 8 : wherein a final PK/PD model includes meal-related parameters; and wherein the estimating of the absorption rates of each meal identified in the meal data includes, for each meal identified in the meal data, obtaining values for the meal-related parameters.

10. A system comprising:

an insulin delivery device including an insulin delivery controller;

one or more processors; and

one or more processor-readable storage media operatively coupled with the one or more processors, the one or more processor-readable storage media storing executable instructions which, when executed by the one or more processors, cause performance of:

obtaining therapy-related data associated with operation of the insulin delivery device for a number of days in the past, the therapy-related data including sensor glucose data for a user and meal data for the user;

determining, from the therapy-related data, an expected value of a fasting blood glucose that corresponds to fasting periods, and an expected value of estimated plasma insulin concentration at the expected value of the fasting blood glucose;

determining at least one adjusted parameter of the insulin delivery controller based on (1) the therapy-related data, and (2) at least one of the expected value of the fasting blood glucose or the expected value of estimated plasma insulin concentration at the expected value of the fasting blood glucose; and

causing the insulin delivery controller to control insulin delivery by the insulin delivery device in accordance with the determined at least one adjusted parameter of the insulin delivery controller.

11. The system of claim 10 , wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of:

determining, from the obtained therapy-related data, an expected value of total daily dose of insulin delivered to the user during the number of days in the past.

12. The system of claim 11 , wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of:

estimating absorption rates of each meal identified in the meal data by using at least one of the determined expected value of total daily dose of insulin, the determined expected value of the fasting blood glucose, or the determined expected value of the estimated plasma insulin concentration; and

generating a pharmacokinetic/pharmacodynamic (PK/PD) model of the user, by adjusting and determining parameters of the PK/PD model to fit the sensor glucose data for well-fit days, wherein the sensor glucose data for well-fit days corresponds to each day of days represented by the obtained therapy-related data, in which the PK/PD model of the user can be fitted to the sensor glucose data using the absorption rates of each meal identified in the meal data.

13. The system of claim 12 , wherein the determining of the at least one adjusted parameter of the insulin delivery controller is based on using the PK/PD model and based on additional therapy-related data associated with further operation of the insulin delivery device.

14. The system of claim 12 , wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of:

adjusting settings of the insulin delivery controller in accordance with the determined at least one adjusted parameter of the insulin delivery controller; and

after adjusting the settings, operating the insulin delivery controller to regulate delivery of insulin from the insulin delivery device to the user.

15. The system of claim 12 , wherein the estimating of the absorption rates of each meal identified in the meal data includes using a known PK/PD model with fixed parameters to estimate the absorption rates based on the expected value of total daily dose of insulin, the expected value of the fasting blood glucose, and the expected value of the estimated plasma insulin concentration.

16. One or more non-transitory processor-readable storage media storing executable instructions which, when executed by one or more processors, cause performance of:

obtaining therapy-related data associated with operation of an insulin delivery device for a number of days in the past, the therapy-related data including sensor glucose data for a user and meal data for the user;

determining, from the therapy-related data, an expected value of a fasting blood glucose that corresponds to fasting periods, and an expected value of estimated plasma insulin concentration at the expected value of the fasting blood glucose;

determining at least one adjusted parameter for an insulin delivery controller of the insulin delivery device based on (1) the therapy-related data, and (2) at least one of the expected value of the fasting blood glucose or the expected value of estimated plasma insulin concentration at the expected value of the fasting blood glucose; and

causing the insulin delivery controller to control insulin delivery by the insulin delivery device in accordance with the determined at least one adjusted parameter.

17. The one or more non-transitory processor-readable storage media of claim 16 , wherein the executable instructions are performed by a computing device in data communication with the insulin delivery device.

18. The one or more non-transitory processor-readable storage media of claim 16 , wherein the determining of the at least one adjusted parameter of the insulin delivery controller is based on using the PK/PD model and based on additional therapy-related data associated with further operation of the insulin delivery device.

19. The one or more non-transitory processor-readable storage media of claim 18 , wherein the determining of the at least one adjusted parameter for the insulin delivery controller comprises:

estimating absorption rates of each meal identified in the meal data by using at least one of an expected value of total daily dose of insulin, the expected value of the fasting blood glucose, or the determined expected value of the estimated plasma insulin concentration; and

generating the PK/PD model of the user, by adjusting and determining parameters of the PK/PD model to fit the sensor glucose data for well-fit days, wherein the sensor glucose data for well-fit days corresponds to each day of days represented by the obtained therapy-related data, in which the PK/PD model of the user can be fitted to the sensor glucose data using the absorption rates of each meal identified in the meal data.

20. The one or more non-transitory processor-readable storage media of claim 16 , wherein the therapy-related data further comprises:

insulin delivery data;

announced mealtime data;

carbohydrate intake estimates for announced meals;

an insulin sensitivity factor (ISF) associated with operation in a manual insulin delivery mode;

carbohydrate ratio (CR) values;

user-entered blood glucose meter measurements; or

a combination thereof.