Medtronic Minimed, Inc.

Sami S Kanderian, Jr

Garry M Steil

John J Shin

Udo Hoss

Martin T Cantwell

John Joseph Mastrototaro

Richard S Yoon

Lu Wang

Kris R Holtzclaw

Peter Hong

Nandita N Patel

Nandita D Dangui

Disclosed herein are techniques related to model predictive control. The techniques may involve generating a desired glucose trajectory that approaches a desired steady state setpoint from a current glucose value over a prediction horizon. The techniques may involve generating a plurality of insulin delivery patterns. Each insulin delivery pattern may correspond to an amount of insulin to be delivered over a control horizon. The techniques may involve generating a plurality of predicted glucose trajectories over the control horizon. Each predicted glucose may be generated based on the current glucose value and a respective insulin delivery pattern. The techniques may involve comparing the desired glucose trajectory against each predicted glucose trajectory and selecting a predicted glucose trajectory that is more similar to the desired glucose trajectory than any other predicted glucose trajectory. The techniques may involve determining an insulin delivery pattern used to generate the selected predicted glucose trajectory.

Model predictive control for diabetes management

A system and method for controlling and monitoring a diabetes-management system through the use of a model that predicts or estimates future dynamic states of glucose and insulin from variables such as insulin delivery or exogenous glucose appearance as well as inherent physiological parameters. The model predictive estimator can be used as an insulin bolus advisor to give an apriori estimate of postprandial glucose for a given insulin delivery profile administered with a known meal to optimize insulin delivery; as a supervisor to monitor the operation of the diabetes-management system; and as a model predictive controller to optimize the automated delivery of insulin into a user's body to achieve a desired blood glucose profile or concentration. Open loop, closed-loop, and semi-closed loop embodiments of the invention utilize a mathematical metabolic model that includes a Minimal Model, a Pump Delivery to Plasma Insulin Model, and a Meal Appearance Rate Model.

Model predictive method and system for controlling and supervising insulin infusion

An infusion system, which may be a closed loop infusion system or 'semi-closed-loop' system, uses state variable feedback to control the rate that fluid is infused into the body of a user. The closed loop infusion system includes a sensor system, a controller, and a delivery system. The 'semi-closed-loop' system further includes prompts that are displayed or sounded or otherwise provide indications to the user prior to fluid delivery. The sensor system includes a sensor for monitoring a condition of the user. The sensor produces a sensor signal, which is representative of the condition of the user. The delivery system infuses a fluid into the user at a rate dictated by the commands from the controller. The system may use three state variables, subcutaneous insulin concentration, plasma insulin concentration, and insulin effect, and corresponding gains, to calculate an additional amount of fluid to be infused as a bolus and to be removed from the basal delivery of the fluid.

Apparatus and method for controlling insulin infusion with state variable feedback

A method of calibrating glucose monitor data includes collecting the glucose monitor data over a period of time at predetermined intervals. It also includes obtaining at least two reference glucose values from a reference source that temporally correspond with the glucose monitor data obtained at the predetermined intervals. Also included is calculating the calibration characteristics using the reference glucose values and corresponding glucose monitor data to regress the obtained glucose monitor data. And, calibrating the obtained glucose monitor data using the calibration characteristics is included. In preferred embodiments, the reference source is a blood glucose meter, and the at least two reference glucose values are obtained from blood tests. In additional embodiments, calculation of the calibration characteristics includes linear regression and, in particular embodiments, least squares linear regression. Alternatively, calculation of the calibration characteristics includes non-linear regression. Data integrity may be verified and the data may be filtered.

Real time self-adjusting calibration algorithm

An infusion system, which may be a closed loop, or 'semi-closed-loop', infusion system, uses state variable feedback to control the rate at which fluid is infused into a user's body. The closed loop system includes a sensor system, a controller, and a delivery system. The 'semi-closed-loop' system further includes prompts that provide indications to the user prior to fluid delivery. The sensor system includes a sensor for monitoring a condition of the user and produces a sensor signal which is representative of the user's condition. The delivery system infuses a fluid into the user at a rate dictated by the commands from the controller. The system may use three state variables, e.g., subcutaneous insulin concentration, plasma insulin concentration, and insulin effect, and corresponding gains, to calculate an additional amount of fluid to be infused with a bolus and to be removed from the basal delivery of the fluid.

A method of calibrating glucose monitor data includes collecting the glucose monitor data over a period of time at predetermined intervals. It also includes obtaining at least two reference glucose values from a reference source that temporally correspond with the glucose monitor data obtained at the predetermined intervals. Also included is calculating the calibration characteristics using the reference glucose values and the corresponding glucose monitor data to regress the obtained glucose monitor data. And calibrating the obtained glucose monitor data using the calibration characteristics is included. In preferred embodiments, the reference source is a blood glucose meter, and the at least two reference glucose values are obtained from blood tests. In additional embodiments, the calculation of the calibration characteristics is obtained using linear regression and in particular embodiments, least squares linear regression. Alternatively, the calculation of the calibration characteristics is obtained using non-linear regression.

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Germantown, MD, United States of America

Sami S Kanderian, Jr