Medtronic Minimed, Inc.

John J. Mastrototaro

Sheldon B. Moberg

Bihong Wu

Garry M. Steil

John C., Jr. Mueller

Andy C. Hayes

Gary L. Williams

Mike Charles Vallet Tolle

Bud Clark

Garry M Steil

Sami S Kanderian, Jr

Kerstin Rebrin

H Bud Clark

Martin T Cantwell

John Joseph Mastrototaro

Sheldon B Moberg

Mark C Estes

Udo Hoss

Gary Williams

John C Mueller, Jr

Frank Saidara

James Kelly Fox

Andrew C Hayes

Antonios Panteleon

Rajiv Shah

William P Van Antwerp

Cary D Talbot

Richard E Purvis

Bradley J Enegren

Nannette M Van Antwerp

John J Shin

Gayane R Voskanyan

Ly Phou

Richard Lemos

Richard Lemos, Jr

Paul V Goode, Jr

Bud H Clark

Andy C Hayes

Gayane Voskanyan

Gary L Williams

John J Mastrototaro

Jr Mueller John C

Jr Sami S Kanderian

Jr Kanderian Sami S

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

A method and/or system may be provided for determining a suggested change in a recommended therapy for a patient based, at least in part, on sensor measurements, and generating an alert to an attendant in a hospital environment upon detection of the suggested change. A method and/or system may also be provided to automatically determine a maximum interval to alert an attendant following receipt of a measurement at an operator interface. A method and/or system may also be provided to blood-glucose sensor calibration.

Closed-loop glucose and/or insulin control system

Apparatuses and methods for medical monitoring physiological characteristics values such as blood glucose levels for the treatment of diabetes, are presented. The apparatuses and methods provide dynamic glucose monitoring functions that perform predictive analysis to anticipate harmful conditions, such as glucose crash and hyperglycemic incidents for a patient. The dynamic functions can also be used to maximize athletic performance and warn of inadequate nocturnal basal rate. Other aspects include advanced alarm and reminder functions, as well as advanced data presentation tools to further facilitate convenient and efficient management of various physiological conditions.

System for monitoring physiological characteristics

Embodiments of the invention provide a dual insertion set for supplying a fluid to a body of a patient and for monitoring a body characteristic of the patient. Typical embodiments of the invention include a base, an infusion portion coupled to a first piercing member and a sensor portion coupled to a second piercing member. The infusion portion includes a cannula coupled to the first piercing member for supplying the fluid to a placement site. The sensor portion includes a sensor coupled to and extending from the base having at least one sensor electrode formed on a substrate and is coupled to the second piercing member in a manner that allows the sensor to be inserted at the placement site. The base is arranged to secure the dual insertion set to skin of the patient. Typically the first and second piercing members are arranged such that when the first and second piercing members are operatively coupled to the base, they are disposed in a spatial orientation designed to inhibit sensor interference that may be caused by compounds present in fluids infused through the cannula.

Combined sensor and infusion set using separated sites

An algorithm and method of making intelligent therapy recommendations for insulin pump parameters is described. The pump parameters include basal rates, carbohydrate-to-insulin ratios (CIR), and insulin sensitivity factors (ISF). A determination of whether a therapy recommendation should be made is based on comparing an updated recommended change with a threshold. The updated recommended change to the pump parameter is made based on a previous recommended change to the pump parameter and the difference between a current blood glucose value and a targeted blood glucose level. The algorithm and method confirms the therapy recommendation is within safety parameters before displaying the therapy recommendation.

Intelligent therapy recommendation algorithim and method of using the same

Disclosed are a method and/or system for determining a suggested change in a recommended therapy for a patient based, at least in part, on sensor measurements, and generating an alert to an attendant in a hospital environment upon detection of the suggested change. In another embodiment, a method and/or system is directed to automatically determining a maximum interval to alert an attendant following receipt of a measurement at an operator interface. In yet another embodiment, a method and/or system is directed to blood-glucose sensor calibration.

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

An infusion system is for infusing a fluid into the body of a patient. The infusion system includes at least one sensor for monitoring blood glucose concentration of the patient and an infusion device for delivering fluid to the patient. The sensor produces at least one sensor signal input. The infusion device uses the at least one sensor signal input and a derivative predicted algorithm to determine future blood glucose levels. The infusion device delivers fluid to the patient when future blood glucose levels are in a patient's target range. The infusion device is capable of suspending and resuming fluid delivery based on future blood glucose levels and a patient's low shutoff threshold. The infusion device suspends fluid delivery when future blood glucose levels falls below the low shutoff threshold. The infusion device resumes fluid delivery when a future blood glucose level is above the low shutoff threshold.

Algorithm sensor augmented bolus estimator for semi-closed loop infusion system

Intelligent therapy recommendation algorithm and method of using the same

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.

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Pasadena, CA, United States of America

Garry M Steil