Medtronic Minimed, Inc.

Pacesetter, Inc.

Ali Dianaty

Anirban Roy

Patrick E Weydt

Benyamin Grosman

Louis J Lintereur

Neha J Parikh

Di Wu

Gabriel A Mouchawar

Richard P Williamson

Yongjian Wu

Reza Shahandeh

Samir Shah

Jorge N Amely-Velez

Thanh Tieu

Robert McCormick

William Winstrom

Brent Croft

Yuxiang Zhong

Pratik J Agrawal

David Dunleavy

Gene A Bornzin

Xiaoyi Min

John W Poore

Zoltan Somogyi

Huzefa F Neemuchwala

Chantal M McMahon

Yougandh Chitre

Afshin Bazargan

Andrea Varsavsky

Eric Allan Larson

David Christopher Antonio

Boyi Jiang

Hans K Wenstad

Samuel Finney

Meng Dai Yu

Jay Sundararajan

Leif Lychou

Erik Montero

David M Aguirre

Andrew P Lynch

Michael Ivey

Virgilio Macion

Johan Dahlgren

Jorge N Amely Velez

Techniques disclosed herein relate to safe correction boluses. In some embodiments, the techniques involve predicting a future glucose level that would result from delivery of a correction bolus. The techniques may also involve comparing the future glucose level to a threshold level for hypoglycemia. The techniques may further involve causing delivery of the correction bolus when the future glucose level is above the threshold level for hypoglycemia.

Safe correction boluses

Techniques disclosed herein involve automatically adjusting a control parameter for an operating mode of a medical device. In some embodiments, the techniques involve obtaining data pertaining to a physiological condition of a patient during operation of the medical device. In some embodiments, the techniques further involve determining an optimized value for the control parameter using the data pertaining to the physiological condition of the patient and a cost function, wherein the cost function disproportionately penalizes for an amount of time that a physiological parameter is below a lower bound of a target range and includes a first weighting factor that is dependent on an amount of time that the physiological parameter is outside of the target range, and a second weighting factor, different from the first weighting factor, dependent on the amount of time that the physiological parameter is below the lower bound of the target range.

Personalized closed loop optimization systems and methods

Infusion devices and related medical devices, patient data management systems, and methods are provided for monitoring a physiological condition of a patient. An exemplary infusion device includes an actuation arrangement operable to deliver fluid to a user, a communications interface to receive measurement data indicative of a physiological condition of the user, a sensing arrangement to obtain contextual measurement data, and a control system coupled to the actuation arrangement, the communications interface and the sensing arrangement to determine a command for autonomously operating the actuation arrangement in a manner that is influenced by the measurement data and the contextual measurement data and autonomously operate the actuation arrangement in accordance with the command to deliver the fluid to the user.

Context-sensitive infusion devices, systems and methods

A method is provided for establishing a communication session with an implantable medical device ('IMD'). The method includes configuring an IMD and an external device to communicate with one another through a protocol that utilizes a dedicated advertisement channel. The advertisement period and the scan period of the protocol are independent of one another such that the advertisement and scan periods at least partially overlap intermittently after a number of cycles. When the external device detects one of the advertisement notices, the method includes establishing a communications link between the external device and the IMD.

Medical device and method for managing advertisement period

Disclosed herein are techniques related to automatically adjusting at least one parameter of an insulin delivery controller of an insulin delivery device to regulate delivery of insulin to a user. In some embodiments, the techniques may involve 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. The techniques may also involve determining at least one adjusted parameter for the insulin delivery controller based on the therapy-related data. Additionally, the techniques may involve causing the insulin delivery controller to adjust at least one setting in accordance with the determined at least one adjusted parameter.

Determination of adjustments to fluid delivery settings

Techniques disclosed herein relate generally to notification generation in a medical device system. In some embodiments, the techniques involve generating a first notification via a medical application executing on a first device, determining that the first notification was not acknowledged within a predetermined time frame, and causing generation of a second notification at a medical device carried by a user to notify the user that the first notification has not been acknowledged.

Backup notifications in a medical device system

A medical device system and related methods of automatically adjusting control parameters of a medical device are disclosed. One method involves obtaining data pertaining to a physiological condition of a patient during operation of the medical device in accordance with the operating mode, determining a plurality of adjusted values for the control parameter based at least in part on the data, determining a respective cost associated with each respective adjusted value for the control parameter based at least in part on the data using a cost function, identifying, from among the plurality of adjusted values, an optimized value from among the plurality of adjusted values, wherein the optimized value has a minimum cost associated therewith from among the plurality of costs, and updating the control parameter at the medical device to the optimized value.

A processor-implemented method for closed-loop control in steady-state conditions includes determining, based on data including historical bolus information but excluding historical basal information, an amount of unmetabolized therapeutic substance in a patient; determining, based on a difference between a most recent measurement of a physiological condition of the patient and a target value for the physiological condition, a first amount or rate of a basal dosage for delivery to the patient; adjusting, based on the amount of unmetabolized therapeutic substance, the first amount or rate of the basal dosage to determine a second amount or rate of the basal dosage; and causing delivery of the second amount or rate of the basal dosage based on communicating the second amount or rate in a delivery command.

Closed-loop control in steady-state conditions

Disclosed herein are techniques related to glucose level management without carbohydrate counting. The techniques may involve obtaining contextual information for a meal, predicting amounts of glucose to be absorbed into a bloodstream of the patient over a duration of time due to consumption of the meal based on the contextual information for the meal, determining one or more amounts of insulin to counteract effects of the predicted amounts of glucose, and affecting insulin therapy based on outputting information indicative of the determined one or more amounts of insulin.

Glucose level management without carbohydrate counting

Techniques related to automatically generating a super bolus may include determining an amount of an augmented meal bolus to be delivered to a patient for regulating the patient's glycemic response to a meal. The amount of the augmented meal bolus may exceed a sufficient amount for counteracting a glucose level increase caused by the meal. In some embodiments, the techniques may further include determining a duration of a postprandial reduction period during which basal dosage deliveries to the patient are to be reduced. In some other embodiments, the techniques may further include delivering the augmented meal bolus to the patient prior to determining whether or not to cause reduction of basal dosage deliveries. More specifically, a glucose level of the patient may be obtained after delivery of the augmented meal bolus, and the obtained glucose level may be used to determine whether to reduce basal dosage deliveries.

Automated super bolus generation

A method of automatically initializing an analyte sensor for a user is disclosed here. A first analyte sensor is operated in a first measurement mode to generate first sensor signals indicative of an analyte level of the user. A second analyte sensor is deployed to measure the analyte level of the user, and is operated in an initialization mode, concurrently with operation of the first analyte sensor in the first measurement mode, to receive sensor configuration data generated by the first analyte sensor. During operation of the second analyte sensor in the initialization mode, the second analyte sensor is calibrated with at least some of the received sensor configuration data. After the calibrating, operation of the second analyte sensor is transitioned from the initialization mode to a second measurement mode during which the second analyte sensor generates second sensor signals indicative of the analyte level of the user.

Analyte sensor configuration and calibration based on data collected from a previously used analyte sensor

Dynamic super bolus generation

Medical devices and related systems and operating methods are provided. A method of operating an infusion device capable of delivering fluid influencing a physiological condition to a patient involves obtaining an event indication, such as a meal indication, determining an initial bolus amount based on the event indication, and determining predicted values for the physiological condition of the patient during a time window into the future based at least in part on the initial bolus amount. When the predicted values violate a threshold during the time window, the control system identifies an adjusted bolus amount that results in the predicted values for the physiological condition satisfying the threshold during the time window from within a search space defined by the initial bolus amount and operates an actuation arrangement of the infusion device to deliver the adjusted bolus amount of the fluid to the patient.

Infusion devices and related meal bolus adjustment methods

A method of controlling an insulin infusion device involves controlling the device to operate in an automatic basal insulin delivery mode, obtaining a blood glucose measurement for the user, and initiating a correction bolus procedure when: the measurement exceeds a correction bolus threshold value; and a maximum basal insulin infusion rate is reached during the automatic basal insulin delivery mode. The correction bolus procedure calculates an initial correction bolus amount, and scales the initial amount to obtain a final correction bolus amount, such that a predicted future blood glucose level resulting from simulated delivery of the final correction bolus amount exceeds a low blood glucose threshold level. The final amount is delivered to the user during operation in the automatic basal insulin delivery mode.

Fluid infusion system that automatically determines and delivers a correction bolus

System and methods for establishing a communication session between an implantable medical device and an external device

Growing community of inventors

Porter Ranch, CA, United States of America

Ali Dianaty