Medtronic Minimed, Inc.

Benyamin Grosman

Anirban Roy

Neha J Parikh

Di Wu

Louis J Lintereur

Patrick E Weydt

Desmond Barry Keenan

John Joseph Mastrototaro

Ali Dianaty

Rebecca K Gottlieb

David Dunleavy

Mahta Sadeghzadeh

Francine R Kaufman

Ulrich H Rankers

Steven J Vargas

David Legray

Do Kim

Yuxiang Zhong

Taly G Engel

Maria Diana Miller

Mikhail Loutseiko

Andrea Varsavsky

Pratik J Agrawal

Alexander S Campbell

Huzefa F Neemuchwala

Chantal M McMahon

Lavie Golenberg

Scott W Lee

Ohad Cohen

Rajiv Shah

Ning Yang

Bradley Chi Liang

Salman Monirabbasi

Keith Nogueira

Xiaolong Li

Peter Ajemba

Mike C Liu

Jeffrey Nishida

Eyal Dassau

Andy Y Tsai

Howard Zisser

Luis A Torres

Elaine Gee

Catherine Tan Fogel

Steven Lai

Sadaf S Seleh

Leonardo Nava-Guerra

David M Sniecinski

Jing Liu

Georgios Mallas

Peter Onesti

Chi A Tran

Francis J Doyle

Lois Javanovic

Techniques disclosed herein relates generally to default carbohydrate consumption counts based on population carbohydrate consumption models. In one example, the techniques involve receiving, from a device, at least one characteristic of a person; accessing a population carbohydrate consumption model which relates the at least one characteristic with carbohydrate intake for a population; determining, based on applying the population carbohydrate consumption model to the at least one characteristic, at least one of: a preliminary carbohydrate count for the person or a default carbohydrate consumption count for the person; and causing delivery of insulin to the person based on communicating, to the device, at least one of: the preliminary carbohydrate count for the person or the default carbohydrate consumption count for the person.

Default carbohydrate consumption counts based on population carbohydrate consumption models

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 relate generally to alert management based on sleeping status. In some embodiments, the techniques involve obtaining user status data indicative of a sleeping status of a user of a medical device, and controlling alert generation and output associated with the medical device in accordance with the sleeping status of the user.

Alert management based on sleeping status

Disclosed herein are techniques related to determination of adjustments to fluid delivery settings. In some embodiments, the techniques may involve accessing insulin delivery data for a user of an insulin delivery device. The insulin delivery data may be indicative of an amount of insulin delivered by the insulin delivery device to the user during a time period. The techniques may also involve analyzing the insulin delivery data to generate an updated insulin sensitivity factor (ISF) value. The techniques may further involve generating, based on the updated ISF value, a recommendation to adjust an ISF value of the insulin delivery device. Additionally, the techniques may involve causing adjustment of the ISF value to the updated ISF value in accordance with the recommendation.

Determination of adjustments to fluid delivery settings

Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device to deliver fluid to a body of a user involves obtaining measurement values for a physiological condition influenced by the fluid, autonomously operating the infusion device to deliver the fluid based at least in part on the measurement values, and detecting a nonactionable condition based on the measurement values. In response to detecting the nonactionable condition, delivery of the fluid is limited while maintaining autonomous operation of the infusion device. In one exemplary embodiment, the nonactionable condition is a rescue condition indicative of the user having consumed fast-acting carbohydrates, and thus insulin delivery may be automatically limited in response to detecting the rescue carbohydrate consumption.

Infusion devices and related rescue detection methods

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

Techniques related to temporary setpoint values are disclosed. The techniques may involve causing operation of a fluid delivery device in a closed-loop mode for automatically delivering fluid based on a difference between a first setpoint value and an analyte concentration value during operation of the fluid delivery device in the closed-loop mode. Additionally, the techniques may involve obtaining a second setpoint value. The second setpoint value may be a temporary setpoint value to be used for a period of time to regulate fluid delivery, and the second setpoint value may be greater than the first setpoint value. The techniques may further involve causing operation of the fluid delivery device for automatically reducing fluid delivery for the period of time based on the second setpoint value.

Regulating delivery of insulin to a body of a user by a fluid delivery device

Disclosed are techniques related to determination of adjustments to fluid delivery settings. The techniques may involve accessing an input meal size category, where the input meal size category is selected from among a plurality of meal size categories via a user interface; accessing a plurality of groups of historical meal events for a person, where each group of the plurality of groups is associated with a respective meal size category of the plurality of meal size categories and includes respective historical meal events for the person categorized in the respective meal size category; identifying a group, in the plurality of groups of historical meal events for the person, having an associated meal size category that matches the input meal size category; identifying a person-specific carbohydrate amount representative of the historical meal events in the identified group; and determining a bolus dosage value of insulin based on the person-specific carbohydrate amount.

Systems, storage media and computer-readable media are provided for translating one or more therapy parameters of a first insulin therapy system to one or more translated therapy parameters for use at a second insulin therapy system that is different than the first insulin therapy system. A therapy profile generated at the first insulin therapy system can be send to a translation service. The therapy profile includes one or more therapy parameters. At least one therapy parameter from the therapy profile can be translated into at least one translated therapy parameter that is mapped to and adapted for use by the second insulin therapy system. The at least one translated therapy parameter can then be sent to the second insulin therapy system for use at the second insulin therapy system.

Translating therapy parameters of an insulin therapy system to translated therapy parameters for use at a different insulin therapy system

A continuous glucose monitoring system may utilize electrode current (Isig) signals, Electrochemical Impedance Spectroscopy (EIS), and Vcntr values to optimize sensor glucose (SG) calculation in such a way as to enable reduction of the need for blood glucose (BG) calibration requests from users.

Methods, systems, and devices for continuous glucose monitoring

Disclosed herein are techniques related to configurable target values. The techniques may involve identifying a target glucose value obtained based on user input. Additionally, the techniques may involve determining a fluid delivery command based on a difference between a measured glucose value and the target glucose value. The techniques may further involve causing fluid delivery by a fluid infusion device in accordance with the fluid delivery command to regulate the measured glucose value to the target glucose value.

Configurable target values

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.

Disclosed herein are techniques related to management of insufficient hypoglycemia response. In some embodiments, the techniques may involve obtaining an amount of insulin remaining in a body of a patient. The techniques may involve also obtaining a quantity of insulin needed by the patient within a future time period. Additionally, the techniques may involve identifying a condition in which the patient's glucose level will continue to decrease during the future time period despite suspension of basal insulin dosage delivery. The condition may be identified based on determining that the amount of insulin remaining in the body is greater than the quantity of insulin needed by the patient within the future time period. Furthermore, the techniques may involve, responsive to identifying the condition, causing performance of an action for preventing the patient's glucose level from falling into a hypoglycemic range in combination with the suspension of basal insulin dosage delivery.

Management of insufficient hypoglycemia response

A system includes one or more processors and one or more processor-readable storage media storing instructions which, when executed by the one or more processors, cause performance of obtaining a first target value for a glucose level of a patient, modifying a glucose setpoint from a second target value to the first target value, and regulating the glucose level of the patient to the modified glucose setpoint based on controlling insulin delivery by an insulin infusion device.

Configurable target glucose values

Growing community of inventors

Valley Village, CA, United States of America

Benyamin Grosman