Insulet Corporation

Joon Bok Lee

Yibin Zheng

Jason B O'Connor

Ashutosh Zade

Jason O'connor

Trang Ly

Steven P Cardinali

Ian T McLaughlin

David I Nazzaro

Thomas Arnold Peyser

Nicholas Conte

Thomas Metzmaker

Mengdi Li

Jennifer Lena Schneider

Eric Benjamin

Daniel Allis

Todd Vienneau

Timothy Henning

Kyle Grover

Connor Gullifer

John D'Arco

Ian Mclaughlin

Matthew Alles

Paul Frederick Bente, Iv

James Graham

Bonnie Dumais

Daniel Murray

Jason O′Connor

Paul Frederick Bente

Steven Cardinali

The exemplary embodiments attempt to identify impending hypoglycemia and/or hyperglycemia and take measures to prevent the hypoglycemia or hyperglycemia. Exemplary embodiments may provide a drug delivery system for delivering insulin and glucagon as needed by a user of the drug delivery system. The drug delivery system may deploy a control system that controls the automated delivery of insulin and glucagon to a patient by the drug delivery system. The control system seeks among other goals to avoid the user experiencing hypoglycemia or hyperglycemia. The control system may employ a clinical decision support algorithm as is described below to control delivery of insulin and glucagon to reduce the risk of hypoglycemia or hyperglycemia and to provide alerts to the user when needed. The control system assesses whether the drug delivery system can respond enough to avoid hypoglycemia or hyperglycemia and generates alerts when manual action is needed to avoid hypoglycemia or hyperglycemia.

Dual hormone delivery system for reducing impending hypoglycemia and/or hyperglycemia risk

Disclosed are examples that include receiving information related to ingestion of a meal. The information may include a coarse indication of a size of the meal, a relative time of ingestion of the meal, and a general composition indication of the meal. A blood glucose measurement value received within a predetermined time range of a relative time of ingestion of the meal may be identified. Settings for a delay and an extend parameter and a delivery constraint may be determined. A meal model may be modified using the determined settings for the delay parameter, the extend parameter, and the delivery constraint. The modified meal model may be used to determine a dose of insulin to be delivered in response to the received information. An instruction indicates a determined dose of insulin to be delivered may be output for delivery to a drug delivery device.

Meal bolus subcategories in model based insulin therapy

Provided is a wearable medical device that includes a processor or logic circuitry. The wearable medical device may include a memory storing instructions that, when executed by the processor or logic circuitry, configure the wearable medical device to determine, by the processor or the logic circuitry, that an event affecting a blood glucose measurement value trend of a user has occurred. Based on the occurrence of the event, the processor or the logic circuitry may select a mode of operation of the analyte sensor, and generate a signal indicating the selected mode of operation. The mode of operation may correspond to a sampling frequency of a physical attribute or physiological condition of a user of the wearable medical device.

Threshold based automatic glucose control response

Exemplary embodiments may enable a user to schedule medicament bolus deliveries, such as insulin boluses, for future dates and times. The exemplary embodiments may provide the ability to delay a scheduled medicament bolus delivery by short periods of time. The user may reschedule a scheduled medicament bolus delivery by entering a new date and/or time for the medicament bolus delivery. Still further, a user may cancel a scheduled medicament bolus delivery. In addition, exemplary embodiments may enable multiple medicament bolus deliveries to be viewed and managed.

Scheduling of medicament bolus deliveries by a medicament delivery device at future dates and times with a computing device

A wearable drug delivery device, techniques, and computer-readable media that provide an application that implements a diabetes treatment plan for a user are described. The drug delivery device may include a controller operable to direct operation of the wearable drug delivery device. The controller may provide a selectable activity mode of operation for the user. Operation of the drug delivery device in the activity mode of operation may reduce a likelihood of hypoglycemia during times of increased insulin sensitivity for the user and may reduce a likelihood of hyperglycemia during times of increased insulin requirements for the user. The activity mode of operation may be manually activated by the user or may be activated automatically by the controller. The controller may automatically activate the activity mode of operation based on a detected activity level of the user and/or a detected location of the user.

Activity mode for artificial pancreas system

The exemplary embodiments may modify a glucose cost component of the cost function of the control loop of an insulin delivery device to compensate for persistent positive low level glucose excursions relative to a target glucose level. The exemplary embodiments may enable use of different glucose cost component functions for different glucose levels of the user. These glucose cost component functions may be employed in piecewise fashion with a different piece being applied for each respective range of glucose level values for the user. The final glucose cost function for calculating the glucose cost component may be a weighted combination of a piecewise glucose cost function and a weighted standard cost function (such as a quadratic function). The weights may reflect the magnitude and/or persistence of glucose excursions relative to a target glucose level.

Medicament delivery device with an adjustable and piecewise analyte level cost component to address persistent positive analyte level excursions

Exemplary embodiments described herein relate to a closed loop artificial pancreas system. The artificial pancreas system seeks to automatically and continuously control the blood glucose level of a user by emulating the endocrine functionality of a healthy pancreas. The artificial pancreas system uses a closed loop control system with a cost function. The penalty function helps to bound the infusion rate of insulin to attempt to avoid hypoglycemia and hyperglycemia. However, unlike conventional systems that use a generic or baseline parameter for a user's insulin needs in a cost function, the exemplary embodiments may use a customized parameter in the cost function that reflects the individualized insulin needs of the user. The use of the customized parameter causes the cost function to result in insulin dosages over time better suited to the individualized insulin needs of the user. This helps to better avoid hypoglycemia and hyperglycemia.

User parameter dependent cost function for personalized reduction of hypoglycemia and/or hyperglycemia in a closed loop artificial pancreas system

Disclosed herein is a method for execution by a drug delivery device for determining an optimal dose of a liquid drug for current cycle of a medication delivery algorithm, the method utilizing a stepwise evaluation of a model and a cost function across a coarse search space consisting of coarse discrete quantities of the drug and a refined search space consisting of refined discrete quantities of the drug.

Optimizing embedded formulations for drug delivery

Described are techniques, processes, devices, computer-readable media that enable provision of an indication of whether it is safe for a person with diabetes to participate in exercise while using a wearable drug delivery system. A processor may receive or obtain physiological data related to a condition of a wearer of the wearable drug delivery system and by evaluating an exercise model that uses inputs related to the physiological data to make the determination of whether it is safe to exercise and output an exercise safety signal. Modifications to the wearer's medication treatment plan and other actions may be based on an outputted exercise safety signal.

Exercise safety prediction based on physiological conditions

Exemplary embodiments may modify the cost function parameters based on current and projected mean outcomes in blood glucose level control performance. The exemplary embodiments may modify the weight coefficient R for the insulin cost so that the value of R is not fixed and is not based solely on clinical determined values. Exemplary embodiments may also adjust the cost function to address persistent low-level blood glucose level excursions for users. The exemplary embodiments may reduce the penalty of the insulin cost by the sum of the converted insulin cost of the glucose excursions above target for a period divided by a number of cycles of average insulin action time. The AID system reduces the insulin cost by the lack of insulin in previous cycles.

Adaptive update of automatic insulin delivery (AID) control parameters

In an automated insulin delivery device, fuzzy logic may be applied to the responding to the possibility of a user taking additional action that may affect the blood glucose concentration. Fuzzy sets may be defined for empirically derived different likelihoods of the user taking such additional action based on correlated factors. A membership function may be provided for each fuzzy set. The membership function may provide a probability of membership in the set based on a parameter. Each fuzzy set may have a response that is reflective of the likelihood of additional user action associated with the fuzzy set. The responses by the AID device to each of these cases may reflect the probability of each such case occurring as evidenced by empirical data.

Use of fuzzy logic in predicting user behavior affecting blood glucose concentration in a closed loop control system of an automated insulin delivery device

Disclosed are techniques to establish a modified pump rate that mitigates the effects of a pump occlusion and enables a recommended dosage of insulin to be output by a pump mechanism over the course of a control cycle. In an example, the pump rate may be reduced by adding a calculated time interval between application of actuation commands to extend the amount of time over which insulin may be output by the pump mechanism.

Techniques to reduce risk of occlusions in drug delivery systems

Disclosed are examples of a device, a system, methods and computer-readable medium products operable to implement functionality to determine and respond to a purpose of a meal. An algorithm or application may receive data that may include data related to a meal purpose from data sources and determine whether any of the data received from the plurality of data sources was received from a direct data source or an indirect data source. The data may be evaluated to determine a purpose of the meal. Based on the results of the evaluation, instructions may be generated to provide an appropriate response based on the determined purpose of the meal. The generated instructions to provide the appropriate response based on the determined purpose of the meal may be output.

Early meal detection and calorie intake detection

Disclosed are techniques to establish initial settings for an automatic insulin delivery device. An adjusted total daily insulin (TDI) factor usable to calculate a TDI dosage may be determined. The adjusted TDI factor may be a TDI per unit of a physical characteristic of the user (e.g., weight) times a reduction factor. The adjusted TDI factor may be compared to a maximum algorithm delivery threshold. Based on the comparison result, the application or algorithm may set a TDI dosage and output a control signal. Blood glucose measurement values may be collected from a sensor over a period of time. A level of glycated hemoglobin of the blood may be determined based on the obtained blood glucose measurement values. In response to the level of glycated hemoglobin, the set TDI dosage may be modified. A subsequent control signal including the modified TDI dosage may be output to actuate delivery of insulin.

Initial total daily insulin setting for user onboarding

Exemplary embodiments may address the problem of missing blood glucose concentration readings from a glucose monitor that transmits blood glucose concentration readings over a wireless connection due to problems with the wireless connection. In the exemplary embodiments, an automated insulin delivery (AID) device uses an estimate in place of a missing blood glucose concentration reading in determining a predicted future blood glucose concentration reading for a user. Thus, the AID device is able to operate normally in generating insulin delivery settings despite not receiving a current blood glucose concentration reading for a current cycle. There is no need to suspend delivery of insulin to the user due to the missing blood glucose concentration reading.

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Acton, MA, United States of America

Joon Bok Lee