Abstract: The disclosed embodiments are directed to methods for adjusting, on a per individual basis, the maximum delivery limit of insulin delivered to a diabetic person via an automatic insulin delivery system, particularly for a diabetic who receives basal or bolus doses of insulin via an automatic insulin delivery system. Various embodiments of the method disclosed herein may be incorporated into a dosing algorithm of an automatic insulin delivery system.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed are processes and techniques implementable by a drug delivery system to maintain optimal drug delivery for a patient according to a treatment plan. The disclosed techniques enable a new drug delivery device that is exchanged for a previous drug delivery device to operate using analyte-based drug delivery control immediately during initialization instead of having to wait for a warm-up period. For example, a drug delivery device may include a processor and a memory storing instructions that, when executed by the processor, operate the drug delivery device to receive a present analyte measurement value from an analyte sensor during an initialization of the drug delivery device, receive backfill values measured by the analyte sensor prior to the initialization, calculate a dosage of a drug using the present analyte measurement value and the backfill values, and deliver the dosage of the drug. Other embodiments are described.

Abstract: Exemplary embodiments may compensate for loss of calibration by sensors that provide sensed analyte measurements to delivery devices. The exemplary embodiments may obtain one or more calibrated analyte measurements, such as by using techniques to obtain that the analyte measurement value that are known to produce properly calibrated analyte values. The one or more calibrated analyte measurements are compared to a recent analyte measurement to determine a negative or positive offset. The offset may be added to the recent analyte measurement to generate an updated analyte measurement that reflects the calibrated analyte measurement. In addition, past analyte measurement values within a time window may be updated as well by adding the offset to the past analyte measurement values. The exemplary embodiments may also compensate for the past over-delivery or under-delivery of an agent by the delivery device due to the loss of calibration.

Abstract: Disclosed are processes and techniques implementable by devices and a drug delivery system to maintain optimal drug delivery for a user according to a user's diabetes treatment plan. The disclosed techniques enable a drug delivery device that received an analyte measurement value from an analyte sensor during a prior cycle time to determine a present analyte measurement value has not been received during a present cycle time. A processor of the drug delivery device may, in response to the determination, initiate an action to obtain the present analyte measure value from another device or an estimate of the present analyte measurement value. Based on an outcome of the initiated action, the processor may calculate a dosage of the drug using the present analyte measurement value or information related to the present analyte measurement value. The drug delivery device may also be operable to output the calculated dosage of the drug.

Abstract: 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.

Abstract: Disclosed herein are systems and methods for the delivery of a co-formulation of insulin and a second drug, such as GLP-1, using an automated insulin delivery system. In a first embodiment, a dose of insulin is calculated by a medication delivery algorithm and a reduction factor is applied to account for the effect of second drug on the user's daily insulin requirement. In a second embodiment of the invention, a total amount of the second drug administered to the user during the past 24 hours is used to modify the correction factor and the insulin-to-carbohydrate ratio used by the medication delivery algorithm to cause a reduction in the insulin delivered to the user to account for the effect of the administration of the second drug portion of the co-formulation.

Abstract: Disclosed herein are systems and methods for the delivery of insulin and pramlintide using an automated insulin delivery system. In a first embodiment, a drug delivery system is configured to deliver independent doses of insulin and pramlintide. The system monitors the user's blood glucose level and determines when a meal is been ingested and, in response, delivers the dose of pramlintide which, in turn alters the required delivery of insulin. In the second embodiment, the drug delivery system is configured to deliver a co-formulation of insulin and pramlintide as basal doses. The total amount of pramlintide delivered in a most recent pre-determine period of time, for example, 24 hours, is used to alter the aggressiveness of the algorithm which determines the basal doses of the co-formulation.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Exemplary embodiments may perform data analytics on data regarding a user of a medicament delivery device and data regarding other users of like medicament delivery devices to provide insights and guide management of the medicament delivery device for the user. The data analytics may be so called “big data” analytics that are performed on large data sets. The data analytics may be performed on various types of data to help determine what actions, if any, the remote management should take. The data analytics may be performed by the software on the remote management device or by other computational resources, such as by other servers or cloud computing resources. Exemplary embodiments may provide for remote management of a medicament delivery device. The management may be remote in that the management is performed via a remote management device, such as a computing device, that is remotely located from the medicament delivery device.

Abstract: Techniques and devices for determining and generating insulin formulations are described.

Abstract: Methods and apparatuses for performing a blood glucose monitoring process are described. For example, an apparatus may include at least one memory and logic coupled to the at least one memory. The logic may operate to determine patient monitoring information associated with a diabetic treatment of a patient, generate at least one monitoring information structure based on the patient monitoring information, generate at least one monitoring information image based on at least a portion of the at least one monitoring information structure, and process the at least one monitoring information image using a computational model to determine a blood glucose condition of the patient. Other embodiments are described.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Exemplary embodiments may provide an improved approach to automated insulin delivery by more accurately estimating the total daily insulin (TDI) of a user. As a result, less insulin is wasted by the delivery system, and the estimate of TDI more closely matches a user's actual daily insulin needs. Hence, the user need not refill the insulin reservoir excessively or need not fret unnecessarily about running out of insulin prematurely. The estimate relies on the history of actual automated insulin deliveries and thus reflects the actual insulin delivered to the user more accurately than conventional approaches.