Infusion devices and related patient management systems and methods are provided. An exemplary method of presenting information pertaining to operation of an infusion device to deliver fluid to a body of a patient involves identifying a plurality of event patterns within different monitoring periods based on measurement values for the patient's condition, prioritizing the identified event patterns based on one or more prioritization criteria, filtering the prioritized list of identified event patterns based on one or more filtering criteria, and then providing a respective pattern guidance display for each identified event pattern remaining in the filtered prioritized list.

An infusion pump assembly includes a reservoir assembly configured to contain an infusible fluid. A motor assembly is configured to act upon the reservoir assembly and dispense at least a portion of the infusible fluid contained within the reservoir assembly. Processing logic is configured to control the motor assembly. The processing logic includes a primary microprocessor configured to execute one or more primary applications written in a first computer language; and a safety microprocessor configured to execute one or more safety applications written in a second computer language.

A closed-loop system for insulin infusion overnight uses a model predictive control algorithm (MPC). Used with the MPC is a glucose measurement error model which was derived from actual glucose sensor error data. That sensor error data included both a sensor artifacts component, including dropouts, and a persistent error component, including calibration error, all of which was obtained experimentally from living subjects. The MPC algorithm advised on insulin infusion every fifteen minutes. Sensor glucose input to the MPC was obtained by combining model-calculated, noise-free interstitial glucose with experimentally-derived transient and persistent sensor artifacts associated with the FreeStyle Navigator Continuous Glucose Monitor System (FSN). The incidence of severe and significant hypoglycemia reduced 2300- and 200-fold, respectively, during simulated overnight closed-loop control with the MPC algorithm using the glucose measurement error model suggesting that the continuous glucose monitoring technologies facilitate safe closed-loop insulin delivery.

An apparatus comprising a user interface configured to generate an electrical signal to start a basal insulin rate test when prompted by a user, an input configured to receive sampled blood glucose data of a patient that is obtained during a specified time duration, including a time duration during delivery of insulin according to a specified basal insulin rate pattern, and a controller communicatively coupled to the input and the user interface. The controller includes an insulin calculation module configured for determining at least one of an amount of basal insulin over-delivered and an amount of basal insulin under-delivered during the basal insulin rate test in trying to meet a target blood glucose baseline. Other devices and methods are disclosed.

An infusion control test system includes a patient bio-simulator configured to generate first simulated biophysical data; an infusion control system configured to determine a first infusion profile in response to the first simulated biophysical data; and an infusion pump simulator configured to simulate a first administration of medication according to the first infusion profile. The patient bio-simulator is further configured to: simulate a patient response to the simulated first administration of medication; and generate second simulated biophysical data by adjusting the first simulated biophysical data according to the patient response.

Provided is a personalized artificial intelligence-based insulin dose determination system includes an interface layer capable of communicating with the insulin pump and the continuous blood glucose system, and signal-processing information from the insulin pump and the continuous blood glucose system, a control layer receiving the signal-processed information from the interface layer and generating output information associated with an insulin infusion amount, an outer safety layer determining whether or not the output information generated from the control layer satisfies a preset threshold condition, and delivering the output information to the interface layer when the output information satisfies the preset threshold condition, and a personalized safety layer receiving prescription information including Total Daily Dose of Insulin (TDD) information of the user from the outside to determine a personalized safety control variable, and transmitting the determined control variable as an input variable of the control layer.

An insulin delivery device includes an insulin injection device in communication with a controller for controlling the insulin injection device. The controller is configured to receive a heart signal from one or more heart sensors, and a blood glucose signal from one or more blood glucose sensors. The controller is further configured to analyze changes in the heart rhythm of the subject based on the heart signal and determine, based on the changes in the heart rhythm and the blood glucose signal, whether the subject is and/or will be experiencing an adverse event. Upon determination that the subject is or will be experiencing an adverse event, the controller determines one or more parameters of delivery of insulin to be delivered to the subject. Finally, the controller is configured to control the injection device to deliver insulin to the subject in accordance with the determined one or more parameters of delivery.

A system for monitoring autonomic nervous system (ANS) function in a subject includes at least one sensor configured to acquire a first physiological signal related to heart activity of the subject, one or more additional sensors configured to acquire one or more physiological signals, a processing unit configured to receive, process and analyze the first and additional physiological signals to monitor/detect, in real time, at least one of an autonomic nervous system dysfunction and a change in a physiological state of the subject and determine an output condition based on the detection, and an output mechanism configured to perform, based on the output condition, generating an alert, initiating a treatment, and/or storing data. At least one of the additional sensors is a blood glucose sensor, a respiration sensor, a sudomotor activity sensor, a pulse wave sensor, an electroencephalography (EEG) sensor, an electromyography (EMG) sensor or a motion sensor.

A system for administration of medicament to a user-body includes an analyte sensor and an automated medicament delivery device. The automated medicament delivery device is configured to determine an actual bolus fraction of a total daily medicament that was delivered as a total daily bolus dose, determine a ratio of the actual bolus fraction relative to a target bolus fraction, and based at least partially on the determined ratio of the actual bolus fraction relative to a target bolus fraction, determine at least one new value of a parameter value utilized by the automated medicament delivery device to determine bolus doses.

This document generally describes systems and methods for simulating a medication delivery system. Some embodiments can include a medication delivery simulation system that simulates an operation of a medication delivery system, such as delivery of a medication and detection of a response to the delivery. The simulation system can be used to simulate at least part of the operation of a medication delivery system and can check operations of one or more components in the medication delivery system, such as a pump, a sensor, a user device (e.g., a mobile device), and a server computing device.