Disclosed herein are devices, methods and systems for monitoring and detection of adverse events in a subject. In an embodiment, 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.

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. In exemplary embodiments, a respective pattern guidance display includes graphical indicia of one or more remedial actions, such as recommended therapy modifications for addressing the respective event pattern.

Methods and systems are disclosed for determining a basal rate adjustment of insulin in a continuous glucose monitoring system of a person with diabetes. A method may include receiving, by at least one computing device, a signal representative of at least one glucose measurement and detecting, by the at least one computing device, a glucose state of the person based on the signal, the detected glucose state including a glucose level of the person and a rate of change of the glucose level. The method may also include calculating, by the at least one computing device, an adjustment to a basal rate of a therapy delivery device based on a control-to-range algorithm and at least one failsafe constraint to account for changes in the insulin sensitivity of the person with diabetes or inaccurate glucose measurement.

A medical device is configured to store, in a session memory, data regarding operational parameters of the medical device, changes to the parameters, and physiological data of a patient associated with the medical device. A clinician may select, via a user interface of the medical device, a portion of the session memory to a remote device with a request for assistance regarding a problem occurring at the medical device. The portion of the session may be reviewed at the remote device in a graphical copy of a user interface of the medical device as it appeared when the data was recorded, and a workflow is generated at the remote device and sent to the medical device to assist the clinician in resolving issues at the medical device. A session may be started without an identification of the patient or a medical component, and the identification updated at a later time.

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.

Medical devices and related patient management systems and methods are provided. An exemplary system includes a medical device to obtain measurement values for a physiological condition in a body of a patient and a computing device to identify a plurality of event patterns within a plurality of monitoring periods based on the measurement values, prioritize the plurality of event patterns based on one or more prioritization criteria, filter the prioritized list of event patterns based on one or more filtering criteria, and generate a snapshot graphical user interface display. The snapshot graphical user interface display includes a graph overlay region having a graphical representation of the measurement values with respect to a time of day and an event detection region below the graph overlay region, where the event detection region includes pattern guidance displays for event patterns of the filtered prioritized list ordered according to the prioritization.

The present invention relates to compensating for brain shift in catheter trajectory planning. First brain shift information is determined from an initial brain image dataset, an initial planning dataset, a patient orientation dataset, and first burr hole dataset. The brain image dataset is updated based on the first brain shift information and a trajectory of a first catheter is updated based on the updated brain image dataset. For a subsequent catheter placement, subsequent brain shift information is determined based on the updated brain image dataset, the patient orientation dataset, and a subsequent burr hole dataset. The brain image dataset is updated again based on the subsequent brain shift information. The re-updated brain image dataset is utilized to update trajectories of the subsequent catheter as well as any preceding catheters.

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.

The subject technology is embodied in a method for predicting a delivery rate of a plurality of drugs dispensed by multiple infusion pumps at a delivery point. The method includes receiving one or more operating parameters related to multiple drug pumps and a carrier fluid pump, wherein each of the drug pumps dispenses a drug, and the carrier fluid pump dispenses a carrier fluid. The method also includes determining a delivery rate of a first drug at the delivery point. This can be done by predicting time variation of a concentration of the first drug at the delivery point based on a mathematical model of a mixed flow through a fluid path that terminates at the delivery point. The mixed flow includes the drugs and the carrier fluid. The model includes the operating parameters and a plurality of flow-parameters related to the mathematical model of the mixed flow.

Systems for predicting a drug delivery profile as described herein include at least one drug pump and/or a controllable valve that produce a drug flow and dispense at least a first drug. The system also includes at least one carrier fluid pump and/or another controllable valve that produces a carrier fluid flow, a flow junction structure configured to receive the drug flow and the carrier fluid flow to produce a mixed flow, and a fluid path for carrying the mixed flow between the flow junction structure and a delivery point. The system further includes a processing device configured to predict the drug delivery profile at the delivery point based on determining a predicted time variation of drug concentration at the delivery point using at least a model of the mixed flow. The model includes a plurality of parameters related to propagation of the mixed flow through the fluid path.