University of Virginia Patent Foundation

Dexcom, Inc.

Stephen D Patek

Marc D Breton

Boris P Kovatchev

Patrick T Keith-Hynes

Basak Ozaslan

Enrique Campos-Náñez

Stephen J Vanslyke

Colleen Hughes Karvetski

Linda Gonder-Frederick

Leon S Farhi

John Lach

Myla D Goldman

Edward A Ortiz

Colleen S Hughes

Boris P Kovachev

Matthew M Engelhard

Colleen S Hughes Karvetski

A computer-implemented method for treating a patient suffering from Type I Diabetes (T1D). The method can include quantifying physical activity (PA) of the patient; calculating an accumulated PA periodically based on the quantified PA, the accumulated PA indicating an aggregate of the PA; and generating an activity informed insulin bolus by adjusting a prevalent functional insulin therapy bolus with a previous activity component, wherein the previous activity component is based on the accumulated daily PA, an activity profile, and an activity factor of the patient. The method can include determining an additional glucose uptake within a time period, the additional glucose uptake being caused by a PA; translating the additional glucose uptake into a number of insulin units with a same BG lowering impact; and generating an activity informed insulin bolus by adjusting a prevalent functional insulin therapy bolus with the insulin units.

System and method for physical activity informed drug dosing

A system, method and non-transient computer readable medium for assessing the opportunity to address either hyperglycemic or hypoglycemic risk (or both) in patients with diabetes based on historical continuous glucose monitoring (CGM) data.

Method, system and computer readable medium for assessing actionable glycemic risk

A flexible system for utilizing data from different monitoring techniques and capable of providing assistance to patients with diabetes at several scalable levels, ranging from advice about long-term trends and prognosis to real-time automated closed-loop control (artificial pancreas). These scalable monitoring and treatment strategies are delivered by a unified system called the Diabetes Assistant (DiAs) platform. The system provides a foundation for implementation of various monitoring, advisory, and automated diabetes treatment algorithms or methods. The DiAs recommendations are tailored to the specifics of an individual patient, and to the patient risk assessment at any given moment.

Unified platform for monitoring and control of blood glucose levels in diabetic patients

An insulin monitoring system includes one or more processors, one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors. The program instructions include: first program instructions to track, in real time, the amount of insulin active in a patient; second program instructions to calculate the amount of insulin need of the patient by tracking the patient's metabolic states; third program instructions to compare the insulin active in the patient with the insulin need of the patient; and fourth program instructions to determine an insulin fault level based on the comparison.

Insulin monitoring and delivery system and method for CGM based fault detection and mitigation via metabolic state tracking

An insulin delivery supervisor (IDS) with a safety analysis and supervision function that can reside between the insulin request and the insulin delivery and can intercept any excessive insulin requests before the insulin was delivered. The IDS can be implemented in any system based on insulin pump or pen and will work with either SMBG or CGM modes of blood glucose monitoring.

Method and system for the safety, analysis and supervision of insulin pump action and other modes of insulin delivery in diabetes

An aspect of an embodiment or partial embodiment of the present invention (or combinations of various embodiments in whole or in part of the present invention) comprises, but not limited thereto, a method and system (and related computer program product) for continually assessing the risk of hypoglycemia for a patient and then determining what action to take based on that risk assessment. A further embodiment results in two outputs: (1) an attenuation factor to be applied to the insulin rate command sent to the pump (either via conventional therapy or via open or closed loop control) and/or (2) a red/yellow/green light hypoglycemia alarm providing to the patient an indication of the risk of hypoglycemia. The two outputs of the CPHS can be used in combination or individually.

Method, system and computer program product for CGM-based prevention of hypoglycemia via hypoglycemia risk assessment and smooth reduction insulin delivery

An amount of glycemic dysfunction associated with mis-timing (e.g., delay) of meal boluses based on replay analysis is determined. The amount of dysfunction of historical or estimated bolusing as compared to an optimally timed bolus based on the replay analysis is quantified and visualized. Inferences may be made about diabetes meal management regarding inputs from a patient.

Evaluation and visualization of glycemic dysfunction

A method and system use mathematical models and available patient information to virtualize a continuous glucose monitoring trace for a period of time. Such a method and system can generate the virtualized trace when episodic patient data is incomplete. Such a method and system can also rely on self-monitored blood glucose measurement information to improve the virtualized continuous glucose monitoring trace.

Method, system, and computer readable medium for virtualization of a continuous glucose monitoring trace

Equivalent insulin pump retrospective virtual basal rates for daily injections are constructed from planned insulin injections according to a virtual basal rate profile developed for a patient, and a database of historical insulin injections, i.e. basal injections actually administered by the patient, providing a unified framework for analysis, design, optimization, and adaptation of MDI (multiple daily injections) and CSII (continuous subcutaneous insulin infusion (i.e. insulin pump)) treatment parameters for patients with diabetes.

Method, system, and computer readable medium for controlling insulin delivery using retrospective virtual basal rates

Adaptive on board estimation of exogenous pharmacon responsive to transient (i.e., impermanent) physiological effects is provided. Dynamically estimating an equivalent amount of an exogenous pharmacon on board (XOB), such as insulin and/or carbohydrates, left in the subject, is based on predictions of glucose time-series data. These estimated values, such as insulin on board (IOB), are useful for diabetes management software, including decision support and/or artificial pancreas (AP) algorithms, for example.

Dynamic equivalent on board estimator

An insulin device configured to control insulin dispensing based on insulin sensitivity. The insulin device includes a processor configured to receive insulin dosing schedule information, psychological stress level data, and body mass index (BMI) data; a sensor configured to generate a blood glucose level measurement. The sensor is calibrated as a function of the psychological stress level data and the BMI data and the processor is configured to monitor and detect changes of the blood glucose level measurement that are determined to have occurred as a function of changes of the psychological stress level data, and identify a time when the BMI data counteracts a detected change in the blood glucose level measurement. The insulin device also includes an insulin dispensing valve controlled by the processor to change the insulin dosing schedule information in accordance with the counteracting BMI data.

System and method for body mass index relation to patient differing psychological stress effect on blood glucose dynamics in patients with insulin dependent diabetes

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Charlottesville, VA, United States of America

Stephen D Patek