University of Virginia Patent Foundation

Abbott Diabetes Care Inc.

Lifescan Corporation

Breton, Marc D.

Boris P Kovatchev

Marc D Breton

Stephen D Patek

Daniel J Cox

Erwin Satrya Budiman

Kenneth J Doniger

Claudio Cobelli

Chiara Dalla Man

Rayna S Robeva

Jennifer Kim Penberthy

J Randall Moorman

William L Clarke

Colleen Hughes Karvetski

Patrick T Keith-Hynes

Christopher R King

David Price

Giovanni Sparacino

Andrea Facchinetti

M Pamela Griffin

Linda Gonder-Frederick

Leon S Farhi

Francesco Micheletto

Randall Moorman

Colleen S Hughes

Erik Otto

Martin Straume

Edward A Ortiz

Pamela Griffin

Alan Coulson

Colleen S Hughes Karvetski

Patek Stephen D

Hughes Karvetski Colleen S

Method, system and computer program product for providing real time detection of analyte sensor sensitivity decline is continuous glucose monitoring systems are provided.

Method, system and computer program product for real-time detection of sensitivity decline in analyte sensors

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

A method, apparatus, and a kit are capable of improving accuracy of CGS devices using dynamic outputs of continuous glucose sensors.

Accuracy of continuous glucose sensors

A system, method and non-transient computer readable medium for tracking hypoglycemia risk in patients with diabetes exercise. A system may include a digital processor configured to execute instructions to receive an input from each available data source of a plurality of intermittently available data sources; determine a plurality of probability signals for impending hypoglycemia, wherein each probability signal is based on one or more of the inputs from the available data sources or a lack of input from an unavailable data source; wherein a probability signal for each unavailable data source is assigned a value corresponding to a zone of uncertainty; and determine an aggregate risk of hypoglycemia based on the plurality of intermittently data sources by aggregating the plurality of probability signals.

System, method and computer readable medium for dynamical tracking of the risk for hypoglycemia in type 1 and type 2 diabetes

A method, system and computer readable medium for tracking changes in average glycemia in diabetes is based on a conceptually new approach to the retrieval of SMBG data. Using the understanding of HbA1c fluctuation as the measurable effect of the action of an underlying dynamical system, SMBG provides occasional glimpses at the state of this system and, using these measurements, the hidden underlying system trajectory can be reconstructed for individual diabetes patients. Using compartmental modeling a new two-step algorithm is provided that includes: (i) real-time estimate of HbA1c from fasting glucose readings, updated with any new incoming fasting SMBG data point(s), and (ii) initialization and calibration of the estimated HbA1c trace with daily SMBG profiles obtained periodically. The estimation of these profiles includes a factorial model capturing daily BG variability within two latent factors.

Tracking changes in average glycemia in diabetics

A computer-implemented method for providing a real-time estimate of glycosylated hemoglobin (HbA1c) of a patient from a self-monitoring blood glucose (SMBG) measurement, and tracking changes in average glycemia of said patient over time is disclosed. The method includes the steps of; a computer computing a surrogate fasting measurement based on SMBG data received from the patient; a computer computing a glycation value using the said surrogate fasting measurement in a predetermined glycation equation; a computer outputting said glycation value as an initial estimate of HbA1c upon initialization of tracking of said patient's average glycemia; a computer updating said glycation value by using an updated SMBG value in said predetermined glycation equation, said updated SMBG value being based on a subsequent computed surrogate fasting measurement; and a computer computing an updated estimate of HbA1c using said initial estimate of HbA1c and said updated glycation value in a predetermined HbA1c estimation equation.

System and method for tracking changes in average glycemia in diabetics

A system, computer program product, method and algorithm for evaluation of blood glucose variability&#x2014;one of the most important parameters of diabetes management. An embodiment of the method may use routine self-monitoring blood glucose (SMBG) data collected over a period of 2-6 weeks, for example, based on a theory of risk analysis of blood glucose data. One aspect may include a method, system and computer program product for computing the Average Daily Risk Range (ADRR)&#x2014;a measure of overall glucose variability. Another aspect may include a method, system, and computer program product for estimating separately the glucose variability in the hypoglycemic range via a Low BG Index (LBGI) and the glucose variability in the high BG range via High BG Index (HBGI) followed by a combination of the two indices into a single variability display.

Method, system and computer program product for evaluation of blood glucose variability in diabetes from self-monitoring data

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Charlottesville, VA, United States of America

Boris P Kovatchev