Abstract: A method, system and related computer program product for tracking the probability of hypoglycemia from routine self-monitoring of blood glucose (SMBG) data in patients with diabetes. A specific bivariate probability distribution of low BG events based jointly on the Low BG Index (LBGI) and the Average Daily Risk Range (ADRR) is used to predict hypoglycemia probability of occurrence from inputted SMBG data. The SMBG data is retrieved from a series of SMBG data of a patient available from the patient's glucose meter and allows tracking of the probability for future hypoglycemia over a predetermined duration, e.g. a 24 or 48 hour period. The tracking includes presentation of visual and/or numerical output, as well as construction of hypoglycemia risk trajectories that would enable warning messages for crossing of predefined thresholds, such as 50% likelihood for upcoming hypoglycemia below 50 mg/dl.

Abstract: A simulation environment for in silico testing of monitoring methods, open-loop and closed-loop treatment strategies in type 1 diabetes. Some exemplary principal components of the simulation environment comprise, but not limited thereto, the following: 1) a “population” of in silico “subjects” with type 1 diabetes in three age groups; 2) a simulator of CGM sensor errors; 3) a simulator of insulin pumps and discrete insulin delivery; 4) an interface allowing the input of user-specified treatment scenarios; and 5) a set of standardized outcome measures and graphs evaluating the quality of the tested treatment strategies. These components can be used separately or in combination for the preclinical evaluation of open-loop or closed-loop control treatments of diabetes.

Abstract: A structure, method, and computer program product for a diabetes control system provides, but is not limited thereto, the following: open-loop or closed-loop control of diabetes that adapts to individual physiologic characteristics and to the behavioral profile of each person. An exemplary aspect to this adaptation is biosystem (patient or subject) observation and modular control. Consequently, established is the fundamental architecture and the principal components for a modular system, which may include algorithmic observers of patients' behavior and metabolic state, as well as interacting control modules responsible for basal rate, insulin boluses, and hypoglycemia prevention.

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

Abstract: 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.

Abstract: Time-varying hyperglycemic stresses are derived from actual ICU patients and applied to non-critically ill virtual patients, using any model of normal glucose-insulin physiology that fulfills certain requirements, in order to model and simulate stress hyperglycemia. Other aspects provide: 1) a methodology to perform sensitivity analyses of the parameters of ICU insulin infusion therapy protocols and to improve the protocols; and 2) a training system for clinicians about the course and management of stress hyperglycemia in the ICU or other facility.

Abstract: A method, system and related computer program product for tracking the probability of hypoglycemia from routine self-monitoring of blood glucose (SMBG) data in patients with diabetes. A specific bivariate probability distribution of low BG events based jointly on the Low BG Index (LBGI) and the Average Daily Risk Range (ADRR) is used to predict hypoglycemia probability of occurrence from inputted SMBG data. The SMBG data is retrieved from a series of SMBG data of a patient available from the patient's glucose meter and allows tracking of the probability for future hypoglycemia over a predetermined duration, e.g., a 24 or 48 hour period. The tracking includes presentation of visual and/or numerical output, as we construction of hypoglycemia risk trajectories that would enable warning messages for crossing of predefined thresholds, such as 50% likelihood for upcoming hypoglycemia below 50 mg/dl.

Abstract: A structure, method, and computer program product for a diabetes control system provides, but is not limited thereto, the following: open-loop or closed-loop control of diabetes that adapts to individual physiologic characteristics and to the behavioral profile of each person. An exemplary aspect to this adaptation is biosystem (patient or subject) observation and modular control. Consequently, established is the fundamental architecture and the principal components for a modular system, which may include algorithmic observers of patients' behavior and metabolic state, as well as interacting control modules responsible for basal rate, insulin boluses, and hypoglycemia prevention.

Abstract: 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.

Abstract: 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.

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

Abstract: A method, system and computer program product for evaluating or determining a user's insulin sensitivity (SI). An initial step or module may include acquiring SMBG readings from a predetermined period. Another step or module may include computing an estimate of insulin sensitivity (SI) from the SMBG readings. Another step or module may include using the estimate of SI to compute individualized carbohydrate ratio. Additionally, another step or module may include using the estimate of SI to compute individualized correction factor. The computation of the two components of an insulin dose calculator, carbohydrate ratio and correction factor, uses this estimate, which allows the tailoring of carbohydrate ratio and correction factor to the present state of the person.

Abstract: A simulator for in-silico testing of Type 1 diabetes patients uses a model that puts in relation plasma concentrations, i.e., glucose G and insulin /, with glucose fluxes, i.e. endogenous glucose production (EGP), glucose rate of appearance (Ra), glucose utilization by the tissues (U), renal extraction (E), and insulin fluxes, i.e., rate of insulin appearance from the subcutaneous tissues (SC) and insulin degradation (D). A module is also included to describe counter-regulation, i.e. glucagon kinetics, secretion and action. A glucagon subcutaneous absorption model enables simulation of dual hormone control.

Abstract: 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.

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

Abstract: A computer-implemented method for providing a real-time estimate of glycosylated hemoglobin (HbAlc) of a patient from a self-monitoring blood glucose (SMBG) measurement, and tracking changes in average glycemia of said patient over time is disclosed.

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

Abstract: 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 end 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.

Abstract: 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.

Abstract: A flexible system capable of 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. A central data exchange node or server collects patient data from individual DiAs devices and provides safety assurance, monitoring, telemedicine and database building for the DiAs system.