Abstract: Continuous Glucose Monitoring (CGM) devices provide glucose concentration measurements in the subcutaneous tissue with limited accuracy and precision. Therefore, CGM readings cannot be incorporated in a straightforward manner in outcome metrics of clinical trials e.g. aimed to assess new glycaemic-regulation therapies. To define those outcome metrics, frequent Blood Glucose (BG) reference measurements are still needed, with consequent relevant difficulties in outpatient settings. Here we propose a “retrofitting” algorithm that produces a quasi continuous time BG profile by simultaneously exploiting the high accuracy of available BG references (possibly very sparsely collected) and the high temporal resolution of CGM data (usually noisy and affected by significant bias).

Abstract: Certain aspects of the present disclosure relate to methods and systems for providing decision support around glucose management for patients with diabetes. Time-varying inputs including blood glucose, meal intake information, and amount of infused insulin are processed using a machine learning model to obtain predicted glucose levels for a plurality of prediction horizons and uncertainties for the predictions. A confidence interval is generated for each prediction and the confidence intervals are compared to hypo- and hyperglycemic thresholds. If a confidence interval is entirely below or entirely above the hypo- and hyperglycemic thresholds, respectively, then a decision support output is provided.

Abstract: Continuous Glucose Monitoring (CGM) devices provide glucose concentration measurements in the subcutaneous tissue with limited accuracy and precision. Therefore, CGM readings cannot be incorporated in a straightforward manner in outcome metrics of clinical trials e.g. aimed to assess new glycaemic-regulation therapies. To define those outcome metrics, frequent Blood Glucose (BG) reference measurements are still needed, with consequent relevant difficulties in outpatient settings. Here we propose a “retrofitting” algorithm that produces a quasi continuous time BG profile by simultaneously exploiting the high accuracy of available BG references (possibly very sparsely collected) and the high temporal resolution of CGM data (usually noisy and affected by significant bias).

Abstract: A method of predicting future blood glucose concentrations of an individual patient includes: selecting an individualized nonlinear physiological model of glucose-insulin dynamics, the selected model having a plurality of model parameters whose values are to be determined; estimating values for each of the model parameters in the plurality of model parameters, a first subset of the model parameters having values estimated from a priori population data and a second subset of the model parameters having values personalized for the individual patient by applying a parameter estimation technique to a priori information and data for the individual patient to obtain a posteriori information; and; applying a nonlinear prediction technique to the selected model using the estimated values for each of the model parameters to obtain a predicted blood glucose concentration of the individual patient at a future time.

Abstract: A method of predicting future blood glucose concentrations of an individual patient includes: identifying an individualized linear black box model of glucose-insulin by estimating a plurality of impulse response functions each accounting for an input-output relation of a plurality of individualized patient data sets, the impulse response functions being functions in a Reproducing Kernel Hilbert Space (RKHS); and applying a linear predicting technique to the selected model using the identified impulse response functions to obtain a predicted blood glucose concentration of the individual patient at a future time.

Abstract: Mitigation of the risk of prolonged hypoglycemia in T1D management requires patient to assume a small dose of fast-acting carbohydrates, called hypotreatment (HT), as soon as hypoglycemia is detected. This invention consists in a method that, on the basis of the datastream generated by a continuous glucose monitoring (CGM) sensor, triggers the assumption of preventive HTs i.e., snacks that, being quickly absorbed into the circulation, avoid, or at least mitigate, a forthcoming hypoglycemic event. The method resorts to the “dynamic risk” (DR) non-linear function, which combines current glycemia with its rate-of-change provided by CGM, adapted to distinguish the severity of the about-to-happen hypoglycemia. The method has been tested in a simulated realistic scenario. Results show that the administration of an HT in advance, as triggered by the new method, brings to a strong reduction of the time that a patient would have spent in hypoglycemia assuming the HT at hypoglycemic threshold crossing.

Abstract: Continuous Glucose Monitoring (CGM) devices provide glucose concentration measurements in the subcutaneous tissue with limited accuracy and precision. Therefore, CGM readings cannot be incorporated in a straightforward manner in outcome metrics of clinical trials e.g. aimed to assess new glycaemic-regulation therapies. To define those outcome metrics, frequent Blood Glucose (BG) reference measurements are still needed, with consequent relevant difficulties in outpatient settings. Here we propose a “retrofitting” algorithm that produces a quasi continuous time BG profile by simultaneously exploiting the high accuracy of available BG references (possibly very sparsely collected) and the high temporal resolution of CGM data (usually noisy and affected by significant bias).

Abstract: Continuous Glucose Monitoring (CGM) devices provide glucose concentration measurements in the subcutaneous tissue with limited accuracy and precision. Therefore, CGM readings cannot be incorporated in a straightforward manner in outcome metrics of clinical trials e.g. aimed to assess new glycaemic-regulation therapies. To define those outcome metrics, frequent Blood Glucose (BG) reference measurements are still needed, with consequent relevant difficulties in outpatient settings. Here we propose a “retrofitting” algorithm that produces a quasi continuous time BG profile by simultaneously exploiting the high accuracy of available BG references (possibly very sparsely collected) and the high temporal resolution of CGM data (usually noisy and affected by significant bias).

Abstract: A device for monitoring a diabetic patient includes continuous glucose monitoring system that is configured to generate glucose data indicative of the patient's actual glucose level. An continuous subcutaneous insulin infusion pump is configured to inject insulin into the patient and that is configured to generate insulin data regarding when and how much insulin has been injected into the patient. A processor, programmed with a discrete-time reiterative filter, calculates a predicted glucose level corresponding to a predicted glucose level currently expected to be sensed by the continuous glucose monitoring system, based on the insulin data and the glucose data over time and is also programed to generate an alert when the actual glucose level is different from the predicted glucose level by a predetermined amount. An alert generating device is coupled to the processor and is configured to generate an aesthetically-sensible event corresponding to the generation of the alert.

Abstract: Continuous Glucose Monitoring (CGM) devices provide glucose concentration measurements in the subcutaneous tissue with limited accuracy and precision. Therefore, CGM readings cannot be incorporated in a straightforward manner in outcome metrics of clinical trials e.g. aimed to assess new glycaemic-regulation therapies. To define those outcome metrics, frequent Blood Glucose (BG) reference measurements are still needed, with consequent relevant difficulties in outpatient settings. Here we propose a “retrofitting” algorithm that produces a quasi continuous time BG profile by simultaneously exploiting the high accuracy of available BG references (possibly very sparsely collected) and the high temporal resolution of CGM data (usually noisy and affected by significant bias).

Abstract: A device for monitoring a diabetic patient includes continuous glucose monitoring system that is configured to generate glucose data indicative of the patient's actual glucose level. An continuous subcutaneous insulin infusion pump is configured to inject insulin into the patient and that is configured to generate insulin data regarding when and how much insulin has been injected into the patient. A processor, programmed with a discrete-time reiterative filter, calculates a predicted glucose level corresponding to a predicted glucose level currently expected to be sensed by the continuous glucose monitoring system, based on the insulin data and the glucose data over time and is also programed to generate an alert when the actual glucose level is different from the predicted glucose level by a predetermined amount. An alert generating device is coupled to the processor and is configured to generate an aesthetically-sensible event corresponding to the generation of the alert.