公开(公告)号：US20240293619A1

公开(公告)日：2024-09-05

申请号：US18614977

申请日：2024-03-25

申请人： Dexcom, Inc.

发明人： Andrea Facchinetti ,  Simone Del Favero ,  Giovanni Sparacino ,  Claudio Cobelli

IPC分类号： A61M5/172 ,  A61B5/00 ,  A61B5/145 ,  A61M5/14 ,  A61M5/142 ,  G16H20/17 ,  G16H40/63 ,  G16H40/67 ,  G16Z99/00

CPC分类号： A61M5/1723 ,  A61B5/14532 ,  A61B5/4839 ,  A61B5/725 ,  A61B5/7282 ,  A61B5/7405 ,  A61B5/742 ,  A61B5/743 ,  A61B5/7455 ,  A61B5/746 ,  A61M5/14 ,  A61M5/14276 ,  G16H20/17 ,  G16H40/63 ,  G16H40/67 ,  G16Z99/00 ,  A61M2202/0468 ,  A61M2205/18

摘要： 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.

公开(公告)号：US20190223807A1

公开(公告)日：2019-07-25

申请号：US16373454

申请日：2019-04-02

申请人： DexCom, Inc.

发明人： Claudio Cobelli ,  Simone Del Favero ,  Andrea Facchinetti ,  Giovanni Sparacino

IPC分类号： A61B5/00 ,  A61B5/145 ,  A61B5/1495

摘要： 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). The inputs of the algorithm are: a CGM time series; some reference BG measurements; a model of blood to interstitial glucose kinetics; and a model of the deterioration in time of sensor accuracy, together with (if available) a priori information (e.g. probabilistic distribution) on the parameters of the model. The algorithm first checks for the presence of possible artifacts or outliers on both CGM datastream and BG references, and then rescales the CGM time series by exploiting a retrospective calibration approach based on a regularized deconvolution method subject to the constraint of returning a profile laying within the confidence interval of the reference BG measurements. As output, the retrofitting algorithm produces an improved “retrofitted” quasi-continuous glucose concentration signal that is better (in terms of both accuracy and precision) than the CGM trace originally measured by the sensor. In clinical trials, the so-obtained retrofitted traces can be used to calculate solid outcome measures, avoiding the need of increasing the data collection burden at the patient level.

公开(公告)号：US11633156B2

公开(公告)日：2023-04-25

申请号：US16373454

申请日：2019-04-02

申请人： DexCom, Inc.

发明人： Claudio Cobelli ,  Simone Del Favero ,  Andrea Facchinetti ,  Giovanni Sparacino

IPC分类号： A61B5/145 ,  A61B5/00 ,  A61B5/1495 ,  G16H50/30 ,  G16H50/20

摘要： 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). The inputs of the algorithm are: a CGM time series; some reference BG measurements; a model of blood to interstitial glucose kinetics; and a model of the deterioration in time of sensor accuracy, together with (if available) a priori information (e.g. probabilistic distribution) on the parameters of the model. The algorithm first checks for the presence of possible artifacts or outliers on both CGM datastream and BG references, and then rescales the CGM time series by exploiting a retrospective calibration approach based on a regularized deconvolution method subject to the constraint of returning a profile laying within the confidence interval of the reference BG measurements. As output, the retrofitting algorithm produces an improved “retrofitted” quasi-continuous glucose concentration signal that is better (in terms of both accuracy and precision) than the CGM trace originally measured by the sensor. In clinical trials, the so-obtained retrofitted traces can be used to calculate solid outcome measures, avoiding the need of increasing the data collection burden at the patient level.

公开(公告)号：US20220202323A1

公开(公告)日：2022-06-30

申请号：US17645715

申请日：2021-12-22

申请人： Dexcom, Inc.

发明人： Simone Del Favero ,  Andrea Facchinetti ,  Simone Faccioli ,  Gianluigi Pillonetto ,  Giovanni Sparacino

IPC分类号： A61B5/145 ,  G01N33/66

摘要： 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.

公开(公告)号：US20160193409A1

公开(公告)日：2016-07-07

申请号：US15067104

申请日：2016-03-10

申请人： DexCom, Inc.

发明人： Andrea Facchinetti ,  Simone Del Favero ,  Giovanni Sparacino ,  Claudio Cobelli

IPC分类号： A61M5/172 ,  A61B5/00 ,  A61B5/145 ,  A61M5/142

CPC分类号： A61M5/1723 ,  A61B5/14532 ,  A61B5/4839 ,  A61B5/725 ,  A61B5/7282 ,  A61B5/7405 ,  A61B5/742 ,  A61B5/743 ,  A61B5/7455 ,  A61B5/746 ,  A61M5/14 ,  A61M5/14276 ,  A61M2202/0468 ,  A61M2205/18 ,  G06F19/3468 ,  G16H40/63

摘要： 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.

公开(公告)号：US20220233152A1

公开(公告)日：2022-07-28

申请号：US17454693

申请日：2021-11-12

申请人： DexCom, Inc.

发明人： Giacomo Cappon ,  Andrea Facchinetti ,  Giovanni Sparacino ,  Simone Del Favero

IPC分类号： A61B5/00 ,  A61B5/145 ,  G16H50/30 ,  G16H50/50

摘要： 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.

公开(公告)号：US10299733B2

公开(公告)日：2019-05-28

申请号：US14770803

申请日：2014-02-20

申请人： DexCom, Inc.

发明人： Claudio Cobelli ,  Simone Del Favero ,  Andrea Facchinetti ,  Giovanni Sparacino

IPC分类号： A61B5/00 ,  A61B5/145 ,  A61B5/1495

摘要： 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). The inputs of the algorithm are: a CGM time series; some reference BG measurements; a model of blood to interstitial glucose kinetics; and a model of the deterioration in time of sensor accuracy, together with (if available) a priori information (e.g. probabilistic distribution) on the parameters of the model. The algorithm first checks for the presence of possible artifacts or outliers on both CGM datastream and BG references, and then rescales the CGM time series by exploiting a retrospective calibration approach based on a regularized deconvolution method subject to the constraint of returning a profile laying within the confidence interval of the reference BG measurements. As output, the retrofitting algorithm produces an improved “retrofitted” quasi-continuous glucose concentration signal that is better (in terms of both accuracy and precision) than the CGM trace originally measured by the sensor. In clinical trials, the so-obtained retrofitted traces can be used to calculate solid outcome measures, avoiding the need of increasing the data collection burden at the patient level.