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    RETROSPECTIVE RETROFITTING METHOD TO GENERATE A CONTINUOUS GLUCOSE CONCENTRATION PROFILE BY EXPLOITING CONTINUOUS GLUCOSE MONITORING SENSOR DATA AND BLOOD GLUCOSE MEASUREMENTS
    14.
    发明申请
    RETROSPECTIVE RETROFITTING METHOD TO GENERATE A CONTINUOUS GLUCOSE CONCENTRATION PROFILE BY EXPLOITING CONTINUOUS GLUCOSE MONITORING SENSOR DATA AND BLOOD GLUCOSE MEASUREMENTS 审中-公开

    公开(公告)号：US20190223807A1

    公开(公告)日：2019-07-25

    申请号：US16373454

    申请日：2019-04-02

    Applicant: DexCom, Inc.

    Inventor： Claudio Cobelli ,  Simone Del Favero ,  Andrea Facchinetti ,  Giovanni Sparacino

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

    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). 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.

    Retrospective retrofitting method to generate a continuous glucose concentration profile by exploiting continuous glucose monitoring sensor data and blood glucose measurements
    16.
    发明授权
    Retrospective retrofitting method to generate a continuous glucose concentration profile by exploiting continuous glucose monitoring sensor data and blood glucose measurements 有权

    公开(公告)号：US12226234B2

    公开(公告)日：2025-02-18

    申请号：US18182444

    申请日：2023-03-13

    Applicant: Dexcom, Inc.

    Inventor： Claudio Cobelli ,  Simone Del Favero ,  Andrea Facchinetti ,  Giovanni Sparacino

    IPC: A61B5/145 ,  A61B5/00 ,  A61B5/1495 ,  G16H10/40 ,  G16H50/20 ,  G16H50/30

    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 glycemic-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.

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