Retrospective retrofitting method to generate a continuous glucose concentration profile by exploiting continuous glucose monitoring sensor data and blood glucose measurements

• US 10,299,733 B2
• Filed: 02/20/2014
• Issued: 05/28/2019
• Est. Priority Date: 02/20/2013
• Status: Active Grant

• Current Assignee: DexCom Incorporated
• Original Assignee: DexCom Incorporated
• Inventors: Cobelli, Claudio, Del Favero, Simone, Facchinetti, Andrea, Sparacino, Giovanni
• Primary Examiner(s): Messersmith, Eric J
• Application Number:
  US14/770,803
• Publication Number:
  US 20160073964A1
• Time in Patent Office: 1,923 Days
• CPC Class Codes: A61B 5/1451 for interstitial fluid
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1495 Calibrating or testing of in-vivo probes
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7289 Retrospective gating, i.e. associating measured signals or images with a physiological event after the actual measurement or image acquisition, e.g. by simultaneously recording an additional physiological signal during the measurement or image acquisition
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

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.

Claims

1. A method for monitoring a glucose level in a user, comprising the steps of:

(a) continuously monitoring glucose levels in the user'"'"'s interstitial fluids and to generating a continuous glucose monitoring (CGM) time series representative thereof;

(b) generating blood glucose (BG) references representative of the user'"'"'s blood glucose levels at discrete time intervals;

(c) detecting outliers and artifacts in both the CGM time series and the BG references and generating a preprocessed CGM signal corresponding to the CGM time series from which any outliers and artifacts are discarded and generating a preprocessed BG signal corresponding to the BG references from which any outliers and artifacts are discarded;

(d) performing a retrospective calibration of the preprocessed CGM signal, employing the preprocessed BG signal, thereby compensating for systematic underestimation and overestimation of CGM time series with respect to reference BG values due to;

blood-to-interstitial glucose kinetics, sensor drift, errors in CGM sensor calibration, and changes in sensor sensitivity, and generating a retrospectively calibrated CGM signal representative thereof by rescaling the calibrated CGM signal so as to stay within a confidence interval of the BG values;

(e) deconvoluting the retrospectively calibrated CGM signal based on a model of blood-to-interstitial glucose kinetics, and thereby generating a retrofitted glucose concentration profile with a predetermined confidence interval, and

(f) displaying an output of the constrained inverse problem solver module, wherein the displayed output is a more accurate and precise indication of the glucose level that obtainable in the absence of the preprocessing, retrospective calibration, and constrained inverse problem solver deconvolution.

2. The method for monitoring a glucose level in a user of claim 1, further comprising employing other selected relevant inputs in the step of detecting outliers and artifacts, wherein the other selected relevant inputs are selected from a group consisting of:

data regarding meals consumed, data regarding treatment taken for hypoglycemia, data regarding drugs taken, data regarding physical activity, and data regarding stress.

3. The method for monitoring a glucose level in a user of claim 1, further comprising the step of employing other selected relevant inputs in the step of performing a retrospective calibration, wherein the other selected relevant inputs are selected from a group consisting of:

data regarding meals consumed, data regarding treatment taken for hypoglycemia, data regarding drugs taken, data regarding physical activity, and data regarding stress.

4. The method for monitoring a glucose level in a user of claim 1, further comprising the step of employing a CGM drift/degradation model and a priori information on model parameters in performing the step of performing a retrospective calibration.

5. The method for monitoring a glucose level in a user of claim 1, further comprising the step of employing a blood-to-interstitial glucose kinetics model in performing the step of performing a retrospective calibration.

6. The method for monitoring a glucose level in a user of claim 1, further comprising the step of employing other selected relevant inputs in the step of deconvoluting the retrospectively calibrated CGM signal, wherein the other selected relevant inputs are selected from a group consisting of:

data regarding meals consumed, data regarding treatment taken for hypoglycemia, data regarding drugs taken, data regarding physical activity, and data regarding stress.

7. The method for monitoring a glucose level in a user of claim 1, further comprising the step of employing a blood-to-interstitial glucose kinetics model in the performing the step of deconvoluting the retrospectively calibrated CGM signal.