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.