Page 191 - Glucose Monitoring Devices

The Bayesian approach applied to the calibration problem 193

FIGURE 9.9
Calibrated CGM profiles (continuous lines) and laboratory references for accuracy
assessment (points) for a representative subject during an in-clinic session on day 7.
From top to bottom: original manufacturer calibration (performed two times per day),
Bayesian calibration algorithm with one calibration per day, one calibration every 2 days
and one calibration every 4 days.
Adapted from Acciaroli G, Vettoretti M, Facchinetti A, Sparacino G, Cobelli C. Reduction of blood glucose
measurements to calibrate subcutaneous glucose sensors: a Bayesian multiday framework. IEEE Transactions on
Biomedical Engineering 2018;65(3):587e595.

number of calibrations, more accurate than the original CGM output given by the
manufacturer. In particular, MARD, PAGE, and CEGA-A resulted, respectively,
15.04%, 74.26%, 66.18% (original manufacturer calibration, on average two
calibrations per day), 8.85%, 97.06%, 94.12% (Bayesian algorithm, one calibration