Martina Vettoretti

Postdoctoral Research Fellow

Department of Information Engineering

University of Padova

Research topics

Major research achievements

Improvement of CGM sensor accuracy and reduction of the number of calibrations per day

Because of the time-variability of sensor sensitivity, CGM sensors need to be periodically recalibrated using few SMBG measurements per day to maintain a good level of sensor accuracy. A new algorithm for the online calibration of CGM sensors was proposed, which properly takes into account the time-variability of sensor sensitivity by a time-varying calibration function. Parameters of the calibration function are determined by Bayesian estimation to compensate the availability of only few SMBG measurements per day with a priori information about parameters’ statistical distribution. In a database of 57 CGM signals collected by the Dexcom G4 Platinum, the Bayesian calibration algorithm was tested with standard calibrations frequency, i.e. two per day, and halved calibrations frequency, i.e. one per day. Results showed that the one-per-day Bayesian calibration drives to accuracy performance similar to the two-per-day Bayesian calibration (11.8% vs 11.7% mean absolute relative difference, respectively) and statistically significantly better of the manufacturer calibration (13.1% mean absolute relative difference). By use of a multiple-day calibration function, we demonstrated in a database of 108 CGM signals collected by the Dexcom G4 Platinum that the Bayesian method allows to further reduce the number of calibrations per day (mean absolute relative difference equal to 11.62% with the new algorithm and one calibration every 4 days vs 12.83% with the manufacturer two-per-day calibration). Similar results were achieved by applying the method to data collected by last generation Dexcom G6 sensor. The proposed Bayesian calibration algorithm is thus effective in improving the accuracy of CGM sensors and importantly allows to reduce the number of SMBG references per day required for calibration without deteriorating sensor performance.

Development of a simulation framework for in silico assessment of CGM nonadjunctive use

Until December 2016, in the United States all CGM sensors in the market were approved for adjunctive use, i.e. to support SMBG in the treatment of T1D but not to substitute it. The recent improvements achieved in the accuracy of last generation CGM sensors suggested that CGM sensors may be accurate enough to be safely used nonadjunctively. Dexcom Inc. (San Diego, CA), one of the leader companies in the worldwide production of CGM sensors, asked to the Food and Drug Administration (FDA) the approval of the Dexcom G5 Mobile sensor for nonadjunctive use. By using the T1D patient decision simulator, which allows simulating the real-life behavior of T1D patients in using SMBG and CGM devices to make treatment decisions, we performed a two-week in silico clinical trial to compare SMBG use to CGM nonadjunctive use in 40.000 virtual subject behaviors reflecting the characteristics of a general T1D population. The results, which confirmed the safety and effectiveness of Dexcom G5 Mobile nonadjunctive use, were presented by Dexcom Inc. at the FDA Clinical Chemistry and Clinical Toxicology Devices Advisory Panel meeting of July 21st, 2016. A positive feedback came from the panel that voted 8/10 in favor of the approval of Dexcom G5 Mobile for nonadjunctive use (Edelman, J Diabetes Sci Technol, 2016). Based on the positive feedback of the panel, on December 20th 2016 the FDA approved the nonadjuctive use of Dexcom G5 Mobile. Few months later, on March 24th, Medicare announced covering Dexcom G5 Mobile for all people with diabetes on intensive insulin therapy.