A Run-to-Run Approach to Enhance Continuous Glucose Monitor Accuracy Based on Continuous Wear

The accuracy of continuous glucose monitors (CGMs) plays a critical role in glucose management and the development of artificial pancreas systems. Current CGM performance can be improved by a run-to-run (R2R) strategy based on continuous wear that personalizes sensor calibration parameters using data from previous weeks' use. The proposed strategy determines personalized calibration curve parameter sets (calibration curve slope and intercept, sensitivity drift curve, mean reference error) by tapping into data that is readily available at the end of each week after weekly new sensor reinsertions, minimizing a cost function that penalizes the deviation between expected and actual blood glucose (BG) values. The algorithm was evaluated on 10 in silico subjects within the UVA/Padova metabolic simulator. The enhanced CGM's BG tracking was significantly improved over the standard CGM, decreasing the summed square error by over 20% in the second week and converging to 50% improvement by the 6th week. The R2R algorithm enhances glucose accuracy week by week based on continuous wear of the device.