Postprandial performance of Dexcom SEVEN PLUS and Medtronic Paradigm Veo: Modeling and statistical analysis ?
Minimizing excessive postprandial glucose excursion is still an unmet need in treating diabetes. This work addresses the analysis and modeling in the postprandial state of two commercial CGM devices. Twelve patients with type 1 diabetes were studied in the postprandial state on four different occasions under controlled conditions. Each time, we performed simultaneous glucose monitoring using the Dexcom SEVEN PLUS (47 datasets) and the Medtronic Paradigm Veo (42 datasets). The following statistical properties of the error signal were analyzed and modeled sequentially for the two devices: the lag time, the error stationarity, the error probability distribution and the time correlation. Finally, models were built for sensor simulation in silico studies. Lag time followed an exponential probability distribution for both monitors (?sevenplus=108, ?veo=1.69). Standard deviation and mean of the error signal, calculated as time-dependent signals across the population of sensors, were time-varying and correlated with the reference value and its rate of change, respectively. In both cases, a high variability of postprandial behaviors was observed. After non-stationarity compensation, a logistic distribution was obtained for the SEVEN PLUS error (close to normal distribution). Regarding the Paradigm Veo, a multimodal distribution was obtained, which turned into normal after elimination of five unstable sensors. Finally, a first order autoregressive model fitted the SEVEN PLUS error time-series while a third-order filter was necessary for the Paradigm Veo. The Paradigm Veo device exhibited greater delay variability with higher delay time and higher probability of abnormal sensor behaviors as compared to the SEVEN PLUS device. In both cases, the observed variability may have important clinical implications in postprandial performance. Therefore, further improvements are needed in calibration algorithms to reduce this variability.
simulation continuous glucose monitoring statistical modeling error analysis modeling errors biomedical systems