Facchinetti Andrea, Sparacino Giovanni, Cobelli Claudio
Department of Information Engineering, University of Padova, Padova, Italy.
J Diabetes Sci Technol. 2010 Jan 1;4(1):4-14. doi: 10.1177/193229681000400102.
Knowing the statistical properties of continuous glucose monitoring (CGM) sensor errors can be important in several practical applications, e.g., in both open- and closed-loop control algorithms. Unfortunately, modeling the accuracy of CGM sensors is very difficult for both experimental and methodological reasons. It has been suggested that the time series of CGM sensor errors can be described as realization of the output of an autoregressive (AR) model of first order driven by a white noise process. The AR model was identified exploiting several reference blood glucose (BG) samples (collected frequently in parallel to the CGM signal), a procedure to recalibrate CGM data, and a linear time-invariant model of blood-to-interstitium glucose (BG-to-IG) kinetics. By resorting to simulation, this work shows that some assumptions made in the Breton and Kovatchev modeling approach may significantly affect the estimated sensor error and its statistical properties.
Three simulation studies were performed. The first simulation was devoted to assessing the influence of CGM data recalibration, whereas the second and third simulations examined the role of the BG-to-IG kinetic model. Analysis was performed by comparing the "original" (synthetically generated) time series of sensor errors vs its "reconstructed" version in both time and frequency domains.
Even small errors either in CGM data recalibration or in the description of BG-to-IG dynamics can severely affect the possibility of correctly reconstructing the statistical properties of sensor error. In particular, even if CGM sensor error is a white noise process, a spurious correlation among its samples originates from suboptimal recalibration or from imperfect knowledge of the BG-to-IG kinetics.
Modeling the statistical properties of CGM sensor errors from data collected in vivo is difficult because it requires perfect calibration and perfect knowledge of BG-to-IG dynamics. Results suggest that correct characterization of CGM sensor error is still an open issue and requires further development upon the pioneering contribution of Breton and Kovatchev.
了解连续血糖监测（CGM）传感器误差的统计特性在多个实际应用中可能很重要，例如在开环和闭环控制算法中。不幸的是，由于实验和方法学原因，对CGM传感器的准确性进行建模非常困难。有人提出，CGM传感器误差的时间序列可以描述为一阶自回归（AR）模型的输出实现，该模型由白噪声过程驱动。利用多个参考血糖（BG）样本（与CGM信号并行频繁采集）、一种重新校准CGM数据的程序以及血糖到组织间液葡萄糖（BG-to-IG）动力学的线性时不变模型，确定了AR模型。通过模拟，这项工作表明，布雷顿和科瓦切夫建模方法中做出的一些假设可能会显著影响估计的传感器误差及其统计特性。
进行了三项模拟研究。第一项模拟致力于评估CGM数据重新校准的影响，而第二项和第三项模拟研究了BG-to-IG动力学模型的作用。通过在时域和频域中比较传感器误差的“原始”（合成生成）时间序列与其“重建”版本进行分析。
即使在CGM数据重新校准或BG-to-IG动力学描述中存在小误差，也会严重影响正确重建传感器误差统计特性的可能性。特别是，即使CGM传感器误差是一个白噪声过程，其样本之间的虚假相关性也源于次优重新校准或对BG-to-IG动力学的不完全了解。
根据体内收集的数据对CGM传感器误差的统计特性进行建模很困难，因为这需要完美校准和对BG-to-IG动力学的完美了解。结果表明，CGM传感器误差的正确表征仍然是一个未解决的问题，需要在布雷顿和科瓦切夫的开创性贡献基础上进一步发展。
