University Institute of Control and Industrial Informatics, Polytechnical University of Valencia, Valencia, Spain.
Diabetes Technol Ther. 2012 Jan;14(1):74-82. doi: 10.1089/dia.2011.0138. Epub 2011 Aug 24.
Continuous glucose monitoring (CGM) devices estimate plasma glucose (PG) from measurements in compartments alternative to blood. The accuracy of currently available CGM is yet unsatisfactory and may depend on the implemented calibration algorithms, which do not compensate adequately for the differences of glucose dynamics between the compartments. Here we propose and validate an innovative calibration algorithm for the improvement of CGM performance.
CGM data from GlucoDay(®) (A. Menarini, Florence, Italy) and paired reference PG have been obtained from eight subjects without diabetes during eu-, hypo-, and hyperglycemic hyperinsulinemic clamps. A calibration algorithm based on a dynamic global model (GM) of the relationship between PG and CGM in the interstitial space has been obtained. The GM is composed by independent local models (LMs) weighted and added. LMs are defined by a combination of inputs from the CGM and by a validity function, so that each LM represents to a variable extent a different metabolic condition and/or sensor-subject interaction. The inputs best suited for glucose estimation were the sensor current I and glucose estimations Ĝ, at different time instants [I(k), I(k)(-1), Ĝ(k)(-1)] (IIG). In addition to IIG, other inputs have been used to obtain the GM, achieving different configurations of the calibration algorithm.
Even in its simplest configuration considering only IIG, the new calibration algorithm improved the accuracy of the estimations compared with the manufacturer's estimate: mean absolute relative difference (MARD)=10.8±1.5% versus 14.7±5.4%, respectively (P=0.012, by analysis of variance). When additional exogenous signals were considered, the MARD improved further (7.8±2.6%, P<0.05).
The LM technique allows for the identification of intercompartmental glucose dynamics. Inclusion of these dynamics into the calibration algorithm improves the accuracy of PG estimations.
连续血糖监测（CGM）设备通过测量替代血液的隔室中的测量值来估计血浆葡萄糖（PG）。目前可用的 CGM 的准确性仍不理想，并且可能取决于所实施的校准算法，这些算法不能充分补偿隔室之间葡萄糖动力学的差异。在这里，我们提出并验证了一种用于改善 CGM 性能的创新校准算法。
从 8 名无糖尿病的受试者在等、低和高血糖高胰岛素夹期间获得了 GlucoDay®（A. Menarini，佛罗伦萨，意大利）和配对的参考 PG 的 CGM 数据。已经获得了一种基于 PG 和间质空间中 CGM 之间关系的动态全局模型（GM）的校准算法。GM 由独立的局部模型（LM）加权和添加组成。LM 由 CGM 的输入和有效性函数的组合定义，因此每个 LM 在不同程度上代表不同的代谢状态和/或传感器-受试者相互作用。最适合葡萄糖估计的输入是传感器电流 I 和不同时间点的葡萄糖估计值 Ĝ，[I(k)，I(k)(-1)， Ĝ(k)(-1)]（IIG）。除了 IIG 之外，还使用了其他输入来获得 GM，从而实现了校准算法的不同配置。
即使在仅考虑 IIG 的最简单配置中，与制造商的估计相比，新的校准算法也提高了估计的准确性：平均绝对相对差异（MARD）=10.8±1.5%比 14.7±5.4%，分别为（P=0.012，方差分析）。当考虑其他外源性信号时，MARD 进一步改善（7.8±2.6%，P<0.05）。
LM 技术允许识别隔室间葡萄糖动力学。将这些动力学纳入校准算法可提高 PG 估计的准确性。
