Vettoretti M, Facchinetti A, Sparacino G, Cobelli C
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:2359-62. doi: 10.1109/EMBC.2015.7318867.
Self-monitoring of blood glucose (SMBG) devices are portable systems that allow measuring glucose concentration in a small drop of blood obtained via finger-prick. SMBG measurements are key in type 1 diabetes (T1D) management, e.g. for tuning insulin dosing. A reliable model of SMBG accuracy would be important in several applications, e.g. in in silico design and optimization of insulin therapy. In the literature, the most used model to describe SMBG error is the Gaussian distribution, which however is simplistic to properly account for the observed variability. Here, a methodology to derive a stochastic model of SMBG accuracy is presented. The method consists in dividing the glucose range into zones in which absolute/relative error presents constant standard deviation (SD) and, then, fitting by maximum-likelihood a skew-normal distribution model to absolute/relative error distribution in each zone. The method was tested on a database of SMBG measurements collected by the One Touch Ultra 2 (Lifescan Inc., Milpitas, CA). In particular, two zones were identified: zone 1 (BG≤75 mg/dl) with constant-SD absolute error and zone 2 (BG>75mg/dl) with constant-SD relative error. Mean and SD of the identified skew-normal distributions are, respectively, 2.03 and 6.51 in zone 1, 4.78% and 10.09% in zone 2. Visual predictive check validation showed that the derived two-zone model accurately reproduces SMBG measurement error distribution, performing significantly better than the single-zone Gaussian model used previously in the literature. This stochastic model allows a more realistic SMBG scenario for in silico design and optimization of T1D insulin therapy.
血糖自我监测（SMBG）设备是便携式系统，可通过手指采血获取的一小滴血来测量葡萄糖浓度。SMBG测量是1型糖尿病（T1D）管理的关键，例如用于调整胰岛素剂量。在一些应用中，如胰岛素治疗的计算机模拟设计和优化，一个可靠的SMBG准确性模型将非常重要。在文献中，描述SMBG误差最常用的模型是高斯分布，然而，该模型过于简单，无法恰当解释观察到的变异性。在此，提出了一种推导SMBG准确性随机模型的方法。该方法包括将葡萄糖范围划分为绝对/相对误差具有恒定标准差（SD）的区域，然后通过最大似然法将偏态正态分布模型拟合到每个区域的绝对/相对误差分布。该方法在由One Touch Ultra 2（加利福尼亚州米尔皮塔斯的LifeScan公司）收集的SMBG测量数据库上进行了测试。具体而言，确定了两个区域：区域1（血糖≤75mg/dl）具有恒定标准差的绝对误差，区域2（血糖>75mg/dl）具有恒定标准差的相对误差。在区域1中，所确定的偏态正态分布的均值和标准差分别为2.03和6.51，在区域2中分别为4.78%和10.09%。视觉预测检查验证表明，所推导的两区模型准确地再现了SMBG测量误差分布，其性能明显优于文献中先前使用的单区高斯模型。这种随机模型为T1D胰岛素治疗的计算机模拟设计和优化提供了更现实的SMBG场景。
