Department of Information Engineering, University of Padova, Padova, Italy.
Diabetes Technol Ther. 2011 Aug;13(8):843-52. doi: 10.1089/dia.2011.0006. Epub 2011 May 11.
The quantitative analysis of glucose time-series can greatly help the management of diabetes. In particular, a static nonlinear transformation, which symmetrizes the distribution of glucose levels by bringing them in the so-called risk space, was proposed previously for both self-monitoring blood glucose and continuous glucose monitoring (CGM) and extensively used in the literature. The continuous nature of CGM data allows us to further refine the risk space concept in order to account for glucose dynamics.
A new dynamic risk (DR) is proposed to explicitly consider the rate of change of glucose as a threat factor for the patient (e.g., risk levels in hypoglycemia and hyperglycemia are amplified in the presence of a decreasing and increasing glucose trend, respectively). The practical calculation of DR is made possible by the use of a regularized deconvolution algorithm that is able to deal with noise in CGM data and with the ill-conditioning of the time-derivative calculation, even in online applications.
Results on simulated and real data show that DR can be effectively computed and fruitfully used in real time (e.g., to generate early warnings of hypo-/hyperglycemic threshold crossings). Further applications of DR in the quantification of the efficiency of glucose control are also suggested.
Exploiting the information on glucose trends empowers the strength of risk measures in interpreting CGM time-series.
葡萄糖时间序列的定量分析可以极大地帮助糖尿病的管理。特别是，以前曾提出过一种静态非线性变换，通过将葡萄糖水平带入所谓的风险空间，使葡萄糖水平的分布对称化，这种变换既适用于自我监测血糖，也适用于连续血糖监测（CGM），并在文献中得到了广泛应用。CGM 数据的连续性使得我们能够进一步细化风险空间概念，以考虑葡萄糖动态。
提出了一种新的动态风险（DR），以明确将葡萄糖变化率作为患者的威胁因素（例如，在存在葡萄糖下降和上升趋势的情况下，低血糖和高血糖的风险水平分别放大）。通过使用正则化反卷积算法，可以实际计算 DR，该算法能够处理 CGM 数据中的噪声以及时间导数计算的不适定性，即使在在线应用中也是如此。
模拟和真实数据的结果表明，DR 可以有效地计算，并在实时中得到有效利用（例如，生成低血糖/高血糖阈值穿越的早期预警）。还提出了 DR 在量化葡萄糖控制效率方面的进一步应用。
利用葡萄糖趋势信息可以增强风险度量在解释 CGM 时间序列方面的作用。
