Schmelzeisen-Redeker Günther, Schoemaker Michael, Kirchsteiger Harald, Freckmann Guido, Heinemann Lutz, Del Re Luigi
Roche Diabetes Care GmbH, Mannheim, Germany
Roche Diabetes Care GmbH, Mannheim, Germany.
J Diabetes Sci Technol. 2015 Aug 4;9(5):1006-15. doi: 10.1177/1932296815590154.
Continuous glucose monitoring (CGM) is a powerful tool to support the optimization of glucose control of patients with diabetes. However, CGM systems measure glucose in interstitial fluid but not in blood. Rapid changes in one compartment are not accompanied by similar changes in the other, but follow with some delay. Such time delays hamper detection of, for example, hypoglycemic events. Our aim is to discuss the causes and extent of time delays and approaches to compensate for these.
CGM data were obtained in a clinical study with 37 patients with a prototype glucose sensor. The study was divided into 5 phases over 2 years. In all, 8 patients participated in 2 phases separated by 8 months. A total number of 108 CGM data sets including raw signals were used for data analysis and were processed by statistical methods to obtain estimates of the time delay.
Overall mean (SD) time delay of the raw signals with respect to blood glucose was 9.5 (3.7) min, median was 9 min (interquartile range 4 min). Analysis of time delays observed in the same patients separated by 8 months suggests a patient dependent delay. No significant correlation was observed between delay and anamnestic or anthropometric data. The use of a prediction algorithm reduced the delay by 4 minutes on average.
Prediction algorithms should be used to provide real-time CGM readings more consistent with simultaneous measurements by SMBG. Patient specificity may play an important role in improving prediction quality.
连续血糖监测（CGM）是支持优化糖尿病患者血糖控制的有力工具。然而，CGM系统测量的是组织间液中的葡萄糖而非血液中的葡萄糖。一个腔室中的快速变化在另一个腔室中不会伴随类似变化，而是会有一定延迟。这种时间延迟妨碍了对例如低血糖事件的检测。我们的目的是讨论时间延迟的原因和程度以及补偿这些延迟的方法。
在一项对37名使用原型葡萄糖传感器的患者的临床研究中获取CGM数据。该研究在2年时间内分为5个阶段。共有8名患者参与了间隔8个月的2个阶段。总共108个包括原始信号的CGM数据集用于数据分析，并通过统计方法进行处理以获得时间延迟的估计值。
原始信号相对于血糖的总体平均（标准差）时间延迟为9.5（3.7）分钟，中位数为9分钟（四分位间距为4分钟）。对间隔8个月的同一患者观察到的时间延迟进行分析表明存在个体差异的延迟。在延迟与既往病史或人体测量数据之间未观察到显著相关性。使用预测算法平均可将延迟减少4分钟。
应使用预测算法来提供与同步指尖血糖测量更一致的实时CGM读数。个体特异性可能在提高预测质量方面发挥重要作用。
