Time Delay of CGM Sensors: Relevance, Causes, and Countermeasures. (4th August 2015)

• Record Type: Journal Article
• Authors: Schmelzeisen-Redeker, Günther; Schoemaker, Michael; Kirchsteiger, Harald; Freckmann, Guido; Heinemann, Lutz; del Re, Luigi
• Is Part Of: Journal of diabetes science and technology. Volume 9:Number 5(2015:Sept.)
• Page Start: 1006
• Page End: 1015
• Publication Date: 2015-08-04
• Subjects: continuous glucose monitoring -- CGM -- MARD -- accuracy -- precision -- performance evaluation -- performance comparison -- time delay; Diabetes -- Periodicals; Medical technology -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462005
• DOI: 10.1177/1932296815590154
• Languages: English
• ISSNs: 1932-2968

Abstract

Background: 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. Methods: 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. Results: 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. Conclusions: 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.