Continuous Glucose Monitoring and Trend Accuracy

Background: Continuous glucose monitoring (CGM) devices are being increasingly used to monitor glycemia in people with diabetes. One advantage with CGM is the ability to monitor the trend of sensor glucose (SG) over time. However, there are few metrics available for assessing the trend accuracy of CGM devices. Aim: The aim of this study was to develop an easy to interpret tool for assessing trend accuracy of CGM data. Methods: SG data from CGM were compared to hourly blood glucose (BG) measurements and trend accuracy was quantified using the dot product. Trend accuracy results are displayed on the Trend Compass, which depicts trend accuracy as a function of BG. A trend performance table and Trend Index (TI) metric are also proposed. The Trend Compass was tested using simulated CGM data with varying levels of error and variability, as well as real clinical CGM data. Results: The results show that the Trend Compass is an effective tool for differentiating good trend accuracy from poor trend accuracy, independent of glycemic variability. Furthermore, the real clinical data show that the Trend Compass assesses trend accuracy independent of point bias error. Finally, the importance of assessing trend accuracy as a function of BG level is highlighted in a case example of low and falling BG data, with corresponding rising SG data. Conclusions: This study developed a simple to use tool for quantifying trend accuracy. The resulting trend accuracy is easily interpreted on the Trend Compass plot, and if required, performance table and TI metric.

[1]  Ali Cinar,et al.  Hypoglycemia Prediction with Subject-Specific Recursive Time-Series Models , 2010, Journal of diabetes science and technology.

[2]  Jaap E Tulleken,et al.  Accuracy and feasibility of point-of-care and continuous blood glucose analysis in critically ill ICU patients , 2006, Critical care.

[3]  Boris Kovatchev,et al.  A Critical Appraisal of the Continuous Glucose–Error Grid Analysis , 2007, Diabetes Care.

[4]  S. Patek,et al.  Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: The Virginia Experience , 2009, Journal of diabetes science and technology.

[5]  Lutz Heinemann,et al.  Comparison of the Numerical and Clinical Accuracy of Four Continuous Glucose Monitors , 2008, Diabetes Care.

[6]  Darrell M. Wilson,et al.  The effect of continuous glucose monitoring in well-controlled type 1 diabetes , 2010 .

[7]  Eyal Dassau,et al.  Real-Time Hypoglycemia Prediction Suite Using Continuous Glucose Monitoring , 2010, Diabetes Care.

[8]  F. Ovalle,et al.  Continuous Glucose Monitoring and Intensive Treatment of Type 1 Diabetes , 2009 .

[9]  Use of real-time continuous glucose monitoring versus traditional self-monitoring of blood glucose levels improves glycaemic control in patients with type 1 diabetes , 2011, Evidence Based Nursing.

[10]  Alex J Sutton,et al.  Glycaemic control in type 1 diabetes during real time continuous glucose monitoring compared with self monitoring of blood glucose: meta-analysis of randomised controlled trials using individual patient data , 2011, BMJ : British Medical Journal.

[11]  Geoffrey McGarraugh.  Alarm Characterization for a Continuous Glucose Monitor That Replaces Traditional Blood Glucose Monitoring , 2010, Journal of diabetes science and technology.

[12]  Michael O'Grady,et al.  Continuous glucose monitoring and intensive treatment of type 1 diabetes. , 2008, The New England journal of medicine.

[13]  Accuracy of the continuous glucose monitoring system in inpatient and outpatient conditions. , 2003, Diabetes & metabolism.

[14]  Geoffrey M. Shaw,et al.  Hypoglycemia Detection in Critical Care Using Continuous Glucose Monitors: An in Silico Proof of Concept Analysis , 2010, Journal of diabetes science and technology.

[15]  B Wayne Bequette,et al.  Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms , 2010, Journal of diabetes science and technology.

[16]  D. Klonoff.  Continuous glucose monitoring: roadmap for 21st century diabetes therapy. , 2005, Diabetes care.

[17]  W. Clarke.  The original Clarke Error Grid Analysis (EGA). , 2005, Diabetes technology & therapeutics.

[18]  L. B. E. A. Hoeks,et al.  Systematic Review Real-time continuous glucose monitoring system for treatment of diabetes: a systematic review , 2011 .

[19]  J. Leahy,et al.  Fully Automated Closed-Loop Insulin Delivery Versus Semiautomated Hybrid Control in Pediatric Patients With Type 1 Diabetes Using an Artificial Pancreas , 2008 .

[20]  Howard C. Zisser,et al.  Fully Integrated Artificial Pancreas in Type 1 Diabetes , 2012, Diabetes.

[21]  D. Cox,et al.  Evaluating the accuracy of continuous glucose-monitoring sensors: continuous glucose-error grid analysis illustrated by TheraSense Freestyle Navigator data. , 2004, Diabetes care.

[22]  Boris Kovatchev,et al.  Continuous Glucose Sensors: Continuing Questions about Clinical Accuracy , 2007, Journal of diabetes science and technology.

[23]  M. Phillip,et al.  Effect of Continuous Glucose Monitoring on Hypoglycemia in Type 1 Diabetes , 2011, Diabetes Care.

[24]  D. Altman,et al.  Measuring agreement in method comparison studies , 1999, Statistical methods in medical research.