Accuracy of a New Real-Time Continuous Glucose Monitoring Algorithm

• Published in: Journal of diabetes science and technology Vol. 4; no. 1; pp. 111 - 118
• Main Authors: Keenan, D. Barry, Cartaya, Raymond, Mastrototaro, John J.
• Format: Journal Article
• Language: English
• Published: United States Diabetes Technology Society 01.01.2010
• Subjects: Adolescent; Adult; Algorithms; Biosensing Techniques - standards; Blood Glucose - analysis; Blood Glucose Self-Monitoring - methods; Blood Glucose Self-Monitoring - standards; Calibration; Computer Systems; Diabetes Mellitus - blood; Efficiency; Female; Humans; Hyperglycemia - blood; Hyperglycemia - diagnosis; Hypoglycemia - blood; Hypoglycemia - diagnosis; Male; Monitoring, Ambulatory - methods; Monitoring, Ambulatory - standards; Original; Randomized Controlled Trials as Topic; Reproducibility of Results; Retrospective Studies
• ISSN: 1932-2968; 1932-3107; 1932-3107
• DOI: 10.1177/193229681000400114

Through minimally invasive sensor-based continuous glucose monitoring (CGM), individuals can manage their blood glucose (BG) levels more aggressively, thereby improving their hemoglobin A1c level, while reducing the risk of hypoglycemia. Tighter glycemic control through CGM, however, requires an accurate glucose sensor and calibration algorithm with increased performance at lower BG levels. Sensor and BG measurements for 72 adult and adolescent subjects were obtained during the course of a 26-week multicenter study evaluating the efficacy of the Paradigm REAL-Time (PRT) sensor-augmented pump system (Medtronic Diabetes, Northridge, CA) in an outpatient setting. Subjects in the study arm performed at least four daily finger stick measurements. A retrospective analysis of the data set was performed to evaluate a new calibration algorithm utilized in the Paradigm Veo insulin pump (Medtronic Diabetes) and to compare these results to performance metrics calculated for the PRT. A total of N = 7193 PRT sensor downloads for 3 days of use, as well as 90,472 temporally and nonuniformly paired data points (sensor and meter values), were evaluated, with 5841 hypoglycemic and 15,851 hyperglycemic events detected through finger stick measurements. The Veo calibration algorithm decreased the overall mean absolute relative difference by greater than 0.25 to 15.89%, with hypoglycemia sensitivity increased from 54.9% in the PRT to 82.3% in the Veo (90.5% with predictive alerts); however, hyperglycemia sensitivity was decreased only marginally from 86% in the PRT to 81.7% in the Veo. The Veo calibration algorithm, with sensor error reduced significantly in the 40- to 120-mg/dl range, improves hypoglycemia detection, while retaining accuracy at high glucose levels.