Comparison of the Numerical and Clinical Accuracy of Four Continuous Glucose Monitors

Abstract

All participants underwent hyperinsulinemic clamps including 1.5-2 h of maintained euglycemia at 5.6 mmol/l followed by descent into hypoglycemia, sustained hypoglycemia at 2.5 mmol/l for 30 min, and recovery. Reference blood glucose sampling was performed every 5 min. The UVA study tested Guardian, DexCom, and Navigator simultaneously; the Profil study tested Glucoday.RESULTS -Regarding numerical accuracy, during euglycemia, the mean absolute relative differences (MARDs) of Guardian, DexCom, Navigator, and Glucoday were 15.2, 21.2, 15.3, and 15.6%, respectively. During hypoglycemia, the MARDs were 16.1, 21.5, 10.3, and 17.5%, respectively. Regarding clinical accuracy, continuous glucose-error grid analysis (CG-EGA) revealed 98.9, 98.3, 98.6, and 95.5% zones A ϩ B hits in euglycemia. During hypoglycemia, zones A ϩ B hits were 84.4, 97.0, and 96.2% for Guardian, Navigator, and Glucoday, respectively. Because of frequent loss of sensitivity, there were insufficient hypoglycemic DexCom data to perform CG-EGA.CONCLUSIONS -The numerical accuracy of Guardian, Navigator, and Glucoday was comparable, with an advantage to the Navigator in hypoglycemia; the numerical errors of the DexCom were ϳ30% larger. The clinical accuracy of the four sensors was similar in euglycemia and was higher for the Navigator and Glucoday in hypoglycemia. Diabetes Care 31:1160-1164, 2008E valuation of the accuracy of continuous glucose monitors (CGMs) is complex for two primary reasons: 1) CGMs assess blood glucose fluctuations indirectly by measuring the concentration of interstitial glucose but are calibrated via self-monitoring to approximate blood glucose; and 2) CGM data reflect an underlying process in time and therefore consist of ordered-in-time highly interdependent data points. Because CGMs operate in the interstitial compartment, which is presumably related to blood via diffusion across the capillary wall (1,2), there are a number of significant challenges in terms of sensitivity, stability, calibration, and physiological time lag between blood and interstitial glucose concentration (1,3-6). In addition, the temporal structure of CGM data poses statistical challenges to the direct use of established accuracy measures, such as correlation or regression, or the clinically based error grid analysis (EGA) (7,8), because these measures judge the quality of approximation of reference blood glucose measurements by readings at isolated points in time, without taking into account the temporal structure of the data. In other words, a random reshuffling of the sensor-reference data pairs in time will not change these accuracy estimates. It is therefore imperative to judge the accuracy of CGMs across several dimensions and to use both numerical and clinical metrics to support this judgment.Defined as the closeness between CGM readings and corresponding in-time reference blood glucose measurements, numerical accuracy is computed by several traditional measures including mean absolute difference (MAD) and mean absolute relative difference (MARD),...

Performance Evaluation of Three Continuous Glucose Monitoring Systems: Comparison of Six Sensors Per Subject in Parallel

“…With the Navigator and the Seven Plus, PARD seemed to slightly improve with sensor usage time. The performance of all systems within the hypoglycemic range (<70 mg/dl) was markedly lower than at higher glucose concentrations, a known fact 3,5,7,12,14,18 that is still dissatisfying for CGM users. While the Guardian achieved a slightly lower MARD than the Seven Plus, its PARD was slightly higher.…”

“…23 In other studies in which more than one sensor was used per subject, only one type of CGM system was used, 3,4,6,[8][9][10][11]15 and in studies that compared multiple CGM systems, only one sensor per subject was used. 2,7,12,19 In this study, both aspects were combined, thus allowing an evaluation in which groups of six sensors (i.e., two sensors of each of three CGM systems in one individual subject) follow identical glycemic excursions. The choice of participants may have a marked influence on the analyzed parameter, 11 this setting, however, allows very high comparability between the CGM systems for both MARD and PARD because data are generated for all systems and all sensors at the same time in the same subject.…”

“…In 2008, the Clinical and Laboratory Standards Institute published POCT05-A, which provides recommendations for study design and parameters of interest in the performance evaluation of CGM systems. 1 Multiple studies investigating CGM systems were published over the years; [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] only one claimed to use procedures recommended in POCT05-A. 13 A comparison of these studies, however, is difficult due to variations in subject groups 11 and study design, including use of different devices for calibration of CGM systems and for reference measurements, either overall or for different study phases.…”

“…13 A comparison of these studies, however, is difficult due to variations in subject groups 11 and study design, including use of different devices for calibration of CGM systems and for reference measurements, either overall or for different study phases. [2][3][4][6][7][8][9][10]12,13,16,17 Given that comparability of different CGM systems is heavily influenced by these factors, it is thus improved when the different CGM systems are worn simultaneously by the same subjects under identical conditions (e.g., in head-to-head comparisons). There are studies in which different CGM systems were compared under such conditions, 2,7,12 but in these cases, only one sensor per CGM system was worn by the subjects.…”

The proposed terminology ‘A1c-derived average glucose’ is inherently imprecise and should not be adopted. Reply to Bloomgarden ZT, Inzucchi SE, Karnieli E, et al. [letter]

“…The study was performed over 21 consecutive working days c Between-analyser precision was determined simultaneously on two different HemoCue B-Glucose meters during a fast-sampling intravenous glucose tolerance test and a hyperinsulinaemic-euglycaemic clamp [8]. Fifty-two patients had 1,684 pairs of samples taken for analysis differences of 15-21% in the range of blood glucose of 3.9-10.0 mmol/l (70-180 mg/dl) [9]. Thus, a combination of frequent multipoint and continuous measurement of plasma glucose appears to be the most feasible approach to obtain reliable mean plasma glucose values.…”