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Multicenter Study
. 2024 Sep;18(5):1044-1051.
doi: 10.1177/19322968241267774. Epub 2024 Aug 19.

Performance of a Novel Continuous Glucose Monitoring Device in People With Diabetes

Julia K Mader  1 , Delia Waldenmaier  2 , Wiebke Mueller-Hoffmann  3 , Katrin Mueller  3 , Michael Angstmann  3 , Gerhard Vogt  3 , Cosima C Rieger  3 , Manuel Eichenlaub  2 , Thomas Forst  4 , Guido Freckmann  2
Affiliations
Multicenter Study

Performance of a Novel Continuous Glucose Monitoring Device in People With Diabetes

Julia K Mader et al. J Diabetes Sci Technol. 2024 Sep.

Abstract

Background: In this multicenter study, performance of a novel continuous glucose monitoring (CGM) system was evaluated.

Methods: Adult participants with diabetes were included in the study. They each wore three sensors of the CGM system on the upper arms for up to 14 days. During four in-clinic visits, frequent comparison measurements with capillary blood glucose (BG) samples were performed. The primary endpoint was the 20/20 agreement rate (AR): the percentage of CGM readings within ±20 mg/dL (at BG values <100 mg/dL) or ±20% (at BG values ≥100 mg/dL) of the comparator. Further evaluations included mean absolute relative difference (MARD) and 20/20 AR in different BG ranges and across the wear time.

Results: Data from 48 participants and 139 sensors were analyzed. During in-clinic sessions the 20/20 AR was 90.5% and the MARD was 9.2%. For BG ranges <70, 70-180, and >180 mg/dL, 20/20 AR was 94.3%, 89.0%, and 92.5%, respectively. At the beginning, middle, and end of sensor wear time, 20/20 AR was 92.8%, 91.5%, and 85.9%, respectively. The 14-day survival probability was 82.4%. Pain and bleeding after sensor insertion were within the expected range. Based on the study outcome, the use of the device is regarded as safe.

Conclusions: The system showed a good performance compared to capillary BG measurements. This level of accuracy could be shown over the entire measurement range, especially in the low glycemic range, and the whole wear time of the sensors. The results of this study are supporting a non-adjunctive use of the device.

Keywords: MARD; accuracy; continuous glucose monitoring; performance.

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Conflict of interest statement

Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: JKM is a member in the advisory boards of Abbott Diabetes Care, Becton-Dickinson/Embecta, Biomea, Eli Lilly, Medtronic, Novo Nordisk, PharmaSens, Roche Diabetes Care, Sanofi, and Viatris; received speaker honoraria from Abbott Diabetes Care, A. Menarini Diagnostics, Becton-Dickinson/Embecta, Boehringer-Ingelheim, Eli Lilly, MedTrust, Novo Nordisk, Roche Diabetes Care, Sanofi, Servier, and Ypsomed; and is shareholder of decide Clinical Software GmbH and elyte Diagnostics GmbH. GF is general manager and medical director of the IfDT (Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany), which carries out clinical studies, eg, with medical devices for diabetes therapy on its own initiative and on behalf of various companies. GF/IfDT have received speakers’ honoraria or consulting fees in the last three years from Abbott, Berlin Chemie, Boydsense, Dexcom, Lilly Deutschland, Novo Nordisk, Perfood, PharmaSens, Roche, Sinocare, Terumo, and Ypsomed. ME and DW are employees of IfDT. WM-H, KM, MA, GV, and CCR are employees of Roche Diabetes Care GmbH, Mannheim, Germany. TF is Chief Medical Officer and Chief Operations Officer of Clinical Research Services, Mannheim, Germany. TF received speaker honoraria or consulting fees in the last three years from Amarin, Astra Zeneca, Bayer, Boehringer-Ingelheim, Cipla, Daiichi-Sankyo, derCampus, Diabetes Academy Bad Mergentheim, Eli Lilly, Fortbildungskolleg, MSD, Novo Nordisk, Roche Diagnostics, Sanofi, Santis, and Sciarc. TF serves as associate editor for Endocrinology, Diabetes & Metabolism.

Figures

Figure 1.
Figure 1.
The Accu-Chek SmartGuide CGM system: applicator (left), sensor with adhesive (middle), and representative depiction of the app (right).
Figure 2.
Figure 2.
Results of the continuous glucose deviation interval and variability analysis (CG-DIVA) of in-clinic session data. (a) Deviation intervals in different glucose ranges covering the Food and Drug Administration iCGM criteria: the dark gray boxes contain 85%, 70%, 80%, and 87% of expected deviations, respectively, and the light gray boxes contain 98%, 99%, and 99% of expected deviations. Black dashes show median deviations. (b) Sensor-to-sensor variability over the whole glucose range. Each sensor with at least three values is represented by median and 90% range. Sensors were ordered by median.
Figure 3.
Figure 3.
Distribution of sensor-specific mean absolute relative differences (MARD) across sensors (n = 132) during in-clinic sessions. MARD: mean absolute relative difference.
Figure 4.
Figure 4.
Consensus Error Grid during in-clinic sessions (a) and in-clinic sessions and home use combined (b). BG: blood glucose; CGM: continuous glucose monitoring.
Figure 5.
Figure 5.
Kaplan-Meier curve including all sensors that delivered at least one valid value (n = 139). Censored sensors are displayed as vertical dashes.
See this image and copyright information in PMC

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