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. 2023 Nov;17(6):1506-1526.
doi: 10.1177/19322968231190941. Epub 2023 Aug 20.

Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting

Guido Freckmann  1   2 , Manuel Eichenlaub  2 , Delia Waldenmaier  2 , Stefan Pleus  2 , Stephanie Wehrstedt  2 , Cornelia Haug  2 , Lilian Witthauer  3   4 , Johan Jendle  1   5 , Rolf Hinzmann  1   6 , Andreas Thomas  1   7 , Elisabet Eriksson Boija  1   8 , Konstantinos Makris  1   9 , Peter Diem  1   10 , Nam Tran  1   11 , David C Klonoff  1   12 , James H Nichols  1   13 , Robbert J Slingerland  1   14
Affiliations

Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting

Guido Freckmann et al. J Diabetes Sci Technol. 2023 Nov.

Abstract

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

Keywords: accuracy; clinical performance evaluation; continuous glucose monitoring; study design.

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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: G.F. is the general manager and medical director of the Institute for Diabetes Technology (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. G.F./IfDT have received research support, speakers’ honoraria, or consulting fees in the last three years from Abbott, Ascensia, Berlin Chemie, Boydsense, Dexcom, Lilly, Metronom, Medtronic, Menarini, MySugr, Novo Nordisk, PharmaSens, Roche, Sanofi, Terumo. M.E., D.W., S.P., S.W., and C.H. are employees of IfDT. R.S. is the chair of the Clinical Chemistry Department of Isala which carries out clinical studies, eg, with medical devices for diabetes therapy on its own initiative and on behalf of various companies. R.S. has received speakers’ honoraria or consulting fees in the last three years from Roche and Menarini. J.J. has for the last three years been a lecturer/member of the scientific advisory board for Abbott, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Medtronic, Nordic InfuCare, NovoNordisk A/S, and Sanofi. R.H. is an employee of Roche Diabetes Care GmbH. L.W. has received funding from the Diabetes Center Berne. E.E.B. and K.M. have no disclosures. D.C.K. is a consultant for Atropos Health, Better Therapeutics, Eoflow, Integrity, Lifecare, Nevro, Novo, Sanofi, and Thirdwayv. N.T. is a consultant for Roche Diagnostics and Radiometer. J.H.N. has received research support from Abbott. P.D. is a board member of PharmaSens. A.T. is a freelance consultant. He has received fees for lectures or consultancy fees from Abbott, Berlin Chemie, Dexcom, Evivamed, Menarini, Novo Nordisk, Roche, and Sanofi in the last three years.

Figures

Figure 1.
Figure 1.
Schematic depiction of the study selection process.
Figure 2.
Figure 2.
Distribution of the years of publication of the 129 performance studies included in this review.
Figure 3.
Figure 3.
Distribution of the total number of subjects in the 129 studies included in this review.
Figure 4.
Figure 4.
Distribution of the number of CGM systems that were tested simultaneously in the 129 studies included in this review.
Figure 5.
Figure 5.
Relative frequencies of capillary, venous, and arterialized-venous sampling of the 167 comparators found in this review.
Figure 6.
Figure 6.
Duration of in-clinic visits of individual studies separated by visits shorter or equal to 14 hours in hours (left) or longer than 14 hours in days (right).
Figure 7.
Figure 7.
Number of studies employing various approaches for deliberate glucose manipulations.
Figure 8.
Figure 8.
(a) Boxplots of percentages of comparator values in the TIR bins from 44 studies. The whiskers indicate minimum/maximum percentages. If multiple comparators were used, then only the distribution of venous measurements during in-clinic visits was analyzed. If the distribution was specified for each CGM system separately, the data were pooled. (b) Estimated mean absolute rate of change (MARoC) of comparator glucose concentrations from results reported in 21 studies.
Figure 9.
Figure 9.
Parameters and methods to characterize point accuracy and their prevalence identified in the 129 studies included in this review.
Figure 10.
Figure 10.
MARDs reported in the 129 studies included in this review with respect to their year of publication (n=189). The data points were spread out within each year to better reflect the number of reported MARDs. Included were only MARD results reported over the full glucose range. If MARD values in specific studies were reported for different comparator compartments, calibration algorithms, or insertion sites, then they were included separately.
Figure 11.
Figure 11.
Usage of different error grids (a) and thresholds for agreement rates (b) expressed as share of all error grids/thresholds in specific time periods. In both panels, the time intervals were chosen so that an approximately equal number of error grid/threshold usages occur in every time interval. In panel (a), the four types of error grids are the Clarke error grid, introduced by Clarke et al, the consensus error grid, introduced by Parkes et al, the CG-EGA, introduced by Kovatchev et al, and the surveillance error grid, introduced by Klonoff et al. In panel (b), the thresholds for switching between absolute (below threshold) and relative (above threshold) differences are characterized. “None” means that only relative differences were used.
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