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Metrics derived from continuous glucose monitoring (CGM) systems are often discordant between systems. A major cause is that CGM systems are not standardized; they use various algorithms and calibration methods, leading to discordant CGM readings across systems. This discordance can be addressed by standardizing CGM performance assessments: If manufacturers aim their CGM systems at the same target, then CGM readings will align across systems. This standardization should include the comparator device, sample origin, and study procedures. With better aligned CGM readings, CGM-derived metrics will subsequently also align better between systems., Competing Interests: 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: SP and ME are employees of Institute for Diabetes Technology (Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm; IfDT), Ulm, Germany.EEB has no disclosures.MF received lecture fees from Menarini.RH—Independent medical & scientific consultant, former employee of Roche Diabetes Care.JJ has been a lecturer/member of the scientific advisory boards at the following companies: Abbott, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Medtronic, Nordic InfuCare, Novo Nordisk, and Sanofi.DCK is a consultant for Afon, Better Therapeutics, Integrity, Lifecare, Nevro, Novo, Samsung, and Thirdwayv. KM has nothing to disclose.JHN has received research support from Abbott.JP—Advisory panel for ROCHE Diabetes Care and Abbott; speaker fees from Insulet and Dexcom.ES is supported by grants from the US National Institutes of Health (NIH) and has received donated materials related to NIH-supported research from Abbott Diabetes Care, Roche Diagnostics, Siemens Diagnostics, Ortho Clinical Diagnostics, Abbott Diagnostics, Asahi Kasei Pharma Corp, GlycoMark Corp.RJS is 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. RJS has received speaker’s honoraria or consulting fees in the last 3 years from Roche and Menarini.AT 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 3 years.NKT is a consultant for Roche Diagnostics and Roche Molecular Systems, received honoraria from Nova Biomedical and Thermo Fisher, and Chair (2024-2026), Critical and Point-of-Care Testing (CPOCT) Division, Association for Diagnostics and Laboratory Medicine (ADLM).LW has nothing to disclose.GF is general manager and medical director of the IfDT, 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 3 years Abbott, Berlin Chemie, Boydsense, Dexcom, Glucoset, i-SENS, Lilly, Menarini, Novo Nordisk, Perfood, Pharmasens, Roche, Sinocare, Terumo, Ypsomed.

Background: The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.S. Food and Drug Administration requirements for "integrated" CGM systems., Methods: The statistical concept of tolerance intervals and data from two approved CGM systems was used to illustrate the CG-DIVA., Results: The CG-DIVA characterizes the expected range of deviations of the CGM system from a comparison method in different glucose concentration ranges and the variability of accuracy within and between sensors. The results of the CG-DIVA are visualized in an intuitive and straightforward graphical presentation. Compared with conventional accuracy characterizations, the CG-DIVA infers the expected accuracy of a CGM system and highlights important differences between CGM systems. Furthermore, it provides information on the incidence of large errors which are of particular clinical relevance. A software implementation of the CG-DIVA is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment)., Conclusions: We argue that the CG-DIVA can simplify the discussion and comparison of CGM accuracy and could replace the high number of conventional approaches. Future adaptations of the approach could thus become a putative standard for the accuracy characterization of CGM systems and serve as the basis for the definition of future CGM performance requirements., Competing Interests: 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 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 on the evaluation of BG meters, with CGM systems and medical devices for diabetes therapy on its own initiative and on behalf of various companies. G.F./IfDT have received speakers’ honoraria or consulting fees from Abbott, Ascensia, Berlin-Chemie, Beurer, BOYDsense, CRF Health, Dexcom, i-SENS, Lilly, Metronom, MySugr, Novo Nordisk, PharmaSens, Roche, Sanofi, Sensile, Terumo, and Ypsomed. ME, DW, SP, and CH are employees of the IfDT. P.S. is an advisor to the IfDT. M.R. is an employee of Diabetes Center Berne. R.H. is an employee of Roche Diabetes Care GmbH. J.J. has received speakers’ honoraria or consulting fees from Abbott, Ascensia, Eli Lilly, Medtronic, Novo Nordisk, and Sanofi. A.T. is an independent consultant for diabetes technology. He was Scientific Director at Medtronic Diabetes Germany until 2020. He participated in advisory boards of the company Dexcom. He has received honoraria for lectures from Dexcom, Abbott and Berlin-Chemie (Menarini), among others. P.D. is a board member of PharmaSens.

Self-monitoring of blood glucose is a key aspect of diabetes management. Depending on the technology used, however, various substances can jeopardize the reliability of the measurements and precipitate complications with potentially life-threatening consequences when blood glucose was deemed well-controlled. As such, it is important for all involved to be aware of those factors. Officially suggested procedures for testing and alternatives have each their own advantages and limitations, and interferences may be found beyond the substances to be tested provided by the various pertinent institutions. This article reviews these pros and cons and illustrates how interference testing beyond established standards contributes to patient safety. Once identified, interfering substances are included in product labeling and healthcare professionals and users need to be trained to be aware of these risks., Competing Interests: 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: RH and BH are employees of Roche Diabetes Care. As proprietor of Dr. Hauss Training & Consulting, OH is providing consulting, training, and medical writing services. No other potential conflicts of interest relevant to this article could be identified.

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., Competing Interests: 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.

In addition to the continuous use, the intermittent use of continuous glucose monitoring (CGM) is an application of CGM, expanding the typical medical use cases. There are a variety of reasons and occasions that speak in favor of using CGM only for a limited time. To date, these circumstances have not been sufficiently discussed. In this article, we define discontinuous or intermittent CGM use, provide reasons for using it, and expand on the benefits and possibilities of using CGM on a temporary basis. We aim to draw attention to this important topic in the discussion of CGM use and give examples for a different method of CGM use. As well, we would like to foster the allocation of CGM to the right patient groups and indications, especially in cases of limited resources. From a global point of view, intermittent CGM use is more likely to occur than continuous use, primarily for economic reasons.

High-quality performance of medical devices for glucose monitoring is important for a safe and efficient usage of this diagnostic option by patients with diabetes. The mean absolute relative difference (MARD) parameter is used most often to characterize the measurement performance of systems for continuous glucose monitoring (CGM). Calculation of this parameter is relatively easy and comparison of the MARD numbers between different CGM systems appears to be straightforward on the first glance. However, a closer look reveals that a number of complex aspects make interpretation of the MARD numbers provided by the manufacturer for their CGM systems difficult. In this review, these aspects are discussed and considerations are made for a systematic and appropriate evaluation of the MARD in clinical trials. The MARD should not be used as the sole parameter to characterize CGM systems, especially when it comes to nonadjunctive usage of such systems.

Background: Measurement results provided by blood glucose monitoring systems (BGMS) can be affected by various influencing factors. For some BGMS using glucose oxidase (GOx)-based test strips, one of these factors is the oxygen partial pressure (pO 2 ) of the applied blood sample. Because assessing the potential influence of pO 2 when measuring capillary blood samples is not straight-forward, we performed a proof of concept study., Method: Influence of pO 2 was investigated for two GOx-based BGMS (BGMS A and B). Measurement results of the GOx-based BGMS were compared with measurement results from a pO 2 -independent BGMS (BGMS C). A total of 119 samples from 60 subjects were measured, twice with BGMS C, then 6 times each with BGMS A and BGMS B or vice versa, and again twice with BGMS C. Immediately afterward, pO 2 was determined. Linear regression analysis based on relative differences between results from BGMS A or BGMS B and results from BGMS C was performed to estimate the degree of pO 2 influence., Results: The relative bias between the lowest and highest pO 2 values differed by 14.3% for BGMS A, indicating a pO 2 influence that might be clinically relevant, and by 9.7% for BGMS B, indicating that pO 2 influence may be too small to be reliably detected because of the BGMS' imprecision., Conclusions: This proof of concept study showed that with the procedures used, a potentially clinically relevant influence of pO 2 in capillary blood samples on GOx-based BGMS could be detected. Further larger-scale studies are needed to verify this influence.

Background: Computer simulation has been shown over the past decade to be a powerful tool to study the impact of medical devices characteristics on clinical outcomes. Specifically, in type 1 diabetes (T1D), computer simulation platforms have all but replaced preclinical studies and are commonly used to study the impact of measurement errors on glycemia., Method: We use complex mathematical models to represent the characteristics of 3 continuous glucose monitoring systems using previously acquired data. Leveraging these models within the framework of the UVa/Padova T1D simulator, we study the impact of CGM errors in 6 simulation scenarios designed to generate a wide variety of glycemic conditions. Assessment of the simulated accuracy of each different CGM systems is performed using mean absolute relative deviation (MARD) and precision absolute relative deviation (PARD). We also quantify the capacity of each system to detect hypoglycemic events., Results: The simulated Roche CGM sensor prototype (RCGM) outperformed the 2 alternate systems (CGM-1 & CGM-2) in accuracy (MARD = 8% vs 11.4% vs 18%) and precision (PARD = 6.4% vs 9.4% vs 14.1%). These results held for all studied glucose and rate of change ranges. Moreover, it detected more than 90% of hypoglycemia, with a mean time lag less than 4 minutes (CGM-1: 86%/15 min, CGM-2: 57%/24 min)., Conclusion: The RCGM system model led to strong performances in these simulation studies, with higher accuracy and precision than alternate systems. Its characteristics placed it firmly as a strong candidate for CGM based therapy, and should be confirmed in large clinical studies.

In general, patients with diabetes performing self-monitoring of blood glucose (SMBG) can strongly rely on the accuracy of measurement results. However, various factors such as application errors, extreme environmental conditions, extreme hematocrit values, or medication interferences may potentially falsify blood glucose readings. Incorrect blood glucose readings may lead to treatment errors, for example, incorrect insulin dosing. Therefore, the diabetes team as well as the patients should be well informed about limitations in blood glucose testing. The aim of this publication is to review the current knowledge on limitations and interferences in blood glucose testing with the perspective of their clinical relevance., Competing Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RH is an employee of Roche Diabetes Care GmbH, Mannheim, Germany. GF, BK, RZ, LH, and OS are members of national and international advisory boards of Roche Diabetes Care GmbH, Mannheim, Germany, (© 2016 Diabetes Technology Society.)

Reliability of blood glucose (BG) measurements is a prerequisite for successful diabetes management. Publications on the evaluation of self-monitored glucose values, however, are frequently characterized by a confusion in terminology. We provide an inventory of key terms such as accuracy, trueness, precision, traceability, calibration, and matrix effect to avoid future misunderstanding. Definitions are taken from the metrological literature and international norms and explained in a language intended for nonspecialists in metrology. The terms are presented in light of the need to apply generally accepted definitions. In addition, a description of requirements and components for a sound evaluation of BG measurement systems is presented. These factors will also enable improvement in future comparisons of study results., (© 2013 Diabetes Technology Society.)