1
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Eichenlaub M, Pleus S, Freckmann G. A Proposal for the Clinical Characterization of Continuous Glucose Monitoring Trend Arrow Accuracy. J Diabetes Sci Technol 2024; 18:800-807. [PMID: 38415676 PMCID: PMC11307235 DOI: 10.1177/19322968241232679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
The assessment and characterization of trend accuracy, that is, the ability of a continuous glucose monitoring (CGM) system to correctly indicate the direction and rate of change (RoC) of glucose levels, has received comparatively little attention in the overall evaluation of CGM performance. As such, only few approaches that examine the trend accuracy have been put forward. In this article, we review existing approaches and propose the clinical trend concurrence analysis (CTCA) which is an adaptation of the conventional trend concurrence analysis. The CTCA is intended to directly evaluate the trend arrows displayed by the CGM systems by characterizing their agreement to suitably categorized comparator RoCs. Here, we call on manufactures of CGM systems to provide the displayed trend arrows for retrospective analysis. The CTCA classifies any deviations between the CGM trend and comparator RoC according to their risk for an adverse clinical event arising from a possibly erroneous treatment decision. For that, the existing rate error grid analysis and a specific set of trend arrow-based insulin dosing recommendations were used. The results of the CTCA are presented in an accessible graphical display and exemplified on data from three CGM systems. With this article, we hope to increase the awareness for the importance and challenges of assessing the accuracy of trend information displayed by CGM systems.
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Affiliation(s)
- Manuel Eichenlaub
- Institut für Diabetes-Technologie,
Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm,
Germany
| | - Stefan Pleus
- Institut für Diabetes-Technologie,
Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm,
Germany
| | - Guido Freckmann
- Institut für Diabetes-Technologie,
Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm,
Germany
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2
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Eichenlaub M, Stephan P, Waldenmaier D, Pleus S, Rothenbühler M, Haug C, Hinzmann R, Thomas A, Jendle J, Diem P, Freckmann G. Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems. J Diabetes Sci Technol 2024; 18:857-865. [PMID: 36329636 PMCID: PMC11307236 DOI: 10.1177/19322968221134639] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
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.
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Affiliation(s)
- Manuel Eichenlaub
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | | | - Delia Waldenmaier
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Stefan Pleus
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | | | - Cornelia Haug
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Rolf Hinzmann
- Roche Diabetes Care GmbH, Mannheim, Germany
- IFCC Scientific Division — Working Group on Continuous Glucose Monitoring (WG-CGM)
| | - Andreas Thomas
- IFCC Scientific Division — Working Group on Continuous Glucose Monitoring (WG-CGM)
- Pirna, Germany
| | - Johan Jendle
- IFCC Scientific Division — Working Group on Continuous Glucose Monitoring (WG-CGM)
- Department of Medical Sciences, Örebro University, Örebro, Sweden
| | - Peter Diem
- IFCC Scientific Division — Working Group on Continuous Glucose Monitoring (WG-CGM)
- Endokrinologie Diabetologie Bern, Bern, Switzerland
| | - Guido Freckmann
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
- IFCC Scientific Division — Working Group on Continuous Glucose Monitoring (WG-CGM)
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3
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Plebani M, Nichols JH, Luppa PB, Greene D, Sciacovelli L, Shaw J, Khan AI, Carraro P, Freckmann G, Dimech W, Zaninotto M, Spannagl M, Huggett J, Kost GJ, Trenti T, Padoan A, Thomas A, Banfi G, Lippi G. Point-of-care testing: state-of-the art and perspectives. Clin Chem Lab Med 2024; 0:cclm-2024-0675. [PMID: 38880779 DOI: 10.1515/cclm-2024-0675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Accepted: 06/10/2024] [Indexed: 06/18/2024]
Abstract
Point-of-care testing (POCT) is becoming an increasingly popular way to perform laboratory tests closer to the patient. This option has several recognized advantages, such as accessibility, portability, speed, convenience, ease of use, ever-growing test panels, lower cumulative healthcare costs when used within appropriate clinical pathways, better patient empowerment and engagement, and reduction of certain pre-analytical errors, especially those related to specimen transportation. On the other hand, POCT also poses some limitations and risks, namely the risk of lower accuracy and reliability compared to traditional laboratory tests, quality control and connectivity issues, high dependence on operators (with varying levels of expertise or training), challenges related to patient data management, higher costs per individual test, regulatory and compliance issues such as the need for appropriate validation prior to clinical use (especially for rapid diagnostic tests; RDTs), as well as additional preanalytical sources of error that may remain undetected in this type of testing, which is usually based on whole blood samples (i.e., presence of interfering substances, clotting, hemolysis, etc.). There is no doubt that POCT is a breakthrough innovation in laboratory medicine, but the discussion on its appropriate use requires further debate and initiatives. This collective opinion paper, composed of abstracts of the lectures presented at the two-day expert meeting "Point-Of-Care-Testing: State of the Art and Perspective" (Venice, April 4-5, 2024), aims to provide a thoughtful overview of the state-of-the-art in POCT, its current applications, advantages and potential limitations, as well as some interesting reflections on the future perspectives of this particular field of laboratory medicine.
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Affiliation(s)
- Mario Plebani
- Department of Medicine, University of Padova, Padova, Italy
| | - James H Nichols
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Peter B Luppa
- Klinikum rechts der Isar der Technischen Universität München, Munich, Germany
| | - Dina Greene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Laura Sciacovelli
- Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Julie Shaw
- Eastern Ontario Regional Laboratories Association (EORLA), Department of Pathology and Laboratory Medicine, The Ottawa Hospital and University of Ottawa, Ottawa, Canada
| | - Adil I Khan
- Department of Pathology & Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Paolo Carraro
- Department of Laboratory Medicine, Venice Hospital, Venice, Italy
| | - Guido Freckmann
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Wayne Dimech
- National Serology Reference Laboratory, Melbourne, Australia
| | | | - Michael Spannagl
- Institute of Laboratory Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Jim Huggett
- National Measurement Laboratory, LGC, Teddington, UK
| | - Gerald J Kost
- POCT - CTR, Pathology and Laboratory Medicine, School of Medicine, University of California, CA, USA
| | - Tommaso Trenti
- Laboratory Medicine and Pathology Department AUSL e AOU Modena, Modena, Italy
| | - Andrea Padoan
- Department of Medicine, DIMED, University of Padova, Padova, Italy
| | - Annette Thomas
- National PoCT Clinical Lead, National Pathology Programme, NHS Wales Executive, Cardiff, Wales, UK
| | - Giuseppe Banfi
- IRCCS Galeazzi-Sant'Ambrogio and Università Vita e Salute San Raffaele, Milan, Italy
| | - Giuseppe Lippi
- Section of Clinical Biochemistry, University of Verona, Verona, Italy
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4
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Selvin E. The Glucose Management Indicator: Time to Change Course? Diabetes Care 2024; 47:906-914. [PMID: 38295402 PMCID: PMC11116920 DOI: 10.2337/dci23-0086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/01/2023] [Indexed: 02/02/2024]
Abstract
Laboratory measurement of hemoglobin A1c (HbA1c) has, for decades, been the standard approach to monitoring glucose control in people with diabetes. Continuous glucose monitoring (CGM) is a revolutionary technology that can also aid in the monitoring of glucose control. However, there is uncertainty in how best to use CGM technology and its resulting data to improve control of glucose and prevent complications of diabetes. The glucose management indicator, or GMI, is an equation used to estimate HbA1c based on CGM mean glucose. GMI was originally proposed to simplify and aid in the interpretation of CGM data and is now provided on all standard summary reports (i.e., average glucose profiles) produced by different CGM manufacturers. This Perspective demonstrates that GMI performs poorly as an estimate of HbA1c and suggests that GMI is a concept that has outlived its usefulness, and it argues that it is preferable to use CGM mean glucose rather than converting glucose to GMI or an estimate of HbA1c. Leaving mean glucose in its raw form is simple and reinforces that glucose and HbA1c are distinct. To reduce patient and provider confusion and optimize glycemic management, mean CGM glucose, not GMI, should be used as a complement to laboratory HbA1c testing in patients using CGM systems.
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Affiliation(s)
- Elizabeth Selvin
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
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5
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Pleus S, Eichenlaub M, Gerber T, Eriksson Boija E, Makris K, Haug C, Freckmann G. Improving the Bias of Comparator Methods in Analytical Performance Assessments Through Recalibration. J Diabetes Sci Technol 2024; 18:686-694. [PMID: 36278402 PMCID: PMC11089879 DOI: 10.1177/19322968221133107] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND In analytical performance studies, the choice of comparator method plays an important role, as studies have shown that there exist relevant systematic differences (bias) between laboratory analyzers. The feasibility of retrospective recalibration of measurement results through comparison with methods or materials of higher metrological order to minimize bias was therefore assessed. METHOD Existing data from performance studies of continuous and blood glucose monitoring systems were retrospectively analyzed. Comparison with a higher-order method was performed for two different data sets. In both cases, subject samples were measured, and a subset was also measured on a higher-order method. Recalibration based on higher-order materials (standard reference material [SRM]) was conducted for two different data sets containing results from SRM and subject samples. Linear regression analysis was performed for each device separately. Resulting equations were applied to the respective complete data set of subject samples. Bias between devices in a data set across all subject samples was assessed before and after recalibration. RESULTS Bias between devices was reduced from -3.6% to +0.6% in one data set and from +11.0% to +0.3% in the other by recalibration based on higher-order method. Using higher-order materials, bias was also reduced by recalibration, but mixed results were found: Bias was reduced from -3.1% to -0.1% in one data set and from -4.3% to -2.7% in the other. CONCLUSIONS Recalibration did lead to a decrease in bias and thus can reduce the impact of the choice of comparator method. The procedure should be verified in a prospectively designed setting.
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Affiliation(s)
- Stefan Pleus
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Manuel Eichenlaub
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | | | - Elisabet Eriksson Boija
- Equalis AB, Uppsala, Sweden
- Working Group on Continuous Glucose Monitoring, Scientific Division, International Federation of Clinical Chemistry and Laboratory Medicine
| | - Konstantinos Makris
- Working Group on Continuous Glucose Monitoring, Scientific Division, International Federation of Clinical Chemistry and Laboratory Medicine
- Department of Clinical Biochemistry, KAT General Hospital, Athens, Greece
| | - Cornelia Haug
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Guido Freckmann
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
- Working Group on Continuous Glucose Monitoring, Scientific Division, International Federation of Clinical Chemistry and Laboratory Medicine
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6
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Erdős B, O'Donovan SD, Adriaens ME, Gijbels A, Trouwborst I, Jardon KM, Goossens GH, Afman LA, Blaak EE, van Riel NAW, Arts ICW. Leveraging continuous glucose monitoring for personalized modeling of insulin-regulated glucose metabolism. Sci Rep 2024; 14:8037. [PMID: 38580749 PMCID: PMC11371931 DOI: 10.1038/s41598-024-58703-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/01/2024] [Indexed: 04/07/2024] Open
Abstract
Continuous glucose monitoring (CGM) is a promising, minimally invasive alternative to plasma glucose measurements for calibrating physiology-based mathematical models of insulin-regulated glucose metabolism, reducing the reliance on in-clinic measurements. However, the use of CGM glucose, particularly in combination with insulin measurements, to develop personalized models of glucose regulation remains unexplored. Here, we simultaneously measured interstitial glucose concentrations using CGM as well as plasma glucose and insulin concentrations during an oral glucose tolerance test (OGTT) in individuals with overweight or obesity to calibrate personalized models of glucose-insulin dynamics. We compared the use of interstitial glucose with plasma glucose in model calibration, and evaluated the effects on model fit, identifiability, and model parameters' association with clinically relevant metabolic indicators. Models calibrated on both plasma and interstitial glucose resulted in good model fit, and the parameter estimates associated with metabolic indicators such as insulin sensitivity measures in both cases. Moreover, practical identifiability of model parameters was improved in models estimated on CGM glucose compared to plasma glucose. Together these results suggest that CGM glucose may be considered as a minimally invasive alternative to plasma glucose measurements in model calibration to quantify the dynamics of glucose regulation.
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Affiliation(s)
- Balázs Erdős
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.
- Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
| | - Shauna D O'Donovan
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Michiel E Adriaens
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Anouk Gijbels
- Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
| | - Inez Trouwborst
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Kelly M Jardon
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Gijs H Goossens
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Lydia A Afman
- Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
| | - Ellen E Blaak
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ilja C W Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
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7
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Eichenlaub M, Pleus S, Rothenbühler M, Bailey TS, Bally L, Brazg R, Bruttomesso D, Diem P, Eriksson Boija E, Fokkert M, Haug C, Hinzmann R, Jendle J, Klonoff DC, Mader JK, Makris K, Moser O, Nichols JH, Nørgaard K, Pemberton J, Selvin E, Spanou L, Thomas A, Tran NK, Witthauer L, Slingerland RJ, Freckmann G. Comparator Data Characteristics and Testing Procedures for the Clinical Performance Evaluation of Continuous Glucose Monitoring Systems. Diabetes Technol Ther 2024; 26:263-275. [PMID: 38194227 PMCID: PMC10979680 DOI: 10.1089/dia.2023.0465] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are <70 mg/dL (3.9 mmol/L) and >300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems.
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Affiliation(s)
- Manuel Eichenlaub
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Stefan Pleus
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
| | | | | | - Lia Bally
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital Bern, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Ronald Brazg
- Rainier Clinical Research Center, Renton, Washington, USA
| | - Daniela Bruttomesso
- Division of Metabolic Disease, Department of Medicine, University of Padua, Padua, Italy
| | - Peter Diem
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- Endokrinologie Diabetologie Bern, Bern, Switzerland
| | - Elisabet Eriksson Boija
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- Equalis AB, Uppsala, Sweden
| | - Marion Fokkert
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- Department of Clinical Chemistry, Isala Clinics, Zwolle, The Netherlands
| | - Cornelia Haug
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Rolf Hinzmann
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- Roche Diabetes Care GmbH, Mannheim, Germany
| | - Johan Jendle
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - David C. Klonoff
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- Diabetes Research Institute of Mills-Peninsula Medical Center, San Mateo, California, USA
| | - Julia K. Mader
- Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
| | - Konstantinos Makris
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- Clinical Biochemistry Department, KAT General Hospital, Athens, Greece
| | - Othmar Moser
- Division of Endocrinology and Diabetology, Medical University of Graz, Graz, Austria
- Department of Exercise Physiology and Metabolism, University of Bayreuth, Bayreuth, Germany
| | - James H. Nichols
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kirsten Nørgaard
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - John Pemberton
- Birmingham Women's and Children's Foundation Trust, Birmingham, United Kingdom
| | - Elizabeth Selvin
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- Department of Cardiovascular and Clinical Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Loukia Spanou
- Department of Endocrinology, Diabetes and Metabolism, Hellenic Red Cross Hospital, Athens, Greece
| | - Andreas Thomas
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- Independent Scientific Consulting, Pirna, Germany
| | - Nam K. Tran
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- Department of Pathology and Laboratory Medicine, University of California Davis Health, Sacramento, California, USA
| | - Lilian Witthauer
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- Diabetes Center Berne, Bern, Switzerland
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital Bern, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Robbert J. Slingerland
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
- Department of Clinical Chemistry, Isala Clinics, Zwolle, The Netherlands
| | - Guido Freckmann
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
- IFCC Scientific Division, Working Group on Continuous Glucose Monitoring
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8
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Tozzo V, Genco M, Omololu SO, Mow C, Patel HR, Patel CH, Ho SN, Lam E, Abdulsater B, Patel N, Cohen RM, Nathan DM, Powe CE, Wexler DJ, Higgins JM. Estimating Glycemia From HbA1c and CGM: Analysis of Accuracy and Sources of Discrepancy. Diabetes Care 2024; 47:460-466. [PMID: 38394636 PMCID: PMC10909686 DOI: 10.2337/dc23-1177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 12/12/2023] [Indexed: 02/25/2024]
Abstract
OBJECTIVE To examine the accuracy of different periods of continuous glucose monitoring (CGM), hemoglobin A1c (HbA1c), and their combination for estimating mean glycemia over 90 days (AG90). RESEARCH DESIGN AND METHODS We retrospectively studied 985 CGM periods of 90 days with <10% missing data from 315 adults (86% of whom had type 1 diabetes) with paired HbA1c measurements. The impact of mean red blood cell age as a proxy for nonglycemic effects on HbA1c was estimated using published theoretical models and in comparison with empirical data. Given the lack of a gold standard measurement for AG90, we applied correction methods to generate a reference (eAG90) that we used to assess accuracy for HbA1c and CGM. RESULTS Using 14 days of CGM at the end of the 90-day period resulted in a mean absolute error (95th percentile) of 14 (34) mg/dL when compared with eAG90. Nonglycemic effects on HbA1c led to a mean absolute error for average glucose calculated from HbA1c of 12 (29) mg/dL. Combining 14 days of CGM with HbA1c reduced the error to 10 (26) mg/dL. Mismatches between CGM and HbA1c >40 mg/dL occurred more than 5% of the time. CONCLUSIONS The accuracy of estimates of eAG90 from limited periods of CGM can be improved by averaging with an HbA1c-based estimate or extending the monitoring period beyond ∼26 days. Large mismatches between eAG90 estimated from CGM and HbA1c are not unusual and may persist due to stable nonglycemic factors.
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Affiliation(s)
- Veronica Tozzo
- Department of Pathology and Center for Systems Biology, Massachusetts General Hospital, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
| | - Matthew Genco
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Cincinnati College of Medicine, Cincinnati, OH
- Medical Service, Cincinnati Veterans Affairs Medical Center, Cincinnati, OH
| | | | - Christopher Mow
- Department of Pathology and Center for Systems Biology, Massachusetts General Hospital, Boston, MA
- Mass General Brigham Enterprise Research IS, Boston, MA
| | - Hasmukh R. Patel
- Department of Pathology and Center for Systems Biology, Massachusetts General Hospital, Boston, MA
| | - Chhaya H. Patel
- Department of Pathology and Center for Systems Biology, Massachusetts General Hospital, Boston, MA
| | - Samantha N. Ho
- Department of Pathology and Center for Systems Biology, Massachusetts General Hospital, Boston, MA
| | - Evie Lam
- Department of Pathology and Center for Systems Biology, Massachusetts General Hospital, Boston, MA
| | - Batoul Abdulsater
- Department of Pathology and Center for Systems Biology, Massachusetts General Hospital, Boston, MA
| | - Nikita Patel
- Department of Pathology and Center for Systems Biology, Massachusetts General Hospital, Boston, MA
| | - Robert M. Cohen
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Cincinnati College of Medicine, Cincinnati, OH
- Medical Service, Cincinnati Veterans Affairs Medical Center, Cincinnati, OH
| | - David M. Nathan
- Diabetes Research Center, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Camille E. Powe
- Diabetes Research Center, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
- Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA
- Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA
| | - Deborah J. Wexler
- Diabetes Research Center, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - John M. Higgins
- Department of Pathology and Center for Systems Biology, Massachusetts General Hospital, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
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9
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Joshi K, Harris M, Cotterill A, Wentworth JM, Couper JJ, Haynes A, Davis EA, Lomax KE, Huynh T. Continuous glucose monitoring has an increasing role in pre-symptomatic type 1 diabetes: advantages, limitations, and comparisons with laboratory-based testing. Clin Chem Lab Med 2024; 62:41-49. [PMID: 37349976 DOI: 10.1515/cclm-2023-0234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 06/06/2023] [Indexed: 06/24/2023]
Abstract
Type 1 diabetes (T1D) is well-recognised as a continuum heralded by the development of islet autoantibodies, progression to islet autoimmunity causing beta cell destruction, culminating in insulin deficiency and clinical disease. Abnormalities of glucose homeostasis are known to exist well before the onset of typical symptoms. Laboratory-based tests such as the oral glucose tolerance test (OGTT) and glycated haemoglobin (HbA1c) have been used to stage T1D and assess the risk of progression to clinical T1D. Continuous glucose monitoring (CGM) can detect early glycaemic abnormalities and can therefore be used to monitor for metabolic deterioration in pre-symptomatic, islet autoantibody positive, at-risk individuals. Early identification of these children can not only reduce the risk of presentation with diabetic ketoacidosis (DKA), but also determine eligibility for prevention trials, which aim to prevent or delay progression to clinical T1D. Here, we describe the current state with regard to the use of the OGTT, HbA1c, fructosamine and glycated albumin in pre-symptomatic T1D. Using illustrative cases, we present our clinical experience with the use of CGM, and advocate for an increased role of this diabetes technology, for monitoring metabolic deterioration and disease progression in children with pre-symptomatic T1D.
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Affiliation(s)
- Kriti Joshi
- Department of Endocrinology and Diabetes, Queensland Children's Hospital, South Brisbane, QLD, Australia
- Children's Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Mark Harris
- Department of Endocrinology and Diabetes, Queensland Children's Hospital, South Brisbane, QLD, Australia
| | - Andrew Cotterill
- Department of Endocrinology and Diabetes, Queensland Children's Hospital, South Brisbane, QLD, Australia
| | - John M Wentworth
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
- Department of Diabetes and Endocrinology, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Jennifer J Couper
- Department of Endocrinology and Diabetes, Women's and Children's Hospital, North Adelaide, SA, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, SA, Australia
| | - Aveni Haynes
- Children's Diabetes Centre, Telethon Kids Institute, The University of Western Australia Perth, Crawley, WA, Australia
| | - Elizabeth A Davis
- Children's Diabetes Centre, Telethon Kids Institute, The University of Western Australia Perth, Crawley, WA, Australia
- Department of Endocrinology and Diabetes, Perth Children's Hospital, Nedlands, WA, Australia
- Centre for Child Health Research, University of Western Australia, Perth, WA, Australia
| | - Kate E Lomax
- Children's Diabetes Centre, Telethon Kids Institute, The University of Western Australia Perth, Crawley, WA, Australia
- Department of Endocrinology and Diabetes, Perth Children's Hospital, Nedlands, WA, Australia
| | - Tony Huynh
- Department of Endocrinology and Diabetes, Queensland Children's Hospital, South Brisbane, QLD, Australia
- Children's Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Department of Chemical Pathology, Mater Pathology, South Brisbane, QLD, Australia
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10
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Gillery P. Biological management of diabetes mellitus, the laboratory medicine specialist and the patient. Clin Chem Lab Med 2024; 62:1-2. [PMID: 37682246 DOI: 10.1515/cclm-2023-0959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Affiliation(s)
- Philippe Gillery
- Laboratory of Biochemistry-Pharmacology-Toxicology, Biology and Pathology Department, University Hospital of Reims, Rue du Général Koenig, F-51092 Reims Cedex, France
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11
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Freckmann G, Eichenlaub M, Waldenmaier D, Pleus S, Wehrstedt S, Haug C, Witthauer L, Jendle J, Hinzmann R, Thomas A, Eriksson Boija E, Makris K, Diem P, Tran N, Klonoff DC, Nichols JH, Slingerland RJ. Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting. J Diabetes Sci Technol 2023; 17:1506-1526. [PMID: 37599389 PMCID: PMC10658695 DOI: 10.1177/19322968231190941] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
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.
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Affiliation(s)
- Guido Freckmann
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Manuel Eichenlaub
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Delia Waldenmaier
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Stefan Pleus
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Stephanie Wehrstedt
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Cornelia Haug
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Lilian Witthauer
- Diabetes Center Berne, Bern, Switzerland
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital Bern, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Johan Jendle
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Rolf Hinzmann
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Roche Diabetes Care GmbH, Mannheim, Germany
| | - Andreas Thomas
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Pirna, Germany
| | - Elisabet Eriksson Boija
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Equalis AB, Uppsala, Sweden
| | - Konstantinos Makris
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Clinical Biochemistry Department, KAT General Hospital, Athens, Greece
| | - Peter Diem
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Endokrinologie Diabetologie Bern, Bern, Switzerland
| | - Nam Tran
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Department of Pathology and Laboratory Medicine, University of California Davis Health, Sacramento, CA, USA
| | - David C. Klonoff
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Diabetes Research Institute, Mills-Peninsula Medical Center, San Mateo, CA, USA
| | - James H. Nichols
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Robbert J. Slingerland
- IFCC Scientific Division - Working Group on Continuous Glucose Monitoring
- Department of Clinical Chemistry, Isala Clinics, Zwolle, the Netherlands
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12
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Valenzano M, Bertolotti IC. Flash glucose monitoring versus oral glucose tolerance test: mind the gap. Acta Diabetol 2023; 60:591-593. [PMID: 36396794 DOI: 10.1007/s00592-022-02006-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 11/03/2022] [Indexed: 11/19/2022]
Affiliation(s)
- Marina Valenzano
- Territorial Department of Diabetology and Endocrinology - Local Health Authority Cuneo 1 (ASL CN1), Via Ospedali 9, 12038, Savigliano, Cuneo, Italy.
| | - Ivan Cibrario Bertolotti
- Institute of Electronics, Information and Telecommunication Engineering, CNR-IEIIT, c.so Duca degli Abruzzi 24, 10129, Torino, Italy
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13
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Pemberton JS, Wilmot EG, Barnard-Kelly K, Leelarathna L, Oliver N, Randell T, Taplin CE, Choudhary P, Adolfsson P. CGM accuracy: Contrasting CE marking with the governmental controls of the USA (FDA) and Australia (TGA): A narrative review. Diabetes Obes Metab 2023; 25:916-939. [PMID: 36585365 DOI: 10.1111/dom.14962] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 12/12/2022] [Accepted: 12/23/2022] [Indexed: 01/01/2023]
Abstract
The National Institute for Clinical Excellence updated guidance for continuous glucose monitoring (CGM) in 2022, recommending that CGM be available to all people living with type 1 diabetes. Manufacturers can trade in the UK with Conformité Européenne (CE) marking without an initial national assessment. The regulatory process for CGM CE marking, in contrast to the Food and Drug Administration (FDA) and Australian Therapeutic Goods Administration (TGA) process, is described. Manufacturers operating in the UK provided clinical accuracy studies submitted for CE marking. Critical appraisal of the studies shows several CGM devices have CE marking for wide-ranging indications beyond available data, unlike FDA and TGA approval. The FDA and TGA use tighter controls, requiring comprehensive product-specific clinical data evaluation. In 2018, the FDA published the integrated CGM (iCGM) criteria permitting interoperability. Applying the iCGM criteria to clinical data provided by manufacturers trading in the UK identified several study protocols that minimized glucose variability, thereby improving CGM accuracy on all metrics. These results do not translate into real-life performance. Furthermore, for many CGM devices available in the UK, accuracy reported in the hypoglycaemic range is below iCGM standards, or measurement is absent. We offer a framework to evaluate CGM accuracy studies critically. The review concludes that FDA- and TGA-approved indications match the available clinical data, whereas CE marking indications can have discrepancies. The UK can bolster regulation with UK Conformity Assessed marking from January 2025. However, balanced regulation is needed to ensure innovation and timely technological access are not hindered.
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Affiliation(s)
- John S Pemberton
- Department of Endocrinology and Diabetes, Birmingham Children's Hospital, Birmingham Women's, and Children's NHS Foundation Trust, Birmingham, UK
| | - Emma G Wilmot
- University Hospitals of Derby and Burton NHS Foundation Trust, Derby, UK
- University of Nottingham, Nottingham, UK
| | | | - Lalantha Leelarathna
- Manchester Diabetes Centre, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Division of Diabetes, Endocrinology and Gastroenterology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Nick Oliver
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | | | - Craig E Taplin
- Department of Endocrinology and Diabetes, Perth Children's Hospital, Perth, Australia
- Telethon Kids Institute, University of Western Australia, Perth, Australia
- Centre for Child Health Research, University of Western Australia, Perth, Australia
| | - Pratik Choudhary
- Leicester Diabetes Center, University of Leicester, Leicester, UK
| | - Peter Adolfsson
- Department of Paediatrics, Kungsbacka Hospital; Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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14
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Klonoff DC, Yeung AM, Huang J, Nichols JH. A Newly FDA-Cleared Benchtop Glucose Analyzer Heralds the Dawn of the Post-YSI 2300 Era. J Diabetes Sci Technol 2023; 17:269-273. [PMID: 36458730 PMCID: PMC10012354 DOI: 10.1177/19322968221139514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Affiliation(s)
- David C. Klonoff
- Diabetes Research Institute, Mills-Peninsula
Medical Center, San Mateo, CA, USA
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15
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Piersanti A, Giurato F, Göbl C, Burattini L, Tura A, Morettini M. Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data. Diabetes Technol Ther 2023; 25:69-85. [PMID: 36223198 DOI: 10.1089/dia.2022.0237] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The advancement of technology in the field of glycemic control has led to the widespread use of continuous glucose monitoring (CGM), which can be nowadays obtained from wearable devices equipped with a minimally invasive sensor, that is, transcutaneous needle type or implantable, and a transmitter that sends information to a receiver or smart device for data storage and display. This work aims to review the currently available software packages and tools for the analysis of CGM data. Based on the purposes of this work, 12 software packages have been identified from the literature, published until December 2021, namely: GlyCulator, EasyGV (Easy Glycemic Variability), CGM-GUIDE© (Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation), GVAP (Glycemic Variability Analyzer Program), Tidepool, CGManalyzer, cgmanalysis, GLU, CGMStatsAnalyser, iglu, rGV, and cgmquantify. Comparison of available software packages and tools has been done in terms of main characteristics (i.e., publication year, presence of a graphical user interface, availability, open-source code, number of citations, programming language, supported devices, supported data format and organization of the data structure, documentation, presence of a toy example, video tutorial, data upload and download, measurement-units conversion), preprocessing procedures, data display options, and computed metrics; also, each of the computed metrics has been analyzed in terms of its adherence to the American Diabetes Association (ADA) 2017 international consensus on CGM data analysis and the ADA 2019 international consensus on time in range. Eventually, the agreement between metrics computed by different software and tools has been investigated. Based on such comparison, usability and complexity of data management, as well as the possibility to perform customized or patients-group analyses, have been discussed by highlighting limitations and strengths, also in relation to possible different user categories (i.e., patients, clinicians, researchers). The information provided could be useful to researchers interested in working in the diabetic research field as to clinicians and endocrinologists who need tools capable of handling CGM data effectively.
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Affiliation(s)
- Agnese Piersanti
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Francesco Giurato
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Christian Göbl
- Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Andrea Tura
- CNR Institute of Neuroscience, Padova, Italy
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
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16
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Gillery P. HbA 1c and biomarkers of diabetes mellitus in Clinical Chemistry and Laboratory Medicine: ten years after. Clin Chem Lab Med 2022; 61:861-872. [PMID: 36239682 DOI: 10.1515/cclm-2022-0894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 09/10/2022] [Indexed: 11/15/2022]
Abstract
Since its discovery in the late 1960s, HbA1c has proven to be a major biomarker of diabetes mellitus survey and diagnosis. Other biomarkers have also been described using classical laboratory methods or more innovative, non-invasive ones. All biomarkers of diabetes, including the historical glucose assay, have well-controlled strengths and limitations, determining their indications in clinical use. They all request high quality preanalytical and analytical methodologies, necessitating a strict evaluation of their performances by external quality control assessment trials. Specific requirements are needed for point-of-care testing technologies. This general overview, which describes how old and new tools of diabetes mellitus biological survey have evolved over the last decade, has been built through the prism of papers published in Clinical Chemistry and Laboratory Medicine during this period.
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Affiliation(s)
- Philippe Gillery
- Laboratory of Biochemistry-Pharmacology-Toxicology, Biology and Pathology Department, University Hospital of Reims, Reims, France.,Laboratory of Medical Biochemistry and Molecular Biology, UMR CNRS/ URCA n°7369, Faculty of Medicine, University of Reims Champagne-Ardenne, Reims, France
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17
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Bailey L. Moving with technological advancements: blood glucose monitoring from a district nurse's perspective. Br J Community Nurs 2022; 27:480-484. [PMID: 36194398 DOI: 10.12968/bjcn.2022.27.10.480] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Capillary blood glucose monitoring is a standard safety protocol before administering insulin. Over the past 12 months, there has been a notable increase in patients under the district nursing service using a flash glucose sensor (FGS), which is a portable technological device inserted into the skin via a stamp-like mechanism. The device sits in the interstitial fluid under the skin; the device can be scanned using a sensor to obtain glucose readings, which can eliminate the need for capillary finger pricking. From experience, some people opt for this device, considering the pain and inconvenience associated with capillary finger pricking. Despite some patients already utilising FGS, some community teams may still have to take a capillary finger prick before insulin administration, depending on local trust policy. Interestingly, while looking into the reasons for this, one discovered some contradictory concerns over the safety of FGS due to a difference in time lag, where interstitial fluid readings differ from blood glucose readings. However, new national guidelines reflect the push towards this technological innovation that could revolutionise patient care in glucose monitoring and diabetes management.
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Affiliation(s)
- Lianne Bailey
- Community Specialist Practitioner Student, Manchester Metropolitan University
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18
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Xu NY, Nguyen KT, DuBord AY, Pickup J, Sherr JL, Teymourian H, Cengiz E, Ginsberg BH, Cobelli C, Ahn D, Bellazzi R, Bequette BW, Gandrud Pickett L, Parks L, Spanakis EK, Masharani U, Akturk HK, Melish JS, Kim S, Kang GE, Klonoff DC. Diabetes Technology Meeting 2021. J Diabetes Sci Technol 2022; 16:1016-1056. [PMID: 35499170 PMCID: PMC9264449 DOI: 10.1177/19322968221090279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.
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Affiliation(s)
- Nicole Y. Xu
- Diabetes Technology Society,
Burlingame, CA, USA
| | | | | | | | | | | | - Eda Cengiz
- University of California, San
Francisco, San Francisco, CA, USA
| | | | | | - David Ahn
- Mary & Dick Allen Diabetes Center
at Hoag, Newport Beach, CA, USA
| | | | | | | | - Linda Parks
- University of California, San
Francisco, San Francisco, CA, USA
| | - Elias K. Spanakis
- Baltimore VA Medical Center,
Baltimore, MD, USA
- University of Maryland, Baltimore,
MD, USA
| | - Umesh Masharani
- University of California, San
Francisco, San Francisco, CA, USA
| | - Halis K. Akturk
- Barbara Davis Center for Diabetes,
University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Sarah Kim
- University of California, San
Francisco, San Francisco, CA, USA
| | - Gu Eon Kang
- The University of Texas at Dallas,
Richardson, TX, USA
| | - David C. Klonoff
- Diabetes Research Institute,
Mills-Peninsula Medical Center, San Mateo, CA, USA
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19
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Freckmann G, Baumstark A, Hinzmann R, Haug C, Pleus S. Comment on "Do We Need the Replacement of YSI 2300? A View from the Clinical Laboratory" by Spanou and Makris. J Diabetes Sci Technol 2022; 16:790-791. [PMID: 34056934 PMCID: PMC9294579 DOI: 10.1177/19322968211014215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Guido Freckmann
- Institut für Diabetes-Technologie,
Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Annette Baumstark
- Institut für Diabetes-Technologie,
Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | | | - Cornelia Haug
- Institut für Diabetes-Technologie,
Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Stefan Pleus
- Institut für Diabetes-Technologie,
Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
- Stefan Pleus, MSc, Institut für
Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm,
Lise-Meitner-Straße 8/2, Ulm, D-89081, Germany.
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20
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Pleus S, Freckmann G, Schauer S, Heinemann L, Ziegler R, Ji L, Mohan V, Calliari LE, Hinzmann R. Self-Monitoring of Blood Glucose as an Integral Part in the Management of People with Type 2 Diabetes Mellitus. Diabetes Ther 2022; 13:829-846. [PMID: 35416589 PMCID: PMC9076772 DOI: 10.1007/s13300-022-01254-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 03/14/2022] [Indexed: 11/25/2022] Open
Abstract
For decades, self-monitoring of blood glucose (SMBG) has been considered a cornerstone of adequate diabetes management. Structured SMBG can follow different monitoring patterns, and it results in improved glycemic control, reduced hypoglycemia, and a better quality of life of people with diabetes. The technology, usability, and accuracy of SMBG systems have advanced markedly since their introduction a few decades ago. Current SMBG systems are small and easy to use, require small (capillary) blood sample volumes, and provide measurement results within seconds. In addition, devices are increasingly equipped with features such as connectivity to other devices and/or digital diaries and diabetes management tools. Although measurement quality can come close to or equal that of the glucose monitoring systems used by healthcare professionals, several available SMBG systems still do not meet internationally accepted accuracy standards, such as the International Organization for Standardization 15197 standard. Reports from China, India, and Brazil based on local experience suggest that in addition of the accuracy issues of SMBG systems, other obstacles also need to be overcome to optimize SMBG usage. Nonetheless, adequate usage of SMBG data is of high relevance for the management of people with type 2 diabetes mellitus.
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Affiliation(s)
- Stefan Pleus
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Guido Freckmann
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | - Sebastian Schauer
- Institut für Diabetes-Technologie, Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm, Ulm, Germany
| | | | - Ralph Ziegler
- Diabetes Clinic for Children and Adolescents, Muenster, Germany
| | - Linong Ji
- Peking University People's Hospital, Peking, China
| | - Viswanathan Mohan
- Dr. Mohan's Diabetes Specialities Centre, Chennai, India
- Madras Diabetes Research Foundation, Chennai, India
| | - Luis Eduardo Calliari
- Pediatric Endocrine Unit, Pediatric Department, Santa Casa School of Medical Department, Santa Casa School of Medical Sciences, Sao Paulo, Brazil
| | - Rolf Hinzmann
- Roche Diabetes Care GmbH, Sandhofer Straße 116, 68305, Mannheim, Germany.
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21
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Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8956850. [PMID: 35449869 PMCID: PMC9017442 DOI: 10.1155/2022/8956850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 03/18/2022] [Indexed: 11/18/2022]
Abstract
Continuous noninvasive blood glucose monitoring and estimation management by using photoplethysmography (PPG) technology always have a series of problems, such as substantial time variability, inaccuracy, and complex nonlinearity. This paper proposes a blood glucose (BG) prediction model for more precise prediction based on BG series decomposition by complete aggregation empirical mode decomposition based on adaptive white noise (CEEMDAN) and the gated recurrent unit (GRU) that is optimized by improved bacterial foraging optimization (IBFO). Hierarchical clustering technology recombines the decomposed BG series according to their sample entropy and the correlations with the original BG trends. Dynamic BG trends are regressed separately for each recombined BG series by the GRU model to realize the more precise estimations, which are optimized by IBFO for its structure and superparameters. Through experiments, the optimized and basic LSTM, RNN, and support vector regression (SVR) are compared to evaluate the performance of the proposed model. The experimental results indicate that the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the 15-min IBFO-GRU prediction is improved on average by about 13.1% and 18.4%, respectively, compared with those of the RNN and LSTM optimized by IBFO. Meanwhile, the proposed model improved the Clarke error grid results by about 2.6% and 5.0% compared with those of the IBFO-LSTM and IBFO-RNN in 30-min prediction and by 4.1% and 6.6% in 15-min ahead forecast, respectively. The evaluation outcomes of our proposed CEEMDAN-IBFO-GRU model have high accuracy and adaptability and can effectively provide early intervention control of the occurrence of hyperglycemic complications.
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22
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Valenzano M, Cibrario Bertolotti I, Grassi G, Broglio F, Valenzano A. Predicting Glycated Hemoglobin Through Continuous Glucose Monitoring in Real-Life Conditions: Improved Estimation Methods. J Diabetes Sci Technol 2022:19322968221081556. [PMID: 35287492 DOI: 10.1177/19322968221081556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The adoption of continuous glucose monitoring (CGM) already helps to improve glycemic control in diabetes. When coupled with appropriate data analysis techniques, CGM also provides dependable estimates for significant metrics, like glycated hemoglobin (HbA1c). Findings from the REALISM-T1D study can boost HbA1c estimation methods in diabetes care and stimulate their use in clinical practice. METHODS Continuous glucose monitoring data of 27 adults affected by type-1 diabetes were acquired by means of G6 (Dexcom, San Diego, CA) sensors for a time span of 120 days. Glycated hemoglobin laboratory assays were performed during the concluding follow-up visits. Data were then analyzed to derive estimates of assay results, taken as the gold standard. RESULTS Bland-Altman (BA) plots show that smart interpolation to patch missing data and a wise choice of interstitial glucose (IG) weighting function, besides a proper mean interstitial glucose (MIG) to HbA1c regression equation, improve HbA1c estimation quality with respect to methods relying on MIG alone. A decrease in the BA plot-related variance of differences with respect to the gold standard confirms the improvement. Wilcoxon signed-rank tests on the bias-compensated mean squared error (MSE) with respect to conventional MIG-based methods show that the improvement is statistically significant with a confidence level better than 95% (P = .0179). CONCLUSIONS Improved HbA1c estimation methods result in better HbA1c prediction quality with respect to those based on MIG alone, thus providing quick, but still relatively accurate feedback to diabetologists. They alleviate the discordances reported in literature and, with further improvements, may become a viable complement/alternative to HbA1c assays.
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Affiliation(s)
- Marina Valenzano
- Division of Endocrinology, Diabetology & Metabolic Diseases, Department of Medical Sciences, University of Turin, Torino, Italy
- Division of Diabetology, Civil Hospital SS. Annunziata, Savigliano, Italy
| | - Ivan Cibrario Bertolotti
- Institute of Electronics, Information Engineering & Telecommunications, National Research Council of Italy, Torino, Italy
| | - Giorgio Grassi
- Department of Endocrinology & Metabolism, Ospedale Molinette, Torino, Italy
| | - Fabio Broglio
- Division of Endocrinology, Diabetology & Metabolic Diseases, Department of Medical Sciences, University of Turin, Torino, Italy
- Division of Diabetology, Civil Hospital SS. Annunziata, Savigliano, Italy
| | - Adriano Valenzano
- Institute of Electronics, Information Engineering & Telecommunications, National Research Council of Italy, Torino, Italy
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23
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Nemat H, Khadem H, Eissa MR, Elliott J, Benaissa M. Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach. IEEE J Biomed Health Inform 2022; 26:2758-2769. [PMID: 35077372 DOI: 10.1109/jbhi.2022.3144870] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Optimal and sustainable control of blood glucose levels (BGLs) is the aim of type-1 diabetes management. The automated prediction of BGL using machine learning (ML) algorithms is considered as a promising tool that can support this aim. In this context, this paper proposes new advanced ML architectures to predict BGL leveraging deep learning and ensemble learning. The deep-ensemble models are developed with novel meta-learning approaches, where the feasibility of changing the dimension of a univariate time series forecasting task is investigated. The models are evaluated regression-wise and clinical-wise. The performance of the proposed ensemble models are compared with benchmark non-ensemble models. The results show the superior performance of the developed ensemble models over developed non-ensemble benchmark models and also show the efficacy of the proposed meta-learning approaches.
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24
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Pullano SA, Greco M, Bianco MG, Foti D, Brunetti A, Fiorillo AS. Glucose biosensors in clinical practice: principles, limits and perspectives of currently used devices. Theranostics 2022; 12:493-511. [PMID: 34976197 PMCID: PMC8692922 DOI: 10.7150/thno.64035] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 10/31/2021] [Indexed: 12/13/2022] Open
Abstract
The demand of glucose monitoring devices and even of updated guidelines for the management of diabetic patients is dramatically increasing due to the progressive rise in the prevalence of diabetes mellitus and the need to prevent its complications. Even though the introduction of the first glucose sensor occurred decades ago, important advances both from the technological and clinical point of view have contributed to a substantial improvement in quality healthcare. This review aims to bring together purely technological and clinical aspects of interest in the field of glucose devices by proposing a roadmap in glucose monitoring and management of patients with diabetes. Also, it prospects other biological fluids to be examined as further options in diabetes care, and suggests, throughout the technology innovation process, future directions to improve the follow-up, treatment, and clinical outcomes of patients.
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Affiliation(s)
| | - Marta Greco
- Department of Health Sciences, Magna Græcia University of Catanzaro, 88100, Catanzaro, Italy
| | - Maria Giovanna Bianco
- Department of Health Sciences, Magna Græcia University of Catanzaro, 88100, Catanzaro, Italy
| | - Daniela Foti
- Department of Experimental and Clinical Medicine, Magna Græcia University of Catanzaro, 88100, Catanzaro, Italy
| | - Antonio Brunetti
- Department of Health Sciences, Magna Græcia University of Catanzaro, 88100, Catanzaro, Italy
| | - Antonino S. Fiorillo
- Department of Health Sciences, Magna Græcia University of Catanzaro, 88100, Catanzaro, Italy
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25
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Shang T, Zhang JY, Thomas A, Arnold MA, Vetter BN, Heinemann L, Klonoff DC. Products for Monitoring Glucose Levels in the Human Body With Noninvasive Optical, Noninvasive Fluid Sampling, or Minimally Invasive Technologies. J Diabetes Sci Technol 2022; 16:168-214. [PMID: 34120487 PMCID: PMC8721558 DOI: 10.1177/19322968211007212] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Conventional home blood glucose measurements require a sample of blood that is obtained by puncturing the skin at the fingertip. To avoid the pain associated with this procedure, there is high demand for medical products that allow glucose monitoring without blood sampling. In this review article, all such products are presented. METHODS In order to identify such products, four different sources were used: (1) PubMed, (2) Google Patents, (3) Diabetes Technology Meeting Startup Showcase participants, and (4) experts in the field of glucose monitoring. The information obtained were filtered by using two inclusion criteria: (1) regulatory clearance, and/or (2) significant coverage in Google News starting in the year 2016, unless the article indicated that the product had been discontinued. The identified bloodless monitoring products were classified into three categories: (1) noninvasive optical, (2) noninvasive fluid sampling, and (3) minimally invasive devices. RESULTS In total, 28 noninvasive optical, 6 noninvasive fluid sampling, and 31 minimally invasive glucose monitoring products were identified. Subsequently, these products were characterized according to their regulatory, technological, and consumer features. Products with regulatory clearance are described in greater detail according to their advantages and disadvantages, and with design images. CONCLUSIONS Based on favorable technological features, consumer features, and other advantages, several bloodless products are commercially available and promise to enhance diabetes management. Paths for future products are discussed with an emphasis on understanding existing barriers related to both technical and non-technical issues.
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Affiliation(s)
- Trisha Shang
- Diabetes Technology Society, Burlingame, California, USA
| | | | - Andreas Thomas
- AGDT (Working group of Diabetes Technology), Germany, Ulm, Germany
| | - Mark A. Arnold
- University of Iowa, Department of Chemistry, Iowa City, Iowa, USA
| | | | | | - David C. Klonoff
- Mills-Peninsula Medical Center, San Mateo, California, USA
- David C. Klonoff, MD, FACP, FRCP (Edin), Fellow AIMBE, Mills-Peninsula Medical Center, 100 South San Mateo Drive, Room 5147, San Mateo, California 94401, USA.
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26
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Klonoff DC, Nguyen KT, Xu NY, Arnold MA. Noninvasive Glucose Monitoring: In God We Trust-All Others Bring Data. J Diabetes Sci Technol 2021; 15:1211-1215. [PMID: 34672216 PMCID: PMC8655290 DOI: 10.1177/19322968211046326] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- David C. Klonoff
- Mills-Peninsula Medical Center,
San Mateo, CA, USA
- David C. Klonoff, MD, FACP, FRCP
(Edin), Fellow AIMBE, Mills-Peninsula Medical Center, 100 South San
Mateo Drive, Room 5147, San Mateo, CA 94401, USA.
| | | | - Nicole Y. Xu
- Diabetes Technology Society,
Burlingame, CA, USA
| | - Mark A. Arnold
- Department of Chemistry, The
University of Iowa, Iowa City, IA, USA
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27
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Gillery P. IFCC Scientific Division: A conductor of standardization in laboratory medicine. Clin Chim Acta 2021; 522:184-186. [PMID: 34364854 DOI: 10.1016/j.cca.2021.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- P Gillery
- International Federation of Clinical Chemistry and Laboratory Medicine, Scientific Division, Chair, Italy; University Hospital of Reims, Department of Biochemistry-Pharmacology-Toxicology, Reims, France; University of Reims Champagne-Ardenne, UMR CNRS/URCA N°7369 MEDyC, Faculty of medicine, Laboratory of Medical Biochemistry and Molecular Biology, Reims, France.
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28
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Zhang Y, Sun J, Liu L, Qiao H. A review of biosensor technology and algorithms for glucose monitoring. J Diabetes Complications 2021; 35:107929. [PMID: 33902999 DOI: 10.1016/j.jdiacomp.2021.107929] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/30/2021] [Accepted: 04/11/2021] [Indexed: 12/24/2022]
Abstract
Diabetes mellitus (DM) has become a serious illness in the whole world. Until now, there is no effective cure for patients with DM. It is well known that the glucose level is one key factor to determine the progress of DM. It is also an important reference to carry out the accurate and timely treatment for patients with DM. In this article, the related biosensors technology that can be utilized to identify and predict glucose level are reviewed in detail, including the algorithms that can help to achieve numerical value of glucose level. Firstly, the biosensor technology based on the physiological fluids are illustrated, including blood, sweat, interstitial fluid, ocular fluid, and other available fluids. Secondly, the algorithms for achieving numerical value of glucose level are investigated, including the physiological model-based method and the machine learning-based method. Finally, the future development trend and challenges of glucose level monitoring are given and the conclusions are drawn.
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Affiliation(s)
- Yaguang Zhang
- The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Jingxue Sun
- The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Liansheng Liu
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China
| | - Hong Qiao
- The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150086, China.
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29
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Shang T, Zhang JY, Bequette BW, Raymond JK, Coté G, Sherr JL, Castle J, Pickup J, Pavlovic Y, Espinoza J, Messer LH, Heise T, Mendez CE, Kim S, Ginsberg BH, Masharani U, Galindo RJ, Klonoff DC. Diabetes Technology Meeting 2020. J Diabetes Sci Technol 2021; 15:916-960. [PMID: 34196228 PMCID: PMC8258529 DOI: 10.1177/19322968211016480] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.
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Affiliation(s)
- Trisha Shang
- Diabetes Technology Society, Burlingame, CA, USA
| | | | | | - Jennifer K. Raymond
- Children’s Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA
| | - Gerard Coté
- Texas A & M University, College Station, Texas, USA
| | | | | | | | | | - Juan Espinoza
- Children’s Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA
| | | | | | | | - Sarah Kim
- University of California San Francisco, San Francisco, CA, USA
| | | | - Umesh Masharani
- University of California San Francisco, San Francisco, CA, USA
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