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The Diabetes Technology Society Error Grid and Trend Accuracy Matrix for Glucose Monitors

  • David C. Klonoff
  • , Guido Freckmann
  • , Stefan Pleus
  • , Boris P. Kovatchev
  • , David Kerr
  • , Chui Tse
  • , Chengdong Li
  • , Michael S.D. Agus
  • , Kathleen Dungan
  • , Barbora Voglová Hagerf
  • , Jan S. Krouwer
  • , Wei An Lee
  • , Shivani Misra
  • , Sang Youl Rhee
  • , Ashutosh Sabharwal
  • , Jane Jeffrie Seley
  • , Viral N. Shah
  • , Nam K. Tran
  • , Kayo Waki
  • , Chris Worth
  • Tiffany Tian, Rachel E. Aaron, Keetan Rutledge, Cindy N. Ho, Alessandra T. Ayers, Amanda Adler, David T. Ahn, Halis Kaan Aktürk, Mohammed E. Al-Sofiani, Timothy S. Bailey, Matt Baker, Lia Bally, Raveendhara R. Bannuru, Elizabeth M. Bauer, Yong Mong Bee, Julia E. Blanchette, Eda Cengiz, James Geoffrey Chase, Kong Y. Chen, Daniel Cherñavvsky, Mark Clements, Gerard L. Cote, Ketan K. Dhatariya, Andjela Drincic, Niels Ejskjaer, Juan Espinoza, Chiara Fabris, G. Alexander Fleming, Monica A.L. Gabbay, Rodolfo J. Galindo, Ana María Gómez-Medina, Lutz Heinemann, Norbert Hermanns, Thanh Hoang, Sufyan Hussain, Peter G. Jacobs, Johan Jendle, Shashank R. Joshi, Suneil K. Koliwad, Rayhan A. Lal, Lawrence A. Leiter, Marcus Lind, Julia K. Mader, Alberto Maran, Umesh Masharani, Nestoras Mathioudakis, Michael McShane, Chhavi Mehta, Sun Joon Moon, James H. Nichols, David N. O’Neal, Francisco J. Pasquel, Anne L. Peters, Andreas Pfützner, Rodica Pop-Busui, Pratistha Ranjitkar, Connie M. Rhee, David B. Sacks, Signe Schmidt, Simon M. Schwaighofer, Bin Sheng, Gregg D. Simonson, Koji Sode, Elias K. Spanakis, Nicole L. Spartano, Guillermo E. Umpierrez, Maryam Vareth, Hubert W. Vesper, Jing Wang, Eugene Wright, Alan H.B. Wu, Sewagegn Yeshiwas, Mihail Zilbermint, Michael A. Kohn
  • Sutter Health
  • Ulm University
  • University of Virginia School of Medicine
  • Centers for Disease Control and Prevention
  • Florida State University
  • Harvard University
  • Ohio State University
  • Institute for Clinical and Experimental Medicine
  • Charles University
  • Krouwer Consulting
  • Los Angeles General Medical Center
  • Imperial College London
  • Kyung Hee University
  • Department of Civil and Environmental Engineering, Rice University
  • Cornell University
  • Indiana University-Purdue University Indianapolis
  • University of California at Davis
  • Gifu University
  • Manchester University NHS Foundation Trust
  • Diabetes Technology Society
  • QuesGen
  • University of Oxford
  • Hoag Memorial Hospital
  • University of Colorado Boulder
  • King Saud University
  • Johns Hopkins Medicine
  • AMCR Institute
  • North kansas City Hospital
  • University of Bern
  • American Diabetes Association
  • Naval Medical Center San Diego
  • Singapore General Hospital
  • Case Western University Hospitals
  • Case Western Reserve University
  • University of California at San Francisco
  • University of Canterbury
  • National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
  • University of Virginia
  • Children’s Mercy Kansas City
  • Texas A&M University
  • Norfolk and Norwich University Hospitals NHS Foundation Trust
  • University of East Anglia, Norwich Medical School
  • University of Nebraska Medical Center
  • Aalborg University
  • Ann and Robert H. Lurie Children's Hospital of Chicago
  • Kinexum
  • Universidade Federal de São Paulo
  • University of Miami Leonard M. Miller School of Medicine
  • Hospital Universitario San Ignacio
  • Science Consulting in Diabetes GmbH
  • University of Bamberg
  • Walter Reed Army Institute of Research
  • King's College London
  • NHS Foundation Trust
  • Oregon Health and Science University
  • Örebro University
  • Joshi Clinic
  • Stanford University School of Medicine
  • Li Ka Shing Knowledge Institute
  • University of Gothenburg
  • Medical University of Graz
  • University of Padua
  • Johns Hopkins University
  • Kangbuk Samsung Hospital
  • Vanderbilt University Medical Center
  • University of Melbourne
  • Emory University
  • Keck School of Medicine of USC
  • Pfützner Science & Health Institute
  • University for Digital Technologies in Medicine and Dentistry
  • University of Michigan, Ann Arbor
  • ADLM—Association for Diagnostics & Laboratory Medicine (formerly AACC)
  • University of California at Los Angeles
  • Cedars-Sinai Health System
  • National Institute of Health Bogotá
  • Steno Diabetes Center Copenhagen
  • Diabetes Center Berne
  • Shanghai Jiao Tong University
  • International Diabetes Center
  • The University of North Carolina at Chapel Hill
  • University of Maryland, Baltimore
  • Boston University
  • University of California at Berkeley
  • Duke University
  • Addis Ababa University

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

Introduction: An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs). Methods: Diabetes Technology Society (DTS) convened 89 international experts in glucose monitoring to (1) smooth the borders of the Surveillance Error Grid (SEG) zones and create a user-friendly tool—the DTS Error Grid; (2) define five risk zones of clinical point accuracy (A-E) to be identical for BGMs and CGMs; (3) determine a relationship between DTS Error Grid percent in Zone A and mean absolute relative difference (MARD) from analyzing 22 BGM and nine CGM accuracy studies; and (4) create trend risk categories (1-5) for CGM trend accuracy. Results: The DTS Error Grid for point accuracy contains five risk zones (A-E) with straight-line borders that can be applied to both BGM and CGM accuracy data. In a data set combining point accuracy data from 18 BGMs, 2.6% of total data pairs equally moved from Zones A to B and vice versa (SEG compared with DTS Error Grid). For every 1% increase in percent data in Zone A, the MARD decreased by approximately 0.33%. We also created a DTS Trend Accuracy Matrix with five trend risk categories (1-5) for CGM-reported trend indicators compared with reference trends calculated from reference glucose. Conclusion: The DTS Error Grid combines contemporary clinician input regarding clinical point accuracy for BGMs and CGMs. The DTS Trend Accuracy Matrix assesses accuracy of CGM trend indicators.

Original languageEnglish
Pages (from-to)1346-1361
Number of pages16
JournalJournal of Diabetes Science and Technology
Volume18
Issue number6
DOIs
StatePublished - Nov 2024
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • accuracy
  • blood glucose
  • continuous glucose monitoring
  • error grid
  • glucose trend
  • surveillance

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