The Diabetes Technology Society Error Grid and Trend Accuracy Matrix for Glucose Monitors
Authors: Klonoff David C. , Freckmann Guido , Pleus Stefan , Kovatchev Boris P. , Kerr David , Tse Chui (Cindy) , Li Chengdong , Agus Michael S. D. , Dungan Kathleen , Voglová Hagerf Barbora , Krouwer Jan S. , Lee Wei-An (Andy) , Misra Shivani , Rhee Sang Youl , Sabharwal Ashutosh , Seley Jane Jeffrie , Shah Viral N. , Tran Nam K. , Waki Kayo , Worth Chris , Tian Tiffany , Aaron Rachel E. , Rutledge Keetan , Ho Cindy N. , Ayers Alessandra T. , Adler Amanda , Ahn David T. , Aktürk Halis Kaan , Al-Sofiani Mohammed E. , Bailey Timothy S. , Baker Matt , Bally Lia , Bannuru Raveendhara R. , Bauer Elizabeth M. , Bee Yong Mong , Blanchette Julia E. , Cengiz Eda , Chase James Geoffrey , Y. Chen Kong , Cherñavvsky Daniel , Clements Mark , Cote Gerard L. , Dhatariya Ketan K. , Drincic Andjela , Ejskjaer Niels , Espinoza Juan , Fabris Chiara , Fleming G. Alexander , Gabbay Monica A. L. , Galindo Rodolfo J. , Gómez-Medina Ana María , Heinemann Lutz , Hermanns Norbert , Hoang Thanh , Hussain Sufyan , Jacobs Peter G. , Jendle Johan , Joshi Shashank R. , Koliwad Suneil K. , Lal Rayhan A. , Leiter Lawrence A. , Lind Marcus , Mader Julia K. , Maran Alberto , Masharani Umesh , Mathioudakis Nestoras , McShane Michael , Mehta Chhavi , Moon Sun-Joon , Nichols James H. , O’Neal David N. , Pasquel Francisco J. , Peters Anne L. , Pfützner Andreas , Pop-Busui Rodica , Ranjitkar Pratistha , Rhee Connie M. , Sacks David B. , Schmidt Signe , Schwaighofer Simon M. , Sheng Bin , Simonson Gregg D. , Sode Koji , Spanakis Elias K. , Spartano Nicole L. , Umpierrez Guillermo E. , Vareth Maryam , Vesper Hubert W. , Wang Jing , Wright Eugene , Wu Alan H.B. , Yeshiwas Sewagegn , Zilbermint Mihail , Kohn Michael A. 
Published: October 2024

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.