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The surveillance error grid.

David C Klonoff1, Courtney Lias2, Robert Vigersky3

  • 1Mills-Peninsula Health Services, San Mateo, CA, USA dklonoff@diabetestechnology.org.

Journal of Diabetes Science and Technology
|January 7, 2015
PubMed
Summary
This summary is machine-generated.

A new Surveillance Error Grid (SEG) offers a modern approach to assess blood glucose monitor accuracy. It provides more precise risk quantification for improved clinical decision-making and regulatory surveillance.

Keywords:
accuracyblood glucoseerror gridmonitorsurveillance

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Area of Science:

  • Medical Devices and Technology
  • Diabetes Management and Monitoring
  • Clinical Risk Assessment

Background:

  • Current error grids for blood glucose (BG) monitors are based on outdated practices and not widely adopted by regulatory agencies.
  • Existing tools like the Clarke Error Grid (CEG) and Parkes Error Grid (PEG) have limitations in assessing clinical risk for BG monitor errors.
  • There is a need for a modern, standardized tool for evaluating the clinical accuracy and performance surveillance of BG monitors.

Purpose of the Study:

  • To introduce and evaluate the Surveillance Error Grid (SEG), a novel tool developed by a multi-stakeholder panel to assess clinical risk associated with BG monitor inaccuracies.
  • To compare the performance and risk stratification capabilities of the SEG against the established CEG and PEG using modeled data.
  • To provide a more granular and precise method for quantifying clinical risk from BG monitor errors for regulatory and manufacturer surveillance.

Main Methods:

  • A new error grid, the SEG, was developed with input from diabetes clinicians, regulatory bodies (FDA), and professional organizations.
  • 206 diabetes clinicians were surveyed regarding the clinical risk of BG measurement errors across four patient scenarios.
  • Modeled BG monitor data, reflecting contemporary meter accuracy and realistic errors, were plotted on SEG, CEG, and PEG for comparative analysis of risk estimates.

Main Results:

  • The SEG demonstrated consistent action boundaries across various patient types and clinician responses, indicating robust applicability.
  • Compared to CEG and PEG, the SEG provided more granular risk estimates and a continuously increasing risk scale, especially for low-risk errors.
  • The SEG classifies data points into 15 risk zones, offering greater precision in quantifying clinical risk than traditional error grids.

Conclusions:

  • The Surveillance Error Grid (SEG) is a modern, precise metric for assessing the clinical accuracy of BG monitors and quantifying associated risks.
  • The SEG's granular risk assessment and continuous scale offer advantages over CEG and PEG, particularly for surveillance programs.
  • This tool is valuable for regulators and manufacturers to monitor and evaluate BG monitor performance effectively.