Computing the Surveillance Error Grid Analysis

Author:

Kovatchev Boris P.1,Wakeman Christian A.1,Breton Marc D.1,Kost Gerald J.2,Louie Richard F.2,Tran Nam K.2,Klonoff David C.3

Affiliation:

1. University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA

2. University of California, Davis, Point-of-Care Testing Center for Teaching and Research, Davis, CA, USA

3. Mills-Peninsula Health Services, San Mateo, CA, USA

Abstract

Introduction: The surveillance error grid (SEG) analysis is a tool for analysis and visualization of blood glucose monitoring (BGM) errors, based on the opinions of 206 diabetes clinicians who rated 4 distinct treatment scenarios. Resulting from this large-scale inquiry is a matrix of 337 561 risk ratings, 1 for each pair of (reference, BGM) readings ranging from 20 to 580 mg/dl. The computation of the SEG is therefore complex and in need of automation. Methods: The SEG software introduced in this article automates the task of assigning a degree of risk to each data point for a set of measured and reference blood glucose values so that the data can be distributed into 8 risk zones. The software’s 2 main purposes are to (1) distribute a set of BG Monitor data into 8 risk zones ranging from none to extreme and (2) present the data in a color coded display to promote visualization. Besides aggregating the data into 8 zones corresponding to levels of risk, the SEG computes the number and percentage of data pairs in each zone and the number/percentage of data pairs above/below the diagonal line in each zone, which are associated with BGM errors creating risks for hypo- or hyperglycemia, respectively. Results: To illustrate the action of the SEG software we first present computer-simulated data stratified along error levels defined by ISO 15197:2013. This allows the SEG to be linked to this established standard. Further illustration of the SEG procedure is done with a series of previously published data, which reflect the performance of BGM devices and test strips under various environmental conditions. Conclusions: We conclude that the SEG software is a useful addition to the SEG analysis presented in this journal, developed to assess the magnitude of clinical risk from analytically inaccurate data in a variety of high-impact situations such as intensive care and disaster settings.

Publisher

SAGE Publications

Subject

Biomedical Engineering,Bioengineering,Endocrinology, Diabetes and Metabolism,Internal Medicine

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