Computing the surveillance error grid analysis: Procedure and examples

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

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


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. 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. 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. 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.

Original languageEnglish (US)
Pages (from-to)673-684
Number of pages12
JournalJournal of diabetes science and technology
Issue number4
StatePublished - 2014


  • Blood glucose monitoring
  • Error grid analysis
  • Hyperglycemia
  • Hypoglycemia
  • Meter errors

ASJC Scopus subject areas

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


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