title: Computing the surveillance error grid analysis: procedure and examples.
abstract: 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.
authors.affiliation: University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA boris@virginia.edu.
authors.forename: Boris P
authors.initials: BP
authors.lastname: Kovatchev
authors.affiliation: University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA.
authors.forename: Christian A
authors.initials: CA
authors.lastname: Wakeman
authors.affiliation: University of Virginia, Center for Diabetes Technology, Charlottesville, VA, USA.
authors.forename: Marc D
authors.initials: MD
authors.lastname: Breton
authors.affiliation: University of California, Davis, Point-of-Care Testing Center for Teaching and Research, Davis, CA, USA.
authors.forename: Gerald J
authors.initials: GJ
authors.lastname: Kost
authors.affiliation: University of California, Davis, Point-of-Care Testing Center for Teaching and Research, Davis, CA, USA.
authors.forename: Richard F
authors.initials: RF
authors.lastname: Louie
authors.affiliation: University of California, Davis, Point-of-Care Testing Center for Teaching and Research, Davis, CA, USA.
authors.forename: Nam K
authors.initials: NK
authors.lastname: Tran
authors.affiliation: Mills-Peninsula Health Services, San Mateo, CA, USA.
authors.forename: David C
authors.initials: DC
authors.lastname: Klonoff
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis; D015190:Blood Glucose Self-Monitoring / Q000706:statistics & numerical data*; D003920:Diabetes Mellitus / Q000097:blood*; D006801:Humans; D006943:Hyperglycemia / Q000097:blood / Q000453:epidemiology; D007003:Hypoglycemia / Q000097:blood / Q000453:epidemiology; D011934:Reagent Strips; D012016:Reference Values; D018570:Risk Assessment; D012984:Software
chemical_list: D001786:Blood Glucose; D011934:Reagent Strips
keywords: blood glucose monitoring; error grid analysis; hyperglycemia; hypoglycemia; meter errors