Computing the Surveillance Error Grid Analysis

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.

[1]  Richard F Louie,et al.  Evaluation of point-of-care glucose testing accuracy using locally-smoothed median absolute difference curves. , 2008, Clinica chimica acta; international journal of clinical chemistry.

[2]  D. Cox,et al.  Biopsychobehavioral model of risk of severe hypoglycemia. Self-management behaviors. , 1999, Diabetes care.

[3]  Christina Schmid,et al.  System Accuracy Evaluation of 43 Blood Glucose Monitoring Systems for Self-Monitoring of Blood Glucose according to DIN EN ISO 15197 , 2012, Journal of diabetes science and technology.

[4]  Richard F Louie,et al.  Assessing the performance of handheld glucose testing for critical care. , 2008, Diabetes technology & therapeutics.

[5]  B H Ginsberg,et al.  A new consensus error grid to evaluate the clinical significance of inaccuracies in the measurement of blood glucose. , 2000, Diabetes care.

[6]  George G Klee,et al.  Glucose meter performance criteria for tight glycemic control estimated by simulation modeling. , 2010, Clinical chemistry.

[7]  D. Cox,et al.  Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose , 1987, Diabetes Care.

[8]  Boris Kovatchev,et al.  The Surveillance Error Grid , 2014, Journal of diabetes science and technology.

[9]  William J Ferguson,et al.  Effects of Humidity on Foil and Vial Packaging to Preserve Glucose and Lactate Test Strips for Disaster Readiness , 2014, Disaster Medicine and Public Health Preparedness.

[10]  William J Ferguson,et al.  Effects of Dynamic Temperature and Humidity Stresses on Point-of-Care Glucose Testing for Disaster Care , 2012, Disaster Medicine and Public Health Preparedness.

[11]  William J Ferguson,et al.  Short-Term Thermal-Humidity Shock Affects Point-of-Care Glucose Testing , 2014, Journal of diabetes science and technology.

[12]  J C Boyd,et al.  Quality specifications for glucose meters: assessment by simulation modeling of errors in insulin dose. , 2001, Clinical chemistry.

[13]  Marc D Breton,et al.  Impact of Blood Glucose Self-Monitoring Errors on Glucose Variability, Risk for Hypoglycemia, and Average Glucose Control in Type 1 Diabetes: An In Silico Study , 2010, Journal of diabetes science and technology.

[14]  Nam K. Tran,et al.  Mapping point-of-care performance using locally-smoothed median and maximum absolute difference curves , 2011, Clinical chemistry and laboratory medicine.

[15]  David E Bruns,et al.  Monte Carlo simulation in establishing analytical quality requirements for clinical laboratory tests meeting clinical needs. , 2009, Methods in enzymology.