Computing the surveillance error grid analysis: Procedure and examples

Journal of Diabetes Science and Technology

Volumn 8, Issue 4, 2014, Pages 673-684

  Kovatchev, Boris P ^a   Wakeman, Christian A ^a   Breton, Marc D ^a   Kost, Gerald J ^b   Louie, Richard F ^b   Tran, Nam K ^b   Klonoff, David C ^c  

^a UNIVERSITY OF VIRGINIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c Mills Peninsula Health Services   (United States)

Author keywords

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

Indexed keywords

GLUCOSE BLOOD LEVEL; TEST STRIP;

ALGORITHM; ANALYSIS; BLOOD; BLOOD GLUCOSE MONITORING; COMPUTER PROGRAM; DIABETES MELLITUS; GLUCOSE BLOOD LEVEL; HUMAN; HYPERGLYCEMIA; HYPOGLYCEMIA; REFERENCE VALUE; RISK ASSESSMENT; STATISTICS AND NUMERICAL DATA; TEST STRIP;

ALGORITHMS; BLOOD GLUCOSE; BLOOD GLUCOSE SELF-MONITORING; DIABETES MELLITUS; HUMANS; HYPERGLYCEMIA; HYPOGLYCEMIA; REAGENT STRIPS; REFERENCE VALUES; RISK ASSESSMENT; SOFTWARE;

EID: 84905923754     PISSN: None     EISSN: 19322968     Source Type: Journal    
DOI: 10.1177/1932296814539590     Document Type: Article

References (16)

1. Klonoff DC, Lias C, Vigersky R, et al. The surveillance error grid. J Diabetes Sci Technol. 2014. doi: 10.1177/1932296814539589.
2. Clarke WL, Cox D, Gonder-Frederick LA, Carter W, Pohl SL. Evaluating the clinical accuracy of self-blood glucose monitoring systems. Diabetes Care. 1987;10:622-628.
3. Parkes JL, Slatin SL, Pardo S, Ginsberg BH. A new consensus error grid to evaluate the clinical significance of inaccuracies in the measurement of blood glucose. Diabetes Care. 2000;23:1143-1148.
4. Freckmann G, Schmid C, Baumstark A, Pleus S, Link M, Haug C. System accuracy evaluation of 43 blood glucose monitoring systems for self-monitoring of blood glucose according to DIN EN ISO 15197. J Diabetes Sci Technol. 2012;6:1060-1075.
5. Breton MD, Kovatchev BP. 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. J Diabetes Sci Technol. 2010;4:562-570.
6. Clarke WL, Cox DJ, Gonder-Frederick LA, Julian DM, Kovatchev BP, Young-Hyman D. The bio-psycho-behavioral model of severe hypoglycemia II: self-management behaviors. Diabetes Care. 1999;22:580-584.
7. Kost GJ, Tran NK, Abad VJ, Louie RF. Evaluation of point-of-care glucose testing accuracy using locally-smoothed median absolute difference curves. Clinica Chimica Acta. 2008;389:31-39.
8. Kost GJ, Tran NK, Louie RF, Gentile NL, Abad VJ. Assessing the performance of handheld glucose testing for critical care. Diabetes Technol Ther. 2008;10:445-451.
9. Kost GJ, Tran NK, Singh H. Mapping point-of-care performance using locally-smoothed median and maximum absolute difference curves. Clin Chem Lab Med. 2011;49:1637-1646.
10. Truong AT, Louie RF, Vy JH, et al. Effects of humidity on foil and vial packaging to preserve glucose and lactate test strips for disaster readiness. Disaster Med Public Health Prep. 2014;8:51-57.
11. Lam M, Louie RF, Curtis CM, et al. Short-term thermalhumidity shock affects point-of-care glucose testing: implications for health professionals and patients. J Diabetes Sci Technol. 2014;8:83-88.
12. Louie RF, Ferguson WJ, Sumner SL, Yu JN, Curtis CM, Kost GJ. Effects of dynamic temperature and humidity stresses on point-of-care glucose testing for disaster care. Disaster Med Public Health Prep. 2012;6:232-240.
13. International Organization for Standardization. Requirements for blood glucose monitoring systems for self-testing in managing diabetes mellitus. 15197:2003.
14. Boyd JC, Bruns DE. Quality specifications for glucose meters: assessment by simulation modeling of errors in insulin dose. Clin Chem. 2001;4:209-214.
15. Boyd JC, Bruns DE. Monte Carlo simulation in establishing analytical quality requirements for clinical laboratory tests: meeting clinical needs. Methods Enzymol. 2009;467: 411-432.
16. Karon BS, Boyd JC, Klee GG. Glucose meter performance criteria for tight glycemic control estimated by simulation modeling. Clin Chem. 2010;56:1091-1097.