Lab Med Online.  2017 Oct;7(4):182-188. 10.3343/lmo.2017.7.4.182.

Evaluation of Cobas b 101 HbA1c Analyzer Performance for Point-of-Care Testing

• Yu HJ
• Lim S
• Kwon MJ
• Woo HY
• Park H

• KMID: 2389658
• DOI: http://doi.org/10.3343/lmo.2017.7.4.182

Abstract

BACKGROUND
The use of point-of-care (POC) devices for evaluating HbA1c is increasing; accordingly, comparisons between these devices and central laboratory methods are important. In the present study, we evaluated the analytical performance of the cobas b 101 analyzer for POC HbA1c testing.
METHODS
The analytical quality of the cobas b 101 system was assessed based on repeatability, within-laboratory precision, linearity, and lot-to-lot reproducibility. Two specimen types, i.e., EDTA whole blood and capillary blood, were examined using the cobas b 101 system and the Variant II Turbo instrument.
RESULTS
The coefficient of variation for within laboratory precision was 5.22% for a normal HbA1c level and 2.56% for a higher HbA1c level. The method showed good linearity, with a coefficient of correlation of 0.990. In a comparison of two different HbA1c disk lots, a strong correlation (r=0.986) and a mean %difference of −2.9% were observed. The cobas b 101 results using EDTA whole blood were strongly correlated with the Variant II Turbo results (r=0.958), with a mean %difference of 0.8%; the cobas b 101 results using capillary blood were strongly correlated with the Variant II Turbo results, using EDTA whole blood (r=0.976), with a mean %difference of 2.0%. A comparison between HbA1c levels in EDTA whole blood and capillary blood obtained using the cobas b 101 showed a strong correlation (r=0.985) and a mean %difference of 1.3%.
CONCLUSIONS
The cobas b 101 analyzer is convenient for the measurement of HbA1c levels for diabetes management.

Keyword

HbA1c; Point-of-care testing; Diabetes

MeSH Terms

Capillaries
Edetic Acid
Methods
Point-of-Care Systems*
Point-of-Care Testing*
Edetic Acid

Figure

• 

  Fig. 1 Lot-to-lot reproducibility of the cobas b 101 HbA1c test. (A) Regression analysis of the cobas b 101 using EDTA venous blood from two different lots. The linear curve and 95% confidence interval [CI] are represented as red and blue lines, respectively. (B) Bland-Altman difference plot summarizing lot-to-lot comparisons.

• 

  Fig. 2 Comparison of the cobas b 101 with the Variant II Turbo. (A) Regression of the cobas b 101 results using EDTA blood against the Variant II Turbo results using EDTA blood. The linear curve and 95% confidence interval [CI] are represented as red and blue lines, respectively. (B) Bland-Altman difference plot summarizing the comparison between the cobas b 101 results using EDTA blood and Variant II Turbo results using EDTA blood. (C) Regression of the cobas b 101 results using capillary blood against the Variant II Turbo results using EDTA blood. The linear curve and 95% confidence interval [CI] are represented as red and blue lines, respectively. (D) Bland-Altman difference plot summarizing the comparison between the cobas b 101 results using capillary blood and Variant II Turbo results using EDTA blood.

• 

  Fig. 3 Regression analysis of the cobas b 101 using two types of samples, EDTA venous whole blood and capillary blood. (A) Regression analysis of the cobas b 101 using two types of samples, EDTA venous blood and capillary blood. The linear curve and 95% confidence interval [CI] are represented as red and blue lines, respectively. (B) Bland-Altman difference plot summarizing the comparison between the venous whole blood and capillary blood samples tested using the cobas b 101.

Reference

1. Gonen B, Rubenstein A, Rochman H, Tanega SP, Horwitz DL. Haemoglobin A1: An indicator of the metabolic control of diabetic patients. Lancet. 1977; 2:734–737.

2. Weykamp C. HbA1c: a review of analytical and clinical aspects. Ann Lab Med. 2013; 33:393–400.

3. Weykamp C, John WG, Mosca A. A review of the challenge in measuring hemoglobin A1c. J Diabetes Sci Technol. 2009; 3:439–445.

4. Cagliero E, Levina EV, Nathan DM. Immediate feedback of HbA1c levels improves glycemic control in type 1 and insulin-treated type 2 diabetic patients. Diabetes Care. 1999; 22:1785–1789.

5. Ferenczi A, Reddy K, Lorber DL. Effect of immediate hemoglobin A1c results on treatment decisions in office practice. Endocr Pract. 2001; 7:85–88.

6. Miller CD, Barnes CS, Phillips LS, Ziemer DC, Gallina DL, Cook CB, et al. Rapid A1c availability improves clinical decision-making in an urban primary care clinic. Diabetes Care. 2003; 26:1158–1163.

7. International Expert Committee. International Expert Committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care. 2009; 32:1327–1334.

8. Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993; 329:977–986.

9. UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet. 1998; 352:837–853.

10. Petersen JR, Omoruyi FO, Mohammad AA, Shea TJ, Okorodudu AO, Ju H. Hemoglobin A1c: assessment of three POC analyzers relative to a central laboratory method. Clin Chim Acta. 2010; 411:2062–2066.

11. Lee JE. Alternative biomarkers for assessing glycemic control in diabetes: fructosamine, glycated albumin, and 1,5-anhydroglucitol. Ann Pediatr Endocrinol Metab. 2015; 20:74–78.

12. Sanchez-Mora C, S Rodriquez-Oliva M, Fernandez-Riejos P, Mateo J, Polo-Padillo J, Goberna R, et al. Evaluation of two HbA1c point-of-care analyzers. Clin Chem Lab Med. 2011; 49:653–657.

13. Wan Mohd Zin RM, Ahmad Kamil ZI, Tuan Soh TR, Embong M, Wan Mohamud WN. Haemoglobin A1c: comparing performance of two point of care devices with laboratory analyser. BMC Res Notes. 2013; 6:540.

14. Lenters-Westra E, Slingerland RJ. Three of 7 hemoglobin A1c point-of-care instruments do not meet generally accepted analytical performance criteria. Clin Chem. 2014; 60:1062–1072.

15. Mayega RW, Guwatudde D, Makumbi FE, Nakwagala FN, Peterson S, Tomson G, et al. Comparison of fasting plasma glucose and haemoglobin A1c point-of-care tests in screening for diabetes and abnormal glucose regulation in a rural low income setting. Diabetes Res Clin Pract. 2014; 104:112–120.

16. Brown M, Kuhlman D, Larson L, Sloan K, Ablah E, Konda K, et al. Does availability of expanded point-of-care services improve outcomes for rural diabetic patients? Prim Care Diabetes. 2013; 7:129–134.

17. Clinical and Laboratory Standards Institute (CLSI). Evaluation of Precision of Quantitative Measurement Procedures; Approved Guideline-Third Edition. CLSI document EP05-A3. Wayne, PA: Clinical and Laboratory Standards Institute;2014.

18. Clinical and Laboratory Standards Institute. Evaluation of Linearity of Quantitative Measurement Procedures: A Statistical Approach; Approved Guideline. CLSI guideline EP06-A. Wayne, PA: Clinical and Laboratory Standards Institute;2003.

19. Clinical and Laboratory Standards Institute (CLSI). Measurement Procedure Comparison and Bias Estimation Using Patient Samples; Approved Guideline-Third Edition. CLSI document EP09-A3. Wayne, PA: Clinical and Laboratory Standards Institute;2013.

20. World Health Organization. Global report on diabetes. Geneva: World Health Organization;2016.

21. International Organization for Standardization. ISO 15197: Determination of performance criteria for in vitro blood glucose monitoring systems for management of human diabetes mellitus. Geneva: International Organization for Standardization;2002.

22. Lenters-Westra E, Slingerland RJ. Six of eight hemoglobin A1c point-of-care instruments do not meet the general accepted analytical performance criteria. Clin Chem. 2010; 56:44–52.

23. National Glycohemoglobin Standardization Program (NGSP). Harmonizing Hemoglobin A1c Testing. Updated on 29 Sep 2016. http://www.ngsp.org/docs/Protocol.pdf.

24. Little RR, Rohlfing CL, Sacks DB. Status of hemoglobin A1c measurement and goals for improvement: from chaos to order for improving diabetes care. Clin Chem. 2011; 57:205–214.

25. National Glycohemoglobin Standardization Program (NGSP). College of American Pathologists (CAP) GH5 Survey Data. Updated on Sep 29 2016. http://www.ngsp.org/CAP/CAP16b.pdf.

26. Shephard MD. Analytical goals for point-of-care testing used for diabetes management in Australian health care settings outside the laboratory. Point Care. 2006; 5:177–185.

27. American Diabetes Association. 6. Glycemic Targets. Diabetes Care. 2017; 40:S48–S56.