Abstract
Background: Allowable total error is derived in many ways, often from data on biological variation in normal individuals. We present a new principle for evaluating allowable total error: What are the diagnostic consequences of allowable total errors in terms of errors in likelihood ratio (LR)? Glycated hemoglobin A1c in blood (HbA1c) in diagnosing diabetes mellitus is used as an example. Allowable total error for HbA1c is 3.0% derived from data on biological variation compared to 6.0% as defined by National Glycohemoglobin Standardization Program (NGSP).
Methods: We estimated a function for LR of HbA1c in diagnosing diabetes mellitus using logistic regression with a clinical database (n=572) where diabetes status was defined by WHO criteria. Then we estimated errors in LR that correspond to errors in the measurement of HbA1c.
Results: Measuring HbA1c 3% too low at HbA1c of 6.5 percentage points (the suggested diagnostic limit) gives a LR of 0.36 times the correct LR, while measuring HbA1c 3% too high gives a LR of 2.77 times the correct LR. The corresponding errors in LR for allowable total error of 6% are 0.13 and 7.69 times the correct LR, respectively.
Conclusions: These principles of evaluating allowable total error can be applied to any diagnostically used analyte where the distribution of the analyte’s concentration is known in patients with and without the disease in a clinically relevant population. In the example used, the allowable total error of 6% leads to very erroneous LRs, suggesting that the NGSP limits of ±6% are too liberal.
Acknowledgments
We thank Frode Width Gran for valuable assistance with data extraction.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Financial support: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
1. Grimes DA, Schulz KF. Refining clinical diagnosis with likelihood ratios. Lancet 2005;365:1500–5.10.1016/S0140-6736(05)66422-7Search in Google Scholar
2. Fraser CG. Biological variation: from principles to practice. Washington, DC: AACC Press, 2001.Search in Google Scholar
3. Klee GG. Establishment of outcome-related analytic performance goals. Clin Chem 2010;56:714–22.10.1373/clinchem.2009.133660Search in Google Scholar PubMed
4. 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;57:205–14.10.1373/clinchem.2010.148841Search in Google Scholar PubMed
5. Gillett MJ. International Expert Committee report on the role of the A1c assay in the diagnosis of diabetes. Diabetes Care 2009;32:1327–34. Clin Biochem Rev 2009;30:197–200.10.2337/dc09-9033Search in Google Scholar PubMed PubMed Central
6. World Health Organization. Use of glycated haemoglobin (HbA1c) in the diagnosis of diabetes mellitus. Abbreviated report of a WHO Consultation. Geneva: World Health Organization, 2011:6–9.Search in Google Scholar
7. National Glycohemoglobin Standardization Program. NGSP News. Available from: http://www.ngsp.org/news.asp. Accessed 18 September, 2014.Search in Google Scholar
8. Westgard QC. Quality requirements. Available from: http://www.westgard.com/biodatabase1.htm. Accessed 18 September, 2014.Search in Google Scholar
9. Rohlfing CL, Parvin CA, Sacks DB, Little RR. Comparing analytic performance criteria: evaluation of HbA1c certification criteria as an example. Clin Chim Acta 2014;433:259–63.10.1016/j.cca.2014.03.034Search in Google Scholar PubMed PubMed Central
10. World Health Organization. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia. Report of a WHO/IDF consultation. Geneva: World Health Organization, 2006:3.Search in Google Scholar
11. Martensson A, Rustad P, Lund H, Ossowicki H. Creatininium reference intervals for corrected methods. Scand J Clin Lab Invest 2004;64:439–41.10.1080/00365510410002832Search in Google Scholar PubMed
12. NOKLUS. Available from: http://www.noklus.no/English.aspx. Accessed 18 September, 2014.Search in Google Scholar
13. Labquality. Available from: http://www.labquality.fi/?lang=en. Accessed 18 September, 2014.Search in Google Scholar
14. Albert A. On the use and computation of likelihood ratios in clinical chemistry. Clin Chem 1982;28:1113–9.10.1093/clinchem/28.5.1113Search in Google Scholar
15. Royston P, Sauerbrei W. Building multivariable regression models with continuous covariates in clinical epidemiology – with an emphasis on fractional polynomials. Methods Inf Med 2005;44:561–71.10.1055/s-0038-1634008Search in Google Scholar
16. Oosterhuis WP. Gross overestimation of total allowable error based on biological variation. Clin Chem 2011;57:1334–6.10.1373/clinchem.2011.165308Search in Google Scholar PubMed
17. Bolann BJ, Asberg A. Analytical quality goals derived from the total deviation from patients’ homeostatic set points, with a margin for analytical errors. Scand J Clin Lab Invest 2004;64:443–50.10.1080/00365510410001167Search in Google Scholar PubMed
18. Weykamp CW, Mosca A, Gillery P, Panteghini M. The analytical goals for hemoglobin A(1c) measurement in IFCC units and National Glycohemoglobin Standardization Program units are different. Clin Chem 2011;57:1204–6.10.1373/clinchem.2011.162719Search in Google Scholar PubMed
19. World Health Organization. Definition, diagnosis and classification of diabetes mellitus and its complications. Report of a WHO consultation. Geneva: World Health Organization, 1999:52.Search in Google Scholar
20. Petersen PH, Jorgensen LG, Brandslund I, De Fine Olivarius N, Stahl M. Consequences of bias and imprecision in measurements of glucose and HbA1c for the diagnosis and prognosis of diabetes mellitus. Scand J Clin Lab Invest Suppl 2005;240:51–60.10.1080/00365510500236135Search in Google Scholar PubMed
21. Tankova T, Chakarova N, Dakovska L, Atanassova I. Assessment of HbA1c as a diagnostic tool in diabetes and prediabetes. Acta Diabetol 2012;49:371–8.10.1007/s00592-011-0334-5Search in Google Scholar PubMed
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