DOI 10.1515/cclm-2013-0356Clin Chem Lab Med 2013; 51(11): e279e281

Letter to the Editor

Bjrn J. Bolann*

Easy verification of clinical chemistry reference intervalsKeywords: accreditation; clinical chemistry tests; refer-ence values.

*Corresponding author: Bjrn J. Bolann, Laboratory of Clinical Biochemistry, Haukeland University Hospital, Bergen, Norway, E-mail: bjorn.bolann@med.uib.no; Department of Clinical Science, University of Bergen, Bergen, Norway

To the Editor,

Reference intervals for clinical chemistry analyses are generally defined as the central 95% interval of results found in healthy persons [1]. According to an interna-tional standard for accreditation of clinical laborato-ries, clinical chemistry laboratories should review their reference intervals regularly [2]. Changes may occur in the reference population, in the specimen handling, in the analytical methods or in analytical quality, and as a result, the reference interval may no longer be repre-sentative of the population for which it is used.

To establish new reference intervals can be compre-hensive and cumbersome work. Therefore, a less demand-ing way of verification is desirable. To a clinical chemistry laboratory, one resource is immediately at hand: patient data in the laboratory information system.

Many attempts have been made to calculate reference intervals from patient data, the obstacle being the problem of separating pathological values from the normals that can be assumed to represent healthy persons. By study-ing patient journals or other laboratory results, patients with conditions that can be expected to give pathological results, may be found and excluded [3].

Assuming that the values from the healthy and sick patients represent different statistical distributions, various mathematical solutions have been proposed to separate them. In 1963, Hoffman published a simple way of separating two overlapping normal distributions, and he used this technique to calculate reference values for fasting blood glucose from patients specimens [4]. This

approach requires that the majority of test results come from non-diseased individuals, i.e., are representative of healthy persons, and that these test results are normally distributed or can be transformed to a normal distribution. Other mathematical solutions have also been suggested [58]. However, such approaches may be complicated and labour-intensive, they may not be accurate enough to establish reference values with sufficient confidence, and to my knowledge, they have not come into widespread use. The aim of this study was to see if Hoffmans algorithm [4] can be used as a simple and rapid tool to check and verify established reference values.

Laboratory test results from outpatients were col-lected from the laboratory information system in Labo-ratory of Clinical Biochemistry, Haukeland University Hospital, Bergen. In outpatients the great majority of results can be expected to be normal. The identity of the patients was not recorded, therefore the study was exempt from approval by the Regional Commit-tee for Medical and Health Research Ethics, but was approved by The Norwegian Data Protection Authority. Results for serum creatinine and lactate dehydrogenase from October 2012 through March 2013 were collected. Only patients that matched age and gender criteria for the respective reference intervals were included. For patients with more than one result, only the first result was used.

The data collected were plotted in a normal probabil-ity graph as shown by Hoffman [4], checked for normal distribution, and if necessary, log transformed to attain a normal distribution. Instead of attempting to define new reference limits from the plot, the laboratorys present ref-erence limits were entered into the graphs to be compared to the collected values. By this approach it can be seen if the laboratorys present reference limits fit the actual population.

Figure 1A shows the results for serum creatinine for male individuals above 16 years of age. The left part of the curve is nearly a straight line, indicating a normal dis-tribution, and can be assumed to represent healthy indi-viduals. The grey line, representing our present reference

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e280Bolann: Easy verification of clinical chemistry reference intervals

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Figure 1Normal quantile plots showing patients results (+) for (A) serum creatinine (n=3131), and (B) log serum lactate dehydrogenase (n=1172), compared to the present corresponding age and gender specific reference interval (solid grey lines).

Table 1Conditions where the method can be used and not used.

Conditions Examples

Can be used All of the following:Analytes with two-sided reference limits. Electrolytes, most enzymes, creatinine, cholesterol,

haematological parameters, many immunological assaysA great majority of normal results.Normal results have (or can be transformed to) an approximately normal distribution.

Cannot be used At least one of the following:Analytes with one-sided reference limits. CRP, folate, tumour markersA high fraction of pathological results. HbA1c, -glutamyltransferaseMany normal results below quantification limit. Some trace elementsTherapeutic limits are used instead of reference limits. Prothrombin time, drugs

interval at 60105 mmol/L, is close to this curve, meaning that our reference interval seems to fit well to the popu-lation for which it is used. At serum creatinine above about 100, the curve of patient data deviates, indicating a different distribution representing the sick population. Figure 1B shows the results for serum lactate dehydroge-nase (LD) after log transformation, for both sexes aged 1069 years. The patient data do not overlap the line of the reference interval (105205 U/L) at any point, showing that the reference interval does not match the correspond-ing healthy population, i.e., the present reference interval is not correct.

In this way the normal probability plot of patients results can be used as a simple check of the present ref-erence interval. If the curves overlap or lie close together (Figure 1A), the reference interval can be assumed to be correct. If, however, there is no overlap between the present reference interval and the distribution of assumed healthy patients values (Figure 1B), then the reference interval is incorrect, and one may get a rough estimate of the magnitude of the error.

The data must be selected from patients where the majority can be supposed to be non-diseased with respect to the component in question, e.g., patients from primary health care. For components where a major part of the results can be expected to be pathological (e.g., haemoglobin A1c or prothrombin time), the approach described here should not be used. Conditions where the method can be used and not used are summarised in Table 1.

For most components it is a fast and easy way to perform the required regular review of established refer-ence limits.

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Bolann: Easy verification of clinical chemistry reference intervalse281

Conflict of interest statement

Authors conflict of interest disclosure: The author stated that there are no conflicts of interest regarding the publi-cation of this article.Research funding: None declared.

Employment or leadership: None declared.Honorarium: None declared.

Received May 14, 2013; accepted June 6, 2013; previously published online June 27, 2013

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