Conventional Population-based Reference Values Research Articles

Biological variation remains relevant

Generation of numerical estimates of the within- and between-subject components of random biological variation has been studied for over four decades. These estimates are applied to set analytical performance goals for imprecision, bias and total analytical error, examine the utility of conventional population-based reference values through investigation of individuality, and assess the significance of changes in serial results from an individual using reference change values. The number of publications still appearing on newly- and well-studied analytes shows the ongoing interest in biological variation. Moreover, the classic works continue to be well cited. New concepts appear regularly. Applications of numerical data are facilitated by the availability of a database ( www.westgard.com/biodatabase-2014-update.htm ) that is updated every two years. However, there have been criticisms over recent times regarding the validity of these data, particularly the heterogeneity of estimates of the components of variation published for a single analyte. This has led to recent publication of the methodology used to generate the database and a plea for harmonisation and standardisation of terms and symbols. A Working Group of EFLM aims to improve the performance of studies by dissemination of a check-list that will stimulate better execution and documentation. Biological variation does remain relevant.

Laboratuvar sonuçlarının değerlendirilmesinde analitlerin biyolojik varyasyon verilerinin önemi

Clinical laboratories should minimize all the sources of variations (preanalytical, analitical and biological variations) and manage them during the entire process leading to the laboratory report. Biological variation data of analytes has been widely used in for many purposes, such as evaluating the significance of changes in serial results which is known as reference change values. Conventional population based reference values do have serious intrinsic problems and thus, there is a need for revisiting practical applications of these values for getting more useful laboratory data. This paper explains the magnitude of within- and between subject biological variation of analytes and details the reference change value applications of biological variation data.

Clinical and preclinical validation of the serum free light chain assay: identification of the critical difference for optimized clinical use

The use of the assay for the measurements of free light chains in serum (sFLCs) is increasing. However, there are technical limitations that potentially affect the use in serial measurements. We need further knowledge on the standards of analytical precision, the utility of conventional population-based reference values and the critical difference (CD) between serial results required for significance. To answer these questions, the biological variation must be known. We determined the biological variation in healthy individuals and patients with plasma cell dyscrasia (PCD). We assessed the imprecision of the analysis in use from FreeLite™. We determined the reference interval (RI) in 170 healthy individuals. The biological variation is identical for healthy individuals and patients with PCD. The imprecision of the sFLC analysis cannot fulfil the desirable performance standards for a laboratory test, but are within the manufacturer's ±20% variation for quality control samples. RI showed a significant increase for κ FLC and κ/λ ratio with age, but not for λ. Critical difference was calculated to be 24% and 23% for κ and λ, respectively. We suggest the use of an age-dependent RI. When monitoring patients with PCD, their own former results are the best reference, and knowledge on CD is a valuable tool, which we describe for the first time. Also, it challenges the recently proposed International Myeloma Working Group 'paraprotein relapse criteria', recommending an increase of more than 25% in the involved FLC to indicate the need for initiation of retreatment. We recommend revision of this criterion.

Using Biological Variation Data for Reference Change Values in Clinical Laboratories

Biological Variation (BV) of quantities examined in laboratory medicine can be described as of three types, namely; variation over the span of life, predictable cyclical variation that can be daily, monthly or seasonal in nature, and random variation [1]. Random variations of analytes which consist of random fluctuation around the setting point of each individual is known as the intra-individual biological variation. Additionally, each person’s setting point may be different from another’s, and the overall variation resulting from this difference is known as inter-individual biological variation. In mathematical terms, these are usually expressed as Coefficients Of Variation (CV) and termed as CVw for intra-individual BV and CVg for inter-individual BV [2]. In laboratory medicine, it is essential to take the BV concept into consideration to provide reliable results. Thus, clinical laboratories should minimize all the sources of variations related laboratory processes, estimate the components of BV and appropriately manage them during the entire process leading to the laboratory report. A comprehensive database constituted from BV data of nearly 320 analytes which is updated every 2 years serves as a useful reference for many clinical laboratories [3,4]. Nonetheless, there are still hundreds of constituents for which BV has not yet been estimated. Future work should focus on these specific tests. Clinicians use several approaches in the interpretation of routine laboratory tests of the patients. These include comparison with predetermined cut-off values or reference values, or a comparison between two sequential results for a specific analyte [5]. Comparison between two sequential results is not as straightforward as it seems. It should be remembered that each result has its own inherent random variation associated with laboratory activity (analytical variation, CVa) and Biological Variation (BV) [6]. It has become clear that conventional population based reference values do have serious intrinsic problems and thus, there is a need for revisiting practical applications of conventional population based reference values for getting more useful laboratory data [7]. A major problem with reference values is that biological quantities are not constants that can be measured once in one reference sample group to provide reference values that are applicable in all situations. Knowledge of the underlying BV of analytes examined in laboratories is vital to understanding the proper generation and application of traditional population based reference values. Using the BV data is the best way to detect changes in a patient’s health status through a comparison between serial analytical results rather than comparison with population based reference values. This is because of the marked individuality of the majority of analytes. Hence, values obtained in consecutive analyses of samples from a patient may fall within the reference range, but show a considerable difference. When the difference exceeds a certain value, known as the Reference Change Value (RCV), a change in the patient’s condition is indicated [7,8].

Inherent biological variation and reference values.

There is a need for revisiting theoretical concepts and practical applications of conventional population-based reference values to make for better clinical use of laboratory data. Knowledge of the underlying biological variation of quantities examined in medical laboratories is vital to understanding the proper generation and application of traditional population-based reference values. Appreciation of the biological changes that occur over the span of life is a necessary prerequisite to deciding whether stratification of reference values according to age is likely to be necessary. Knowledge of the detail of predictable biological cyclical rhythms is required for correct clinical interpretation of laboratory data and appropriate collection of specimens at times relevant to the clinical purpose. Quantitative data on inherent within- and between-subject biological components of variation have shown the marked individuality of most quantities of interest in laboratory medicine. This individuality casts light on why examinations are not generally very successfully applied in population screening or case-finding. Consideration of individuality demonstrates why stratification of reference values is often very advantageous. Individuality provides an indisputable argument for better use of individual specific reference values.

Biological variation data are necessary prerequisites for objective autoverification of clinical laboratory data

The wealth of quantitative data on random biological variation has been used for setting quality specifications, assessing the utility of conventional reference values, and deciding of the significance of changes in serial laboratory results. Most analytes have marked individuality and this makes conventional population-based reference values of low utility. In consequence, reference limits are not ideal for autoverification strategies. Clinical decision limits may be better criteria for holding results for verification by laboratory professionals. Changes in serial results are significant only when the reference change value is exceeded. Such values can be generated by all laboratories and can be implemented, not only to flag reports, but also in delta checking and autoverification since these are objective rather than empirical. We have put these considerations into operation into our laboratory. Apart from special cases, our general approach is that results flagged as having changed 0.05<P<0.01, flagged as just outside the reference limits, or not flagged in any way, are autoverified and reported to the user without intervention. Only results outside pre-set clinical limits and those that have changed highly significantly P<0.01 are held for verification by clinical scientists and medical staff. This strategy allows autoverification of ca. 60% of reports.

The index of individuality is often a misinterpreted quantity characteristic.

The concept of the "index of individuality" was introduced by Eugene Harris in 1974. The index of individuality, calculated as (CV(A)2 + CV(I)2)(1/2)/CV(G), where CV(A), CV(I), and CV(G) are analytical, within-subject, and between-subject coefficients of variation respectively, has been used by many to investigate the utility of conventional population-based reference values. For a high index of individuality, > 1.4, it has been said that reference intervals will be more useful than for a low index, < 0.6. The validity of these concepts is investigated here and a number of our findings are at odds with the generally held opinion. The index of individuality has no impact on the fraction of individuals classified using population-based reference values, as long as the change in concentration from the usual state is of the same absolute magnitude and one sample is assayed to detect disease. However, when a measurement falling outside a reference limit is repeated in order to verify the finding, the index of individuality has considerable influence. For quantities with very low indices, the repeat test result, will be close to the first and give no new information, whereas for quantities with high indices, a repeat test will decrease the number of true positives and false positives.

Biological variation of acute phase proteins.

The analytical, within-subject and between-subject components of variation were estimated for serum albumin, transthyretin, alpha 1-acid glycoprotein, alpha 1-antichymotrypsin, haptoglobin, beta 2-microglobulin and C-reactive protein in a cohort of 19 apparently healthy subjects over 20 weeks. Desirable analytical goals based on biological variation should be able to be met except for serum albumin and beta 2-microglobulin for which methodological improvement is warranted. All proteins showed marked individuality which casts doubt on the utility of conventional population-based reference values as interpretative criteria. The critical differences required for significance of changes in serial results differ markedly from protein to protein and the data presented allow generation of objective criteria for monitoring individuals.

Biologic variation of common hematologic laboratory quantities in the elderly.

Analytic, within-subject, and between-subject biologic variations were estimated for leukocytes, erythrocytes, hemoglobin, hematocrit, mean cell volume (MCV), mean cell hemoglobin (MCH), mean cell hemoglobin content (MCHC), platelets, and a three-component differential count (lymphocytes, monocytes, and granulocytes in terms of both concentration and percentage of leukocytes) in cohorts of 12 male and 12 female healthy elderly subjects. The assays were performed with an Ortho ELT-800 automated analyzer. The estimates of within-subject biologic variation were similar to published data on young subjects, indicating that this aspect of homeostasis is not compromised in the elderly. The data were used to derive objective analytic goals; goals were surpassed except for assays of erythrocytes, hematocrit, and the derived MCV, MCH, and MCHC. The changes required for serial results to be significantly different were determined and found to be generally valid because most quantities have no heterogeneity of within-subject variation. All quantities had significant individuality; in consequence, conventional population-based reference values are of limited utility, and screening using reference limits will not detect latent or early disease in many subjects.

Biological variability of 26 clinical chemistry analytes in elderly people.

Analytical, within-subject, and between-subject components of variation were estimated for 26 clinical chemistry analytes from duplicate analyses of 10 specimens collected from 27 healthy elderly subjects over a period of 20 weeks. Within-subject variations were similar to those generated previously by us in younger subjects. We conclude, therefore, that homeostasis is not compromised by age alone, and biological variability does not increase simply with age. All analytes except serum water had marked individuality, showing that conventional population-based reference values are of limited utility. The critical differences required for two results to be significantly (P less than or equal to 0.05) changed are not the same as those that prompt action by clinicians. Although heterogeneity of within-subject variation does exist, we believe that the critical differences generated will be useful in routine clinical decision making.

Generation and application of data on biological variation in clinical chemistry.

Most clinical chemical analytes vary in a random manner around a homeostatic set point. Replicate analyses of a series of specimens collected from a group of subjects allows estimation of analytical, within and between subject components of variation. The preferred experimental procedures and statistical methods for evaluation of data and analysis of variance are described; a detailed example is provided in the Appendix. The many uses of data on biological variation in clinical chemistry are reviewed, including setting analytical goals, deciding the significance of changes in serial results from an individual, evaluating the utility of conventional population-based reference values in patient management, and other applications.

Variability of Capillary Plasma Glucose in Healthy Individuals in Repeated 75 g Oral Glucose Tolerance Tests

A series of ten 75 g oral glucose tolerance tests was carried out on each of 14 apparently healthy subjects. The analytical, intra-individual and interindividual components of variation were calculated by nested ANOVA following duplicate analyses of capillary plasma glucose under optimal conditions. The analyses met the objectively set analytical goal of CV less than or equal to 2.2%. There was considerable intra-individual variation, but no subject was ever classified as diabetic or having impaired glucose tolerance. Recent criteria for interpretation of results must be used since conventional population-based reference values are not adequate. Serial results from an individual must differ by more than 0.7 mmol/L for fasting specimens and at least 1.6 mmol/L for other specimens for a statistically significant (P less than or equal to 0.05) change to have occurred.

Desirable performance characteristics and clinical utility of immunoglobulin and light-chain assays derived from data on biological variation.

Using automated assays of IgG, IgA, and IgM, and kappa (kappa) and lambda (lambda) light-chains, we assessed the analytical, intra-individual, and interindividual components of variation in a cohort of 12 apparently healthy subjects to examine the utility of the Kallestad Immunochemical Evaluation system. The immunoglobulins and light-chains had small intra-individual variation, and, in consequence, good analytical precisions (CV) of 2.3%, 2.5%, 3.0%, 2.4%, and 2.4% are required for IgG, IgA, IgM, kappa, and lambda assays, respectively. All of these analytes, and the derived kappa/lambda and heavy-/light-chain ratios, have marked individuality. Conventional population-based reference values are of limited utility; therefore, the assays are unlikely to have high diagnostic sensitivity but will be very useful for monitoring individuals. The kappa/lambda ratio may be of particular value in monitoring because a change exceeding 4% does imply a significant (P less than or equal to 0.05) difference in serial results.

Assay of serum fructosamine that minimizes standardization and matrix problems: use to assess components of biological variation.

Methodological problems with the assay of fructosamine in serum--standardization, matrix effects, and dependence on buffer pH--have been minimized with a method involving colorimetric assay of each specimen and subsequent re-assay after standard addition of 1-deoxy-1-morpholinofructose. Absorbance at optimum wavelength of 540 nm varies linearly with fructosamine concentration to at least 5.5 mmol/L, and between-run precision is about 6% for both patients' specimens and quality control materials. Correction of fructosamine to serum albumin of 40 g/L minimizes the effect of albumin while maintaining transferability of data and reference values. From data on biological variation, the analytical goal for precision (CV) is less than or equal to 2.6%. The square root of the ratio of intra- to interindividual variance is low, indicating that fructosamine concentrations have a high index of individuality; thus conventional population-based reference values are of limited use. Although this assay may be useful in monitoring disease, we doubt that it provides a valid screening test for diabetes.