Biological variation database: structure and criteria used for generation and update.

BACKGROUND: Numerical data on the components of biological variation (BV) have many uses in laboratory medicine, including in the setting of analytical quality specifications, generation of reference change values and assessment of the utility of conventional reference values. METHODS: Generation of a series of up-to-date comprehensive database of components of BV was initiated in 1997, integrating the more relevant information found in publications concerning BV. A scoring system was designed to evaluate the robustness of the data included. The database has been updated every 2 years, made available on the Internet and derived analytical quality specifications for imprecision, bias and total allowable error included in the tabulation of data. RESULTS AND CONCLUSIONS: Our aim here is to document, in detail, the methodology we used to evaluate the reliability of the included data compiled from the published literature. To date, our approach has not been explicitly documented, although the principles have been presented at many symposia, courses and conferences.

Harmonization in hemolysis detection and prevention. A working group of the Catalonian Health Institute (ICS) experience.

BACKGROUND: Hemolysis is the main cause of non-quality samples in clinical laboratories, producing the highest percentage of rejections in the external assurance programs of preanalytical quality. The objective was to: 1) study the agreement between the detection methods and quantification of hemolysis; 2) establish comparable hemolysis interference limits for a series of tests and analytical methods; and 3) study the preanalytical variables which most influence hemolysis production. METHODS: Different hemoglobin concentration standards were prepared using the reference method. Agreement was studied between automated methods [hemolytic indexes (HI)] and reference method, as well as the interference according to hemolysis degree in various biochemical tests was measured. Preanalytical variables which could influence hemolysis production were studied: type of extraction, type of tubes, transport time, temperature and centrifugation conditions. RESULTS: Good agreement was obtained between hemoglobin concentrations measured using the reference method and HI, for the most of studied analyzers, particularly those giving quantitative HI. The hemolysis interference cut-off points obtained for the majority of tests studied (except LDH, K) are dependent on the method/analyzer utilized. Furthermore, discrepancies have been observed between interference limits recommended by the manufacturer. The preanalytical variables which produce a lower percentage of hemolysis rejections were: centrifugation at the extraction site, the use of lower volume tubes and a transport time under 15 min at room temperature. CONCLUSIONS: The setting of interference limits (cut-off) for each used test/method, and the study of preanalytical variability will assist to the results harmonization for this quality indicator.

Within-subject biological variation in disease: collated data and clinical consequences.

Quantitative data on the components of biological variation (BV) are used for several purposes, including calculating the reference change value (RCV) required for the assessment of the significance of changes in serial results in an individual. Pathology may modify the set point in diseased patients and, more importantly, the variation around that set-point. Our aim was to collate all published BV data in situations other than health. We report the within-subject coefficient of variation (CV(I)) for 66 quantities in 34 disease states. We compared the results with the CV(I) determined in healthy individuals and examined whether the data derived in specific diseases could be useful for clinical applications. For the majority of quantities studied, CV(I) values are of the same order in disease and health: thus the use of RCV derived from healthy subjects for monitoring patients would be reasonable. However, for a small number of quantities considered to be disease specific markers, the CV(I) differed from those in health. This could mean that RCV derived from healthy CV(I) may be inappropriate for monitoring patients in certain diseases. Hence, disease-specific RCVs may be clinically useful.

Analytical quality specifications for common reference intervals.

Interpretation of laboratory test results requires comparison to some type of reference value or reference interval. These comparisons can be cross-sectional (population-based reference interval and cut-off values) or longitudinal (reference change value). Quality specifications for cross-sectional comparison have been established by determining the influence of analytical bias and imprecision on the percentage of the healthy population falling outside the reference limits, when sharing population-based reference intervals in a Gaussian distribution of results. Quality specifications for longitudinal comparisons are equally important and are often overlooked, since less work has been done in this area. Some criteria suggest that a difference between consecutive results designates a true change in a patient health status when the difference is higher than the within-subject biological variation plus the within-laboratory analytical variation. In this chapter we discuss the clinical considerations and laboratory-related factors that must be considered when quality specifications are applied to sharing reference comparisons. Real life experience shows that different analytical methods can produce comparable results when common quality goals are established, and quality can be achieved through a willingness to work together. Within the existing organization, the current specifications for analytical quality and a dedication to quality health care makes it possible to achieve transferability between laboratories within a geographic area.