A database with reliable information to derive definitive analytical quality specifications for a large number of clinical laboratory tests was prepared in this work. This was achieved by comparing and correlating descriptive data and relevant observations with the biological variation information, an approach that had not been used in the previous efforts of this type. The material compiled in the database was obtained from published articles referenced in BIOS, CURRENT CONTENTS, EMBASE and MEDLINE using "biological variation & laboratory medicine" as key words, as well as books and doctoral theses provided by their authors. The database covers 316 quantities and reviews 191 articles, fewer than 10 of which had to be rejected. The within- and between-subject coefficients of variation and the subsequent desirable quality specifications for precision, bias and total error for all the quantities accepted are presented. Sex-related stratification of results was justified for only four quantities and, in these cases, quality specifications were derived from the group with lower within-subject variation. For certain quantities, biological variation in pathological states was higher than in the healthy state. In these cases, quality specifications were derived only from the healthy population (most stringent). Several quantities (particularly hormones) have been treated in very few articles and the results found are highly discrepant. Therefore, professionals in laboratory medicine should be strongly encouraged to study the quantities for which results are discrepant, the 90 quantities described in only one paper and the numerous quantities that have not been the subject of study.

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.

The RCV concept is an approach that can be offered by laboratories to assess changes in serial results. The RCV data in this study are presented as a point of departure for a widely applicable objective guide to interpret changes in serial results.

To determine the influence of biological variation on the reliability of data from different types of urine specimens, we measured nine analytes in first-morning, randomly collected, and 24-h samples of urine from 53 healthy individuals (14 men and 39 women). The urines were collected once a week for 10 weeks. The data obtained were used as a basis for specimen collection and to gain insight into the influence of urine quantities in the diagnosis, screening, and monitoring of patients. We found that 24-h urine expressed in output rather than concentration units is the most reliable specimen for diagnosis and monitoring for most of the analytes studied. On the basis of the ratio between estimated within- and between-subject variation, the tests with greatest medical usefulness for diagnosis and screening of specific pathologies are those measuring protein and sodium. Moreover, the results indicate that urine creatinine may be a poor test for diagnosis, monitoring, and screening.

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