Each of 102 Nordic routine clinical biochemistry laboratories collected blood samples from at least 25 healthy reference individuals evenly distributed for gender and age, and analysed 25 of the most commonly requested serum/plasma components from each reference individual. A reference material (control) consisting of a fresh frozen liquid pool of serum with values traceable to reference methods (used as the project "calibrator" for non-enzymes to correct reference values) was analysed together with other serum pool controls in the same series as the project samples. Analytical data, method data and data describing the reference individuals were submitted to a central database for evaluation and calculation of reference intervals intended for common use in the Nordic countries. In parallel to the main project, measurements of commonly requested haematology properties on EDTA samples were also carried out, mainly by laboratories in Finland and Sweden. Aliquots from reference samples were submitted to storage in a central bio-bank for future establishment of reference intervals for other properties. The 25 components were, in alphabetical order: alanine transaminase, albumin, alkaline phosphatase, amylase, amylase pancreatic, aspartate transaminase, bilirubins, calcium, carbamide, cholesterol, creatine kinase, creatininium, gamma-glutamyltransferase, glucose, HDL-cholesterol, iron, iron binding capacity, lactate dehydrogenase, magnesium, phosphate, potassium, protein, sodium, triglyceride and urate.

Background: The aim of this study was to develop new methods for partitioning biochemical reference data, covering in particular nongaussian distributions. Methods: We recently proposed partitioning criteria for gaussian distributions. These criteria relate to proportions of the subgroups outside each of the reference limits of the combined distribution (proportion criteria) and to distances between the subgroup distributions as correlates of these proportions (distance criteria). However, distance criteria do not seem to be ideal for nongaussian distributions because a generally valid relationship between proportions and distances cannot be established for these. Results: Proportion criteria appear preferable to distance criteria for two additional reasons: (a) The prevalences of the subgroup populations may have a considerable effect on stratification, but these are hard to account for by using distance criteria. Two methods to handle prevalences are described, the root method and the multiplication method. (b) Tied reference values, another complication of the partitioning problem, could also be hard to take care of using distance criteria. Some solutions to the problems caused by tied reference values are suggested. Conclusions: Partitioning of biochemical reference data should preferably be based on proportion criteria; this is

Background: The aim of this study was to develop new and useful criteria for partitioning reference values into subgroups applicable to gaussian distributions and to distributions that can be transformed to gaussian distributions. Methods: The proposed criteria relate to percentages of the subgroups outside each of the reference limits of the combined distribution. Critical values suggested as partitioning criteria for these percentages were derived from analytical bias quality specifications for using common reference intervals throughout a geographic area. As alternative partitioning criteria to the actual percentages, these were transformed mathematically to critical distances between the reference limits of the subgroup distributions, to be applied to each pair of reference limits, the upper and the lower, at a time. The new criteria were tested using data on various plasma proteins collected from ∼500 reference individuals, and the outcomes were compared with those given by the currently widely applied and recommended partitioning model of Harris and Boyd, the “Harris-Boyd model”. Results: We suggest 4.1% as the critical minimum percentage outside that would justify partitioning into subgroups, and 3.2% as the critical maximum percentage outside that would justify combining them. Percentages between these two values should be classified as marginal, implying that nonstatistical considerations are required to make the final decision on partitioning. The correlation between the critical percentages and the critical distances was mathematically precise in the new model, whereas this correlation is rather approximate in the Harris-Boyd model because focus on the difference between means in this model makes high precision hard to achieve. The application examples suggested that the new model is more radical than the Harris-Boyd model. Conclusions: New percentage and distance criteria, to be used for partitioning gaussian-distributed data, have been developed. The distance criteria, applied separately to both reference limit pairs of the subgroup distributions, seemed more reliable and correlated more accurately with the critical percentages than the distance criteria of the Harris-Boyd model. As opposed to the Harris-Boyd model, the new model is easily adjustable to new critical values of the percentages, should they need to be changed in the future.

Methods:We studied the specificity of Horn's test algorithm (probability of false detection of outliers), using Monte Carlo computer simulations performed on 13 types of probability distributions covering a wide range of positive and negative skewness. Distributions with 3% of the original observations replaced by random outliers were used to also examine the sensitivity of the test (probability of detection of true outliers). Three data transformations were used: the Box and Cox function (used in the original Horn's test), the Manly exponential function, and the John and Draper modulus function. Results: For many of the probability distributions, the specificity of Horn's algorithm was rather poor compared with the theoretical expectation. The cause for such poor performance was at least partially related to remaining nongaussian kurtosis (peakedness). The sensitivity showed great variation, dependent on both the type of underlying distribution and the location of the outliers (upper and/or lower tail). Conclusion: Although Horn's algorithm undoubtedly is an improvement compared with older methods for out-

In the Nordic Reference Interval Project (NORIP), data from 102 Nordic clinical chemical laboratories were obtained. Each laboratory reported analytical data on up to 25 of the most commonly used clinical biochemical properties, including results from each of a minimum of 25 reference individuals. A reference material consisting of a liquid frozen pool of serum with values traceable to reference methods (used as the project "calibrator" for non-enzymes to correct reference values) was measured together with other serum pool controls in each laboratory in the same analytical series as the project samples. The data on the controls were used to evaluate the analytical quality of the routine methods. For reference interval calculations, only such reference values on enzymes were accepted that were obtained by applying the International Federation of Clinical Chemistry (IFCC) compatible methods (37 degrees C), while "calibrator"-corrected reference values were used in the cases of non-enzymes. For each property, gender- and age-specific reference intervals were estimated, based on simple non-parametric calculations and using objective criteria to perform partitioning into subgroups. It is concluded that the same reference intervals are applicable in all five Nordic countries. The following descriptive data for the considered properties are presented in the tables: number of measurement values from each country and measurement system, certified/indicative target values for controls, differences between methods and measurement systems together with coefficients of variation, effects of control correction on the measurement values, differences between subgroups as determined by age, gender, country and material, and comparison of the new reference intervals with those presented in standard textbooks. The 25 components involved in this project were (listed in alphabetical order): Alanine transaminase, albumin, alkaline phosphatase, amylase, amylase pancreatic type, aspartate transaminase, bilirubin, calcium, carbamide, cholesterol, creatine kinase, creatininium, gamma-glutamyltransferase, glucose, HDL-cholesterol, iron, iron-binding capacity, lactate dehydrogenase, magnesium, phosphate, potassium, protein, sodium, triglyceride and urate.