Reference limits of plasma and serum creatinine concentrations from intra-laboratory data bases of several German and Italian medical centres: Comparison between direct and indirect procedures.
BACKGROUND
The current dogma of establishing intra-laboratory reference limits (RLs) and their periodical reviewing cannot be fulfilled by most laboratories due to the expenses involved. Thus, most laboratories adopt external sources for their RLs often neglecting the problems of transferability. Therefore, several attempts were undertaken to derive RLs from the large data pools stored in modern laboratory information systems. These attempts were further developed to a more sophisticated indirect procedure. The new model can be considered a combined approach because it pre-excludes some subjects by direct criteria. In the current study, the new concept was applied to estimate RLs for serum and plasma creatinine from several German and Italian laboratories.


METHODS
A smoothed kernel density function was estimated for the distribution of the total mixed data of the sample group (combined data of non-diseased and diseased subjects). It was assumed that the "central" part of the distribution of all data represents the non-diseased ("healthy") population. The central part was defined by truncation points using an optimisation method, and was used to estimate a Gaussian distribution of the values of presumably non-diseased subjects after Box-Cox transformation of the empirical data. This distribution was now considered as the distribution of the non-diseased subgroup. The percentiles of this parametrical distribution were calculated to obtain RLs.


RESULTS
RLs determined by the indirect combined decomposition technique led to similar RLs as the classical direct method. Furthermore, the RLs obtained from 14 laboratories in 2 different European regions reflected the well-known differences of various analytical procedures. Stratification for gender and age was necessary. With rising age, an increase of the upper RL and of the reference range was observed. Hospitalization appeared also to affect the RLs. The new approach led to RLs in an artificially mixed population of diseased and non-diseased subjects (selected by clinical criteria) which were identical to RLs determined by a direct method applied to the non-diseased subgroup.


CONCLUSIONS
The proposed strategy of combining exclusion criteria with a resolution technique led to plausible retrospective RLs from intra-laboratory data pools for creatinine. Differences between laboratories were mainly due to the well-known bias of the different analytical procedures.
Arzideh
Farhad
F
Institut für Statistik, Universität Bremen, Bremen, Germany.
Wosniok
Werner
W
Haeckel
Rainer
R
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001774:Blood Chemical Analysis / Q000379:methods* / Q000592:standards; D003404:Creatinine / Q000097:blood*; D016208:Databases, Factual; D005260:Female; D005858:Germany; D006801:Humans; D007558:Italy; D007753:Laboratories; D008297:Male; D008875:Middle Aged; D012016:Reference Values; D012737:Sex Factors; D055815:Young Adult
D003404:Creatinine