Biological variation and reference change value of the estimated glomerular filtration rate in humans: A systematic review and meta-analysis

Background

Knowledge of the biological variation of serum or plasma creatinine (Cr) and the estimated glomerular filtration rate (eGFR) is important for understanding disease dynamics in Chronic Kidney Disease (CKD). The aim of our study was to determine the magnitude of random fluctuation of eGFR by determining its reference change value (RCV).

Methods

We performed a systematic review and meta-analysis of studies on biological variation of Cr. Relevant studies were identified by systematic literature search on PubMed. Additional studies were retrieved from the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database. Random-effects meta-analysis was conducted to derive an overall estimate of intra-individual variation of creatinine (CV_ICr). Based on our estimate of CV_ICr and RCV for Cr, the RCV for the eGFR was determined.

Results

Among identified studies, 37 met our inclusion criteria. Meta-analysis of all studies yielded a CV_ICr of 5.2% (95% confidence interval [CI] 4.6–5.8%), however high between-study heterogeneity (I² = 82.3%) was found. Exclusion of outliers led to a significant reduction of heterogeneity while still including 85% of all studies and resulted in a slightly lower CV_ICr of 5.0% (95% CI 4.7–5.4%). Assuming an analytical variation of CV_A 1.1%, we found an overall RCV for eGFR of ±16.5%. After exclusion of outlier studies, we found a minimum conservative RCV for eGFR of ±12.5%.

Conclusion

The RCV of the eGFR represents a valuable tool for clinicians to discern true changes in kidney function from random fluctuation.

An appraisal of statistical procedures used in derivation of reference intervals

When conducting studies to derive reference intervals (RIs), various statistical procedures are commonly applied at each step, from the planning stages to final computation of RIs. Determination of the necessary sample size is an important consideration, and evaluation of at least 400 individuals in each subgroup has been recommended to establish reliable common RIs in multicenter studies. Multiple regression analysis allows identification of the most important factors contributing to variation in test results, while accounting for possible confounding relationships among these factors. Of the various approaches proposed for judging the necessity of partitioning reference values, nested analysis of variance (ANOVA) is the likely method of choice owing to its ability to handle multiple groups and being able to adjust for multiple factors. Box-Cox power transformation often has been used to transform data to a Gaussian distribution for parametric computation of RIs. However, this transformation occasionally fails. Therefore, the non-parametric method based on determination of the 2.5 and 97.5 percentiles following sorting of the data, has been recommended for general use. The performance of the Box-Cox transformation can be improved by introducing an additional parameter representing the origin of transformation. In simulations, the confidence intervals (CIs) of reference limits (RLs) calculated by the parametric method were narrower than those calculated by the non-parametric approach. However, the margin of difference was rather small owing to additional variability in parametrically-determined RLs introduced by estimation of parameters for the Box-Cox transformation. The parametric calculation method may have an advantage over the non-parametric method in allowing identification and exclusion of extreme values during RI computation.

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.