•BV data for testosterone, FSH, prolactin, LH and DHEA-S in 38 men by CV-ANOVA is presented.•BV data of prolactin and DHEA-S in men is reported for the first time.•Low II suggests RCV is valuable for ...interpretation of an individual’s test results.•The APS for imprecision for the hormones varied between 4 and 11 %.•No hormone concentration differences between the five nationalities studied.
Knowledge of biological variation (BV) of hormones is essential for interpretation of laboratory tests and for diagnostics of endocrinological and reproductive diseases. There is a lack of robust BV data for many hormones in men.
We used serum samples collected weekly over 10 weeks from the European Biological Variation Study (EuBIVAS) to determine BV of testosterone, follicle-stimulating hormone (FSH), prolactin, luteinizing hormone (LH) and dehydroepiandrosterone sulfate (DHEA-S) in 38 men. We derived within-subject (CVI) and between-subject (CVG) BV estimates by CV-ANOVA after trend, outlier, and homogeneity analysis and calculated reference change values, index of individuality (II), and analytical performance specifications.
The CVI estimates were 10 % for testosterone, 8 % for FSH, 13 % for prolactin, 22 % for LH, and 9 % for DHEA-S, respectively. The IIs ranged between 0.14 for FSH to 0.66 for LH, indicating high individuality.
In this study, we have used samples from the highly powered EuBIVAS study to derive BV estimates for testosterone, FSH, prolactin, LH and DHEA-S in men. Our data confirm previously published BV estimates of testosterone, FSH and LH. For prolactin and DHEA-S BV data for men are reported for the first time.
Biological variation (BV) data can be used to set analytical performance specifications (APS) for lipid assays. Poor performance will impact upon the efficacy of international guidelines for ...cardiovascular risk assessment (CVR) and relevant clinical decision limits. This systematic review applies the Biological Variation Data Critical Appraisal Checklist (BIVAC) to published studies of BV of CVR biomarkers enabling metanalysis of the data.
Studies of BV of total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides and apolipoproteins A1 and B, retrieved using a systematic literature search, were evaluated and graded using the BIVAC. Meta-analysis of CVI and CVG estimates were performed utilizing weightings based upon BIVAC grades and the width of the data confidence intervals.
Applying the BIVAC, ten publications were graded as D, 43 as C, 5 as B and 1 as A (fully compliant). A total of 196 CVI and 87 CVG estimates were available for the different lipid measurands. The meta-analysis-derived BV data estimates were generally concordant with those in the online 2014 BV database.
Application of BIVAC identifies BV data suitable for many important applications including setting APS. Additionally, this review identifies a need for new BIVAC compliant studies to deliver BV reference data in different subpopulations.
•Reliable biological variation (BV) estimates are necessary for optimal diagnosis and monitoring of cardiovascular risk.•The Biological Variation Data Critical Appraisal Checklist has been applied to systematically evaluate BV studies for lipids•This study provides updated evidence-based estimates of CVI and CVG values for lipids from BIVAC-compliant studies, delivered by meta-analysis•Quality assessed BV data will in the future be made available in the EFLM Biological Variation Database.
Retrospective studies frequently assume analytes long-term stability at ultra-low temperatures. However, these storage conditions, common among biobanks and research, may increase the preanalytical ...variability, adding a potential uncertainty to the measurements. This study is aimed to evaluate long-term storage stability of different analytes at <-70 °C and to assess its impact on the reference change value formula.
Twenty-one analytes commonly measured in clinical laboratories were quantified in 60 serum samples. Samples were immediately aliquoted and frozen at <-70 °C, and reanalyzed after 11 ± 3.9 years of storage. A change in concentration after storage was considered relevant if the percent deviation from the baseline measurement was significant and higher than the analytical performance specifications.
Preanalytical variability (CV
) due to storage, determined by the percentage deviation, showed a noticeable dispersion. Changes were relevant for alanine aminotransferase, creatinine, glucose, magnesium, potassium, sodium, total bilirubin and urate. No significant differences were found in aspartate aminotransferase, calcium, carcinoembryonic antigen, cholesterol, C-reactive protein, direct bilirubin, free thryroxine, gamma-glutamyltransferase, lactate dehydrogenase, prostate-specific antigen, triglycerides, thyrotropin, and urea. As nonnegligible, CV
must remain included in reference change value formula, which was modified to consider whether one or two samples were frozen.
After long-term storage at ultra-low temperatures, there was a significant variation in some analytes that should be considered. We propose that reference change value formula should include the CV
when analyzing samples stored in these conditions.
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.
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.
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.
Background Cardiac troponins (cTn) are specific markers for cardiac damage and acute coronary syndromes. The availability of new high-sensitivity assays allows cTn detection in healthy people, thus ...permitting the estimation of biological variation (BV) of cTn. The knowledge of BV is important to define analytical performance specifications (APS) and reference change values (RCVs). The aim of this study was to estimate the within- and between-subject weekly BV (CVI, CVG) of cTnI applying two high-sensitivity cTnI assays, using European Biological Variation Study (EuBIVAS) specimens. Methods Thirty-eight men and 53 women underwent weekly fasting blood drawings for 10 consecutive weeks. Duplicate measurements were performed with Singulex Clarity (Singulex, USA) and Siemens Atellica (Siemens Healthineers, Germany). Results cTnI was measurable in 99.4% and 74.3% of the samples with Singulex and Atellica assays, respectively. Concentrations were significantly higher in men than in women with both methods. The CVI estimates with 95% confidence interval (CI) were for Singulex 16.6% (15.6-17.7) and for Atellica 13.8% (12.7-15.0), with the observed difference likely being caused by the different number of measurable samples. No significant CVI differences were observed between men and women. The CVG estimates for women were 40.3% and 36.3%, and for men 65.3% and 36.5% for Singulex and Atellica, respectively. The resulting APS and RCVs were similar for the two methods. Conclusions This is the first study able to estimate cTnI BV for such a large cohort of well-characterized healthy individuals deriving objective APS and RCV values for detecting significant variations in cTnI serial measurements, even within the 99th percentile.
Abstract
Objectives
The estimates of biological variation (BV) have traditionally been determined using direct methods, which present limitations. In response to this issue, two papers have been ...published addressing these limitations by employing indirect methods. Here, we present a new procedure, based on indirect methods that analyses data collected within a multicenter pilot study. Using this method, we obtain CV
I
estimates and calculate confidence intervals (CI), using the EFLM-BVD CV
I
estimates as gold standard for comparison.
Methods
Data were collected over a 18-month period for 7 measurands, from 3 Spanish hospitals; inclusion criteria: patients 18–75 years with more than two determinations. For each measurand, four different strategies were carried out based on the coefficient of variation ratio (rCoeV) and based on the use of the bootstrap method (OS1, RS2 and RS3). RS2 and RS3 use symmetry reference change value (RCV) to clean database.
Results
RS2 and RS3 had the best correlation for the CV
I
estimates with respect to EFLM-BVD. RS2 used the symmetric RCV value without eliminating outliers, while RS3 combined RCV and outliers. When using the rCoeV and OS1 strategies, an overestimation of the CV
I
value was obtained.
Conclusions
Our study presents a new strategy for obtaining robust CV
I
estimates using an indirect method together with the value of symmetric RCV to select the target population. The CV
I
estimates obtained show a good correlation with those published in the EFLM-BVD database. Furthermore, our strategy can resolve some of the limitations encountered when using direct methods such as calculating confidence intervals.
Testing for thyroid disease constitutes a high proportion of the workloads of clinical laboratories worldwide. The setting of analytical performance specifications (APS) for testing methods and ...aiding clinical interpretation of test results requires biological variation (BV) data. A critical review of published BV studies of thyroid disease related measurands has therefore been undertaken and meta-analysis applied to deliver robust BV estimates.
A systematic literature search was conducted for BV studies of thyroid related analytes. BV data from studies compliant with the Biological Variation Data Critical Appraisal Checklist (BIVAC) were subjected to meta-analysis. Global estimates of within subject variation (CV
) enabled determination of APS (imprecision and bias), indices of individuality, and indicative estimates of reference change values.
The systematic review identified 17 relevant BV studies. Only one study (EuBIVAS) achieved a BIVAC grade of A. Methodological and statistical issues were the reason for B and C scores. The meta-analysis derived CV
generally delivered lower APS for imprecision than the mean CV
of the studies included in this systematic review.
Systematic review and meta-analysis of studies of BV of thyroid disease biomarkers have enabled delivery of well characterized estimates of BV for some, but not all measurands. The newly derived APS for imprecision for both free thyroxine and triiodothyronine may be considered challenging. The high degree of individuality identified for thyroid related measurands reinforces the importance of RCVs. Generation of BV data applicable to multiple scenarios may require definition using "big data" instead of the demanding experimental approach.
An external quality control program distributes same control samples to various laboratories and evaluates results obtained with a common criterion. The aim of this work is to summarize the evolution ...of various types of external programs, to point out the progresses ant to preclude practical consequences of the participant laboratories.
The material consists on a brief revision of the different types of external programs that have been used for the last forty years. The method is the critical analysis of the strong and weak points of each program model, from the light of our experience. External quality assessment (EQA) programs were initiated at half the XX century, evidencing big discrepancies among laboratory results. EQA were developed in various countries and some mechanisms to harmonize them were proposed: to establish common performance specifications derived from biological variation, to use EQS as educational tool. Since the 2000 important advances were seen: to focus EQA to assure the adequate clinical use of laboratory tests, to use commutable controls, to harmonize the different EQA models, to promote a forum for co-operation and exchange of knowledge on quality-related matters for EQA organizers.
To participate in an EQA with commutable-reference method assigned values controls allows to know the real inaccuracy of results and their impact on patient' samples. To participate in a EQA with non commutable controls allows to know whether the individual laboratory performance agrees with that from other laboratories using same analytical method.
Abstract
Background
Hematological parameters have many applications in athletes, from monitoring health to uncovering blood doping. This study aimed to deliver biological variation (BV) estimates for ...9 hematological parameters by a Biological Variation Data Critical Appraisal Checklist (BIVAC) design in a population of recreational endurance athletes and to assess the effect of self-reported exercise and health-related variables on BV.
Methods
Samples were drawn from 30 triathletes monthly for 11 months and measured in duplicate for hematological measurands on an Advia 2120 analyzer (Siemens Healthineers). After outlier and homogeneity analysis, within-subject (CVI) and between-subject (CVG) BV estimates were delivered (CV-ANOVA and log-ANOVA, respectively) and a linear mixed model was applied to analyze the effect of exercise and other related variables on the BV estimates.
Results
CVI estimates ranged from 1.3% (95%CI, 1.2-1.4) for mean corpuscular volume to 23.8% (95%CI, 21.6-26.3) for reticulocytes. Sex differences were observed for platelets and OFF-score. The CVI estimates were higher than those reported for the general population based on meta-analysis of eligible studies in the European Biological Variation Database, but 95%CI overlapped, except for reticulocytes, 23.9% (95%CI, 21.6-26.5) and 9.7% (95%CI, 6.4-11.0), respectively. Factors related to exercise and athletes’ state of health did not appear to influence the BV estimates.
Conclusions
This is the first BIVAC-compliant study delivering BV estimates that can be applied to athlete populations performing high-level aerobic exercise. CVI estimates of most parameters were similar to the general population and were not influenced by exercise or athletes’ state of health.
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