Background Interpretation of the complete blood count (CBC) parameters requires reliable biological variation (BV) data. The aims of this study were to appraise the quality of publications reporting BV data for CBC parameters by applying the BV Data Critical Appraisal Checklist (BIVAC) and to deliver global BV estimates based on BIVAC compliant studies. Methods Relevant publications were identified by a systematic literature search and evaluated for their compliance with the 14 BIVAC criteria, scored as A, B, C or D, indicating decreasing compliance. Global CVI and CVG estimates with 95% CI were delivered by a meta-analysis approach using data from BIVAC compliant papers (grades A–C). Results In total, 32 studies were identified; four received a BIVAC grade A, 2 B, 20 C and 6 D. Meta-analysis derived CVI and CVG estimates were generally lower or in line with those published in a historical BV database available online. Except for reticulocytes, CVI estimates of erythrocyte related parameters were below 3%, whereas platelet (except MPV and PDW) and leukocyte related parameters ranged from 5% to 15%. Conclusions A systematic review of CBC parameters has provided updated, global estimates of CVI and CVG that will be included in the newly published European Federation of Clinical Chemistry and Laboratory Medicine BV Database.

BackgroundCardiac 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.MethodsThirty-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).ResultscTnI 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.ConclusionsThis 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.

Biological variation (BV) has multiple applications in a variety of fields of clinical laboratory. The use of BV in statistical modeling is twofold. On the one hand, some models are used for the generation of BV estimates (within- and between-subject variability). Other models are built based on BV in combination with other factors to establish ranges of normality that will help the clinician interpret serial results for the same subject. There are two types of statistical models for the calculation of BV estimates: A. Direct methods, prospective studies designed to calculate BV estimates; i. Classic model: developed by Harris and Fraser, revised by the Working Group on Biological Variation of the European Federation of Laboratory Medicine. ii. Mixed-effect models. iii. Bayesian model. B. Indirect methods, retrospective studies to derive BV estimates from large databases of results. Big data. Understanding the characteristics of these models is crucial as they determine their applicability in different settings and populations. Models for defining ranges that help in the interpretation of individual serial results include: A. Reference change value and B. Bayesian data network. In summary, this review provides an overview of the models used to define BV components and others for the follow-up of patients. These models should be exploited in the future to personalize and improve the information provided by the clinical laboratory and get the best of the resources available.