Biological variation (BV) data have many applications in laboratory medicine. However, these depend on the availability of relevant and robust BV data fit for purpose. BV data can be obtained through different study designs, both by experimental studies and studies utilizing previously analysed routine results derived from laboratory databases. The different BV applications include using BV data for setting analytical performance specifications, to calculate reference change values, to define the index of individuality and to establish personalized reference intervals. In this review, major achievements in the area of BV from last decade will be presented and discussed. These range from new models and approaches to derive BV data, the delivery of high-quality BV data by the highly powered European Biological Variation Study (EuBIVAS), the Biological Variation Data Critical Appraisal Checklist (BIVAC) and other standards for deriving and reporting BV data, the EFLM Biological Variation Database and new applications of BV data including personalized reference intervals and measurement uncertainty.
Biological variation (BV) describes the variation observed in the concentration or activity of different constituents in a person, reflecting the regulation by homeostatic processes in the body . In a steady state setting, the concentration of most measurands is characterized by random variation around a homeostatic set point, whereas the concentration of some measurands is also influenced by different life phases or predictable cyclic variation. The within-subject variation BV (CVI) denotes the variation of the concentration/activity of a measurand around a homeostatic set point within a single individual in steady state, whereas the between-subject BV (CVG) denotes the variation between the homeostatic set points of different individuals.
BV data have many different applications in laboratory medicine. A major use of BV data is for setting analytical performance specifications (APS) for imprecision, bias, total error and measurement uncertainty. These and other characteristics can be established on estimates of within- and between-subject BV, utilizing different formulae. The utility of the conventional population-based reference intervals can be assessed by the index of individuality (II), which usually is calculated as the ratio between CVI and CVG. Estimates of CVI and analytical imprecision can also be used to calculate reference change values (RCVs) to assess the probability that a difference between two consecutive results in an individual can be explained by analytical and within-subject biological variation . Also, a model for calculating personalized reference intervals (prRI) based on BV data has recently been published . This utilizes previous test results of a subject in a steady-state condition and estimates of the individual’s BV, derived either for the relevant population or for the individual.
Different sources of BV data have been available in the last decades . In the 1st European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Strategic Conference in 2014, it was, however, recognised that much of the available BV data were compromised because of uncertainty around the estimates or other factors affecting fitness for purpose. Thus, this limited the utility of these data for the many different BV applications. Furthermore, it highlighted the need for critical appraisal of existing BV data as well as for new studies to generate high-quality data . As a result of the Strategic Conference, different EFLM Working Groups and Task Groups set in motion initiatives to improve on the availability of quality-assessed BV data and their applications . In this review, an update on the achievements and developments driven by the EFLM Working Group for Biological Variation (WG-BV) , the EFLM Task Group for the Biological Variation Database (TG-BVD)  and other key players in the field of BV the last 10 years will be presented, with suggestions for future developments, focussing on the following:
New mathematical models to calculate BV estimates.
Delivery of high-quality BV data from the highly powered European Biological Variation Study (EuBIVAS).
The Biological Variation Data Critical Appraisal Checklist (BIVAC), a standard for evaluating BV publications.
The EFLM Biological Variation Database.
Standards for reporting of BV data and BV terminology.
New applications of BV data.
New mathematical models to calculate BV estimates
BV studies have historically been undertaken as prospective experimental studies. The methodology for this was first described by Fraser and Harris . In essence, a group of reference individuals is selected and sampled at regular time intervals, with a strictly controlled preanalytical phase and then analysed under standardized conditions. Duplicate analysis of the samples is recommended to estimate the CVA component directly. The resulting data are examined for clinical events, trends and statistical outliers as well as homogeneity. The CVA, CVI, and CVG estimates are thereafter derived by traditional statistical approaches such as e.g. ANOVA. More details about this study design and traditional calculations can be found in .
The last decade, three new approaches have been developed to estimate BV components. The two first models, CV-ANOVA  and the Bayesian approach , are based on data derived from experimental studies, in principle using the study design of Fraser and Harris  to estimate BV components. The third model uses retrospectively collected data, extracted from laboratory databases, for data mining studies , , .
The CV-ANOVA approach to calculate BV estimates
Different ANOVA methods have over time been used to derive BV data, historically mostly standard ANOVA and ln-ANOVA. The standard ANOVA, based on a nested ANOVA approach, is performed on the raw data, after exclusion of outliers, ensuring a steady state situation by trend analysis and assessment of variance homogeneity. The resulting within-subject standard deviation (SDI) estimate is divided by the total mean to derive the CVI estimate. In the ln-ANOVA method, data are first ln-transformed and the nested ANOVA is performed on the transformed results. The estimated components from the ANOVA are then transformed back and become CV values on the original scale. A new ANOVA-method, a CV-ANOVA method – the Røraas method – was published in 2016  and has since then been widely used. It is based on CV transformation with normalization/standardisation of each person’s data by dividing by that person’s mean value, and then performing ANOVA on these results, which provides estimates of analytical and within-subject variation in the form of CV values. However, this does not provide an estimate of the CVG, since each individual has a mean value of 1. Thus, by this approach, the CVG must be estimated by the standard or alternatively by the ln-ANOVA approach, if data are not normally distributed. These three different methods, standard ANOVA, ln-ANOVA and CV-ANOVA have been compared in computer simulations for different distributions of raw data , i.e. normal distribution, ln-normal distribution and mirrored ln-normal distribution. The CV-ANOVA method performed well for all types of distributions, as opposed to the other ANOVA methods.
The Bayesian approach of calculating BV estimates
An approach utilizing Bayesian statistics to derive BV data, based on the same experimental study design as that recommended by Fraser and Harris , was published by Røraas in 2019 . This Bayesian model disregards the assumption of normality and is more robust to extreme observations by using adaptive Student-t distributions instead of normal distributions. The advantage over traditional methods such as ANOVA is that laborious statistical operations, associated with possible subjectivity in data trimming to achieve homogeneity and exclusion of outliers, are not required. Furthermore, the Bayesian model delivers individual within-person BV estimates (CVP) that can be used to explore heterogeneity of data, asses if the data can be generalised for the whole population, assess relevant sub-groups or to identify individuals not belonging to the group. It is then also possible to assess correlations between the CVP and e.g. age or homeostatic set points and to calculate personalized reference intervals, prRI, directly, as described later. The model utilizes, if available, prior knowledge to make more precise inference from previous performed studies. This is particularly valuable if previous data on BV for the measurand, or related measurands, are available. Some studies that have applied both ANOVA and Bayes methods on the same data set have reported obtaining similar BV results with both methods, however, this may depend on distribution of the data [10, 12].
Using previously analysed data derived from laboratory information management systems to calculate BV estimates
This model uses a different approach for the generation of BV data, unlike the two previous models, as it utilizes already available data, analysed as part of routine follow-up of patients, held in laboratory databases. This is achieved by extracting results from patient cohorts from laboratory information management systems (LIMS) consisting of a large number of individuals, where two or more samples routinely have been analysed for the same measurand. Assessment of such data collected for diagnostic or monitoring purposes also provides an opportunity to assess sub-groups including different states of health, the effect of time between sampling, or other factors, without the efforts of prospectively collecting large data sets. For many analytes requested for a patient, the concentrations of the analytes are not impacted by non-relevant pathology and may represent values obtained for the healthy population. This “big data” or “data mining” approach is particularly useful if the measurand in question is not present in apparently healthy subjects (e.g. unusual proteins found in myeloma), if the concentration of the measurand is significantly different from what is found in healthy individuals (e.g. HbA1c in diabetes mellitus) or if it is unacceptable or unethical to collect specimens from individuals, for example children. Studies detailing utilizing data derived from laboratory databases for estimating e.g. CVI and RCVs for e.g. routine chemistry, endocrine and haematology tests have reported results more or less equivalent to those delivered by standard, prospective methods [11, 12, 14, 15]. However, most such studies do not report measures of uncertainty, which is a great limitation. However, one recently published study has now proposed how this can be done . Further work is required to identify the strengths and limitations of this retrospective, data mining approach to deliver BV data and to meet challenges in measurand data distributions (e.g. skewed data), and other aspects allowing to fine-tuning this approach further.
Delivery of high-quality BV estimates – the European Biological Variation Study (EuBIVAS)
The EFLM WG-BV decided in 2014 to design and establish the EuBIVA with the aim of providing updated high-quality BV estimates for many measurands, derived from a highly powered and rigorously executed BV study . Briefly, EuBIVAS involved six European laboratories (Milan, Italy; Bergen, Norway; Madrid, Spain; Padua, Italy; Istanbul, Turkey; Assen, The Netherlands). Following a detailed screening of enrolled participants, 91 healthy volunteers (38 males and 53 females; age range, 21–69 years) were included in the study. All involved laboratories followed the same protocol for the pre-analytical phase, with all participants compiling an enrolment questionnaire to verify their health status and to collect information regarding their lifestyle. Fasting blood samples were drawn for 10 consecutive weeks at each participating laboratory. The samples were stored at −80 °C until shipped on dry-ice and analysed in duplicate for a high number of measurands at the San Raffaele Hospital in Milan .
BV estimates for all participants, males, females, and if considered relevant for the specific measurands for female subgroups (above and below 50 years, in reflection of menopausal status), were for most EuBIVAS measurands estimated by the CV-ANOVA method . The EuBIVAS approach presents a number of benefits. Firstly, it is sufficiently powered to enable subgroup analysis. This has allowed gender specific data and for some measurands also menopausal age specific data , , , , , , ,  which until this point have been unavailable for many measurands. Secondly, the BV estimates have been obtained based on current best-practice recommendations for study design [25, 26]. EuBIVAS has so far delivered BV estimates for 81 different measurands; with data for two target measurands (cardiac troponin and serum creatinine) obtained using two different analytical methods [20, 27]. The results of most of the studies are summarized in . For 10 of these measurands, no previous BV studies had been carried out and further studies for measurands for where there as of yet are no available BV data, are also on the way.
For haematology measurands, given the requirement for fresh whole blood, the analytical approach used by EuBIVAS was not possible. Two different projects, in Italy , ,  and Turkey , have been carried out to deliver high-quality BV data for complete blood count, utilizing fresh samples. While the analytical approach necessarily varies from that employed within EuBIVAS, all other elements of these studies were similar. In addition to updating the BV estimates of complete blood count parameters currently in use, these studies also provided BV data for some parameters where this was lacking.
The EuBIVAS study might be described as an exemplar of the classical approach to delivery and reporting for BV data. It has delivered BV estimates for many measurands that are lower than those previously reported. This is probably due to tight control of pre-analytical factors, the use of modern examination methodology, and the critically important application of correct statistical approach to data handling with e.g. assessment of outliers, variance homogeneity and trend, according to best practice recommendations. The absence of clear differences between the subject cohorts from Turkey, Norway, The Netherlands, Spain, and Italy, confirms that the obtained data are transportable across health care systems indicating that they may be used to deliver APS for systems to be used internationally. It is reassuring that this consistency was also demonstrated using principal component analysis, an unsupervised machine learning approach . It should be noted that a possible disadvantage of the meticulously generated EuBIVAS data could be that they are “too good”, having been delivered under rigorously controlled conditions that do not reflect routine practice. The EuBIVAS estimates may thus be more useful for setting APS than for RCV calculations or to calculate personalized reference intervals. Instead, data mining from results in LIMS might deliver BV data that more accurately reflect the “real life” situation that can be used for e.g. RCV  and personalized reference intervals. Furthermore, there is still a great lack of studies on different age groups and states of health, even though high-quality studies for population subgroups such athletes and pregnant women have also been published in the last 10 years , , , , , . For many states of health, however, it may be that using the approach of extracting data from LIMS is a more pragmatic approach to delivery of required data sets, rather than highly powered experimental studies like the EuBIVAS.
The Biological Variation Data Critical Appraisal Checklist (BIVAC)
A high number of BV studies has been published in the last four decades. Though historical BV studies were typically designed according to that time’s standard, many do not to fulfil today’s standard for study design and execution, or they utilized analytical methods that are now considered obsolete. Thus, much of the historical BV data may be compromised with uncertainty or considered unfit for use today. Following the 1st Strategic Conference of EFLM defining APS in November 2014, the EFLM TG-BVD was established within the WG-BV, with the objective to appraise the quality of BV data that is publicly available. The result of this work was, among others, the Biological Variation Data Critical Appraisal Checklist (BIVAC), a standard for evaluating BV studies . The BIVAC is designed to assess the quality of BV publications by addressing essential elements that may impact upon veracity and utility of the BV estimates. It consists of 14 quality items and focuses on the effect of study design, the measurement procedure and statistical handling of data on BV estimates. The individual quality items can be awarded scores A, B, C and for some essential items, also D, indicating decreasing compliance with the checklist. The lowest score obtained for any quality item decides the overall grade. A BIVAC grade A indicates that the publication is fully compliant with all BIVAC quality items. If the lowest score for any quality item is a B, then the overall grade is a B and similarly C or D if the lowest score is a C or D, respectively. Studies receiving a D grade are not considered fit for purpose. Systematic reviews of BV studies for many different measurands have shown that the majority of historical studies receive a BIVAC grade C, mostly related to statistical items such as analysis of variance homogeneity and outliers [23, 40], , , , , , , , . Since the publication of BIVAC in 2017, however, an increasing number of BV studies fulfil the criteria for a high-quality study and are fully BIVAC compliant, as illustrated by the papers included in the Clinical Chemistry and Laboratory Medicine special issue on BV in 2022 [4, 49]. The BIVAC also provides, in combination with the Biological Variation Data Reporting Checklist , a framework that may help those planning BV studies both to perform and publish their study in an appropriate manner.
The EFLM Biological Variation Database
Historically, different sources of BV data have been available, also online. After the Strategic Conference, one of the main objectives of the WG-BV and TG-BVD was to establish a new database with quality-assessed BV data, available to users worldwide. The “EFLM Biological Variation Database” was launched during the EuroMedLab in Barcelona in May 2019 and is available at www.biologicalvariation.eu. To populate the database, systematic literature searches for BV studies for relevant measurands have been performed and identified publications appraised by the BIVAC. Both the BIVAC scores as well as a BV minimum data set, encompassing around 30 descriptive items are published in the database for all included measurands, thus offering a detailed and updated source of quality-assessed data. Also, a meta-analysis approach to pool estimates from BIVAC compliant studies with similar study design to provide global BV estimates was developed . In the EFLM Biological Variation Database, meta-analyses are automatically performed for studies of acceptable study design (BIVAC grade A–C), when performed in healthy adults with more than two samples collected per individual and with biweekly to monthly samplings. The meta-analysis report estimates for CVI and CVG with confidence intervals (CI), based on a weighted median approach where the study design as well as the BIVAC grade is taken into account . As of December 2022, global BV results derived by meta-analysis have been published for 139 measurands, as well as several thousand detailed BV data sets and more than 560 BV studies (Figure 1). In addition to presenting BV estimates for each evaluated paper and the summary BV estimates derived by meta-analysis, the database also provides automatic calculation of RCV and the most common APS used in laboratory practice. These applications are available when accessing the global meta-analysis BV estimates (Figure 1) .
In today’s database, global BV estimates are published based on data derived from healthy adults with biweekly to monthly sampling. However, data from studies performed in different states of health, other age groups and sampling intervals are also included in the database. In the future, meta-analyses will also be provided for these different settings and states of health. As of yet, BV data derived from data mining of retrospectively collected data have not been included in the meta-analysis, as these are, with one very recent exception , not accompanied by CI, which is one of the prerequisites to be included. Furthermore, a standard for assessing the quality of such kind of studies is lacking. Such a standard will be developed, prior to including these types of studies in the meta-analysis that provides global BV estimates in the database.
Reporting of BV data and BV terminology
The Biological Variation Data Reporting Checklist
There have historically been no internationally recognized standards for production, reporting and transmission of BV data. The WG-BV published in 2015 the Biological Variation Data Reporting Checklist , which identifies key elements required in published BV studies. The reporting checklist is based on the same structure as the STARD  and identifies six main items for focus with a number of sub items including 1) title/abstract/keywords, 2) introduction, 3) methods, 4) data analysis, 5) results and 6) discussion. The sub items have been additionally mapped to a minimum data set domains previously identified by the WG.
Studies complying with this checklist and the BIVAC can be considered fit for purpose, include essential statistical analyses such as outlier and variance homogeneity testing, use recommended terminology and report BV estimates accompanied by key metadata. This is essential as these BV data are reference data; they need to be applied with care, with understanding of their provenance and intrinsic characteristics if they are to be transported safely and effectively into clinical practice across health care systems.
The EFLM BV groups have developed further materials to support the delivery and reporting of studies. This work has initially focused on the “classical” prospective experimental approach to delivery of BV data. Access is available online in the form of an interactive mind map  and an overview of the structure is presented in Figure 2. The content includes embedded links and documents that can be used to assess the veracity of data from existing BV studies and to enable the design of new BV experiments. It reflects what is believed to be the current state of the art and is in a format that draws parallels with that of STARD to be used as a possible publication structure to guide researchers (Figure 2). This represents an initial iteration as there is a degree of complexity now arising in the subject area as a consequence of the emergence of new approaches to the generation of data sets as described in this review. However, through time the map can be adapted dynamically to address emergent developments, thus enabling assurance of quality of newer data sets. One of the challenges faced by those assessing (e.g. BIVAC scoring) publications of BV data is that key metadata required to enable the process are missing or poorly described. In practice, this means well executed studies deliver data that are devalued and non-translatable as a consequence of deficiencies in reporting. The mind map will enable users to check that existing and new studies are compliant with a proposed structure, reporting a considered minimum data set that includes the embedded BIVAC. Further development needs to include consideration of data mining approaches and differing data management.
Terminology for components of BV
Over years, a wide range of terms and symbols have been used to describe the components of BV in published literature. In 2015, a recommendation on harmonized use of terminology in the field of BV was published  and these definitions and abbreviations, as used in this review, are recommended the EFLM WG-BV and TG-BVD as well as by Clinical Chemistry and Laboratory Medicine . Additionally, in 2016, the EFLM WG-BV proposed and got accepted the establishment of a Medical Subject Heading (MeSH) term for “Biological Variation” in the National Library of Medicine. This MeSH term has been available from December 2017, thus facilitating systematic searches for BV publications for current publications.
New application of BV data
Reference change values and index of individuality
Most BV application have been published several decades ago, as reviewed in . This includes the use of the index of individuality, which is used to assess the value of using population-based reference interval or RCV for monitoring of an individual. The RCV enumerates the value that the difference between two test results in the same individual can be, with a certain probability, explained by analytical and within-subject biological variation. The RCV has earlier been calculated as a symmetrical value, whereas it should in fact be calculated with asymmetrical limits derived from a ln RCV method . In the EFLM Biological Variation Database , the published RCVs are based on the asymmetrical approach.
Analytical performance specifications
In the consensus document from the 1st Strategic Conference of the EFLM in 2014 it was agreed that APS can be established using three different models . Model 1 is based on the effect of analytical performance on clinical outcomes. Model 2 is based on components of BV and Model 3 is based on state of the art. In the Biological Variation Database, model 2 based APS utilizing the global BV estimates are automatically calculated for imprecision, bias, total error and measurement uncertainty, presented as minimum, desirable and optimal . These first three are presented in the database  with the pro and con for each  and are also described in detail elsewhere . In addition, APS for maximum allowable standard measurement uncertainty (MAu) is included. When using MAu bias should, in principle, be eliminated, and all the remaining sources of variation added linearly as variances. Accordingly, the MAu can be set as 0.5 × CVI, and the maximum expanded allowable measurement uncertainty (MAU) as k × 0.5 × CVI. The “k” is the coverage factor, for example, 2 or 3, to obtain a certain confidence level (95 or 99). The most used coverage factor is 2. Thus, MAU can be calculated as MAU<2 × 0.5 * CVI.
However, when calculating APS, we also must consider for which situations or scenario we are going to use them. Different APS will probably be relevant if these are applied in e.g. internal quality control, in External Quality Assurance (EQA) schemes, to evaluate lot to lot variations, in clinical guidelines. To set correct APS is an ongoing and difficult process. The Cutting Edge of Laboratory Medicine (CELME) conference, to be arranged in 2023 , will have the title “Analytical performance specifications: Moving from models to practical recommendations” and aims to take this area forward.
Personalized reference intervals
When using laboratory tests for the diagnosis and monitoring of patients, a reliable reference to which the results can be compared is required. Today, most reference data are derived from the population, derived either by experimental reference interval studies or by utilizing laboratory test results stored in LIMS. However, such reference data have limitations when used as the reference for an individual, especially for measurands with a low index of individuality. Thus, patients’ test results preferably should be compared with their own, individualized reference intervals, i.e. a personalized RI (prRI). Recently, a new model for calculating prRI has been developed, utilizing BV data [2, 56], , , , . The model is based on the homeostatic set point (HSP) and the total variation around the HSP (TVset) of the analytes. To derive the prRI, firstly the HSP of the measurand for the individual being assessed, must be estimated. This is achieved by calculating the mean of previous test results obtained in a steady state situation. Thereafter, the prRI can be constructed by prediction intervals in two ways: 1) using the person’s own within-person biological variation estimate (SDP) or 2) using the within-subject biological variation estimate (SDI) derived from a population similar to the person being monitored.
The prRI can thereafter be calculated as
where HSP is the homeostatic set point; k is a constant depending on the type of distribution (normal distribution (z) in case of population-based SDI and t distribution in case of SDP) and the probability, n is the number of previous test results and the SDB refers to either the SDP or the SDI and the SDA the analytical variation. To get a reliable estimate of the SDP, more than five previous test results from a steady state situation for an individual is required. This will often be a limitation, and it may therefore be easier to construct the prRI using the SDI. In this setting, at least three samples are required to estimate the HSP . The two models will give rather similar prRIs if the presuppositions of the models are fulfilled.
It is well known that clinicians use the limits of a population-based reference intervals to act on patients’ results, although these limits are not the same as clinical decision/action limits . It is possible to estimate prRIs for all measurands where there are repeated measurements from a steady state situation and as such, these may represent personalized action limits. The calculation of prRI can be integrated in laboratory information systems and has the potential to be useful in diagnosing and follow up of patients. However, there are unresolved questions considering the clinical use of prRIs e.g., no studies have demonstrated their actual benefit to patients. Furthermore, clinical decision/action limits are set independently of reference intervals, and they are, unlike reference limits, similar for all kinds of measurement procedures. A further question that remains is if clinical decision/action limits should be personalized, such as for example each person having their own diagnostic cut off for a diabetes mellitus diagnosis or for being treated for hypercholesterolemia. This will probably be dependent if the measurand itself is part of the pathology or if it is only a marker of pathology.
In the later years, there has been a great progress in the methodology of deriving, calculating, estimating and reporting BV data and derived parameters. The results of many of these initiatives have been incorporated in the EFLM Biological Variation Database, which provides quality-assessed BV data, with automatically calculated BV applications, freely available for users worldwide. Furthermore, new applications for use of BV data, such as prRI and MAU have been developed. Clinical Chemistry and Laboratory Medicine has been a key partner for the EFLM BV groups and others in driving the area of BV forward, including by publishing a Special Issue on Biological Variation in 2022. This special issue contains 21 articles on BV and related aspects [4, 49]. We indeed congratulate Clinical Chemistry and Laboratory Medicine with its 60th anniversary.
For the future, it is important to take into account the fact that there is a lack of robust and high-quality BV for many measurands, population groups and settings, and furthermore, it is important to be aware that some BV data may be associated with a large uncertainty that should be considered whenever these data are used for different BV application. Importantly, we must in the future address how BV data can best benefit screening, diagnosing and monitoring of patients and thereby improve patient outcomes.
Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
Informed consent: Not applicable.
Ethical approval: Not applicable.
1. Sandberg, S, Røraas, T, Aarsand, AK. Biological variation and analytical performance specifications (Internet). In: Rifai, N, Chiu, RWK, Young, I, Burnham, CAD, Wittver, CT, editors. Tietz textbook of Laboratory medicine, 7th ed. St Lous: Elsevier; 2022:335–56 pp.Search in Google Scholar
2. Coşkun, A, Sandberg, S, Unsal, I, Cavusoglu, C, Serteser, M, Kilercik, M, et al.. Personalized reference intervals in laboratory medicine: a new model based on within-subject biological variation. Clin Chem 2021;67:374–84. https://doi.org/10.1093/clinchem/hvaa233.Search in Google Scholar PubMed
3. Carobene, A. Reliability of biological variation data available in an online database: need for improvement. Clin Chem Lab Med 2015;53:871–7. https://doi.org/10.1515/cclm-2014-1133.Search in Google Scholar PubMed
4. Sandberg, S, Carobene, A, Aarsand, AK. Biological variation – eight years after the 1st Strategic Conference of EFLM. Clin Chem Lab Med 2022;60:465–8. https://doi.org/10.1515/cclm-2022-0086.Search in Google Scholar PubMed
7. Fraser, CG, Harris, EK. Generation and application of data on biological variation in clinical chemistry. Crit Rev Clin Lab Sci 1989;27:409–37. https://doi.org/10.3109/10408368909106595.Search in Google Scholar PubMed
8. Aarsand, AK, Webster, C, Coskun, A, Gonzales-Lao, E, Diaz-Garzon, J, Roraas, T, et al.. EFLM biological variation database. Available from: https://biologicalvariation.eu [Accessed 10 Dec 2022].Search in Google Scholar
9. Røraas, T, Støve, B, Petersen, PH, Sandberg, S. Biological variation: the effect of different distributions on estimated within-person variation and reference change values. Clin Chem 2016;62:725–36. https://doi.org/10.1373/clinchem.2015.252296.Search in Google Scholar PubMed
10. Røraas, T, Sandberg, S, Aarsand, AK, Støve, B. A Bayesian approach to biological variation analysis. Clin Chem 2019;65:995–1005. https://doi.org/10.1373/clinchem.2018.300145.Search in Google Scholar PubMed
11. Jones, GRD. Estimates of within-subject biological variation derived from pathology databases: an approach to allow assessment of the effects of age, sex, time between sample collections, and analyte concentration on reference change values. Clin Chem 2019;65:579–88. https://doi.org/10.1373/clinchem.2018.290841.Search in Google Scholar PubMed
12. Røys, EÅ, Guldhaug, NA, Viste, K, Jones, GD, Alaour, B, Sylte, MS, et al.. Sex hormones and adrenal steroids: biological variation estimated using direct and indirect methods. Clin Chem 2023;69:100–9. https://doi.org/10.1093/clinchem/hvac175.Search in Google Scholar PubMed
13. Marques-Garcia, F, Boned, B, González-Lao, E, Braga, F, Carobene, A, Coskun, A, et al.. Critical review and meta-analysis of biological variation estimates for tumor markers. Clin Chem Lab Med 2022;60:494–504. https://doi.org/10.1515/cclm-2021-0725.Search in Google Scholar PubMed
14. Cembrowski, GS, Lyon, AW, McCudden, C, Qiu, Y, Xu, Q, Mei, J, et al.. Transformation of sequential hospital and outpatient laboratory data into between-day reference change values. Clin Chem 2022;68:595–603. https://doi.org/10.1093/clinchem/hvab271.Search in Google Scholar PubMed
15. Marqués-García, F, Nieto-Librero, A, González-García, N, Galindo-Villardón, P, Martínez-Sánchez, LM, Tejedor-Ganduxé, X, et al.. Within-subject biological variation estimates using an indirect data mining strategy. Spanish multicenter pilot study (BiVaBiDa). Clin Chem Lab Med 2022;60:1804–12. https://doi.org/10.1515/cclm-2021-0863.Search in Google Scholar PubMed
16. Carobene, A, Strollo, M, Jonker, N, Barla, G, Bartlett, WA, Sandberg, S, et al.. Sample collections from healthy volunteers for biological variation estimates’ update: a new project undertaken by the Working Group on Biological Variation established by the European Federation of Clinical Chemistry and Laboratory Medicine. Clin Chem Lab Med 2016;54:1599–608. https://doi.org/10.1515/cclm-2016-0035.Search in Google Scholar PubMed
17. Aarsand, AK, Díaz-Garzón, J, Fernandez-Calle, P, Guerra, E, Locatelli, M, Bartlett, WA, et al.. The EuBIVAS: within- and between-subject biological variation data for electrolytes, lipids, urea, uric acid, total protein, total bilirubin, direct bilirubin, and glucose. Clin Chem 2018;64:1380–93. https://doi.org/10.1373/clinchem.2018.288415.Search in Google Scholar PubMed
18. Carobene, A, Aarsand, AK, Guerra, E, Bartlett, WA, Coskun, A, Díaz-Garzón, J, et al.. European biological variation study (EuBIVAS): within- and between-subject biological variation data for 15 frequently measured proteins. Clin Chem 2019;65:1031–41. https://doi.org/10.1373/clinchem.2019.304618.Search in Google Scholar PubMed
19. Bottani, M, Banfi, G, Guerra, E, Locatelli, M, Aarsand, AK, Coşkun, A, et al.. European Biological Variation Study (EuBIVAS): within- and between-subject biological variation estimates for serum biointact parathyroid hormone based on weekly samplings from 91 healthy participants. Ann Transl Med 2020;8:855. https://doi.org/10.21037/atm-19-4498.Search in Google Scholar PubMed PubMed Central
20. Ceriotti, F, Marco, JDG, Fernández-Calle, P, Maregnani, A, Aarsand, AK, Coskun, A, et al.. The European Biological Variation Study (EuBIVAS): weekly biological variation of cardiac troponin I estimated by the use of two different high-sensitivity cardiac troponin I assays. Clin Chem Lab Med 2020;58:1741–7. https://doi.org/10.1515/cclm-2019-1182.Search in Google Scholar PubMed
21. Cavalier, E, Lukas, P, Bottani, M, Aarsand, AK, Ceriotti, F, Coşkun, A, et al.. European biological variation study (EuBIVAS): within- and between-subject biological variation estimates of β-isomerized C-terminal telopeptide of type I collagen (β-CTX), N-terminal propeptide of type I collagen (PINP), osteocalcin, intact fibroblast growth factor 23 and uncarboxylated-unphosphorylated matrix-gla protein—a cooperation between the EFLM working group on biological variation and the international osteoporosis foundation-international federation of clinical chemistry committee on bone metabolism. Osteoporos Int 2020;31:1461–70. https://doi.org/10.1007/s00198-020-05362-8.Search in Google Scholar PubMed
22. Clouet-Foraison, N, Marcovina, SM, Guerra, E, Aarsand, AK, Coşkun, A, Díaz-Garzón, J, et al.. Analytical performance specifications for lipoprotein(a), apolipoprotein B-100, and apolipoprotein A-I using the biological variation model in the EuBIVAS population. Clin Chem 2020;66:727–36. https://doi.org/10.1093/clinchem/hvaa054.Search in Google Scholar PubMed
23. Carobene, A, Guerra, E, Marqués-García, F, Boned, B, Locatelli, M, Coşkun, A, et al.. Biological variation of morning serum cortisol: updated estimates from the European Biological Variation Study (EuBIVAS) and meta-analysis. Clin Chim Acta 2020;509:268–72. https://doi.org/10.1016/j.cca.2020.06.038.Search in Google Scholar PubMed
24. Carobene, A, Aarsand, AK, Coşkun, A, Díaz-Garzón, J, Locatelli, M, Fernandez-Calle, P, et al.. Biological variation of serum iron from the European biological variation study (EuBIVAS). Clin Chem Lab Med 2023;61:e57–60. https://doi.org/10.1515/cclm-2022-1091.Search in Google Scholar PubMed
25. Aarsand, AK, Røraas, T, Fernandez-Calle, P, Ricos, C, Díaz-Garzón, J, Jonker, N, et al.. The biological variation data critical appraisal checklist: a standard for evaluating studies on biological variation. Clin Chem 2018;64:501–14. https://doi.org/10.1373/clinchem.2017.281808.Search in Google Scholar PubMed
26. Bartlett, WA, Braga, F, Carobene, A, Coskun, A, Prusa, R, Fernandez-Calle, P, et al.. A checklist for critical appraisal of studies of biological variation. Clin Chem Lab Med 2015;53:879–85. https://doi.org/10.1515/cclm-2014-1127.Search in Google Scholar PubMed
27. Carobene, A, Marino, I, Coskun, A, Serteser, M, Unsal, I, Guerra, E, et al.. The EuBIVAS project: within-and between-subject biological variation data for serum creatinine using enzymatic and alkaline picrate methods and implications for monitoring. Clin Chem 2017;63:1527–36. https://doi.org/10.1373/clinchem.2017.275115.Search in Google Scholar PubMed
28. Carobene, A, Aarsand, AK, Bartlett, WA, Coskun, A, Diaz-Garzon, J, Fernandez-Calle, P, et al.. The European Biological Variation Study (EuBIVAS): a summary report. Clin Chem Lab Med 2022;60:505–17. https://doi.org/10.1515/cclm-2021-0370.Search in Google Scholar PubMed
29. Buoro, S, Carobene, A, Seghezzi, M, Manenti, B, Dominoni, P, Pacioni, A, et al.. Short- and medium-term biological variation estimates of red blood cell and reticulocyte parameters in healthy subjects. Clin Chem Lab Med 2018;56:954–63. https://doi.org/10.1515/cclm-2017-0902.Search in Google Scholar PubMed
30. Buoro, S, Seghezzi, M, Manenti, B, Pacioni, A, Carobene, A, Ceriotti, F, et al.. Biological variation of platelet parameters determined by the Sysmex XN hematology analyzer. Clin Chim Acta 2017;470:125–32. https://doi.org/10.1016/j.cca.2017.05.004.Search in Google Scholar PubMed
31. Buoro, S, Carobene, A, Seghezzi, M, Manenti, B, Pacioni, A, Ceriotti, F, et al.. Short- and medium-term biological variation estimates of leukocytes extended to differential count and morphology-structural parameters (cell population data) in blood samples obtained from healthy people. Clin Chim Acta 2017;473:147–56. https://doi.org/10.1016/j.cca.2017.07.009.Search in Google Scholar PubMed
32. Coskun, A, Carobene, A, Kilercik, M, Serteser, M, Sandberg, S, Aarsand, AK, et al.. Within-subject and between-subject biological variation estimates of 21 hematological parameters in 30 healthy subjects. Clin Chem Lab Med 2018;58:618–28.10.1515/cclm-2017-1155Search in Google Scholar PubMed
33. Carobene, A, Campagner, A, Uccheddu, C, Banfi, G, Vidali, M, Cabitza, F. The multicenter European Biological Variation Study (EuBIVAS): a new glance provided by the Principal Component Analysis (PCA), a machine learning unsupervised algorithms, based on the basic metabolic panel linked measurands. Clin Chem Lab Med 2022;60:556–68. https://doi.org/10.1515/cclm-2021-0599.Search in Google Scholar PubMed
34. Diaz-Garzon, J, Fernandez-Calle, P, Aarsand, AK, Sandberg, S, Coskun, A, Carobene, A, et al.. Long-term within- and between-subject biological variation of 29 routine laboratory measurands in athletes. Clin Chem Lab Med 2022;60:618–28. https://doi.org/10.1515/cclm-2021-0910.Search in Google Scholar PubMed
35. Diaz–Garzon, J, Fernandez-Calle, P, Aarsand, AK, Sandberg, S, Buno, A. Biological variation of venous acid-base status measurands in athletes. Clin Chim Acta 2021;523:497–503. https://doi.org/10.1016/j.cca.2021.11.001.Search in Google Scholar PubMed
36. Kristoffersen, AH, Petersen, PH, Sandberg, S. A model for calculating the within-subject biological variation and likelihood ratios for analytes with a time-dependent change in concentrations; exemplified with the use of D-dimer in suspected venous thromboembolism in healthy pregnant women. Ann Clin Biochem 2012;49:561–9. https://doi.org/10.1258/acb.2012.011265.Search in Google Scholar PubMed
37. Kristoffersen, AH, Petersen, PH, Bjørge, L, Røraas, T, Sandberg, S. Concentration of fibrin monomer in pregnancy and during the postpartum period. Ann Clin Biochem 2019;89:73–9. https://doi.org/10.1177/0004563219869732.Search in Google Scholar PubMed
38. Kristoffersen, AH, Petersen, PH, Røraas, T, Sandberg, S. Estimates of within-subject biological variation of protein C, antithrombin, protein S free, protein S activity, and activated protein C resistance in pregnant women. Clin Chem 2017;63:898–907. https://doi.org/10.1373/clinchem.2016.265900.Search in Google Scholar PubMed
39. Kristoffersen, AH, Petersen, PH, Bjørge, L, Røraas, T, Sandberg, S. Within-subject biological variation of activated partial thromboplastin time, prothrombin time, fibrinogen, factor VIII and von Willebrand factor in pregnant women. Clin Chem Lab Med 2018;56:1297–308. https://doi.org/10.1515/cclm-2017-1220.Search in Google Scholar PubMed
40. Diaz-Garzón, J, Fernandez-Calle, P, Minchinela, J, Aarsand, AK, Bartlett, WA, Aslan, B, et al.. Biological variation data for lipid cardiovascular risk assessment biomarkers. A systematic review applying the biological variation data critical appraisal checklist (BIVAC). Clin Chim Acta 2019;495:467–75. https://doi.org/10.1016/j.cca.2019.05.013.Search in Google Scholar PubMed
41. Fernández-Calle, P, Díaz-Garzón, J, Bartlett, W, Sandberg, S, Braga, F, Beatriz, B, et al.. Biological variation estimates of thyroid related measurands – meta-analysis of BIVAC compliant studies. Clin Chem Lab Med 2022;60:483–93. https://doi.org/10.1515/cclm-2021-0904.Search in Google Scholar PubMed
42. Diaz-Garzon, J, Fernandez-Calle, P, Sandberg, S, Özcürümez, M, Bartlett, WA, Coskun, A, et al.. Biological variation of cardiac troponins in health and disease: a systematic review and meta-analysis. Clin Chem 2021;67:256–64. https://doi.org/10.1093/clinchem/hvaa261.Search in Google Scholar PubMed
43. Carobene, A, Lao, EG, Simon, M, Locatelli, M, Coşkun, A, Díaz-Garzón, J, et al.. Biological variation of serum insulin: updated estimates from the European Biological Variation Study (EuBIVAS) and meta-analysis. Clin Chem Lab Med 2022;60:479–82. https://doi.org/10.1515/cclm-2020-1490.Search in Google Scholar PubMed
44. Jonker, N, Aslan, B, Boned, B, Marqués-García, F, Ricós, C, Alvarez, V, et al.. Critical appraisal and meta-analysis of biological variation estimates for kidney related analytes. Clin Chem Lab Med 2022;60:469–78. https://doi.org/10.1515/cclm-2020-1168.Search in Google Scholar PubMed
45. Ricos, C, Fernandez-Calle, P, Gonzales-Lao, E, Simon, M, Diaz-Garzon, J, Boned, B, et al.. Critical appraisal and meta-analysis of BV studies on glycosylated albumin, glucose, and HbA1c. Adv Lab Med 2020;1:23–9.10.1515/almed-2020-0029Search in Google Scholar
46. Coskun, A, Braga, F, Carobene, A, Ganduxe, XT, Aarsand, AK, Fernandez-Calle, P, et al.. Systematic review and meta-analysis of within-subject and between-subject biological variation estimates of 20 haematological parameters. Clin Chem Lab Med 2020;58:25–32. https://doi.org/10.1515/cclm-2019-0658.Search in Google Scholar PubMed
47. Coşkun, A, Aarsand, AK, Braga, F, Carobene, A, Díaz-Garzón, J, Fernandez-Calle, P, et al.. Systematic review and meta-analysis of within-subject and between-subject biological variation estimates of serum Zinc, Copper and Selenium. Clin Chem Lab Med 2022;60:479–82. https://doi.org/10.1515/cclm-2021-0723.Search in Google Scholar PubMed
48. González-Lao, E, Corte, Z, Simón, M, Ricos, C, Coskun, A, Braga, F, et al.. Systematic review of the biological variation data for diabetes related analytes. Clin Chim Acta 2019;488:61–7. https://doi.org/10.1016/j.cca.2018.10.031.Search in Google Scholar PubMed
49. Sandberg, S, Carobene, A, Aarsand, AK, Issue editors. Biological variation – 8 years after the 1st Strategic Conference of EFLM (special issue). Clin Chem Lab Med 2022;60:462–644.10.1515/cclm-2022-0086Search in Google Scholar PubMed
50. STARD guidelines. Available from: https://www.equator-network.org/reporting-guidelines/stard/ [Accessed 10 Dec 2022].Search in Google Scholar
52. Simundic, AM, Kackov, S, Miler, M, Fraser, CG, Petersen, PH. Terms and symbols used in studies on biological variation: the need for harmonization. Clin Chem 2015;61:438–9. https://doi.org/10.1373/clinchem.2014.233791.Search in Google Scholar PubMed
54. Sandberg, S, Fraser, CG, Horvath, AR, Jansen, R, Jones, G, Oosterhuis, W, et al.. Defining analytical performance specifications: consensus statement from the 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine. Clin Chem Lab Med 2015;53:833–5. https://doi.org/10.1515/cclm-2015-0067.Search in Google Scholar PubMed
56. Coskun, A, Sandberg, S, Unsal, I, Cavusoglu, C, Serteser, M, Kilercik, M, et al.. Personalized reference intervals: using estimates of within-subject or within-person biological variation requires different statistical approaches. Clin Chim Acta 2021;524:201–2. https://doi.org/10.1016/j.cca.2021.10.034.Search in Google Scholar PubMed
57. Coskun, A, Theodorsson, E, Oosterhuis, WP, Sandberg, S. Measurement uncertainty for practical use. Clin Chim Acta 2022;531:352–60. https://doi.org/10.1016/j.cca.2022.04.1003.Search in Google Scholar PubMed
58. Coskun, A, Sandberg, S, Unsal, I, Yavuz, FG, Cavusoglu, C, Serteser, M, et al.. Personalized reference intervals – statistical approaches and considerations. Clin Chem Lab Med 2022;60:629–35.10.1515/cclm-2021-1066Search in Google Scholar PubMed
59. Coskun, A, Sandberg, S, Unsal, I, Serteser, M, Aarsand, AK. Personalized reference intervals: from theory to practice. Crit Rev Clin Lab Sci 2022;59:1–16.10.1080/10408363.2022.2070905Search in Google Scholar PubMed
60. Carobene, A, Banfi, G, Locatelli, M, Vidali, M. Personalized Reference Intervals: from the statistical significance to the clinical usefulness. Clin Chim Acta 2021;524:203–4. https://doi.org/10.1016/j.cca.2021.10.036.Search in Google Scholar PubMed
61. Ozarda, Y, Sikaris, K, Streichert, T, Macri, J, (C-RIDL) IC on R intervals and, DL. Distinguishing reference intervals and clinical decision limits – a review by the IFCC Committee on Reference Intervals and Decision Limits. Crit Rev Clin Lab Sci 2018;55:420–31. https://doi.org/10.1080/10408363.2018.1482256.Search in Google Scholar PubMed
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