The Biological Variation Data Critical Appraisal Checklist: A Standard for Evaluating Studies on Biological Variation

Biological variation of methylmalonic acid in urine in spanish population

BACKGROUND-AIM

Methylmalonic acid (MMA) is currently the best biomarker of functional vitamin B12 deficiency. However, for a correct interpretation of the patient's results it is necessary to know its biological variation (BV). No BV data are available for urine MMA values, as measured by mass spectrometry. Hence, the aim of this study was to estimate the within- and between-person coefficients of variation (CVw, CVg) for MMA in a healthy population, and the associated index of individuality (II), as well as to define quality specifications based on BV and the reference change value (RCV).

METHODS

Random urine samples from 34 healthy volunteers were collected over four consecutive weeks. Samples were stored at -80 °C until analysis in a single analytical run. MMA excretion was quantified by tandem liquid chromatography coupled to mass spectrometry (HPLC-MS/MS). Results were normalized to urine creatinine. The coefficients of variation were estimated by CV-ANOVA. Confidence intervals (95 %) were calculated. Quality specifications were defined according to international recommendations.

RESULTS

A total of 128 samples were included. The coefficients of variation were CVw = 35.7 % (26.1-45.3) and CVg = 67.7 % (58.3-77.0). The associated II was 0.5 and the RCV was 88.1 %.

CONCLUSION

Considering the II obtained, MMA in urine has high individuality, therefore, RCV is better to evaluate serial clinical results. Our results will contribute to a better clinical interpretation of this biomarker and will represent a great aid when defining analytical performance specifications for this magnitude.

Within- and between-subject biological variation estimates for the enumeration of lymphocyte deep immunophenotyping and monocyte subsets

OBJECTIVES

This study aimed to deliver biological variation (BV) estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping (memory T/B cells, regulatory T cells, etc.) and classical, intermediate, and nonclassical monocyte subsets based on the full spectrum flow cytometry (FS-FCM) and a Biological Variation Data Critical Appraisal Checklist (BIVAC) design.

METHODS

Samples were collected biweekly from 60 healthy Chinese adults over 10 consecutive two-week periods. Each sample was measured in duplicate within a single run for lymphocyte deep immunophenotyping and monocyte subset determination using FS-FCM, including the percentage (%) and absolute count (cells/μL). After trend adjustment, a Bayesian model was applied to deliver the within-subject BV (CVI) and between-subject BV (CVG) estimates with 95 % credibility intervals.

RESULTS

Enumeration (% and cells/μL) for 25 types of lymphocyte deep immunophenotyping and three types of monocyte subset percentages showed considerable variability in terms of CVI and CVG. CVI ranged from 4.23 to 47.47 %. Additionally, CVG ranged between 10.32 and 101.30 %, except for CD4+ effector memory T cells re-expressing CD45RA. No significant differences were found between males and females for CVI and CVG estimates. Nevertheless, the CVGs of PD-1+ T cells (%) may be higher in females than males. Based on the desired analytical performance specification, the maximum allowable imprecision immune parameter was the CD8+PD-1+ T cell (cells/μL), with 23.7 %.

CONCLUSIONS

This is the first study delivering BV estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping, along with classical, intermediate, and nonclassical monocyte subsets, using FS-FCM and adhering to the BIVAC design.

Analytical performance specifications based on biological variation data - considerations, strengths and limitations

Analytical performance specifications (APS) are typically established through one of three models: (i) outcome studies, (ii) biological variation (BV), or (iii) state-of-the-art. Presently, The APS can, for most measurands that have a stable concentration, be based on BV. BV based APS, defined for imprecision, bias, total allowable error and allowable measurement uncertainty, are applied to many different processes in the laboratory. When calculating APS, it is important to consider the different APS formulae, for what setting they are to be applied and if they are suitable for the intended purpose. In this opinion paper, we elucidate the background, limitations, strengths, and potential intended applications of the different BV based APS formulas. When using BV data to set APS, it is important to consider that all formulae are contingent on accurate and relevant BV estimates. During the last decade, efficient procedures have been established to obtain reliable BV estimates that are presented in the EFLM biological variation database. The database publishes detailed BV data for numerous measurands, global BV estimates derived from meta-analysis of quality-assured studies of similar study design and automatic calculation of BV based APS.

What the Milan conference has taught us about analytical performance specification model definition and measurand allocation

Analytical performance specifications (APS) represent the criteria that specify the quality required for laboratory test information to satisfy clinical needs. In 2014 the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) considered timely to update the topic of APS by organizing a conference in Milan in which some strategic concepts were proposed. Here I summarize the essential points representing the EFLM Strategic Conference heritage and discuss the approaches that will permit us to become more concrete, including roles and main actions expected from each of involved stakeholders for contributing a quantum leap forward in the way of practicality of Milan consensus about APS.

Applying the Milan models to setting analytical performance specifications - considering all the information

Analytical performance specifications (APS) are used for decisions about the required analytical quality of pathology tests to meet clinical needs. The Milan models, based on clinical outcome, biological variation, or state of the art, were developed to provide a framework for setting APS. An approach has been proposed to assign each measurand to one of the models based on a defined clinical use, physiological control, or an absence of quality information about these factors. In this paper we propose that in addition to such assignment, available information from all models should be considered using a risk-based approach that considers the purpose and role of the actual test in a clinical pathway and its impact on medical decisions and clinical outcomes in addition to biological variation and the state-of-the-art. Consideration of APS already in use and the use of results in calculations may also need to be considered to determine the most appropriate APS for use in a specific setting.

Analytical performance specifications for trace elements in biological fluids derived from six countries federated external quality assessment schemes over 10 years

OBJECTIVES

This article defines analytical performance specifications (APS) for evaluating laboratory proficiency through an external quality assessment scheme.

METHODS

Standard deviations for proficiency assessment were derived from Thompson's characteristic function applied to robust data calculated from participants' submissions in the Occupational and Environmental Laboratory Medicine (OELM) external quality assurance scheme for trace elements in serum, whole blood and urine. Characteristic function was based on two parameters: (1) β - the average coefficient of variation (CV) at high sample concentrations; (2) α - the average standard deviation (SD) at low sample concentrations. APSs were defined as 1.65 standard deviations calculated by Thompson's approach. Comparison between OELM robust data and characteristic function were used to validate the model.

RESULTS

Application of the characteristic function allowed calculated APS for 18 elements across three matrices. Some limitations were noted, particularly for elements (1) with no sample concentrations near analytical technique limit of detection; (2) exhibiting high robust CV at high concentration; (3) exhibiting high analytical variability such as whole blood Tl and urine Pb; (4) with an unbalanced number of robust SD above and under the characteristic function such as whole blood Mn and serum Al and Zn.

CONCLUSIONS

The characteristic function was a useful means of deriving APS for trace elements in biological fluids where biological variation data or outcome studies were not available. However, OELM external quality assurance scheme data suggests that the characteristic functions are not appropriate for all elements.

The biological variation of insulin resistance markers: data from the European Biological Variation Study (EuBIVAS)

OBJECTIVES

An insulin resistant state is characteristic of patients with type 2 diabetes, polycystic ovary syndrome, and metabolic syndrome. Identification of insulin resistance (IR) is most readily achievable using formulae combining plasma insulin and glucose results. In this study, we have used data from the European Biological Variation Study (EuBIVAS) to examine the biological variability (BV) of IR using the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and the Quantitative Insulin sensitivity Check Index (QUICKI).

METHODS

Ninety EuBIVAS non-diabetic subjects (52F, 38M) from five countries had fasting HOMA-IR and QUICKI calculated from plasma glucose and insulin samples collected concurrently on 10 weekly occasions. The within-subject (CVI) and between-subject (CVG) BV estimates with 95 % CIs were obtained by CV-ANOVA after analysis of trends, variance homogeneity and outlier removal.

RESULTS

The CVI of HOMA-IR was 26.7 % (95 % CI 25.5-28.3), driven largely by variability in plasma insulin and the CVI for QUICKI was 4.1 % (95 % CI 3.9-4.3), reflecting this formula's logarithmic transformation of glucose and insulin values. No differences in values or BV components were observed between subgroups of men or women below and above 50 years.

CONCLUSIONS

The EuBIVAS, by utilising a rigorous experimental protocol, has produced robust BV estimates for two of the most commonly used markers of insulin resistance in non-diabetic subjects. This has shown that HOMA-IR, in particular, is highly variable in the same individual which limits the value of single measurements.

A standard to report biological variation data studies - based on an expert opinion

There is a need for standards for generation and reporting of Biological Variation (BV) reference data. The absence of standards affects the quality and transportability of BV data, compromising important clinical applications. To address this issue, international expert groups under the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) have developed an online resource (https://tinyurl.com/bvmindmap) in the form of an interactive mind map that serves as a guideline for researchers planning, performing and reporting BV studies. The mind map addresses study design, data analysis, and reporting criteria, providing embedded links to relevant references and resources. It also incorporates a checklist approach, identifying a Minimum Data Set (MDS) to enable the transportability of BV data and incorporates the Biological Variation Data Critical Appraisal Checklist (BIVAC) to assess study quality. The mind map is open to access and is disseminated through the EFLM BV Database website, promoting accessibility and compliance to a reporting standard, thereby providing a tool to be used to ensure data quality, consistency, and comparability of BV data. Thus, comparable to the STARD initiative for diagnostic accuracy studies, the mind map introduces a Standard for Reporting Biological Variation Data Studies (STARBIV), which can enhance the reporting quality of BV studies, foster user confidence, provide better decision support, and be used as a tool for critical appraisal. Ongoing refinement is expected to adapt to emerging methodologies, ensuring a positive trajectory toward improving the validity and applicability of BV data in clinical practice.

Biological Variation Data in Triathletes for Metabolism and Growth-Related Biomarkers Included in the Athlete Biological Passport

BACKGROUND

When using biological variation (BV) data, BV estimates need to be robust and representative. High-endurance athletes represent a population under special physiological conditions, which could influence BV estimates. Our study aimed to estimate BV in athletes for metabolism and growth-related biomarkers involved in the Athlete Biological Passport (ABP), by 2 different statistical models.

METHODS

Thirty triathletes were sampled monthly for 11 months. The samples were analyzed for human growth hormone (hGH), insulin-like growth factor-1 (IGF-1), insulin-like growth factor binding protein 3 (IGFBP-3), insulin, and N-terminal propeptide of type III procollagen (P-III-NP) by immunoassay. Bayesian and ANOVA methods were applied to estimate within-subject (CVI) and between-subject BV.

RESULTS

CVI estimates ranged from 7.8% for IGFBP-3 to 27.0% for insulin, when derived by the Bayesian method. The 2 models gave similar results, except for P-III-NP. Data were heterogeneously distributed for P-III-NP for the overall population and in females for IGF-1 and IGFBP-3. BV components were not estimated for hGH due to lack of steady state. The index of individuality was below 0.6 for all measurands, except for insulin.

CONCLUSIONS

In an athlete population, to apply a common CVI for insulin would be appropriate, but for IGF-1 and IGFBP-3 gender-specific estimates should be applied. P-III-NP data were heterogeneously distributed and using a mean CVI may not be representative for the population. The high degree of individuality for IGF-1, IGFBP-3, and P-III-NP makes them good candidates to be interpreted through reference change values and the ABP.

Analytical performance and user-friendliness of four commercially available point-of-care devices for C-reactive protein

INTRODUCTION

Proper implementation of Point-of-Care testing (POCT) for C-reactive protein (CRP) in primary care can decrease the inappropriate use of antibiotics, thereby tackling the problem of growing antimicrobial resistance.

OBJECTIVE

The analytical performance and user-friendliness of four POCT-CRP assays were evaluated: QuikRead go easy, LumiraDx, cobas b 101 and Afinion 2.

MATERIALS AND METHODS

Imprecision was evaluated using plasma pools in addition to manufacturer-specific control material. Trueness was assessed by verification of traceability to ERM-DA474/IFCC in parallel to method comparison towards the central laboratory CRP method (cobas c 503) using i) retrospectively selected plasma samples (n = 100) and ii) prospectively collected capillary whole blood samples (n = 50). User-friendliness was examined using a questionnaire.

RESULTS

Between-day imprecision on plasma pools varied from 4.5 % (LumiraDx) to 11.5 % (QuikRead). Traceability verification revealed no significant difference between cobas c 503 CRP results and the ERM-DA474/IFCC certified value. cobas b 101 and Afinion achieved the best agreement with the central laboratory method. LumiraDx and QuikRead revealed a negative mean difference, with LumiraDx violating the criterion of > 95 % of POCT-CRP-results within ± 20 % of the comparison method. Regarding user-friendliness, Afinion obtained the highest Likert-scores.

CONCLUSION

The analytical performance and user-friendliness of POCT-CRP devices varies among manufacturers, emphasizing the need for quality assurance supervised by a central laboratory.

Biological Variation Estimates for Plasma Copeptin and Clinical Implications

BACKGROUND

Plasma copeptin measurement is useful for the differential diagnoses of polyuria-polydipsia syndrome. It has also been proposed as a prognostic marker for cardiovascular diseases. However, limited information is available about the within- (CVI) and between-subject (CVG) biological variation (BV). This study presents BV estimates for copeptin in healthy individuals.

METHODS

Samples were collected weekly from 41 healthy subjects over 5 weeks and analyzed using the BRAHMS Copeptin proAVP KRYPTOR assay after at least 8 h of food and fluid abstinence. Outlier detection, variance homogeneity, and trend analysis were performed followed by CV-ANOVA for BV and analytical variation (CVA) estimation with 95% confidence intervals. Reference change values (RCVs), index of individuality (II), and analytical performance specification (APS) were also calculated.

RESULTS

The analysis included 178 results from 20 males and 202 values from 21 females. Copeptin concentrations were significantly higher in males than in females (mean 8.5 vs 5.2 pmol/L, P < 0.0001). CVI estimates were 18.0% (95% CI, 15.4%-21.6%) and 19.0% (95% CI, 16.4%-22.6%), for males and females, respectively; RCVs were -35% (decreasing value) and 54% (increasing value). There was marked individuality for copeptin. No result exceeded the diagnostic threshold (>21.4 pmol/L) for arginine vasopressin resistance.

CONCLUSIONS

The availability of BV data allows for refined APS and associated II, and RCVs applicable as aids in the serial monitoring of patients with specific diseases such as heart failure. The BV estimates are only applicable in subjects who abstained from oral intake due to the rapid and marked effects of fluids on copeptin physiology.

Biological variation of inflammatory and iron metabolism markers in high-endurance recreational athletes; are these markers useful for athlete monitoring?

OBJECTIVES

To deliver biological variation (BV) data for serum hepcidin, soluble transferrin receptor (sTfR), erythropoietin (EPO) and interleukin 6 (IL-6) in a population of well-characterized high-endurance athletes, and to evaluate the potential influence of exercise and health-related factors on the BV.

METHODS

Thirty triathletes (15 females) were sampled monthly (11 months). All samples were analyzed in duplicate and BV estimates were delivered by Bayesian and ANOVA methods. A linear mixed model was applied to study the effect of factors related to exercise, health, and sampling intervals on the BV estimates.

RESULTS

Within-subject BV estimates (CVI) were for hepcidin 51.9 % (95 % credibility interval 46.9-58.1), sTfR 10.3 % (8.8-12) and EPO 27.3 % (24.8-30.3). The mean concentrations were significantly different between sex, but CVI estimates were similar and not influenced by exercise, health-related factors, or sampling intervals. The data were homogeneously distributed for EPO but not for hepcidin or sTfR. IL-6 results were mostly below the limit of detection. Factors related to exercise, health, and sampling intervals did not influence the BV estimates.

CONCLUSIONS

This study provides, for the first time, BV data for EPO, derived from a cohort of well-characterized endurance athletes and indicates that EPO is a good candidate for athlete follow-up. The application of the Bayesian method to deliver BV data illustrates that for hepcidin and sTfR, BV data are heterogeneously distributed and using a mean BV estimate may not be appropriate when using BV data for laboratory and clinical applications.

A model for managing quality control for a network of clinical chemistry instruments measuring the same analyte

OBJECTIVES

Monitoring quality control for a laboratory or network with multiple instruments measuring the same analyte is challenging. We present a retrospective assessment of a method to detect medically significant out-of-control error conditions across a group of instruments measuring the same analyte. The purpose of the model was to ensure that results from any of several instruments measuring the same analytes in a laboratory or a network of laboratories provide comparable results and reduce patient risk. Limited literature has described how to manage QC in these very common situations.

METHODS

Single Levey-Jennings control charts were designed using peer group target mean and control limits for five common clinical chemistry analytes in a network of eight analyzers in two different geographical sites. The QC rules used were 13s/22s/R4s, with the mean being a peer group mean derived from a large population of the same instrument and the same QC batch mean and a group CV. The peer group data used to set the target means and limits were from a quality assurance program supplied by the instrument supplier. Both statistical and clinical assessments of significance were used to evaluate QC failure. Instrument bias was continually monitored.

RESULTS

It was demonstrated that the biases of each instrument were not statistically or clinically different compared to the peer group's average over six months from February 2023 until July 2023. Over this period, the error rate determined by the QC model was consistent with statistical expectations for the 13s/22s/R4s rule. There were no external quality assurance failures, and no detected error exceeded the TEa (medical impact). Thus, the combined statistical/clinical assessment reduced unnecessary recalibrations and the need to amend results.

CONCLUSIONS

This paper describes the successful implementation of a quality control model for monitoring a network of instruments, measuring the same analytes and using externally provided quality control targets. The model continually assesses individual instrument bias and imprecision while ensuring all instruments in the network meet clinical goals for quality. The focus of this approach is on detecting medically significant out-of-control error conditions.

The biological variation of serum 1,25-dihydroxyvitamin D and parathyroid hormone, and plasma fibroblast growth factor 23 in healthy individuals

BACKGROUND AND AIMS

Measuring 1,25-dihydroxyvitamin D (1,25(OH)2D), parathyroid hormone 1-84 (PTH 1-84) and intact FGF23 (iFGF23) is crucial for diagnosing a variety of diseases affecting bone and mineral homeostasis. Biological variability (BV) data are important for defining analytical quality specifications (APS), the usefulness of reference intervals, and the significance of variations in serial measurements in the same subject. The aim of this study was to pioneer the provision of BV estimates for 1,25(OH)2D and to improve existing BV estimates for iFGF23 and PTH 1-84.

MATERIALS AND METHODS

Serum and plasma-EDTA samples of sixteen healthy subjects have been collected for seven weeks and measured in duplicate by chemiluminescent immunoassay on the DiaSorin Liaison platform. After variance verification, within-subject (CVI) and between-subject (CVG) BV estimates were assessed by either standard ANOVA, or CV-ANOVA. The APSs were calculated according to the EFLM-BV-model.

RESULTS

We found the following CVI estimates with 95% confidence intervals:1,25(OH)2D, 22.2% (18.9-26.4); iFGF23, 16.1% (13.5-19.5); and PTH 1-84, 17.9% (14.8-21.8). The CVG were: 1,25(OH)2D, 21.2% (14.2-35.1); iFGF23, 21.1% (14.5-35.8); and PTH 1-84, 31.1% (22.1-50.8).

CONCLUSIONS

We report for the first time BV estimates for 1,25(OH)2D and enhance existing data about iFGF23-BV and PTH 1-84-BV through cutting-edge immunometric methods.

Short-term biological variation of serum tryptase

OBJECTIVES

Serum tryptase is a biomarker of mast cell activation. Among others, it is used in the diagnosis of anaphylaxis where a significant increase during the acute phase supports the diagnosis. When evaluating changes in biomarker levels, it is of utmost importance to consider the biological variation of the marker. Therefore, the aim of this study was to evaluate the short-term biological variation of serum tryptase.

METHODS

Blood samples were drawn at 9 AM three days in a row from apparently healthy subjects. On day two, additional blood samples were drawn every third hour for 12 h. The tryptase concentration was measured in serum using a fluoroenzyme immunoassay (ImmunoCAP™, Thermo Fisher Scientific). Linear mixed-effects models were used to calculate components of biological variation.

RESULTS

In 32 subjects, the overall mean concentration of tryptase was 4.0 ng/mL (range, 1.3-8.0 ng/mL). The within-subject variation was 3.7 % (95 % confidence interval (CI) 3.0-4.4 %), the between-subject variation was 31.5 % (95 % CI 23.1-39.8 %), and the analytical variation was 3.4 % (95 % CI 2.9-4.1 %). The reference change value was 13.3 % for an increase in tryptase at a 95 % level of significance. No significant day-to-day variation was observed (p=0.77), while a minute decrease in the serum concentration was observed during the day (p<0.0001).

CONCLUSIONS

Serum tryptase is a tightly regulated biomarker with very low within-subject variation, no significant day-to-day variation, and only minor semidiurnal variation. In contrast, a considerable between-subject variation exists. This establishes serum tryptase as a well-suited biomarker for monitoring.

Biological variation of serum neopterin concentrations in apparently healthy individuals

OBJECTIVES

The aims of this study were to determine the biological variation (BV), reference change value (RCV), index of individuality (II), and quality specifications for serum neopterin concentrations; a measurand provided by clinical laboratories as an indicator of cellular immunity.

METHODS

The study delivered serum samples collected for 10 consecutive weeks from 12 apparently healthy individuals (3 male, 9 female). Serum neopterin concentrations were measured using high-performance liquid chromatography with fluorometric detection. The data analysis was performed using an online statistical tool and addressed published criteria for estimation of biological variation.

RESULTS

The mean neopterin concentration was 5.26 nmol/L. The within-subject biological variation (CVI) with 95 % confidence interval (CI) of neopterin serum concentrations was 11.54 % (9.98-13.59), and the between-subject biological variation (CVG) with 95 % CI was 43.27 % (30.52-73.67). The neopterin asymmetrical RCV was -24.9 %/+33.1 %, and the II was 0.27. The desirable quality specifications for neopterin were <5.77 % for precision, <11.20 % for bias, and <20.72 % for total allowable error (TEa). When analytical variation was used instead of CVI to calculate TEa, the desirable TEa was <18.39.

CONCLUSIONS

This study determined BV data for neopterin, an indicator of cell-mediated immune response. Asymmetric RCV values, of 24.9 % decrease or a 33.1 % increase between consecutive measurements indicate significant change. The II of 0.27 indicates a high degree of individuality, therefore that it is appropriate to consider the use of personal reference data and significance of change rather than the reference interval as points of reference for the evaluation of neopterin serum concentrations.

The European biological variation study (EuBIVAS): Biological variation data for testosterone, follicle stimulating hormone, prolactin, luteinizing hormone and dehydroepiandrosterone sulfate in men

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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 of PIVKA-II in blood serum of healthy subjects measured by automated electrochemiluminescent assay

Background

Prothrombin/Protein Induced by Vitamin K Absence-II (PIVKA-II) is a candidate biomarker of hepatocellular cancer, recommended both for diagnostics and monitoring. The aim was to evaluate biological variation (BV) of serum PIVKA-II.

Methods

Within-subject (CVI) and between-subject (CVG) BV estimates were assessed in 14 healthy volunteers in a 6-week protocol. Serum concentrations of PIVKA-II were measured by a Roche Elecsys PIVKA-II diagnostic kit (cobas e8000). Precision (CVA) was assessed from duplicate measurements of all volunteers' samples. Two methods were used for the estimation of CVI: SD-ANOVA and CV-ANOVA method. We calculated the index of individuality (II) and reference change value. The experiment was fully compliant with EFLM database checklist.

Results

The CVI of PIVKA-II in healthy persons, as calculated by two statistical methods, were 8.2% (SD-ANOVA with CVA of 3.2%) and 9.4% (CV-ANOVA) with CVA of 2.7%). The CVG was 19.5% (SD-ANOVA), and respective II and RCV were 0.42 and 24.4%.

Conclusions

CVI and CVG of PIVKA-II were 8.2% and 19.5%, respectively, with CVA below 4%. The low II and RCV below 25% enable the use of this biomarker both for diagnostics and monitoring. More data are needed before the introduction of PIVKA-II into clinical practice.

Biological variation estimates of Alzheimer's disease plasma biomarkers in healthy individuals

INTRODUCTION

Blood biomarkers have proven useful in Alzheimer's disease (AD) research. However, little is known about their biological variation (BV), which improves the interpretation of individual-level data.

METHODS

We measured plasma amyloid beta (Aβ42, Aβ40), phosphorylated tau (p-tau181, p-tau217, p-tau231), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) in plasma samples collected weekly over 10 weeks from 20 participants aged 40 to 60 years from the European Biological Variation Study. We estimated within- (CVI ) and between-subject (CVG ) BV, analytical variation, and reference change values (RCV).

RESULTS

Biomarkers presented considerable variability in CVI and CVG . Aβ42/Aβ40 had the lowest CVI (≈ 3%) and p-tau181 the highest (≈ 16%), while others ranged from 6% to 10%. Most RCVs ranged from 20% to 30% (decrease) and 25% to 40% (increase).

DISCUSSION

BV estimates for AD plasma biomarkers can potentially refine their clinical and research interpretation. RCVs might be useful for detecting significant changes between serial measurements when monitoring early disease progression or interventions. Highlights Plasma amyloid beta (Aβ42/Aβ40) presents the lowest between- and within-subject biological variation, but also changes the least in Alzheimer's disease (AD) patients versus controls. Plasma phosphorylated tau variants significantly vary in their within-subject biological variation, but their substantial fold-changes in AD likely limits the impact of their variability. Plasma neurofilament light chain and glial fibrillary acidic protein demonstrate high between-subject variation, the impact of which will depend on clinical context. Reference change values can potentially be useful in monitoring early disease progression and the safety/efficacy of interventions on an individual level. Serial sampling revealed that unexpectedly high values in heathy individuals can be observed, which urges caution when interpreting AD plasma biomarkers based on a single test result.

Sex-related differences in within-subject biological variation estimates for 22 essential and non-essential amino acids

BACKGROUND

Measurement of serum amino acid (AA) concentrations is important in particular for the diagnosis and monitoring of inborn errors of AA metabolism. To ensure optimal clinical interpretation of AAs, reliable biological variation (BV) data are essential. In the present study, we derived BV data for 22 non-essential, conditionally essential, and essential AAs and assessed differences in BV of AAs related to sex.

METHODS

Morning blood samples were drawn from 66 subjects (31 males and 35 females) once a week for 10 consecutive weeks. All samples were analyzed in duplicate using liquid chromatography-tandem mass-spectrometry. The data were assessed for outliers, trends, normality and variance homogeneity analysis prior to estimating within-subject (CVI) and between-subject (CVG) BV.

RESULTS

CVI estimates ranged from 9.0 % for histidine (male) to 33.0 % for taurine (male). CVI estimates in males and females were significantly different for all AAs except for aspartic acid, citrulline and phenylalanine, in most cases higher in females than in males. Apart from for arginine, CVG estimates in males and females were similar.

CONCLUSIONS

In this highly powered BV study, we provide updated BV estimates for 22 AAs and demonstrate that for most AAs, CVI estimates differ between males and females, with implications for interpretation and use of AAs in clinical practice.

Bias in Laboratory Medicine: The Dark Side of the Moon

Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.

Defining allowable total error limits in the clinical laboratory

Allowable total error (ATE) are performance specification limits predefined for a variety of laboratory analytes. These limits define the maximum amount of error that is allowed for an assay when judging acceptability of a new assay during method verification/validation, evaluating patient or instrument comparison data, or in designing a quality control strategy. There are several widely available resources and models that can serve as a guide in selecting ATE. They may be based on legal requirements or set by providers of proficiency testing (PT) and external quality assessment schemes (EQAS). ATE can be also determined by professional expert groups or be based on biological variation of an analyte. Because there are several resources to choose from, there have been several attempts in reaching consensus on which ATE resource should be given preference. This chapter reviews several of these resources in more detail and discusses the difference between allowable total error (ATE) and observed total analytical error (TAE).

Biological variation estimates for serum neurofilament light chain in healthy subjects

OBJECTIVES

Neurofilament light chain (NfL) is an emerging biomarker of neurodegeneration disorders. Knowledge of the biological variation (BV) can facilitate proper interpretation between serial measurements. Here BV estimates for serum NfL (sNfL) are provided.

METHODS

Serum samples were collected weekly from 24 apparently healthy subjects for 10 consecutive weeks and analyzed in duplicate using the Siemens Healthineers sNfL assay on the Atellica® IM Analyzer. Outlier detection, variance homogeneity analyses, and trend analysis were performed followed by CV-ANOVA to determine BV and analytical variation (CVA) estimates with 95%CI and the associated reference change values (RCV) and analytical performance specifications (APS).

RESULTS

Despite observed differences in sNfL concentrations between males and females, BV estimates remained consistent across genders. Both within-subject BV (CVI) for males (10.7%, 95%CI; 9.2-12.6) and females (9.1%, 95%CI; 7.8-10.9) and between-subject BV (CVG) for males (26.1%, 95%CI; 18.0-45.6) and females (30.2%, 95%CI; 20.9-53.5) were comparable. An index of individuality value of 0.33 highlights significant individuality, indicating the potential efficacy of personalized reference intervals in patient monitoring.

CONCLUSIONS

The established BV estimates for sNfL underscore its potential as a valuable biomarker for monitoring neurodegenerative diseases, offering a foundation for improved decision-making in clinical settings.

Biological variation of plasma 25-Hydroxyvitamin D3, Serum vitamin B12, folate and ferritin in Turkish healthy subject

Biological variation (BV) plays a crucial role in determining analytical performance specifications, assessing serial measurements of individuals, and establishing the use of population-based reference intervals. Our study aimed to calculate the BV and BV-based quality goals of 25-hydroxyvitamin D3 (25-OH D3), ferritin, folate and vitamin B12 tests. We included a total of 22 apparently healthy volunteers (9 women and 13 men) aged 18-55 years in the study that we conducted in Turkey. Blood samples were collected from the participants once a week for five weeks. Serum ferritin, folate and vitamin B12 levels were measured using immunochemical method, while plasma 25-OH D3 levels were determined using the high-performance liquid chromatography method. Analysis of variance (ANOVA) was used to estimate analytical variation(CVA), within-subject BV(CVI) and between-subject BV(CVG). The individuality index (II) and reference change value (RCV) were calculated based on these data. The CVI of 25-OH D3, ferritin, folate, and vitamin B12 were found to be 1.8% (0.6%-2.5%), 16.9% (14.4%-20.2%), 10.7% (9.2%-12.7%), and 8.6% (6.8%-10.5%), respectively. CVG were 44.2% (34.3%-69.9%), 132% (87.7%-238%), 19.4% (14.4%-28.8%), and 39.6% (29.8%-59.0%) for the same biomarkers, while CVA were 3.2% (2.81%-3.71%), 3.5% (3.1%-4.1%), 4.0% (3.5%-4.6%), and 7.5% (6.6%-8.6%), respectively. The II values for 25-OH D3, ferritin, folate, and vitamin B12 were calculated as 0.04, 0.13, 0.55, and 0.22, respectively. The RCV were 10.2%, 47.8%, 31.7%, and 31.6%, respectively. Because the tests analyzed in this study exhibit high individuality, RCV should be preferred rather than population-based reference ranges in clinical interpretation of results.

Recommendation for the design of stability studies on clinical specimens

OBJECTIVES

Knowledge of the stability of analytes in clinical specimens is a prerequisite for proper transport and preservation of samples to avoid laboratory errors. The new version of ISO 15189:2022 and the European directive 2017/746 increase the requirements on this topic for manufacturers and laboratories. Within the project to generate a stability database of European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group Preanalytical Phase (WG-PRE), the need to standardise and improve the quality of published stability studies has been detected, being a manifest deficit the absence of international guidelines for the performance of stability studies on clinical specimens.

METHODS

These recommendations have been developed and summarised by consensus of the WG-PRE and are intended primarily to improve the quality of sample stability claims included in information for users provided by assay supplier companies, according to the requirements of the new European regulations and standards for accreditation.

RESULTS

This document provides general recommendations for the performance of stability studies, oriented to the estimation of instability equations in the usual working conditions, allowing flexible adaptation of the maximum permissible error specifications to obtain stability limits adapted to the intended use.

CONCLUSIONS

We present this recommendation based on the opinions of the EFLM WG-PRE group for the standardisation and improvement of stability studies, with the intention to improve the quality of the studies and the transferability of their results to laboratories.

PON-Fold: Prediction of Substitutions Affecting Protein Folding Rate

Most proteins fold into characteristic three-dimensional structures. The rate of folding and unfolding varies widely and can be affected by variations in proteins. We developed a novel machine-learning-based method for the prediction of the folding rate effects of amino acid substitutions in two-state folding proteins. We collected a data set of experimentally defined folding rates for variants and used them to train a gradient boosting algorithm starting with 1161 features. Two predictors were designed. The three-class classifier had, in blind tests, specificity and sensitivity ranging from 0.324 to 0.419 and from 0.256 to 0.451, respectively. The other tool was a regression predictor that showed a Pearson correlation coefficient of 0.525. The error measures, mean absolute error and mean squared error, were 0.581 and 0.603, respectively. One of the previously presented tools could be used for comparison with the blind test data set, our method called PON-Fold showed superior performance on all used measures. The applicability of the tool was tested by predicting all possible substitutions in a protein domain. Predictions for different conformations of proteins, open and closed forms of a protein kinase, and apo and holo forms of an enzyme indicated that the choice of the structure had a large impact on the outcome. PON-Fold is freely available.

Biological variation estimates for spot urine analytes and analyte/creatinine ratios in 33 healthy subjects

OBJECTIVES

Urine samples are frequently used in the clinical practice. In our study, we aimed to calculate the biological variations (BV) of analytes and analyte/creatinine ratios measured in spot urine.

METHODS

Second-morning spot urine samples were collected from 33 (16 female, 17 male) healthy volunteers once weekly for 10 weeks and analyzed in the Roche Cobas 6,000 instrument. Statistical analyzes were performed using BioVar, an online BV calculation software. The data were evaluated in terms of normality, outliers, steady state, homogeneity of the data, and BV values were obtained by analysis of variance (ANOVA). A strict protocol was established for within-subject (CVI) and between-subject (CVG) estimates for both genders.

RESULTS

There was a significant difference between female/male CVI estimates of all analytes except potassium, calcium and magnesium. No difference was found in CVG estimates. When the analytes that had a significant difference in CVI estimates in spot urine analytes were compared to creatinine, it was observed that the significant difference between the genders disappeared. There was no significant difference between female/male CVI and CVG estimates in all spot urine analyte/creatinine ratios.

CONCLUSIONS

Since the CVI estimates of analyte/creatinine ratios are lower, it would be more reasonable to use them in result reporting. Reference ranges should be used with caution, since II values of almost all parameters are between 0.6 and 1.4. The CVI detection power of our study is 1, which is the highest value.

Systematic review and meta-analysis of within-subject and between-subject biological variation data of coagulation and fibrinolytic measurands

OBJECTIVES

The diagnosis and monitoring of bleeding and thrombotic disorders depend on correct haemostatic measurements. The availability of high-quality biological variation (BV) data is important in this context. Many studies have reported BV data for these measurands, but results are varied. The present study aims to deliver global within-subject (CVI) and between-subject (CVG) BV estimates for haemostasis measurands by meta-analyses of eligible studies, by assessment with the Biological Variation Data Critical Appraisal Checklist (BIVAC).

METHODS

Relevant BV studies were graded by the BIVAC. Weighted estimates for CVI and CVG were obtained via meta-analysis of the BV data derived from BIVAC-compliant studies (graded A-C; whereby A represents optimal study design) performed in healthy adults.

RESULTS

In 26 studies BV data were reported for 35 haemostasis measurands. For 9 measurands, only one eligible publication was identified and meta-analysis could not be performed. 74% of the publications were graded as BIVAC C. The CVI and CVG varied extensively between the haemostasis measurands. The highest estimates were observed for PAI-1 antigen (CVI 48.6%; CVG 59.8%) and activity (CVI 34.9%; CVG 90.2%), while the lowest were observed for activated protein C resistance ratio (CVI 1.5%; CVG 4.5%).

CONCLUSIONS

This study provides updated BV estimates of CVI and CVG with 95% confidence intervals for a wide range of haemostasis measurands. These estimates can be used to form the basis for analytical performance specifications for haemostasis tests used in the diagnostic work-up required in bleeding- and thrombosis events and for risk assessment.

Short-term biological variation of plasma uracil in a Caucasian healthy population

OBJECTIVES

Plasma uracil is a new biomarker to assess the activity of dihydropyrimidine dehydrogenase before cancer treatment with fluoropyrimidine drugs. Knowledge on the biological variation of plasma uracil is important to assess the applicability of plasma uracil as a biomarker of drug tolerance and efficacy.

METHODS

A total of 33 apparently healthy individuals were submitted to sequential blood draws for three days. On the second day, blood draws were performed every third hour for 12 h. Plasma uracil was quantified by LC-MS/MS. The within-subject (CVI) and between-subject (CVG) biological variation estimates were calculated using linear mixed-effects models.

RESULTS

The overall median value of plasma uracil was 10.6 ng/mL (range 5.6-23.1 ng/mL). The CVI and CVG were 13.5 and 22.1%, respectively. Plasma uracil remained stable during the day, and there was no day-to-day variation observed. No differences in biological variation components were found between sex and no correlation to age was found. Four samples were calculated to be required to estimate the homeostatic set-point ±15% with 95% confidence.

CONCLUSIONS

Plasma uracil is subject to tight homeostatic regulation without semidiurnal and day-to-day variation, however between-subject variation exists. This emphasizes plasma uracil as a well-suited biomarker for evaluation of dihydropyrimidine dehydrogenase activity, but four samples are required to establish the homeostatic set-point in a patient.

Biological variation of CA 15-3, CA 125 and HE 4 on lithium heparinate plasma in apparently healthy Caucasian volunteers

OBJECTIVES

Tumor markers are well-known for being important tools in the support of diagnosis, monitoring of treatment efficacy and follow-up of cancers. CA 125, CA 15-3 and HE 4 have demonstrated potential efficacy in other clinical indications. The main objective was to evaluate the biological variation of these glycoproteins using two different immunoassays in an apparently healthy Caucasian population.

METHODS

Nineteen healthy volunteers including 11 women and 8 men were sampled weekly for 5 consecutive weeks. Samples were analyzed in duplicate on Lumipulse® G600II (Fujirebio) and on the Cobas e602 (Roche Diagnostics) analyzers. After assessment of normality, exclusion of outliers and analysis of homogeneity of variance, analytical variation (CVA), within-subject biological variation (CVI) and between-subject biological variation (CVG) were determined using a nested ANOVA.

RESULTS

CVA, CVI and CVG were determined on both analyzers and both genders. For CA 125, the CVA ranges from 1.0 to 3.4%, the CVI from 5.7 to 13.8% and the CVG from 32.2 to 42.9%. For CA 15-3, the CVA is between 1.1 and 3.4%, the CVI between 3.9 and 6.5% and the CVG between 43.7 and 196.9%. Lastly, HE 4 has CVA values between 1.4 and 2.4%, CVI between 5.1 and 10.5% and CVG between 7.1 and 12.6%.

CONCLUSIONS

Our study provided updated data on the biological variation of CA 125, HE 4 and CA 15-3. These data allow to improve the clinical interpretation and thus the management of the patient.

Delta checks

Delta check is an electronic error detection tool. It compares the difference in sequential results within a patient against a predefined limit, and when exceeded, the delta check rule is considered triggered. The patient results should be withheld for review and troubleshooting before releasing to the clinical team for patient management. Delta check was initially developed as a tool to detect wrong-blood-in-tube (sample misidentification) errors. It is now applied to detect errors more broadly within the total testing process. Recent advancements in the theoretical understanding of delta check has allowed for more precise application of this tool to achieve the desired clinical performance and operational set up. In this Chapter, we review the different pre-implementation considerations, the foundation concepts of delta check, the process of setting up key delta check parameters, performance verification and troubleshooting of a delta check flag.

Physiological Rhythms and Biological Variation of Biomolecules: The Road to Personalized Laboratory Medicine

The concentration of biomolecules in living systems shows numerous systematic and random variations. Systematic variations can be classified based on the frequency of variations as ultradian (<24 h), circadian (approximately 24 h), and infradian (>24 h), which are partly predictable. Random biological variations are known as between-subject biological variations that are the variations among the set points of an analyte from different individuals and within-subject biological variation, which is the variation of the analyte around individuals’ set points. The random biological variation cannot be predicted but can be estimated using appropriate measurement and statistical procedures. Physiological rhythms and random biological variation of the analytes could be considered the essential elements of predictive, preventive, and particularly personalized laboratory medicine. This systematic review aims to summarize research that have been done about the types of physiological rhythms, biological variations, and their effects on laboratory tests. We have searched the PubMed and Web of Science databases for biological variation and physiological rhythm articles in English without time restrictions with the terms “Biological variation, Within-subject biological variation, Between-subject biological variation, Physiological rhythms, Ultradian rhythms, Circadian rhythm, Infradian rhythms”. It was concluded that, for effective management of predicting, preventing, and personalizing medicine, which is based on the safe and valid interpretation of patients’ laboratory test results, both physiological rhythms and biological variation of the measurands should be considered simultaneously.

Managing ethylenediaminetetraacetic acid (EDTA) interference in EDTA contaminated samples - selectivity in reporting analytes

BACKGROUND

The widely accepted practice of not reporting any results from ethylenediaminetetraacetic acid (EDTA) contaminated samples necessitates repeat phlebotomy and could delay clinical management decisions. EDTA, however, interferes variably or not at all in assays. EDTA concentration in contaminated samples, like serum indices, therefore, could be used to selectively report the result of analytes not affected at measured EDTA concentration.

METHODS

A serum pool, level 1 and level 3 internal quality control materials were spiked with tripotassium-EDTA to create samples with EDTA concentration up to 6.0 mmol/L. EDTA and 45 common and critically important analytes were measured on Abbott Architect to identify EDTA concentrations for analytes where the change in concentration exceeded their respective reference change value (RCV) for unidirectional change at 95% probability.

RESULTS

Serum potassium increased and calcium decreased exceeding RCV at 0.17 mmol/L EDTA. Alkaline phosphatase (ALP) decreased exceeding RCV at EDTA >1.86 mmol/L. The decrease in iron did not exceed a wide RCV of 61.9% until maximum spiked EDTA but exceeded the desirable specification for allowable total error (30.7%) at EDTA >1.86 mmol/L. The small decrease in magnesium did not exceed RCV. EDTA up to the concentration in blood collection tubes did not affect the results of any other measured analyte.

CONCLUSIONS

Only serum potassium, calcium, ALP and iron studies, of the 45 analytes studied, should not be reported in EDTA contaminated samples. EDTA concentration cut-offs for selective reporting would further facilitate reporting of these analytes in EDTA contaminated samples.

Long-Term Within- and Between-Subject Biological Variation Data of Hematological Parameters in Recreational Endurance Athletes

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.

Definition and application of performance specifications for measurement uncertainty of 23 common laboratory tests: linking theory to daily practice

Laboratories should estimate and validate [using analytical performance specifications (APS)] the measurement uncertainty (MU) of performed tests. It is therefore essential to appropriately define APS for MU, but also to provide a perspective on suitability of the practical application of these APS. In this study, 23 commonly ordered measurands were allocated to the models defined during the 2014 EFLM Strategic Conference to derive APS for MU. Then, we checked if the performance of commercial measuring systems used in our laboratory may achieve them. Most measurands (serum alkaline phosphatase, aspartate aminotransferase, creatine kinase, γ-glutamyltransferase, lactate dehydrogenase, pancreatic amylase, total proteins, immunoglobulin G, A, M, magnesium, urate, and prostate-specific antigen, plasma homocysteine, and blood red and white cells) were allocated to the biological variation (BV) model and desirable APS were defined accordingly (2.65%, 4.75%, 7.25%, 4.45%, 2.60%, 3.15%, 1.30%, 2.20%, 2.50%, 2.95%, 1.44%, 4.16%, 3.40%, 3.52%, 1.55%, and 5.65%, respectively). Desirable APS for serum total cholesterol (3.00%) and urine albumin (9.00%) were derived using outcome-based model. Lacking outcome-based information, serum albumin, high-density lipoprotein cholesterol, triglycerides, and blood platelets were temporarily reallocated to BV model, the corresponding desirable APS being 1.25%, 2.84%, 9.90%, and 4.85%, respectively. A mix between the two previous models was employed for serum digoxin, with a 6.00% desirable APS. In daily practice by using our laboratory systems, 16 tests fulfilled desirable and five minimum APS, while two (serum albumin and plasma homocysteine) exceeded goals, needing improvements.

Sex Hormones and Adrenal Steroids: Biological Variation Estimated Using Direct and Indirect Methods

BACKGROUND

Biological variation (BV) data may be used to develop analytical performance specifications (APS), reference change values (RCV), and support the applicability of population reference intervals. This study estimates within-subject BV (CVI) for several endocrine biomarkers using 3 different methodological approaches.

METHODS

For the direct method, 30 healthy volunteers were sampled weekly for 10 consecutive weeks. Samples were analyzed in duplicate for 17-hydroxyprogesterone (17-OHP), androstenedione, cortisol, cortisone, estradiol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), sex hormone-binding globulin (SHBG), and testosterone. A CV-ANOVA with outlier removal and a Bayesian model were applied to derive the CVI. For estradiol, FSH and LH, only the male subgroup was included. In the indirect method, using the same analytes and groups, pairs of sequential results were extracted from the laboratory information system. The total result variation for individual pairs was determined by identifying a central gaussian distribution in the ratios of the result pairs. The CVI was then estimated by removing the effect of analytical variation.

RESULTS

The estimated CVI from the Bayesian model (μCVP(i)) in the total cohort was: 17-OHP, 23%; androstenedione, 20%; cortisol, 18%; cortisone, 11%; SHBG, 7.4%; testosterone, 16%; and for the sex hormones in men: estradiol, 14%; FSH, 8%; and LH, 26%. CVI-heterogeneity was present for most endocrine markers. Similar CVI data were estimated using the CV-ANOVA and the indirect method.

CONCLUSIONS

Similar CVI data were obtained using 2 different direct and one indirect method. The indirect approach is a low-cost alternative ensuring implementation of CVI data applicable for local conditions.

Biological variation: recent development and future challenges

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.

Personalized reference intervals: from theory to practice

Using laboratory test results for diagnosis and monitoring requires a reliable reference to which the results can be compared. Currently, most reference data is derived from the population, and patients in this context are considered members of a population group rather than individuals. However, such reference data has limitations when used as the reference for an individual. A patient's test results preferably should be compared with their own, individualized reference intervals (RI), i.e. a personalized RI (prRI).The prRI is based on the homeostatic model and can be calculated using an individual's previous test results obtained in a steady-state situation and estimates of analytical (CV_A) and biological variation (BV). BV used to calculate the prRI can be obtained from the population (within-subject biological variation, CV_I) or an individual's own data (within-person biological variation, CV_P). Statistically, the prediction interval provides a useful tool to calculate the interval (i.e. prRI) for future observation based on previous measurements. With the development of information technology, the data of millions of patients is stored and processed in medical laboratories, allowing the implementation of personalized laboratory medicine. PrRI for each individual should be made available as part of the laboratory information system and should be continually updated as new test results become available.In this review, we summarize the limitations of population-based RI for the diagnosis and monitoring of disease, provide an outline of the prRI concept and different approaches to its determination, including statistical considerations for deriving prRI, and discuss aspects which must be further investigated prior to implementation of prRI in clinical practice.

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.

Internal quality control - past, present and future trends

Objectives

This paper offers an historical view, through a summary of the internal quality control (IQC) models used from second half of twentyth century to those performed today and wants to give a projection on how the future should be addressed.

Methods

The material used in this work study are all papers collected referring IQC procedures. The method used is the critical analysis of the different IQC models with a discussion on the weak and the strong points of each model.

Results

First models were based on testing control materials and using multiples of the analytical procedure standard deviation as control limits. Later, these limits were substituted by values related with the intended use of test, mainly derived from biological variation. For measurands with no available control material methods based on replicate analysis of patient' samples were developed and have been improved recently; also, the sigma metrics that relates the quality desired with the laboratory performance has resulted in a highly efficient quality control model. Present tendency is to modulate IQC considering the workload and the impact of analytical failure in the patent harm.

Conclusions

This paper remarks the strong points of IQC models, indicates the weak points that should be eliminated from practice and gives a future projection on how to promote patient safety through laboratory examinations.

Biological variation estimates obtained from Chinese subjects for 32 biochemical measurands in serum

OBJECTIVES

The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) have established a program of work to make available, and to enable delivery of well characterized data describing the biological variation (BV) of clinically important measurands. Guided by the EFLM work the study presented here delivers BV estimates obtained from Chinese subjects for 32 measurands in serum.

METHODS

Samples were drawn from 48 healthy volunteers (26 males, 22 females; age range, 21-45 years) for 5 consecutive weeks at Chinese laboratory. Sera were stored at -80 °C before triplicate analysis of all samples on a Cobas 8000 modular analyzer series. Outlier and homogeneity analyses were performed, followed by CV-ANOVA, to determine BV estimates with confidence intervals.

RESULTS

The within-subject biological variation (CVI) estimates for 30 of the 32 measurands studied, were lower than listed on the EFLM database; the exceptions were alanine aminotransferase (ALT), lipoprotein (a) (LP(a)). Most of the between-subject biological variation (CVG) estimates were lower than the EFLM database entries.

CONCLUSIONS

This study delivers BV data for a Chinese population to supplement the EFLM BV database. Population differences may have an impact on applications of BV Data.

Harmonization Status of Serum Ferritin Measurements and Implications for Use as Marker of Iron-Related Disorders

BACKGROUND

Serum ferritin is considered a suitable biomarker of iron-related disorders. However, data about the comparability of results among commercial measuring systems (MSs) are contradictory. We performed an intercomparison study aimed at verifying the current interassay variability and its impact on clinical application of the test. Obtaining this information is vital because manufacturers continue to claim calibration alignment to different WHO preparations, which are not related to each other in terms of traceability.

METHODS

Four widely used MSs were evaluated. The interassay agreement was verified using 39 human serum pools. The recovery of WHO International Standard (IS) 94/572 (the only reference material available at the time of the study) was evaluated, after assessing the material commutability. Finally, an approach for harmonizing ferritin results was proposed.

RESULTS

Highly significant differences (P < 0.00001) among ferritin concentrations assayed by different MSs were detected and the interassay CV (median 22.9%; interquartile range 21.8-25.5) overlapped the desirable intermethod bias (24.6%). IS 94/572 was commutable for use only with Access and Centaur, with Access being the only MS correctly recovering its assigned value. Accordingly, we used regression data against Access to recalibrate MSs, indirectly aligning them to IS 94/572, with a substantial improvement in degree of harmonization and traceability to higher-order reference.

CONCLUSIONS

The harmonization among evaluated ferritin MSs is far from optimal, with the implementation of traceability to different WHO ISs being a factor of confusion. A recalibration approach, however, would permit measurement harmonization, allowing the use of common decision thresholds.

Practical application of European biological variation combined with Westgard Sigma Rules in internal quality control

OBJECTIVES

Westgard Sigma Rules is a statistical tool available for quality control. Biological variation (BV) can be used to set analytical performance specifications (APS). The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) regularly updates BV data. However, few studies have used robust BV data to determine quality goals and design a quality control strategy for tumor markers. The aim of this study was to derive APS for tumor markers from EFLM BV data and apply Westgard Sigma Rules to establish internal quality control (IQC) rules.

METHODS

Precision was calculated from IQC data, and bias was obtained from the relative deviation of the External quality assurance scheme (EQAS) group mean values and laboratory-measured values. Total allowable error (TEa) was derived using EFLM BV data. After calculating sigma metrics, the IQC strategy for each tumor marker was determined according to Westgard Sigma Rules.

RESULTS

Sigma metrics achieved for each analyte varied with the level of TEa. Most of these tumor markers except neuron-specific enolase reached 3σ or better based on TEa_min. With TEa_des and TEa_opt set as the quality goals, almost all analytes had sigma values below 3. Set TEa_min as quality goal, each analyte matched IQC muti rules and numbers of control measurements according to sigma values.

CONCLUSIONS

Quality goals from the EFLM BV database and Westgard Sigma Rules can be used to develop IQC strategy for tumor markers.

Within-subject biological variation estimates using an indirect data mining strategy. Spanish multicenter pilot study (BiVaBiDa)

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.