European Biological Variation Study (EuBIVAS): within- and between-subject biological variation estimates for serum thyroid biomarkers based on weekly samplings from 91 healthy participants

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.

Evaluation of the Comparability of Wantai Wan200+ Instrument with Routine Laboratory Assays for 21 Different Analytes

Background: We compared the performance of 21 different assays performed by the Wantai Wan200+ (Wantai BioPharm, Beijing, China) with respect to other methods in use at the University Hospital of Padova (AOPD), Italy. Methods: The plasma (P) or serum (S) of 5027 leftover samples, collected from May to Sept 2023, was either analyzed or frozen at -20 °C. Beckman DXI800 (DXI), Roche Cobas 8000 e801 (RC), Snibe Maglumi 4000 plus (SM), DiaSorin Liaison XL (DL) and Binding Site Optilite (BS) equipment were used at the AOPD. P-procalcitonin (PCT), DXI; P-Troponin I (TnI), DXI; S-CA125, DXI; S-free PSA (f-PSA), DXI; S-total PSA (t-PSA), DXI; S-IL6, SM; P-Troponin T (TnT), RC; P-NT-proBNP, RC; P-Neuron-Specific Enolase (NSE), RC; S-CA15-3, DL; S-CA19-9, DL; S-AFP, DL; and S-CEA, DL were tested in fresh samples. P-Myoglobin (Myo), DXI; P-Cyfra21-1, RC; S-β2 microglobulin (B2MIC), BS; S-HE4, SM; S-PGI, SM; S-PGII, SM; S-CA72-4, SM; and S-CA50, SM were analyzed in frozen and thawed samples. Bland-Altman (BA), Passing-Bablok (PB) and Cohen's Kappa (CKa) metrics were used as statistics. Results: An excellent comparability profile was found for 11 analytes. For example, the t-PSA CKa was 0.94 (95%CI: 0.90 to 0.98), and the PB slope and intercept were 1.02 (95%CI: 0.99 to 1.03) and 0.02 (95%CI: 0.01 to 0.03), respectively; the BA bias was 2.25 (95%CI: -0.43 to 4.93). Ten tested measurands demonstrated a suboptimal comparability profile. Biological variation in EFLM (EuBIVAS) performance specifications was evaluated to assess the clinical relevance of measured biases. Conclusions: Evaluation of the Wantai Wan200+'s performance suggests that between-method differences did not exceed the calculated bias. Metrological traceability may influence the comparisons obtained for some measurands.

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.

Twin study: genotype-dependent epigenetic factors affecting free thyroxine levels in the normal range

Aim: To explore the clinical application of DNA methylation affecting thyroid function, we evaluated the association of DNA methylation with free thyroxine (FT4) and TSH measurements in monozygotic twins. Materials & methods: Discordant pairs for FT4 or TSH levels were examined for the relationship between the within-pair difference of each measurement and the DNA methylation levels using epigenome-wide association studies. The contribution of polymorphisms to the methylation sensitivity was also examined. Results: We found two CpG sites significantly associated with FT4 levels, and also some CpG sites showing significant differences in their methylation levels within FT4-discordant pairs depending on the polymorphism in EPHB2. Conclusion: The FT4 level may be associated with a combination of methylation and polymorphisms in the EPHB2 gene.

Biological variation in thyroid function tests in older adults and clinical implications

OBJECTIVE

Interpreting thyroid function tests can be challenging due to inherent variation, and the need for tests rises with age. While age-related changes in thyrotropin (TSH) levels are known, the biological variation in older adults remains unclear.

DESIGN

We recruited nineteen 65-99-year-old (older adults) without thyroid disease for monthly blood sampling for 1 year.

PATIENTS AND MEASUREMENTS

Serum was stored at -20C°, and TSH, total thyroxine (TT4) and total triiodothyronine (TT3) were analysed in random order in a single batch for each participant. Results were compared to test results from 15 euthyroid men aged 24-53 years (younger adults) collected previously using a similar methodology.

RESULTS

Interindividual coefficients of variation in older/younger adults were 46.7%/44.0% for TSH, 12.7%/19.5% for TT4 and 14.6%/22.4% for TT3. Intraindividual coefficients of variation (CVI ) were 19.0%/25.4% for TSH, 5.5%/10.8% for TT4 and 6.9%/13.2% for TT3. The index of individuality was below 0.6 for all hormones in all age groups. The number of samples required to determine the homoeostatic set-point at 10% precision in older adults was 14-21 for TSH and 2 for TT4 and TT3. TT4 in older adults was the only parameter in any group with comparable CVI between individuals (p = .22).

CONCLUSIONS

CVI for TT4 and TT3 was halved in older compared to younger adults with two tests of TT4 needed to describe the individual set-point. Similar CVI between older adults caused TT4 to provide a reliable estimate of thyroid function, and the added value of measuring thyroxine could improve clinical practice.

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.

Practical delta check limits for tumour markers in different clinical settings

OBJECTIVES

Few studies have reported on delta checks for tumour markers, even though these markers are often evaluated serially. Therefore, this study aimed to establish a practical delta check limit in different clinical settings for five tumour markers: alpha-fetoprotein, cancer antigen 19-9, cancer antigen 125, carcinoembryonic antigen, and prostate-specific antigen.

METHODS

Pairs of patients' results (current and previous) for five tumour markers between 2020 and 2021 were retrospectively collected from three university hospitals. The data were classified into three subgroups, namely: health check-up recipient (subgroup H), outpatient (subgroup O), and inpatient (subgroup I) clinics. The check limits of delta percent change (DPC), absolute DPC (absDPC), and reference change value (RCV) for each test were determined using the development set (the first 18 months, n=179,929) and then validated and simulated by applying the validation set (the last 6 months, n=66,332).

RESULTS

The check limits of DPC and absDPC for most tests varied significantly among the subgroups. Likewise, the proportions of samples requiring further evaluation, calculated by excluding samples with both current and previous results within the reference intervals, were 0.2-2.9% (lower limit of DPC), 0.2-2.7% (upper limit of DPC), 0.3-5.6% (absDPC), and 0.8-35.3% (RCV99.9%). Furthermore, high negative predictive values >0.99 were observed in all subgroups in the in silico simulation.

CONCLUSIONS

Using real-world data, we found that DPC was the most appropriate delta-check method for tumour markers. Moreover, Delta-check limits for tumour markers should be applied based on clinical settings.

Biological variation of serum thyrotropin and thyroid hormones concentrations determined at 8-week intervals for 1 year in clinically healthy cats

BACKGROUND

Cats commonly develop thyroid disease but little is known about the long-term biological variability of serum thyroid hormone and thyrotropin (thyroid-stimulating hormone; TSH) concentrations.

OBJECTIVES

We aimed to determine the long-term biological variation of thyroid hormones and TSH in clinically healthy cats.

METHODS

A prospective, observational study was carried out. Serum samples for analysis of total thyroxine (T4, by radioimmunoassay [RIA] and homogenous enzyme immunoassay [EIA]), triiodothyronine (T3 ), free T4 (by dialysis), and TSH were obtained every 8 weeks for 1 year from 15 healthy cats, then frozen until single-batch analysis. Coefficients of variation (CV) within individual cats (CV I ) and among individual cats (CV G ), as well as the variation between duplicates (ie, analytical variation [CV A ]) were determined with restricted maximum likelihood estimation. The indices of individuality (IoI) and reference change values (RCVs) for each hormone were calculated.

RESULTS

Some thyroid hormones showed similar (total T4 by EIA) or greater (TSH) interindividual relative to intraindividual variation resulting in intermediate to high IoI, consistent with previous studies evaluating the biological variation of these hormones weekly for 5-6 weeks. By contrast, total T4 (by RIA) and free T4 had a low IoI. Total T3 had a high ratio ofCV A toCV I ; therefore, interindividual variation could not be distinguished from analytical variation. No seasonal variability in the hormones could be demonstrated.

CONCLUSIONS

Clinicians might improve the diagnosis of feline thyroid disease by establishing baseline concentrations for analytes with intermediate-high IoI (total T4, TSH) for individual cats and applying RCVs to subsequent measurements.

A mathematical algorithm to harmonize measurements for thyroid-stimulating hormone between instruments

BACKGROUND

Thyroid-stimulating hormone (TSH) is measured differently between diagnostic units using different devices, which makes cross-comparisons challenging. Here, we have developed a mathematical algorithm to harmonize TSH measurements between 2 instruments, the Abbott ISR2000 and the Siemens ADVIA Centaur XP.

METHODS

Applying the principle of the maximum allowable error between the standard curve and real signal values, the minimum number of comparison samples required for TSH hormone detection was calculated for both instruments. Next, a mathematical algorithm describing the relationship between TSH standard curves from both instruments was established. The algorithm was then tested on sample measurements from both instruments, with signals transformed to Siemens ADVIA Centuar XP-type data. Finally, test results were assessed where the relative error was 

RESULTS

Before conversion, the mean percentage error between the TSH results of samples measured on both instruments was 23.20% (>1/2 TEa). After algorithmic transformation, the average percentage error was reduced to 7.93% (<1/2 TEa).

CONCLUSIONS

Our algorithm enables TSH measurements across different instruments to be comparable, and provides a method to harmonize TSH data between laboratories that utilize different instrumentation.

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Key Words

• Comparability
• Conversion
• Determination
• Mathematical algorithm
• Thyroid-stimulating hormone (TSH)

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MESH Headings

• Humans
• Thyrotropin
• Algorithms

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Grants Collapse

Affiliation(s)

Xin Zhou Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610041, China; Jintang First People's Hospital, Chengdu 610400, China
Zaishuan Liu Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610041, China
Yining Ma College of Mathematics of Sichuan University, Chengdu 610065, China
Chongwei Zhang Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610041, China
Yongkang Wu Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu 610041, China; Jintang First People's Hospital, Chengdu 610400, China.

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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.

Free thyroxine measurement in clinical practice: how to optimize indications, analytical procedures, and interpretation criteria while waiting for global standardization

Thyroid dysfunctions are among the most common endocrine disorders and accurate biochemical testing is needed to confirm or rule out a diagnosis. Notably, true hyperthyroidism and hypothyroidism in the setting of a normal thyroid-stimulating hormone level are highly unlikely, making the assessment of free thyroxine (FT4) inappropriate in most new cases. However, FT4 measurement is integral in both the diagnosis and management of relevant central dysfunctions (central hypothyroidism and central hyperthyroidism) as well as for monitoring therapy in hyperthyroid patients treated with anti-thyroid drugs or radioiodine. In such settings, accurate FT4 quantification is required. Global standardization will improve the comparability of the results across laboratories and allow the development of common clinical decision limits in evidence-based guidelines. The International Federation of Clinical Chemistry and Laboratory Medicine Committee for Standardization of Thyroid Function Tests has undertaken FT4 immunoassay method comparison and recalibration studies and developed a reference measurement procedure that is currently being validated. However, technical and implementation challenges, including the establishment of different clinical decision limits for distinct patient groups, still remain. Accordingly, different assays and reference values cannot be interchanged. Two-way communication between the laboratory and clinical specialists is pivotal to properly select a reliable FT4 assay, establish reference intervals, investigate discordant results, and monitor the analytical and clinical performance of the method over time.

Comparison of four indirect (data mining) approaches to derive within-subject biological variation

OBJECTIVES

Within-subject biological variation (CV _i ) is a fundamental aspect of laboratory medicine, from interpretation of serial results, partitioning of reference intervals and setting analytical performance specifications. Four indirect (data mining) approaches in determination of CV _i were directly compared.

METHODS

Paired serial laboratory results for 5,000 patients was simulated using four parameters, d the percentage difference in the means between the pathological and non-pathological populations, CV _i the within-subject coefficient of variation for non-pathological values, f the fraction of pathological values, and e the relative increase in CV _i of the pathological distribution. These parameters resulted in a total of 128 permutations. Performance of the Expected Mean Squares method (EMS), the median method, a result ratio method with Tukey's outlier exclusion method and a modified result ratio method with Tukey's outlier exclusion were compared.

RESULTS

Within the 128 permutations examined in this study, the EMS method performed the best with 101/128 permutations falling within ±0.20 fractional error of the 'true' simulated CV _i , followed by the result ratio method with Tukey's exclusion method for 78/128 permutations. The median method grossly under-estimated the CV _i . The modified result ratio with Tukey's rule performed best overall with 114/128 permutations within allowable error.

CONCLUSIONS

This simulation study demonstrates that with careful selection of the statistical approach the influence of outliers from pathological populations can be minimised, and it is possible to recover CV _i values close to the 'true' underlying non-pathological population. This finding provides further evidence for use of routine laboratory databases in derivation of biological variation components.

Biological variation estimates of thyroid related measurands - meta-analysis of BIVAC compliant studies

OBJECTIVES

Testing for thyroid disease constitutes a high proportion of the workloads of clinical laboratories worldwide. The setting of analytical performance specifications (APS) for testing methods and aiding clinical interpretation of test results requires biological variation (BV) data. A critical review of published BV studies of thyroid disease related measurands has therefore been undertaken and meta-analysis applied to deliver robust BV estimates.

METHODS

A systematic literature search was conducted for BV studies of thyroid related analytes. BV data from studies compliant with the Biological Variation Data Critical Appraisal Checklist (BIVAC) were subjected to meta-analysis. Global estimates of within subject variation (CV_I) enabled determination of APS (imprecision and bias), indices of individuality, and indicative estimates of reference change values.

RESULTS

The systematic review identified 17 relevant BV studies. Only one study (EuBIVAS) achieved a BIVAC grade of A. Methodological and statistical issues were the reason for B and C scores. The meta-analysis derived CV_I generally delivered lower APS for imprecision than the mean CV_A of the studies included in this systematic review.

CONCLUSIONS

Systematic review and meta-analysis of studies of BV of thyroid disease biomarkers have enabled delivery of well characterized estimates of BV for some, but not all measurands. The newly derived APS for imprecision for both free thyroxine and triiodothyronine may be considered challenging. The high degree of individuality identified for thyroid related measurands reinforces the importance of RCVs. Generation of BV data applicable to multiple scenarios may require definition using "big data" instead of the demanding experimental approach.

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

OBJECTIVES

The European Biological Variation Study (EuBIVAS), which includes 91 healthy volunteers from five European countries, estimated high-quality biological variation (BV) data for several measurands. Previous EuBIVAS papers reported no significant differences among laboratories/population; however, they were focused on specific set of measurands, without a comprehensive general look. The aim of this paper is to evaluate the homogeneity of EuBIVAS data considering multivariate information applying the Principal Component Analysis (PCA), a machine learning unsupervised algorithm.

METHODS

The EuBIVAS data for 13 basic metabolic panel linked measurands (glucose, albumin, total protein, electrolytes, urea, total bilirubin, creatinine, phosphatase alkaline, aminotransferases), age, sex, menopause, body mass index (BMI), country, alcohol, smoking habits, and physical activity, have been used to generate three databases developed using the traditional univariate and the multivariate Elliptic Envelope approaches to detect outliers, and different missing-value imputations. Two matrix of data for each database, reporting both mean values, and "within-person BV" (CV_P) values for any measurand/subject, were analyzed using PCA.

RESULTS

A clear clustering between males and females mean values has been identified, where the menopausal females are closer to the males. Data interpretations for the three databases are similar. No significant differences for both mean and CV_Ps values, for countries, alcohol, smoking habits, BMI and physical activity, have been found.

CONCLUSIONS

The absence of meaningful differences among countries confirms the EuBIVAS sample homogeneity and that the obtained data are widely applicable to deliver APS. Our data suggest that the use of PCA and the multivariate approach may be used to detect outliers, although further studies are required.

The European Biological Variation Study (EuBIVAS): a summary report

Biological variation (BV) data have many important applications in laboratory medicine. Concerns about quality of published BV data led the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) 1st Strategic Conference to indicate need for new studies to generate BV estimates of required quality. In response, the EFLM Working Group on BV delivered the multicenter European Biological Variation Study (EuBIVAS). This review summarises the EuBIVAS and its outcomes. Serum/plasma samples were taken from 91 ostensibly healthy individuals for 10 consecutive weeks at 6 European centres. Analysis was performed by Siemens ADVIA 2400 (clinical chemistry), Cobas Roche 8000, c702 and e801 (proteins and tumor markers/hormones respectively), ACL Top 750 (coagulation parameters), and IDS iSYS or DiaSorin Liaison (bone biomarkers). A strict preanalytical and analytical protocol was applied. To determine BV estimates with 95% CI, CV-ANOVA after analysis of outliers, homogeneity and trend analysis or a Bayesian model was applied. EuBIVAS has so far delivered BV estimates for 80 different measurands. Estimates for 10 measurands (Non-HDL Cholesterol, S100-β protein, neuron-specific enolase, soluble transferrin receptor, intact fibroblast growth-factor-23, uncarboxylated-unphosphorylated matrix-Gla protein, human epididymis protein-4, free, conjugated and %free prostate-specific antigen), prior to EuBIVAS, have not been available. BV data for creatinine and troponin I were obtained using two analytical methods in each case. The EuBIVAS has delivered high-quality BV data for a wide range of measurands. The BV estimates are for many measurands lower than those previously reported, having an impact on the derived analytical performance specifications and reference change values.