Reference Change Values for Brain Natriuretic Peptides Revisited

Within-subject biological variation estimated using real-world data strategies (RWD): a systematic review

Biological variation (BV) is defined as the variation in the concentration of a measurand around the homeostatic set point. This is a concept introduced by Fraser and Harris in the second part of the twentieth century. BV is divided into two different estimates: within-subject BV (CVI) and between-subject BV (CVG). Biological variation studies of biomarkers have been gaining importance in recent years due to the potential practical application of these estimates. The main applications of BV in the clinical laboratory include: the establishment of Analytical Performance Specifications (APS), estimation of the individual's homeostatic set point (HSP), calculation of Reference Change Value (RCV), estimation of individuality index calculation (II), and establishment of personalized reference intervals (prRI). The classic models for obtaining BV estimates have been the most used to date. In these studies, a target population ("normal" population), a sampling frequency and time, and a number of samples per individual, among other factors, are defined. The Biological Variation Data Critical Appraisal Checklist (BIVAC) established by the Task Group-Biological Variation Database (TG-BVD) of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) represents a guide for the evaluation and performance of these direct studies. These methods have limitations because they are laborious, expensive, invasive, and are based on an ideal population. In recent years, models have been proposed to obtain BV estimates based on the Real-World Data (RWD) strategy. In this case, we move from a model with a low number of individuals (direct methods) to a population model using the data stored in the Laboratory Information System (LIS). RWD methods are presented as an alternative to overcome the limitations of direct methods. Currently, there is little scientific evidence on the application of RWD models since only five papers have been published. In these papers, three different working algorithms are proposed (Loh et al., Jones et al., and Marques-Garcia et al.). These algorithms are divided into three fundamental stages for their development: patient data and study design, database(s) cleaning, and statistical strategies for obtaining BV estimates. When working with large amounts of data, RWD methods allow us to subdivide the population and thus obtain estimates into subgroups, what would be more difficult using direct methods. Of the three algorithms proposed, the algorithm developed in the Spanish multicenter project BiVaBiDa is the most complete, as it overcomes the limitations of the other two, including the possibility of calculating the confidence interval of the BV estimate. RWD methods also have limitations such as the anonymization of data and the standardization of electronic medical records, as well as the statistical complexity associated with data analysis. It is necessary to continue working on the development of RWD algorithms that allow us to obtain BV estimates that, which are as robust as possible.

Association between NT-proBNP changes and clinical outcomes in paediatric patients with heart failure: Insights from PANORAMA-HF and PARADIGM-HF

AIMS

The PANORAMA-HF trial demonstrated significant N-terminal pro-B-type natriuretic peptide (NT-proBNP) reductions in paediatric patients with left ventricular systolic dysfunction with sacubitril/valsartan or enalapril treatment over 52 weeks. This post hoc analysis aims to correlate changes in NT-proBNP levels with clinical outcomes in PANORAMA-HF patients receiving either sacubitril/valsartan or enalapril. Additionally, NT-proBNP reductions in the paediatric population were compared with a subset of adult heart failure with reduced ejection fraction (HFrEF) patients from the PARADIGM-HF trial.

METHODS AND RESULTS

This post hoc analysis utilized data from Part 2 of the PANORAMA-HF trial. Associations between baseline NT-proBNP levels, changes post-baseline and the risk of HF clinical events in paediatric patients on sacubitril/valsartan or enalapril were assessed. The paediatric HF population from PANORAMA-HF was categorized into age groups (AG): AG1 (aged 6 to <18 years), AG2a (aged 2 to <6 years) and AG3a (aged 1 month to <2 years). The Cox proportional hazard model evaluated the relationship between NT-proBNP and clinical outcomes. Analysis of 361 paediatric patients (sacubitril/valsartan, n = 179; enalapril, n = 182) demonstrated overall higher baseline NT-proBNP levels in younger AGs. At Week 52, both treatment groups exhibited reduced NT-proBNP levels across all AGs. Reductions were comparable between sacubitril/valsartan and enalapril, with a numerically greater reduction observed in adult patients versus children. Strong associations between NT-proBNP levels and HF clinical outcomes were observed in paediatric populations in PANORAMA-HF and in adult DCM patients with HFrEF in PARADIGM-HF. Doubling of NT-proBNP levels was associated with a ≥1.7-fold increased risk of HF clinical events, while halving of the levels correlated with a 52% reduction in the risk of clinical events.

CONCLUSIONS

This is the first prospective, randomized large-scale study to demonstrate a strong correlation between NT-proBNP levels and risks of HF clinical events in paediatric patients with HF.

Estimation of the biological variation of IGF-I in multimorbid geriatric patients and its clinical implications

PURPOSE

IGF-I is a well-established biomarker for detecting abnormalities in the growth hormone axis and evaluating effectiveness of growth hormone (GH) treatment. Common age-related diseases, such as sarcopenia are associated with impairments in the GH axis, making targeted GH therapy a potential treatment option. Nonetheless, data on the biological variation of IGF-I in older patients are missing, potentially leading to inaccurate interpretation of IGF-I concentrations in the diagnostic and therapeutic process. Our study aims to address this gap.

METHODS

We conducted a retrospective analysis of IGF-I concentrations measured in samples from the geriatric outpatient facility of the Ludwig-Maximilians-University Hospital, Munich and the respective patient data from the MUnich SArcopenia Registry (MUSAR). Using a mixed-effects model, we estimated the intraindividual biological coefficient of variation (CVi). We calculated the Reference Change Values (RCV) and the Index of Individuality (II).

RESULTS

246 serum samples from 89 patients (mean age 83 years, range 70-97) were analyzed. The CVi ranged from 13.4 to 15.6%, with a mean of 14.7%. RCV was 30.7% for a decrease and 44.3% for an increase in IGF-I concentrations. The II was 0.44.

CONCLUSION

The CVi of IGF-I in our cohort differs from that previously described in younger and healthier populations and is therefore crucial for identifying significant changes in this geriatric cohort. The high degree of individuality also supports the application of personalized reference intervals. Our study provides data on the biological variation of IGF-I concentrations in geriatric patients; the calculated RCVs have the potential to refine interpretation.

Biological Variation of Erythrocyte Total, Metal-Free, and Zinc Protoporphyrin IX in Patients with Erythropoietic Protoporphyria and Healthy Subjects: Implications for Clinical Interpretation and Monitoring

BACKGROUND

There is a lack of robust biological variation (BV) data for porphyria-related biomarkers. Our study aimed to estimate BV of erythrocyte total, metal-free, and zinc protoporphyrin IX in patients with erythropoietic protoporphyria (EPP) and healthy subjects and to explore the clinical implications of these data.

METHODS

Fourteen patients with EPP and 15 healthy subjects were sampled quarterly for 2 years, and erythrocyte protoporphyrin analyses were performed in duplicate in all samples. A Bayesian method was used to estimate the within-subject (CVI) and personal (CVP(i)) BV.

RESULTS

Based on clinical and laboratory assessments, EPP patients were stable during the study, with only 2 data points excluded. CVI in the EPP cohort was estimated as 9.8% (95% credible interval 8.5%-11.5%) for erythrocyte total protoporphyrin, 10.5% (9.0%-12.3%) for metal-free protoporphyrin, and 5.9% (4.3%-8.0%) for zinc protoporphyrin. Baseline metal-free protoporphyrin ranged from 6.9 to 139.8 µmol/L, but the CVP(i)s derived for each patient were similar (20th and 80th percentile of predicted distribution 9.5%-11.5%), and data were homogeneously distributed. Metal-free protoporphyrin was not measurable in the healthy cohort. Data for zinc protoporphyrin were heterogeneously distributed in both study cohorts.

CONCLUSIONS

The EPP patients had different set points for metal-free protoporphyrin, but the CVP(i) was similar, supporting the use of the same treatment goals when monitoring. This study is the first to use Bayesian analysis to demonstrate that personal BV is similar in patients with stable, chronic disease and different set points.

Biological variation of serum cholinesterase activity in healthy subjects

OBJECTIVES

Serum cholinesterase (ChE) 3.1.1.8 is measured to assess exposure to organophosphorus pesticides and determine deficiency related to prolonged apnea after the induction of anesthesia with certain drugs and less often as an indicator of liver function. Biological variation (BV) is an accepted endogenous source that contributes to the total variation in laboratory medicine. No data on the BV of serum ChE have been found in the European Federation of Clinical Chemistry and Laboratory Medicine BV database. Thus, this study aimed to contribute to the data on BV of serum ChE activity.

METHODS

Detailed inclusion and exclusion criteria were used for the enrollment of 20 (10 women and 10 men, 8-10 weeks) ostensibly healthy volunteers from Turkey. The serum ChE activity was measured on Roche Cobas c501. Statistical analyses included the detection of outliers, control for the normality of distribution, checking steady-state condition, assessment for homogeneity, subgroup analysis, analysis of variance with 95 % confidence intervals, and estimation of analytical performance specifications (APS).

RESULTS

After exclusion, 332 results were included in the study. The within-subject BV of men (3.5 % [2.9-4.2 %]) was lower than that of women (4.8 % [4.1-5.8 %]). Between-subject BV of men and women were 15.9 % [10.5-32.4 %] and 12.3 % [8.4-22.6 %], respectively. The index of individuality was 0.18 and reference change value (RCV) was +9.1 %/-8.3 %. The calculated desirable APS for imprecision and bias were 1.7 and 3.2 %, respectively.

CONCLUSIONS

We believe that this study will contribute to the BV data on serum ChE activity. The prominent individuality of serum ChE activity favors the use of RCV instead of population-based reference intervals for more reliable follow-up.

Unveiling the biological variation of lymphocyte subset counts: Insights from a full-spectrum flow cytometer and the BD MultitestTM 6-Color TBNK kit

BACKGROUND AND AIMS

To estimate the biological variation (BV) for lymphocyte subset counts in healthy adults based on full-spectrum flow cytometry (FS-FCM) and the most commonly used BD MultitestTM 6-Color TBNK kit in China.

MATERIALS AND METHODS

The study was designed according to the BV Data Critical Appraisal Checklist (BIVAC). Peripheral blood samples were collected from 60 healthy adults every two weeks for a period of 20 weeks (10 samples from each subject). Lymphocyte subsets were quantified using FS-FCM and the kit mentioned above. Bayesian models were used to analyze within-subject BV (CVI) and between-subject BV (CVG). Accordingly, the analytical performance specifications (APS) and more were derived. Additionally, the allowable total error (TEa) derived from the BV data in this study was compared with that based on state-of-the-art (SOTA).

RESULTS

The CVIs for the percentages of CD3+, CD3+CD4+, CD3+CD8+, CD3-CD19+, and CD3-CD16/CD56+ cells were 3.60 %, 7.05 %, 4.19 %, 10.73 %, and 19.17 %, respectively. The CVIs for the absolute counts were 13.99 %, 13.51 %, 16.19 %, 16.30 %, and 28.64 %, respectively. The CVGs for the percentages were 11.78 %, 21.33 %, 35.20 %, 33.69 %, and 44.36 %, and those for the absolute counts were 30.27 %, 28.84 %, 43.11 %, 46.69 %, and 49.21 %, respectively. No significant differences were observed in the CVI and CVG of males and females. The maximum allowable imprecision parameter based on the BV model was absolute CD3-CD16/56+ cell counts (14.3 %). For most lymphocyte subset parameters, TEa based on SOTA in China was less than the optimal TEa obtained from the BV data of this study.

CONCLUSIONS

To the best of our knowledge, this study is the first to estimate the BV of lymphocyte subset counts based on FS-FCM and the clinically commonly used BD MultitestTM 6-Color TBNK kit.

Determination of the biological variation and reference change value of lipoprotein (a)

BACKGROUND

Understanding lipoprotein (a) [Lp(a)] measurement variability is essential in establishing its coronary heart disease (CHD) association, and optimizing assessment and management of atherosclerotic cardiovascular disease (ASCVD) risk. We established the components of biological variation (BV) and reference change value (RCV) of Lp(a) in a UK cohort.

METHOD

22 healthy individuals were recruited to the study. Blood samples were collected for six consecutive weeks and analysed in duplicate using the Lp(a) assay by Sentinel Diagnostics on the Beckman Coulter AU5800. Outlier, heterogeneity, normality, and trend analysis were performed, followed by CV-ANOVA to determine estimates of BV, adhering to the 14 BIVAC quality items. RCV was calculated based on estimated CVA and CVI.

RESULTS

Four participants were excluded from the analysis as their mean Lp(a) levels fell below the functional sensitivity of the assay. Mean Lp(a) concentration ranged from 14 to 241 nmol/L. The overall estimate of CVI for all participants was 10.9% (95% CI of 9.1 - 13.0%). The RCV for Lp(a) was +31.6%/-24.0%.

CONCLUSION

Our study obtained a CVI estimate for Lp(a) that aligned consistently with recent studies adhering to the quality specifications outlined in the BIVAC checklist. The CVI estimate was significantly lower than Lp(a) estimates reported in studies up to 2003. The CVI estimate highlights the limitations of relying solely on a single Lp(a) measurement for prognosticating ASCVD risk and identifying candidates for novel Lp(a) therapies, particularly when the measured value is near clinical decision thresholds.

Proposal for delta check limits of frequently requested hormones using real-world data

Introduction

Research on delta check limits (DCLs) for hormones is limited, yet some laboratories apply arbitrary DCLs. We aimed to propose DCLs for commonly requested hormones.

Materials and methods

This study analyzed 59,657 paired results for adrenocorticotropic hormone (ACTH), cortisol, parathyroid hormone (PTH), prolactin, insulin, testosterone, and thyroglobulin from five Korean university hospitals. Delta check limits were established using the absolute delta difference (absDD) and absolute delta percent change (absDPC) with 5% cutoff for inpatients/emergencies (IE), outpatients (O) and both (combined; mean of them). Proportions outside the DCLs were compared across groups.

Results

Using absDD and absDPC, each group's DCLs showed 4.3% to 6.4% of values outside the DCLs, aligning with the 5% cutoff (excluding group IE for insulin, testosterone, and thyroglobulin due to < 1000 data pairs). Delta check limits of absDD differed between groups for ACTH, cortisol, PTH, and prolactin, while for absDPC, differences were seen only for ACTH and prolactin. Cross-validation revealed IE and O groups differed outside DCLs of absDD for ACTH, cortisol, and PTH, but only ACTH with absDPC. Combined DCLs of absDD showed ACTH and cortisol exceeded limits in 7.2% and 9.0% in IE, but only 2.6% and 0.6% in O. With absDPC, ACTH differed (10.4% in IE, 2.8% in O), while cortisol, PTH, and prolactin ranged from 4.0% to 6.1%.

Conclusions

Combined DCLs of absDPC are recommended for cortisol, PTH, and prolactin, while ACTH requires separate DCLs on clinical settings. These DCLs from real-world data provide a foundation for establishing DCLs of hormones in clinical laboratories.

Estimating Reference Change Values Using Routine Patient Data: A Novel Pathology Database Approach

BACKGROUND

The reference change value (RCV) is calculated by combining the within-subject biological variation (CVI) and local analytical variation (CVA). These calculations do not account for the variation seen in preanalytical conditions in routine practice or CVI in patients presenting for treatment. As a result, the RCVs may not reflect routine practice or align with clinicians' experiences. We propose a novel RCV approach based on routine patient data that is potentially more clinically relevant.

METHODS

This study used the refineR algorithm to determine RCVs using serial patient data extracted from a local Laboratory Information System (LIS). The model was applied to biomarkers with a range of result ratio distributions varying from normal to log-normal. Results were compared against conventional formula-based RCVs using CVI estimates from a state-of-the-art biological variation study. Monte Carlo simulations were also used to validate the LIS data approach.

RESULTS

The RCVs estimated from LIS data were: 11-deoxycortisol (men): -70%/+196%, 17-hydroxyprogesterone (men): -49%/+100%, albumin: -10%/+11%, androstenedione (men): -47%/+96%, cortisol (men): -54%/+51%, cortisone (men): -32%/+51%, creatinine: -16%/+14%, phosphate (women): -23%/+29%, phosphate (men): -27%/+29%, testosterone (men): -38%/+60%. The formula-based RCV estimates showed similar but slightly lower results, and the Monte Carlo simulations confirmed the applicability of the new approach.

CONCLUSIONS

RCVs may be estimated from patient results without prior assumptions about the shape of the ratios between serial results. Laboratories can determine RCVs based on local practice and population.

Time-dependent characteristics of analytical measurands

OBJECTIVES

Biological variation is a relevant component of diagnostic uncertainty. In addition to within-subject and between-subject variation, preanalytical variation also includes components that contribute to biological variability. Among these, daily recurring, i.e., diurnal physiological variation is of particular importance, as it contains both a random and a non-random component if the exact time of blood collection is not known.

METHODS

We introduce four time-dependent characteristics (TDC) of diurnal variations for measurands to assess the relevance and extent of time dependence on the evaluation of laboratory results.

RESULTS

TDC address (i) a threshold for considering diurnality, (ii) the expected relative changes per time unit, (iii) the permissible time interval between two blood collections at different daytimes within which the expected time dependence does not exceed a defined analytical uncertainty, and (iv) a rhythm-expanded reference change value. TDC and their importance will be exemplified by the measurands aspartate aminotransferase, creatine kinase, glucose, thyroid stimulating hormone, and total bilirubin. TDCs are calculated for four time slots that reflect known blood collection schedules, i.e., 07:00-09:00, 08:00-12:00, 06:00-18:00, and 00:00-24:00. The amplitude and the temporal location of the acrophase are major determinates impacting the diagnostic uncertainty and thus the medical interpretation, especially within the typical blood collection time from 07:00 to 09:00.

CONCLUSIONS

We propose to check measurands for the existence of diurnal variations and, if applicable, to specify their time-dependent characteristics as outlined in our concept.

Within- and between-subject biological variation data for whole blood HbA1c from 38 apparently healthy Turkish subjects

HbA1c plays an important role in the diagnosis and treatment of diabetes and is a valuable biomarker for evaluating glycemic control and predicting the risk of vascular complications. The study aimed to determine the biological variation (BV) for HbA1c and thereby contribute to analytical performance specifications, reference change values, and index of individuality. Fasting venous whole blood samples were collected from 38 presumably healthy subjects (20 females, 18 males) once a week for ten weeks, and analyzed in duplicate using the Roche Cobas c501 analyzer. BioVar, an online R-based biological variation analysis tool, was used for the statistical analysis. BV values were obtained by analysis of variance (ANOVA) after outlier detection, normality tests, steady-state, and homogeneity checks. The within-subject biological variation for HbA1c was 2.9%, and the between-subject biological variation was 7.9%. The index of the individuality of HbA1c was 0.37. Derived desirable analytical goals for imprecision, bias, total allowable error, and maximum expanded allowable measurement uncertainty were 1.4%, 1.8%, 4.2%, and 2.9% respectively. The reference change value is more appropriate for interpreting HbA1c results than a population-based reference interval.

Clinical utility of personalized reference intervals for CEA in the early detection of oncologic disease

OBJECTIVES

Personalized reference intervals (prRI) have been proposed as a diagnostic tool for assessing measurands with high individuality. Here, we evaluate clinical performance of prRI using carcinoembryonic antigen (CEA) for cancer detection and compare it with that of reference change values (RCV) and other criteria recommended by clinical guidelines (e.g. 25 % of change between consecutive CEA results (RV25) and the cut-off point of 5 μg/L (CP5)).

METHODS

Clinical and analytical data from 2,638 patients collected over 19 years were retrospectively evaluated. A total 15,485 CEA results were studied. For each patient, we calculated prRI and RCV using computer algorithms based on the combination of different strategies to assess the number of CEA results needed, consideration of one or two limits of reference interval and the intraindividual biological variation estimate (CVI) used: (a) publicly available (CVI-EU), (b) CVI calculated using an indirect method (CVI-NOO) and (c) within-person BV (CVP). For each new result identified falling outside the prRI, exceeding the RCV interval, RV25 or CP5, we searched for records identifying the presence of tumour at 3 and 12 months after the test. The sensitivity, specificity and predictive power of each strategy were calculated.

RESULTS

PrRI approaches derived using CVI-EU, and both limits of reference interval achieve the best sensitivity (87.5 %) and NPV (99.3 %) at 3 and 12 months of all evaluated criteria. Only 3 results per patients are enough to calculate prRIs that reach this diagnostic performance.

CONCLUSIONS

PrRI approaches could be an effective tool to rule out new oncological findings during the active surveillance of patients.

A New Concept for Reference Change Values-Regression to the Population Mean

BACKGROUND

Reference change values (RCV) are used to indicate a change in analyte concentration that is unlikely to be due to random variation in the patient or the measurement. Current theory describes RCV relative to a first measurement result (X1). We investigate an alternative view predicting the starting point for RCV calculations from X1 and its location in the reference interval.

METHODS

Data for serum sodium, calcium, and total protein from the European Biological Variation study and from routine clinical collections were analyzed for the effect of the position of X1 within the reference interval on the following result from the same patient. A model to describe the effect was determined, and an equation to predict the RCV for a sample in a population was developed.

RESULTS

For all data sets, the midpoints of the RCVs were dependent on the position of X1 in the population. Values for X1 below the population mean were more likely to be followed by a higher result, and X1 results above the mean were more likely to be followed by lower results. A model using population mean, reference interval dispersion, and result diagnostic variation provided a good fit with the data sets, and the derived equation predicted the changes seen.

CONCLUSIONS

We have demonstrated that the position of X1 within the reference interval creates an asymmetrical RCV. This can be described as a regression to the population mean. Adding this concept to the theory of RCVs will be an important consideration in many cases.

Variations in tumor growth, intra-individual biological variability, and the interpretation of changes

OBJECTIVES

The identification of changes in tumor markers (TMs) in cancer patients that indicate response to treatment, stabilization or disease progression is a challenge for laboratory medicine. Several approaches have been proposed: assessing percentage increases, applying discriminant values, and estimating half-life (t1/2) or doubling time (DT). In all of them it is assumed that the TM is a surrogate of the variation in tumor size. In general this variation is time-dependent, but this is not the case of intraindividual biological variability (CVi), which can range from 6 % in CA15-3 to 22 % in CA125. When decisions are made on the basis of DT or t1/2, these values can be affected by the CVi; if it is very large, the growth rate very slow and the period of time between determinations very short, the result obtained for DT may be due mainly to the CVi. The aim of this study is to establish the relationship between the CVi and temporal variables.

METHODS

We related equations for calculating DT and t1/2 to the reference change values in tumor markers.

RESULTS

The application of the formula obtained allows the calculation of the optimal time between measurements to ensure that the influence of the CVi is minimal in different types of tumors and different scenarios.

CONCLUSIONS

Intraindividual variation affects the calculation of DT and t1/2. It is necessary to establish the minimum time between two measurements to ensure that the CVi does not affect their calculation or lead to misinterpretation.

Accuracy of glomerular filtration rate estimation using creatinine and cystatin C for identifying and monitoring moderate chronic kidney disease: the eGFR-C study

Background

Estimation of glomerular filtration rate using equations based on creatinine is widely used to manage chronic kidney disease. In the UK, the Chronic Kidney Disease Epidemiology Collaboration creatinine equation is recommended. Other published equations using cystatin C, an alternative marker of kidney function, have not gained widespread clinical acceptance. Given higher cost of cystatin C, its clinical utility should be validated before widespread introduction into the NHS.

Objectives

Primary objectives were to: (1) compare accuracy of glomerular filtration rate equations at baseline and longitudinally in people with stage 3 chronic kidney disease, and test whether accuracy is affected by ethnicity, diabetes, albuminuria and other characteristics; (2) establish the reference change value for significant glomerular filtration rate changes; (3) model disease progression; and (4) explore comparative cost-effectiveness of kidney disease monitoring strategies.

Design

A longitudinal, prospective study was designed to: (1) assess accuracy of glomerular filtration rate equations at baseline (n = 1167) and their ability to detect change over 3 years (n = 875); (2) model disease progression predictors in 278 individuals who received additional measurements; (3) quantify glomerular filtration rate variability components (n = 20); and (4) develop a measurement model analysis to compare different monitoring strategy costs (n = 875).

Setting

Primary, secondary and tertiary care.

Participants

Adults (≥ 18 years) with stage 3 chronic kidney disease.

Interventions

Estimated glomerular filtration rate using the Chronic Kidney Disease Epidemiology Collaboration and Modification of Diet in Renal Disease equations.

Main outcome measures

Measured glomerular filtration rate was the reference against which estimating equations were compared with accuracy being expressed as P30 (percentage of values within 30% of reference) and progression (variously defined) studied as sensitivity/specificity. A regression model of disease progression was developed and differences for risk factors estimated. Biological variation components were measured and the reference change value calculated. Comparative costs of monitoring with different estimating equations modelled over 10 years were calculated.

Results

Accuracy (P30) of all equations was ≥ 89.5%: the combined creatinine-cystatin equation (94.9%) was superior (p < 0.001) to other equations. Within each equation, no differences in P30 were seen across categories of age, gender, diabetes, albuminuria, body mass index, kidney function level and ethnicity. All equations showed poor (< 63%) sensitivity for detecting patients showing kidney function decline crossing clinically significant thresholds (e.g. a 25% decline in function). Consequently, the additional cost of monitoring kidney function annually using a cystatin C-based equation could not be justified (incremental cost per patient over 10 years = £43.32). Modelling data showed association between higher albuminuria and faster decline in measured and creatinine-estimated glomerular filtration rate. Reference change values for measured glomerular filtration rate (%, positive/negative) were 21.5/-17.7, with lower reference change values for estimated glomerular filtration rate.

Limitations

Recruitment of people from South Asian and African-Caribbean backgrounds was below the study target.

Future work

Prospective studies of the value of cystatin C as a risk marker in chronic kidney disease should be undertaken.

Conclusions

Inclusion of cystatin C in glomerular filtration rate-estimating equations marginally improved accuracy but not detection of disease progression. Our data do not support cystatin C use for monitoring of glomerular filtration rate in stage 3 chronic kidney disease.

Trial registration

This trial is registered as ISRCTN42955626.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 11/103/01) and is published in full in Health Technology Assessment; Vol. 28, No. 35. See the NIHR Funding and Awards website for further award information.

Biological variation of 16 biochemical analytes estimated from a large clinicopathologic database of dogs and cats

BACKGROUND

Biochemical measurements are commonly evaluated using population-based reference intervals; however, there is a growing trend toward reassessing results with within-subject variation (CVI).

OBJECTIVES

We aimed to estimate the CVI of 16 biochemical analytes using a large database of dogs and cats, which refers to the results of routine health checkups.

METHODS

Pairs of sequential results for 16 analytes were extracted from a database of adult patients. The second result was divided by the first result to produce the ratio of sequential results (rr), and the frequency distribution of rr was plotted. From the plots, the coefficient of variation (CVrr) was calculated. Analytical variation (CVA) was calculated using quality control data, and CVI was estimated as follows:CV I = CV rr / 2 1 / 2 2 - CV A 2 1 / 2 . Estimated CVI was compared with previously reported CVI using the Bland-Altman plot analysis.

RESULTS

From the database, 9078 data points from 3610 dogs and 3743 data points from 1473 cats were extracted, with 5468 data pairs for dogs and 2270 for cats. Sampling intervals ranged from 10 to 1970 days (median 366) for dogs and 23 to 1862 days (median 365) for cats. Bland-Altman analysis showed most CVI plots fell within the limits of agreement; however, positive fixed biases were observed in both dogs and cats.

CONCLUSIONS

Our study introduces a novel approach of estimating CVI using routine health checkup data in dogs and cats. Despite biases, our method holds promise for clinical application in assessing the significance of measurement result differences.

Emerging Biomarkers in the Laboratory and in Practice: A Novel Approach to Diagnosing Heart Failure in Diabetes

The Biomarkers for the Diagnosis of Heart Failure in Diabetes webinar was hosted by Diabetes Technology Society on September 20, 2023, with the objective to review current evidence and management practices of biomarker screening for heart failure in people with diabetes. The webinar discussed (1) the four stages of heart failure, (2) diabetes and heart failure, (3) natriuretic peptide and troponin for diagnosing heart failure in diabetes, (4) emerging composite and investigational biomarkers for diagnosing heart failure, and (5) prevention of heart failure progression. Experts in heart failure from the fields of clinical chemistry, cardiology, and diabetology presented data about the importance of screening for heart failure as an often-unnoticed complication of people with type 1 and type 2 diabetes.

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.

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.

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.

Evaluation of Serum Creatinine Levels with Reference Change Value in Patients Receiving Colistin Treatment

OBJECTIVE

In this study, we aimed to evaluate the serum creatinine (SCr) levels with the reference change value (RCV) in patients receiving colistin treatment.

METHODS

We retrospectively recorded the SCr levels of 47 patients receiving colistin treatment before treatment and on days 3 and 7 after treatment. RCV was calculated with the asymmetrical RCV formula (Z = 1.64, P < .05). Percent (%) increase in the SCr results of the patients was compared with RCV and values exceeding RCV were regarded as statistically significant.

RESULTS

The RCV was calculated as 15.6% for SCr. Compared with pretreatment values, SCr value on day 3 was 32/47 and on day 7 it was 36/47; as these results exceeded RCV, they were considered statistically significant.

CONCLUSION

Use of RCV in the interpretation of results between serial measurements will provide a more rapid and sensitive method when making decisions.

Biological variation of biochemical urine and serum analytes in healthy dogs

BACKGROUND

Biological variation (BV) of urinary (U) biochemical analytes has not been described in absolute terms, let alone as a ratio of the U-creatinine or fractional excretion in healthy dogs. These analytes are potential diagnostic tools for different types of kidney damage and electrolyte disorders in dogs.

OBJECTIVES

We aimed to investigate the BV of specific gravity, osmolality, creatinine, urea, protein, glucose, chloride, sodium, potassium, calcium, and phosphate in urine from healthy pet dogs.

METHODS

Blood and urine samples from 13 dogs were collected once weekly for 8 weeks. Samples were analyzed in duplicate and in randomized order. For each sample, U-analyte and serum concentrations were measured, and U-analyte/U-creatinine and fractional excretion (FE) were calculated. Components of variance, estimated by restricted maximum likelihood, were used to determine within-subject variation (CVI ), between-subject variation (CVG ), and analytical variation (CVA ). Index of individuality (II) and reference change values were calculated.

RESULTS

CVI for all urine analytes varied between 12.6% and 35.9%, except for U-sodium, U-sodium/U-Cr, and FE-sodium, which had higher CVI s (59.5%-60.7%). For U-protein, U-sodium, U-potassium, U-sodium/U-creatinine, FE-urea, FE-glucose, FE-sodium, FE-potassium, and FE-phosphate II were low, indicating that population-based RIs were appropriate. The remaining analytes had an intermediate II, suggesting that population-based RIs should be used with caution.

CONCLUSION

This study presents information on the biological variation of urinary and serum biochemical analytes from healthy dogs. These data are important for an appropriate interpretation of laboratory results.

Biomarkers for the Diagnosis of Heart Failure in People with Diabetes: A Consensus Report from Diabetes Technology Society

Diabetes Technology Society assembled a panel of clinician experts in diabetology, cardiology, clinical chemistry, nephrology, and primary care to review the current evidence on biomarker screening of people with diabetes (PWD) for heart failure (HF), who are, by definition, at risk for HF (Stage A HF). This consensus report reviews features of HF in PWD from the perspectives of 1) epidemiology, 2) classification of stages, 3) pathophysiology, 4) biomarkers for diagnosing, 5) biomarker assays, 6) diagnostic accuracy of biomarkers, 7) benefits of biomarker screening, 8) consensus recommendations for biomarker screening, 9) stratification of Stage B HF, 10) echocardiographic screening, 11) management of Stage A and Stage B HF, and 12) future directions. The Diabetes Technology Society panel recommends 1) biomarker screening with one of two circulating natriuretic peptides (B-type natriuretic peptide or N-terminal prohormone of B-type natriuretic peptide), 2) beginning screening five years following diagnosis of type 1 diabetes (T1D) and at the diagnosis of type 2 diabetes (T2D), 3) beginning routine screening no earlier than at age 30 years for T1D (irrespective of age of diagnosis) and at any age for T2D, 4) screening annually, and 5) testing any time of day. The panel also recommends that an abnormal biomarker test defines asymptomatic preclinical HF (Stage B HF). This diagnosis requires follow-up using transthoracic echocardiography for classification into one of four subcategories of Stage B HF, corresponding to risk of progression to symptomatic clinical HF (Stage C HF). These recommendations will allow identification and management of Stage A and Stage B HF in PWD to prevent progression to Stage C HF or advanced HF (Stage D HF).

Biological Variation and Reference Change Value of Routine Hematology Measurands in a Population of Managed Bottlenose Dolphins (Tursiops truncatus)

Hematological analyses are particularly useful in assessing a dolphin's health status. However, the creation of appropriate reference intervals for this species is difficult due to the low number of reference individuals. The implementation of individual reference intervals (iRIs) allows researchers to overcome this limitation and, moreover, also consider the within-individual variability. The aims of this study were (1) to evaluate the biological variations in some hematological measurands, including erythrocytes (RBC), hematocrit (Hct), mean cellular volume and hemoglobin content (MCV and MCHC, respectively), RBC distribution width (RDW), leukocytes (WBC), and platelets (PLT); and (2) to calculate the index of individuality (IoI) and reference change value (RCV), which enable the production of iRIs, in healthy managed bottlenose dolphins. Seven dolphins were included, and the results of six hematological exams were analyzed for each animal. Analytical imprecision (CV_a), within-dolphin variation (CV_i), and between-dolphins variations (CV_g) were calculated, and the IoI and RCV were derived for each measurand. All the hematological measurands had intermediate IoI except WBC, for which Iol was low. The calculated RCV ranged from 10.33% (MCV) to 186.51% (WBC). The results reveal that the majority of hematological measurands have an intermediate level of individuality in dolphins, and thus the application of iRIs is appropriate. The calculated RCV can also be applied to other managed dolphins and could be useful in interpreting serial CBC exams.

Biological variation of biochemical analytes determined at 8-week intervals for 1 year in clinically healthy cats

BACKGROUND

Biological variation helps determine whether population-based or subject-based reference intervals are more appropriate to assess changes in serial analytical values. Previous studies have investigated the biological variation of biochemical analytes weekly or with variable frequency over 5-14 weeks in cats, but none have considered biological variation at less frequent intervals over 1 year.

OBJECTIVES

We aimed to evaluate the long-term biological variation of 19 biochemical analytes in clinically healthy cats.

METHODS

A prospective, observational study in which 15 clinically healthy, client-owned cats were sampled for serum biochemical analyses every 8 weeks for 1 year. Frozen serum samples were single-batch analyzed. Restricted maximum likelihood estimation was used to determine the coefficients of variation (CV), describing variation within each cat, between cats, and the analytical variation. These CVs were used to determine the indices of individuality and reference change values (RCVs).

RESULTS

Albumin, alkaline phosphatase, creatine kinase, and globulin had high indices of individuality, indicating that they are best evaluated by RCVs. Phosphorus, potassium, chloride, sodium, symmetric dimethylarginine, and total CO₂ had low indices of individuality, indicating that population-based reference intervals are appropriate. Alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, calcium, cholesterol, creatinine, glucose, total bilirubin, and total protein had intermediate indices of individuality, indicating that RCVs may provide additional insight into the interpretation of analyte measurements beyond the population-based reference intervals.

CONCLUSIONS

For many analytes, the biological variation detected was similar to that reported in prior studies. Clinicians should consider the biological variation of analytes to best interpret clinically relevant changes in serial analyte measurements.

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.

Reference change value of global longitudinal strain in clinical practice: A test-rest quality implementation project

BACKGROUND

Reference change value (RCV) is used to assess the significance of the difference between two measurements after accounting for pre-analytic, analytic, and within-subject variability. The objective of the current study was to define the RCV for global longitudinal strain (GLS) using different semi-automated software in standard clinical practice.

METHODS

Using a test-retest study design, we quantified the median coefficient of variation (CV) for GLS using AutoStrain and Automated Cardiac Motion Quantification (aCMQ) by Philips. Triplane left-ventricular ejection fraction (LVEF) was measured for comparison. Multivariable regression analysis was performed to determine factors influencing test-retest CV including image quality and the presence of segmental wall motion abnormalities (WMA). RCV was reported using a standard formula assuming two standard deviations for repeated measurements; results were also translated into Bayesian probability. Total measurement variation was described in terms of its three different components: pre-analytic (acquisition), analytic (measuring variation), and within-subject (biological) variation.

RESULT

Of the 44 individuals who were screened, 41 had adequate quality for strain quantification. The mean age of the cohort was 56.4 ± 16.8 years, 41% female, LVEF was 55.8 ± 9.8% and the median and interquartile range for LV GLS was -17.2 [-19.3 to -14.8]%. Autostrain was more time efficient (80% less analysis time) and had a lower total median CV than aCMQ (CV = 7.4% vs. 17.6%, p < .001). The total CV was higher in patients with WMA (6.4% vs. 13.2%, p = .035). In non-segmental disease, the CV translates to a RCV of 15% (corresponding to a probability of real change of 80%). Assuming a within-subject variability of 4.0%, the component analysis identified that inter-reader variability accounts for 3.7% of the CV, while acquisition variability accounts for 4.0%.

CONCLUSION

Using test-retest analysis and CVs, we find that an RCV of 15% for GLS represents an optimistic estimate in routine clinical practice. Based on our results, a higher RCV of 17%-21% is needed in order to provide a high probability of clinically meaningful change in GLS in all comers. The methodology presented here for determining measurement reproducibility and RCVs is easily translatable into clinical practice for any imaging parameter.

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.

Short-term biological variation of serum glial fibrillary acidic protein

OBJECTIVES

Serum glial fibrillary acidic protein (GFAP) is an emerging biomarker for intracerebral diseases and is approved for clinical use in traumatic brain injury. GFAP is also being investigated for several other applications, where the GFAP changes are not always outstanding. It is thus essential for the interpretation of GFAP to distinguish clinical relevant changes from natural occurring biological variation. This study aimed at estimating the biological variation of serum GFAP.

METHODS

Apparently healthy subjects (n=33) had blood sampled for three consecutive days. On the second day, blood was also drawn every third hour from 9 AM to 9 PM. Serum GFAP was measured by Single Molecule Array (Simoa™). Components of biological variation were estimated in a linear mixed-effects model.

RESULTS

The overall median GFAP value was 92.5 pg/mL (range 34.4-260.3 pg/mL). The overall within- (CV_I) and between-subject variations (CV_G) were 9.7 and 39.5%. The reference change value was 36.9% for an increase. No day-to-day variation was observed, however semidiurnal variation was observed with increasing GFAP values between 9 AM and 12 PM (p<0.00001) and decreasing from 12 to 9 PM (p<0.001).

CONCLUSIONS

Serum GFAP exhibits a relatively low CV_I but a considerable CV_G and a marked semidiurnal variation. This implies caution on the timing of blood sampling and when interpreting GFAP in relation to reference intervals, especially in conditions where only small GFAP differences are observed.

The intra-individual variation of cardiac troponin I: the effects of sex, age, climatic season, and time between samples

OBJECTIVES

Knowing the intra-individual variation (CVi), also termed within subject biological variation, of an analyte is essential to properly interpret apparent changes in concentration. While there have been many studies assessing the CVi of cardiac troponin (cTnI), they have been limited in looking at CVi in different settings, and there is no data available on whether CVi might change in different settings.

METHODS

We used our large cTnI data bank to look at the CVi of cTnI in Emergency Department (ED) patients who had an acute myocardial infarction event excluded. We looked at the effects of gender, age, climatic season, and time between samples to assess whether CVi changed. To assess the effect of age, after exclusion, we collected two samples from each subject for each study which were used to calculate the CVi between those identified groups. There were 139 males and 98 females aged <65 years and 109 males and 98 females aged ≥65 years. For gender and season, there were 122 males and 94 females in the summer period and 126 males and 102 females in the winter period. To assess long term variation there were 195 males and 153 females who had further admissions after more than 12 months.

RESULTS

For the four variables listed, there were no significant differences in within individual variation (CVi), but there was a significant difference in between individual variation (CVg) for men and women with regard to age. The Index of Individuality (II) was <0.20 for all conditions studied. We noted that >90% of subjects had an reference change value (RCV) <9 ng/L.

CONCLUSIONS

Because troponin concentration in patients without an identified cardiac condition change so little, delta changes are potentially of great value in assessing patients in the ED. Significant delta changes in troponin can occur without the 99th percentile being exceeded.

Impact of ultra-low temperature long-term storage on the preanalytical variability of twenty-one common biochemical analytes

OBJECTIVES

Retrospective studies frequently assume analytes long-term stability at ultra-low temperatures. However, these storage conditions, common among biobanks and research, may increase the preanalytical variability, adding a potential uncertainty to the measurements. This study is aimed to evaluate long-term storage stability of different analytes at <-70 °C and to assess its impact on the reference change value formula.

METHODS

Twenty-one analytes commonly measured in clinical laboratories were quantified in 60 serum samples. Samples were immediately aliquoted and frozen at <-70 °C, and reanalyzed after 11 ± 3.9 years of storage. A change in concentration after storage was considered relevant if the percent deviation from the baseline measurement was significant and higher than the analytical performance specifications.

RESULTS

Preanalytical variability (CV_P) due to storage, determined by the percentage deviation, showed a noticeable dispersion. Changes were relevant for alanine aminotransferase, creatinine, glucose, magnesium, potassium, sodium, total bilirubin and urate. No significant differences were found in aspartate aminotransferase, calcium, carcinoembryonic antigen, cholesterol, C-reactive protein, direct bilirubin, free thryroxine, gamma-glutamyltransferase, lactate dehydrogenase, prostate-specific antigen, triglycerides, thyrotropin, and urea. As nonnegligible, CV_P must remain included in reference change value formula, which was modified to consider whether one or two samples were frozen.

CONCLUSIONS

After long-term storage at ultra-low temperatures, there was a significant variation in some analytes that should be considered. We propose that reference change value formula should include the CV_P when analyzing samples stored in these conditions.

Estimating the within‐subject (CV I ) and between‐subject (CV G ) biological variation of serum tryptase

Background

Methods

Results

Conclusions

Biological variation of cardiac myosin-binding protein C in healthy individuals

OBJECTIVES

Cardiac myosin-binding protein C (cMyC) is a novel biomarker of myocardial injury, with a promising role in the triage and risk stratification of patients presenting with acute cardiac disease. In this study, we assess the weekly biological variation of cMyC, to examine its potential in monitoring chronic myocardial injury, and to suggest analytical quality specification for routine use of the test in clinical practice.

METHODS

Thirty healthy volunteers were included. Non-fasting samples were obtained once a week for ten consecutive weeks. Samples were tested in duplicate on the Erenna® platform by EMD Millipore Corporation. Outlying measurements and subjects were identified and excluded systematically, and homogeneity of analytical and within-subject variances was achieved before calculating the biological variability (CVI and CVG), reference change values (RCV) and index of individuality (II).

RESULTS

Mean age was 38 (range, 21-64) years, and 16 participants were women (53%). The biological variation, RCV and II with 95% confidence interval (CI) were: CVA (%) 19.5 (17.8-21.6), CVI (%) 17.8 (14.8-21.0), CVG (%) 66.9 (50.4-109.9), RCV (%) 106.7 (96.6-120.1)/-51.6 (-54.6 to -49.1) and II 0.42 (0.29-0.56). There was a trend for women to have lower CVG. The calculated RCVs were comparable between genders.

CONCLUSIONS

cMyC exhibits acceptable RCV and low II suggesting that it could be suitable for disease monitoring, risk stratification and prognostication if measured serially. Analytical quality specifications based on biological variation are similar to those for cardiac troponin and should be achievable at clinically relevant concentrations.

Critical review and meta-analysis of biological variation estimates for tumor markers

OBJECTIVES

Biological variation data (BV) can be used for different applications, but this depends on the availability of robust and relevant BV data. In this study, we aimed to summarize and appraise BV studies for tumor markers, to examine the influence of study population characteristics and concentrations on BV estimates and to discuss the applicability of BV data for tumor markers in clinical practice.

METHODS

Studies reporting BV data for tumor markers related to gastrointestinal, prostate, breast, ovarian, haematological, lung, and dermatological cancers were identified by a systematic literature search. Relevant studies were evaluated by the Biological Variation Data Critical Appraisal Checklist (BIVAC) and meta-analyses were performed for BIVAC compliant studies to deliver global estimates of within-subject (CV_I) and between-subject (CV_G) BV with 95% CI.

RESULTS

The systematic review identified 49 studies delivering results for 22 tumor markers; four papers received BIVAC grade A, 3 B, 27 C and 15 D. Out of these, 29 CV_I and 29 CV_G estimates met the criteria to be included in the meta-analysis. Robust data are lacking to conclude on the relationship between BV and different disease states and tumor marker concentrations.

CONCLUSIONS

This review identifies a lack of high-quality BV studies for many tumor markers and a need for delivery of BIVAC compliant studies, including in different, disease states and tumor marker concentrations. As of yet, the state-of-the-art may still be the most appropriate model to establish analytical performance specifications for the majority of tumor markers.

GFR estimation is complicated by a high incidence of non-steady-state serum creatinine concentrations at the emergency department

Background

Acquiring a reliable estimate of glomerular filtration rate (eGFR) at the emergency department (ED) is important for clinical management and for dosing renally excreted drugs. However, renal function formulas such as CKD-EPI can give biased results when serum creatinine (SCr) is not in steady-state because the assumption that urinary creatinine excretion is constant is then invalid. We assessed the extent of this by analysing variability in SCr in patients who visited the ED of a tertiary care centre.

Methods

Data from ED visits at the University Medical Centre Utrecht, the Netherlands between 2012 and 2019 were extracted from the Utrecht Patient Oriented Database. Three measurement time points were defined for each visit: last SCr measurement before visit as baseline (SCr-BL), first measurement during visit (SCr-ED) and a subsequent measurement between 6 and 24 hours during admission (SCr-H1). Non-steady-state SCr was defined as exceeding the Reference Change Value (RCV), with 15% decrease or 18% increase between successive SCr measurements. Exceeding the RCV was deemed as a significant change.

Results

Of visits where SCr-BL and SCr-ED were measured (N = 47,540), 28.0% showed significant change in SCr. Of 17,928 visits admitted to the hospital with a SCr-H1 after SCr-ED, 27,7% showed significant change. More than half (55%) of the patients with SCr values available at all three timepoints (11,054) showed at least one significant change in SCr over time.

Conclusion

One third of ED visits preceded and/or followed by creatinine measurement show non-stable serum creatinine concentration. At the ED automatically calculated eGFR should therefore be interpreted with great caution when assessing kidney function.

Biological variation of secretoneurin; a novel cardiovascular biomarker implicated in arrhythmogenesis

BACKGROUND

Secretoneurin is a novel prognostic biomarker that may predict mortality in heart failure and the occurrence of ventricular arrhythmias. This study reports the within subject variation (CVI), between subject variation (CVG), reference change values (RCV) and index of individuality (II) of secretoneurin.

METHODS

Thirty healthy volunteers were included. Non-fasting samples were obtained between 8 and 10 am once a week for ten weeks. Secretoneurin was analyzed in duplicate using ELISA. No outliers were present according to Burnett and Reeds' criteria. Simple linear regression did not identify significant trends. Variance homogeneity in the analytical variance and CVI were tested using Cochrane's and Bartlett's tests and four participants were excluded. Calculation of CVI, CVG and RCV were done on ln transformed data as described by Fokkema, the II was calculated using retransformed data.

RESULTS

The median age of the participants was 36 years and 53% were female. Non-fasting glucose, eGFR(CKD-EPI), cTnT and NT-proBNP concentrations were within the normal range. Median secretoneurin concentrations were 38 pmol/L (women) and 33 pmol/L (men), p-value < 0.001. CVI and CVG were 9.8% (CI 8.7% to 11.0%) and 20.0 (CI 15.4% to 28.0%), respectively. RCV were 38.7% (CI 35.5% to 42.7%) and -27.9 (CI -29.9 to -26.2) and the II were 0.60 (CI 0.42-0.78). No gender differences were present.

CONCLUSION

Secretoneurin has a fairly low CVI, CVG, RCV and II, indicating that it could be suitable as a diagnostic or prognostic biomarker and that delta values in serial samplings may be preferable for identifying clinical changes.

Reference change value and measurement uncertainty in the evaluation of tumor markers

The use of measurement uncertainty among clinical laboratories becomes widespread. Measurement uncertainty can be reported with the result, as well as be used in certain reference change value (RCV) calculation equations. RCV is especially recommended for use in tests with a low individuality index. In our study, we calculated the measurement uncertainty of AFP, CA 125, CA 15-3, CA 19-9, CEA tumor markers with the ISO TS 20914:2019. We compared results with limits. Two Beckman Coulter DXI-800 (Minnesota, USA) autoanalysers' results were used. We calculated the RCV values using the classical Fraser method, logarithmic Lund Method, and Clinical Laboratory Standards Institute (CLSI) method as Minimal Difference (MD). We found the same permissible measurement uncertainty limit as 15.97% for all five tumor markers. The highest RCV value was found as 90% upstream for AFP test with Lund logarithmic approach, the lowest RCV value was found as 12% for CEA with MD, all other RCV results were between these two values. We do not recommend the use of MD, as values for Biological variation are not used in the MD approach. We also recommend using the logarithmic approach, although it gives higher results. There are also clinical studies on the significance of tumor markers in a follow-up that show different results. These differences may be because the studies are conducted with different systems. Therefore, each laboratory needs to calculate its own RCV values. We also recommend informing the clinicians about the tests with high measurement uncertainty.

Short- and long-term biological variation of cardiac troponin I in healthy individuals, and patients with end-stage renal failure requiring haemodialysis or cardiomyopathy

Objectives High-sensitivity (hs) cardiac troponin (cTn) assays can quantitate small fluctuations in cTn concentration. Determining biological variation allows calculation of reference change values (RCV), to define significant changes. We assessed the short- and long-term biological variation of cardiac troponin I (cTnI) in healthy individuals and patients with renal failure requiring haemodialysis or cardiomyopathy. Methods Plasma samples were collected hourly for 4 h and weekly for seven further weeks from 20 healthy individuals, 9 renal failure patients and 20 cardiomyopathy patients. Pre- and post-haemodialysis samples were collected weekly for 7 weeks. Samples were analysed using a hs-cTnI assay (Abbott Alinity ci-series). Within-subject biological variation (CVI), analytical variation (CVA) and between-subject biological variation (CVG) was used to calculate RCVs and index of individuality (II). Results For healthy individuals, CVI, CVA, CVG, RCV and II values were 8.8, 14.0, 43.1, 45.8% and 0.38 respectively for short-term, and 41.4, 14.0, 25.8, 121.0% and 1.69 for long-term. For renal failure patients, these were 2.6, 5.8, 50.5, 17.6% and 0.30 respectively for short-term, and 19.1, 5.8, 11.2, 55.2% and 1.78 for long-term. For cardiomyopathy patients, these were 4.2, 10.0, 65.9, 30.0% and 0.16 respectively for short-term, and 17.5, 10.0, 63.1, 55.8% and 0.32 for long-term. Mean cTnI concentration was lower post-haemodialysis (15.2 vs. 17.8 ng/L, p < 0.0001), with a 16.9% mean relative change. Conclusions The biological variation of cTnI is similar between end-stage renal failure and cardiomyopathy patients, but proportionately greater in well-selected healthy individuals with very low baseline cTnI concentrations.

Estimation of inter-laboratory reference change values from external quality assessment data

Introduction

It is common for patients to switch between several healthcare providers. In this context, the long-term follow-up of medical conditions based on laboratory test results obtained from different laboratories is a challenge. The measurement uncertainty in an inter-laboratory context should also be considered in data mining research based on routine results from randomly selected laboratories. As a proof-of-concept study, we aimed at estimating the inter-laboratory reference change value (IL-RCV) for exemplary analytes from publicly available data on external quality assessment (EQA) and biological variation.

Materials and methods

External quality assessment data of the Reference Institute for Bioanalytics (RfB, Bonn, Germany) for serum creatinine, calcium, aldosterone, PSA, and of whole blood HbA1c from campaigns sent out in 2019 were analysed. The median CVs of all EQA participants were calculated based on 8 samples from 4 EQA campaigns per analyte. Using intra-individual biological variation data from the EFLM database, positive and negative IL-RCV were estimated with a formula based on log transformation under the assumption that the analytes under examination have a skewed distribution.

Results

We estimated IL-RCVs for all exemplary analytes, ranging from 13.3% to 203% for the positive IL-RCV and - 11.8% to - 67.0% for the negative IL-RCV (serum calcium - serum aldosterone), respectively.

Conclusion

External quality assessment data together with data on the biological variation – both freely available – allow the estimation of inter-laboratory RCVs. These differ substantially between different analytes and can help to assess the boundaries of interoperability in laboratory medicine.

Biological variation of proprotein convertase subtilisin/kexin type 9 (PCSK9) in human serum

BACKGROUND

Proprotein convertase subtilisin/kexin type 9 (PCSK9) is involved in the regulation of LDL receptors. Inhibition of PCSK9 increase uptake of LDL-particles and pathogen-associated molecular patterns (PAMPs). The aim of our study was to evaluate biological variation of serum PCSK9.

METHODS

Within-subject (CV_I) and between-subject (CV_G) biological variations were assessed in 14 healthy volunteers in a 6-week protocol (7 samples, equidistant time intervals). Serum concentration of PCSK9 was measured by a Quantikine ELISA assay (R&D systems, Bio-Techne Ltd., UK) on a DS2 ELISA reader (Dynex Technologies GmbH, Germany). Precision (CV_A) was assessed by duplicate measurements. Two methods with different levels of robustness were used for the estimation of CV_I, SD-ANOVA and CV-ANOVA method. We calculated the index of individuality and reference change values. The experiment was fully compliant with EFLM database checklist.

RESULTS

The within-subject values of PCSK9 in healthy persons, as calculated by two statistical methods, were 23.2% (SD-ANOVA with CV_A of 5.6%) and 26.6% (CV-ANOVA with CV_A of 4.8%). The CV_G was 10.9% (SD-ANOVA), index of individuality and RCV were 2.13 and 66.3%, respectively.

CONCLUSIONS

The high index of individuality indicates that common reference intervals can be used to interpret serum PSCK9 values.

Managing biological variation data: modern approaches for study design and clinical application

For more than one half-century, variability observed in clinical test result measurements has been ascribed to three major independent factors: (i) pre-analytical variation, occurring at sample collection and processing steps; (ii) analytical variation of the test method for which measurements are taken, and; (iii) biological variation (BV). Appreciation of this last source of variability is the major goal of this review article. Several recent advances have been made to generate, collate, and utilize BV data of biomarker tests within the clinical laboratory setting. Consideration of both prospective and retrospective study designs will be addressed. The prospective/direct study design will be described in accordance with recent recommendations discussed in the framework of a newly-developed system of checklist items. Potential value of retrospective/indirect study design, modeled on data mining from cohort studies or pathology laboratory information systems (LIS), offers an alternative approach to obtain BV estimates for clinical biomarkers. Moreover, updates to BV databases have made these data more current and widely accessible. Principal aims of this review are to provide the clinical laboratory scientist with a historical framework of BV concepts, to highlight useful applications of BV data within the clinical laboratory environment, and to discuss key terms and concepts related to statistical treatment of BV data.

Order of draw of blood samples affect potassium results without K-EDTA contamination during routine workflow

Introduction

A specific sequence is recommended for filling blood tubes during blood collection to prevent erroneous test results due to carryover of additives. However, requirement of this procedure is still debatable. This study was aimed to investigate the potassium ethylenediaminetetraacetic acid (K-EDTA) contamination in blood samples taken after a tube containing the additive during routine workflow. The study was also carried out to examine the effect of order of draw on potassium results, regardless of K-EDTA contamination.

Materials and methods

In 388 outpatients, to determine the probability of K-EDTA cross-contamination, blood was drawn sequentially into a serum tube, followed by a tube containing K-EDTA, and by another serum tube. In another 405 outpatients, to evaluate the effect of order of draw blood unrelated to K-EDTA contamination, two serum tube were successively collected. Potassium was measured on Cobas 6000 c501 analyser (Roche Diagnostic GmbH, Mannheim, Germany) by indirect ion selective electrode method.

Results

Of paired samples collected before and after a K-EDTA tube, 24% had a potassium difference of above 0.3 mmol/L. However, no EDTA contamination was detected in these samples as well as 95% confidence intervals (CI) of limits of agreement for calcium were within the allowable error limits based on reference change values. Interestingly, of blood samples drawn successively, 24% had also a difference greater than 0.3 mmol/L for potassium.

Conclusion

Incorrect order of draw using closed blood collection system does not cause K-EDTA contamination, even in routine workflow. However, regardless of K-EDTA contamination, order of draw has significant influence on the potassium results.

Clinical relevance of biological variation of cardiac troponins

The high-sensitivity immunoassays for cardiac troponin I (hs-cTnI) and cardiac troponin T (hs-cTnT) are recommended by all the most recent international guidelines as gold standard laboratory methods for the detection of myocardial injury and diagnosis of acute myocardial infarction (AMI). In this review article, the Authors aimed at discussing the relevant biochemical, physiological, and clinical issues related to biological variability of cTnI and cTnT. Cardiac troponins, measured with hs-cTn methods, show a better clinical profile than the other cardio-specific biomarkers (such as the natriuretic peptides, BNP and NT-proBNP). In particular, the hs-cTn methods are characterized by a low intra-individual index of variation (<0.6) and reduced analytical imprecision (about 5% CV) at the clinical cut-off value (i.e., the 99th percentile URL value). Moreover, recent studies have reported that differences between two hs-cTn measured values (RCV) >30% can be considered statistically significant. These favourable biological characteristics and analytical performance of hs-cTn methods significantly improved the accuracy in the diagnostic process of acute coronary syndromes (ACS) in patients admitted to emergence department. In addition, several studies have demonstrated the clinical usefulness of cardiovascular risk evaluation with hs-cTn methods in some groups of patients with clinical conditions at high cardiovascular risk (such as systemic hypertension, severe obesity, diabetes mellitus, renal insufficiency, and chronic obstructive pulmonary disease). However, screening programs in the general population with hs-cTn methods for cardiovascular risk stratification require further investigation to define the optimal target populations, timing of measurement, and preventive interventions.

Biological variation of serum neurofilament light chain

OBJECTIVES

The neurofilament light chain (NfL) has emerged as a versatile biomarker for CNS-diseases and is approaching clinical use. The observed changes in NfL levels are frequently of limited magnitude and in order to make clinical decisions based on NfL measurements, it is essential that biological variation is not confused with clinically relevant changes. The present study was designed to evaluate the biological variation of serum NfL.

METHODS

Apparently healthy individuals (n=33) were submitted to blood draws for three days in a row. On the second day, blood draws were performed every third hour for 12 h. NfL was quantified in serum using the Simoa™ HD-1 platform. The within-subject variation (CV_I) and between-subject variation (CV_G) were calculated using linear mixed-effects models.

RESULTS

The overall median value of NfL was 6.3 pg/mL (range 2.1-19.1). The CV_I was 3.1% and the CV_G was 35.6%. An increase in two serial measurements had to exceed 24.3% to be classified as significant at the 95% confidence level. Serum NfL levels remained stable during the day (p=0.40), whereas a minute variation (6.0-6.6 pg/mL) was observed from day-to-day (p=0.02).

CONCLUSIONS

Serum NfL is subject to tight homeostatic regulation with none or neglectable semidiurnal and day-to-day variation, but considerable between-subject variation exists. This emphasizes serum NfL as a well-suited biomarker for disease monitoring, but warrants caution when interpreting NfL levels in relation to reference intervals in a diagnosis setting. Furthermore, NfL's tight regulation requires that the analytical variation is kept at a minimum.

Intra-Individual Variation and Reliability of Biomarkers of the Antioxidant Defense System by Considering Dietary and Lifestyle Factors in Premenopausal Women

Epidemiological studies frequently rely on a single biomarker measurement to assess the relationship between antioxidant status and diseases. This bears an inherent risk for misclassification, if the respective biomarker has a high intra-individual variability. The present study investigates the intra-individual variation and reliability of enzymatic and non-enzymatic biomarkers of the antioxidant system in premenopausal women. Forty-four apparently healthy females provided three consecutive fasting blood samples in a four-week rhythm. Analyzed blood biomarkers included Trolox equivalent antioxidant capacity (TEAC), catalase, glutathione peroxidase, glutathione, vitamin C, bilirubin, uric acid, coenzyme Q10, tocopherols, carotenoids and retinol. Intra- and inter-individual variances for each biomarker were estimated before and after adjusting for relevant influencing factors, such as diet, lifestyle and use of contraceptives. Intraclass correlation coefficient (ICC), index of individuality, reference change value and number of measurements needed to confine attenuation in regression coefficients were calculated. Except for glutathione and TEAC, all biomarkers showed a crude ICC ≥ 0.50 and a high degree of individuality indicating that the reference change value is more appropriate than population-based reference values to scrutinize and classify intra-individual changes. Apart from glutathione and TEAC, between 1 and 9 measurements were necessary to reduce attenuation in regression coefficients to 10%. The results indicate that the majority of the assessed biomarkers have a fair to very good reliability in healthy premenopausal women, except for glutathione and TEAC. To assess the status of the antioxidant system, the use of multiple measurements and biomarkers is recommended.

High-sensitivity troponins in dialysis patients: variation and prognostic value

Background

Dialysis patients have a high prevalence of cardiovascular mortality but also elevated cardiac troponins (cTns) even without signs of cardiac ischaemia. The study aims to assess variation and prognostic value of high-sensitivity cTnI and cTnT in prevalent dialysis patients.

Methods

In 198 prevalent haemodialysis (HD) and 78 peritoneal dialysis (PD) patients, 4-monthly serum troponin I and T measurements were obtained. Reference change values (RCVs) were used for variability assessment and competing-risk regression models for survival analyses; maximal follow-up was 50 months.

Results

HD and PD patients had similar troponin levels [median (interquartile range) troponin I: 25 ng/L (14–43) versus 21 ng/L (11–37), troponin T: 70 ng/L (44–129) versus 67 ng/L (43–123)]. Of troponin I and T levels, 42% versus 98% were above the decision level of myocardial infarction. RCVs were +68/−41% (troponin I) and +29/−23% (troponin T). Increased variability of troponins related to higher age, male sex, protein-energy wasting and congestive heart failure, but not ischaemic heart disease or dialysis form. Elevated troponin T, but not troponin I, predicted death after adjusting for confounders.

Conclusions

A large proportion of prevalent dialysis patients without current established or ongoing cardiac events have elevated levels of high-sensitivity cTns. Mortality risk was doubled in patients with persistently high troponin T levels. The large intraindividual variation of cTns suggests that serial measurements and reference change levels may be used to improve diagnostic utility. However, evidence-based recommendations require more data from large studies of dialysis patients with cardiac events.