Prediction interval: A powerful statistical tool for monitoring patients and analytical systems

Monitoring is indispensable for assessing disease prognosis and evaluating the effectiveness of treatment strategies, both of which rely on serial measurements of patients' data. It also plays a critical role in maintaining the stability of analytical systems, which is achieved through serial measurements of quality control samples. Accurate monitoring can be achieved through data collection, following a strict preanalytical and analytical protocol, and the application of a suitable statistical method. In a stable process, future observations can be predicted based on historical data collected during periods when the process was deemed reliable. This can be evaluated using the statistical prediction interval. Statistically, prediction interval gives an "interval" based on historical data where future measurement results can be located with a specified probability such as 95%. Prediction interval consists of two primary components: (i) the set point and (ii) the total variation around the set point which determines the upper and lower limits of the interval. Both can be calculated using the repeated measurement results obtained from the process during its steady-state. In this paper, (i) the theoretical bases of prediction intervals were outlined, and (ii) its practical application was explained through examples, aiming to facilitate the implementation of prediction intervals in laboratory medicine routine practice, as a robust tool for monitoring patients' data and analytical systems.

Harmonizing the post-analytical phase: focus on the laboratory report

The final, post-analytical, phase of laboratory testing is increasingly recognized as a fundamental step in maximizing quality and effectiveness of laboratory information. There is a need to close the loop of the total testing cycle by improving upon the laboratory report, and its notification to users. The harmonization of the post-analytical phase is somewhat complicated, mainly because it calls for communication that involves parties speaking different languages, including laboratorians, physicians, information technology specialists, and patients. Recently, increasing interest has been expressed in integrated diagnostics, defined as convergence of imaging, pathology, and laboratory tests with advanced information technology (IT). In particular, a common laboratory, radiology and pathology diagnostic reporting system that integrates text, sentinel images and molecular diagnostic data to an integrated, coherent interpretation enhances management decisions and improves quality of care.

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.

Are tube fill volumes below 90% a rejection criterion for all coagulation tests?

BACKGROUND

Rejected samples lead to prolonged turnaround time and delayed diagnosis and treatment of patients. This study was conducted to determine minimum acceptable sample volume in Sarstedt brand coagulation tubes to reduce high sample rejection rate.

METHODS

Blood samples were drawn from 20 participants (10 healthy volunteers and 10 patients receiving oral anticoagulant) into coagulation tubes. Six samples were taken from each participant, with tube fill volumes of 100%, 90%, 80%, 70%, 60%, and 50%. Prothrombin time (PT), active partial thromboplastin time (aPTT), and fibrinogen tests were analyzed.

RESULTS

According to quality performance specifications, the tube fill volume must be at least 70% for PT and aPTT and 50% for fibrinogen. There was no statistical difference in samples from healthy volunteers for PT, aPTT, and fibrinogen tests when the minimum tube fill volume was at least 80%, 90%, and 50%, respectively. These percentages were 50%, 70%, and 60%, respectively, in patients receiving oral anticoagulant.

CONCLUSIONS

Sarstedt tubes meet quality standard specifications at a 70% fill rate for PT and aPTT and a 50% fill rate for fibrinogen. Comprehensive studies with larger populations are needed to accept these values as sample acceptance criteria for the laboratory.

Biological variation of urinary protein: Creatinine ratio and urine specific gravity in cats

BACKGROUND

Laboratory results are influenced by presence and severity of disease, as well as preanalytical factors, analytical variation, and biological variation. Biological variation data for urinary protein: creatinine ratio (UPC) and urine specific gravity (USG) in cats are lacking.

OBJECTIVES

Determine the biological variation of UPC and USG in cats.

ANIMALS

Eighty healthy client-owned cats.

METHODS

Prospective study. Urine was collected on days 0, 14, and 56 from all 80 cats to investigate the persistence of borderline or overt proteinuria or suboptimal urine concentration. In 15 of these cats, urine was collected weekly from day 0 to 42 to calculate the index of individuality (II) and reference change value (RCV), and on days 56 and 57 to evaluate day-to-day variability of UPC and USG.

RESULTS

Borderline or overt proteinuria (UPC ≥0.2) was present in 18/80 (23%) cats at baseline and persisted on 3 occasions in 2 months in 8/18 (44%) cats. Urine concentration was suboptimal at inclusion (USG <1.035) in 8/80 (10%) cats and at all 3 time points during 2 months in 3/8 (38%) cats. The II of UPC and USG indicated intermediate individuality. The 1-sided RCV was 82% for UPC and 36% for USG. Proteinuria substage was identical on 2 consecutive days in 13/15 (87%) cats, and urine concentrating ability remained the same in all 15 cats.

CONCLUSIONS AND CLINICAL IMPORTANCE

A >82% increase in UPC in a healthy cat is not solely attributable to physiological and analytical variation. For USG, a decrease of >36% is considered clinically relevant.

Biological Variation in Biochemistry Analytes in Laboratory Guinea Pigs (Cavia porcellus)

Biological variation (BV) describes the physiological random fluctuation around a homeostatic set point, which is a characteristic of all blood measurands (analytes). That variation may impact the clinical relevance of the changes that are observed in the serial results for an individual. Biological variation is represented mathematically by the coefficient of variation (CV) and occurs within each individual (CVI) and between individuals in a population (CVG). Biological variation data can be used to assess whether population-based reference or subject-based reference intervals should be used for the interpretation of laboratory results through the calculation of the index of individuality (IoI). This study aimed to determine the biological variations, calculate the IoI and reference change values (RCV) of clinical chemistry analytes in an outbred strain colony of Hartley guinea pigs (GPs), and set the quality specifications for clinical chemistry analytes. Blood was collected from 16 healthy adult laboratory colony GPs via jugular venipuncture at weekly intervals over six weeks. All the samples were frozen and analyzed in a single run. Analytical, CVI, and CVG biological variations, together with the IoI and RCV, were calculated for each measurand. Based on the estimated BV, the calculated IoI was low for glucose, so individual reference intervals (RCV) should be used. The majority of the measurands should be interpreted using both population-based and subject-based reference intervals as the IoIs were intermediate.

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.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Reference intervals for thyroid disorders calculated by indirect method and comparison with reference change values

Introduction

The aim of the study was to calculate reference intervals (RIs) for thyroid stimulating hormone (TSH), free thyroxine (fT4) and free triiodothyronine (fT3) and evaluate the clinical significance of these intervals by use of reference change values (RCV) of the analytes.

Materials and methods

Laboratory patient data between August and December 2021 were evaluated for the study. A total of 188,912 patients with TSH, fT4, fT3, anti-thyroid peroxidase antibodies (Anti-TPO) and anti-thyroglobulin antibodies (Anti-Tg) results were evaluated. All measurements were performed on Cobas c801 (Roche Diagnostics, Penzberg, Germany) using electrochemiluminescence immunoassay technology. Estimated RIs were compared with manufacturer's by means of RCVs of analytes.

Results

Thyroid stimulating hormone values didn't differ significantly by gender and age. The combined RIs for whole group (N = 28,437) was found as 0.41-4.37 mIU/mL. Free T4 values (11.6-20.1 pmol/L, N = 13,479 in male; 10.5-19.5 pmol/L, N = 17,634 female) and fT3 values (3.38-6.35 pmol/L, N = 2,516 in male; 3.39-5.99 pmol/L, N = 3,348 pmol/L in female) significantly differed by gender (P < 0.050). Both fT4 and fT3 values also showed significant differences in age subgroups comparisons. So, male and female RIs were represented separately for age subgroups. When compared with manufacturer's RIs, TSH whole group and fT4 subgroups RIs didn't exceed the analytes' RCVs, but this difference was greater for fT3.

Conclusions

Reference interval estimation by use of indirect method out of laboratory data may be more accurate than manufacturer provided RIs. This population based RIs evaluated using RCV of analytes may provide useful information in clinical interpretation of laboratory results.

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.

Personalized reference intervals: from theory to practice

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

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.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

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.

Transformation of Sequential Hospital and Outpatient Laboratory Data into Between-Day Reference Change Values

BACKGROUND

Serial differences between intrapatient consecutive measurements can be transformed into Taylor series of variation vs time with the intersection at time = 0 (y0) equal to the total variation (analytical + biological + preanalytical). With small preanalytical variation, y0, expressed as a percentage of the mean, is equal to the variable component of the reference change value (RCV) calculation: (CVA2 + CVI2)1/2.

METHODS

We determined the between-day RCV of patient data for 17 analytes and compared them to healthy participants' RCVs. We analyzed 653 consecutive days of Dartmouth-Hitchcock Roche Modular general chemistry data (4.2 million results: 60% inpatient, 40% outpatient). The serial patient values of 17 analytes were transformed into 95% 2-sided RCV (RCVAlternate), and 3 sets of RCVhealthy were calculated from 3 Roche Modular analyzers' quality control summaries and CVI derived from biological variation (BV) studies using healthy participants.

RESULTS

The RCVAlternate values are similar to RCVhealthy derived from known components of variation. For sodium, chloride, bicarbonate calcium, magnesium, phosphate, alanine aminotransferase, albumin, and total protein, the RCVs are equivalent. As expected, increased variation was found for glucose, aspartate aminotransferase, creatinine, and potassium. Direct bilirubin and urea demonstrated lower variation.

CONCLUSIONS

Our RCVAlternate values integrate known and unknown components of analytic, biologic, and preanalytic variation, and depict the variations observed by clinical teams that make medical decisions based on the test values. The RCVAlternate values are similar to the RCVhealthy values derived from known components of variation and suggest further studies to better understand the results being generated on actual patients tested in typical laboratory environments.

Annual biological variation and personalized reference intervals of clinical chemistry and hematology analytes

OBJECTIVES

A large number of people undergo annual health checkup but accurate laboratory criterion for evaluating their health status is limited. The present study determined annual biological variation (BV) and derived parameters of common laboratory analytes in order to accurately evaluate the test results of the annual healthcare population.

METHODS

A total of 43 healthy individuals who had regular healthcare once a year for six consecutive years, were enrolled using physical, electrocardiogram, ultrasonography and laboratory. The annual BV data and derived parameters, such as reference change value (RCV) and index of individuality (II) were calculated and compared with weekly data. We used annual BV and homeostatic set point to calculate personalized reference intervals (RI_per) which were compared with population-based reference intervals (RI_pop).

RESULTS

We have established the annual within-subject BV (CV_I), RCV, II, RI_per of 24 commonly used clinical chemistry and hematology analytes for healthy individuals. Among the 18 comparable measurands, CV_I estimates of annual data for 11 measurands were significantly higher than the weekly data. Approximately 50% measurands of II were <0.6, the utility of their RI_pop were limited. The distribution range of RI_per for most measurands only copied small part of RI_pop with reference range index for 8 measurands <0.5.

CONCLUSIONS

Compared with weekly BV, for annual healthcare individuals, annual BV and related parameters can provide more accurate evaluation of laboratory results. RI_per based on long-term BV data is very valuable for "personalized" diagnosis on annual health assessments.

The development of autoverification system of lymphocyte subset assays on the flow cytometry platform

OBJECTIVES

Peripheral blood lymphocyte subsets are important parameters for monitoring immune status; however, lymphocyte subset detection is time-consuming and error-prone. This study aimed to explore a highly efficient and clinically useful autoverification system for lymphocyte subset assays performed on the flow cytometry platform.

METHODS

A total of 94,402 lymphocyte subset test results were collected. To establish the limited-range rules, 80,427 results were first used (69,135 T lymphocyte subset tests and 11,292 NK, B, T lymphocyte tests), of which 15,000 T lymphocyte subset tests from human immunodeficiency virus (HIV) infected patients were used to set customized limited-range rules for HIV infected patients. Subsequently, 13,975 results were used for historical data validation and online test validation.

RESULTS

Three key autoverification rules were established, including limited-range, delta-check, and logical rules. Guidelines for addressing the issues that trigger these rules were summarized. The historical data during the validation phase showed that the total autoverification passing rate of lymphocyte subset assays was 69.65% (6,941/9,966), with a 67.93% (5,268/7,755) passing rate for T lymphocyte subset tests and 75.67% (1,673/2,211) for NK, B, T lymphocyte tests. For online test validation, the total autoverification passing rate was 75.26% (3,017/4,009), with 73.23% (2,191/2,992) for the T lymphocyte subset test and 81.22% (826/1,017) for the NK, B, T lymphocyte test. The turnaround time (TAT) was reduced from 228 to 167 min using the autoverification system.

CONCLUSIONS

The autoverification system based on the laboratory information system for lymphocyte subset assays reduced TAT and the number of error reports and helped in the identification of abnormal cell populations that may offer clues for clinical interventions.

The European Biological Variation Study (EuBIVAS): Biological Variation Data for Coagulation Markers Estimated by a Bayesian Model

BACKGROUND

For biological variation (BV) data to be safely used, data must be reliable and relevant to the population in which they are applied. We used samples from the European Biological Variation Study (EuBIVAS) to determine BV of coagulation markers by a Bayesian model robust to extreme observations and used the derived within-participant BV estimates [CVP(i)] to assess the applicability of the BV estimates in clinical practice.

METHOD

Plasma samples were drawn from 92 healthy individuals for 10 consecutive weeks at 6 European laboratories and analyzed in duplicate for activated partial thromboplastin time (APTT), prothrombin time (PT), fibrinogen, D-dimer, antithrombin (AT), protein C, protein S free, and factor VIII (FVIII). A Bayesian model with Student t likelihoods for samples and replicates was applied to derive CVP(i) and predicted BV estimates with 95% credibility intervals.

RESULTS

For all markers except D-dimer, CVP(i) were homogeneously distributed in the overall study population or in subgroups. Mean within-subject estimates (CVI) were <5% for APTT, PT, AT, and protein S free, <10% for protein C and FVIII, and <12% for fibrinogen. For APTT, protein C, and protein S free, estimates were significantly lower in men than in women ≤50 years.

CONCLUSION

For most coagulation markers, a common CVI estimate for men and women is applicable, whereas for APTT, protein C, and protein S free, sex-specific reference change values should be applied. The use of a Bayesian model to deliver individual CVP(i) allows for improved interpretation and application of the data.

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.

Biological variation and analytical goals of four thyroid function biomarkers in healthy European volunteers

BACKGROUND

Interpretation of thyroid function tests by means of biological variation (BV) data is essential to identify significant changes between serial measurements at an individual level. Data on thyroid parameters in adults are limited.

OBJECTIVES

We aimed at determining the BV of four thyroid function test (thyroid-stimulating hormone (TSH), free thyroxin (FT4), free triiodothyronine (FT3) and thyroglobulin (Tg)) by applying recent recommendations to acquire BV data on a latest generation of immunoassay.

METHODS

Nineteen healthy volunteers (8 males and 11 females) were drawn every week during 5 consecutive weeks. Samples were analysed in duplicate on the Cobas 602 analyzer (Roche Diagnostics). After normality assessment, outlier exclusion and homogeneity of variance analysis, analytical variation (CV_A ), within-subject biological variation (CV_I ) and between-subject biological variation (CV_G ) were determined using nested ANOVA.

RESULTS

CV_A , CV_I and CV_G were 0.9%, 19.7% and 37.6% for TSH; 3.6%, 4.6% and 10.8% for FT4; 2.2%, 6.0% and 8.6% for FT3; and 0.9%, 15.4% and 84.9% for Tg. Index of individuality (II) for all parameters was between 0.2 and 0.7. The percentage above which the change between two measures is truly significant (reference change value) was 54.7% for TSH, 16.2% for FT4, 17.7% for FT3 and 42.8% for Tg.

CONCLUSION

Based on recent international recommendations, our study provides updated BV data for four thyroid function tests in European healthy volunteers. Reliable BV characteristics, and especially RCV, can facilitate the interpretation of consecutive thyroid function tests in an individual and therefore have the potential to efficiently support clinical decisions regarding thyroid diseases.

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

OBJECTIVES

Thyroid biomarkers are fundamental for the diagnosis of thyroid disorders and for the monitoring and treatment of patients with these diseases. The knowledge of biological variation (BV) is important to define analytical performance specifications (APS) and reference change values (RCV). The aim of this study was to deliver BV estimates for thyroid stimulating hormone (TSH), free thyroxine (FT4), free triiodothyronine (FT3), thyroglobulin (TG), and calcitonin (CT).

METHODS

Analyses were performed on serum samples obtained from the European Biological Variation Study population (91 healthy individuals from six European laboratories; 21-69 years) on the Roche Cobas e801 at the San Raffaele Hospital (Milan, Italy). All samples from each individual were evaluated in duplicate within a single run. The BV estimates with 95% CIs were obtained by CV-ANOVA, after analysis of variance homogeneity and outliers.

RESULTS

The within-subject (CV _I ) BV estimates were for TSH 17.7%, FT3 5.0%, FT4 4.8%, TG 10.3, and CT 13.0%, all significantly lower than those reported in the literature. No significant differences were observed for BV estimates between men and women.

CONCLUSIONS

The availability of updated, in the case of CT not previously published, BV estimates for thyroid markers based on the large scale EuBIVAS study allows for refined APS and associated RCV applicable in the diagnosis and management of thyroid and related diseases.

Biological variation of major gut-derived uremic toxins in the serum of healthy adult cats

Background

Biological variation of serum indoxyl sulfate (IS), p‐cresol sulfate (pCS), and trimethylamine‐n‐oxide (TMAO) concentrations in cats is unknown.

Objectives

To determine short‐ and medium‐term biological variation, index of individuality (II), and reference change values for serum IS, pCS, and TMAO concentrations in healthy adult cats. To determine the effect of feeding on serum concentrations.

Animals

Twelve healthy adult cats.

Methods

Prospective, cohort study. Seven serum samples over a 12‐hour period (short‐term) and 5 serum samples over a 19‐day period (medium‐term) were collected. Serum concentrations of total IS, pCS, and TMAO were measured every 2 hours in a 12‐hour period (hours 0‐12) after a meal in 9 cats and compared to concentrations in a nonfed state.

Results

For IS, the II was high using short‐term (1.96) and low using medium‐term (0.65) biological variation estimates. Individuality was intermediate for pCS (short‐term, 0.98; medium‐term, 1.17) and TMAO (short‐term, 1.47; medium‐term, 0.83). Serum IS, pCS, and TMAO concentrations were significantly lower in a fed state compared to a nonfed state at hours 4, 6, 8, and 12; at hours 4 and 6; and at hours 2, 4, 6, 8, 10, 12, respectively.

Conclusion and Clinical Importance

Population‐based reference intervals with reference to the subject‐based interval can be used to monitor serum pCS and TMAO concentrations. For IS, a subject‐based and a population‐based reference interval is best for short‐term and medium‐term monitoring, respectively. To compare serial measurements, it would be prudent to collect samples at the same time of day and consistently in either a fed or nonfed state.

Comparative evaluation of methods to determine intra-individual reference ranges in nutrition support team (NST)-related tests

BACKGROUND

The intra-individual reference range is generally narrower than the commonly used reference range. Consequently, close monitoring of changes in the laboratory test results of individuals based on the inter-individual reference range remains challenging.

METHODS

We examined the determination of individual reference ranges using four indicators of nutritional conditions: transferrin (TRF), albumin (ALB), retinol-binding protein (RBP), and transthyretin (TTR). The subjects comprised 20 healthy individuals and blood samples were collected and tested five times at 2-week intervals. We used the measurement results for the four indicators and examined individual reference ranges using four methods, including calculation methods based on the reference change value and Bayesian inference.

RESULTS

The resulting intra-individual reference ranges were narrower than the currently used inter-individual reference range for all measurements using four methods. Furthermore, the intra-individual coefficient of variation [CV (intra)] was smaller than the inter-individual coefficient of variation [CV (inter)] for TRF, RBP, and TTR for all 20 subjects. The means CV (intra) for the four indicators were also lower than the corresponding CV (inter).

CONCLUSIONS

The intra-individual reference range can be used to validate the standard deviation and coefficient of variation for currently used indicators. Moreover, Bayesian methods are speculated to be the most versatile.

Utilization of biological variation data in the interpretation of laboratory test results - survey about clinicians' opinion and knowledge

Introduction

To interpret test results correctly, understanding of the variations that affect test results is essential. The aim of this study is: 1) to evaluate the clinicians’ knowledge and opinion concerning biological variation (BV), and 2) to investigate if clinicians use BV in the interpretation of test results.

Materials and methods

This study uses a questionnaire comprising open-ended and close-ended questions. Questions were selected from the real-life numerical examples of interpretation of test results, the knowledge about main sources of variations in laboratories and the opinion of clinicians on BV. A total of 399 clinicians were interviewed, and the answers were evaluated using a scoring system ranked from A (clinician has the highest level of knowledge and the ability of using BV data) to D (clinician has no knowledge about variations in laboratory). The results were presented as number (N) and percentage (%).

Results

Altogether, 60.4% of clinicians have knowledge of pre-analytical and analytical variations; but only 3.5% of them have knowledge related to BV. The number of clinicians using BV data or reference change value (RCV) to interpret measurements results was zero, while 79.4% of clinicians accepted that the difference between two measurements results located within the reference interval may be significant.

Conclusions

Clinicians do not use BV data or tools derived from BV such as RCV to interpret test results. It is recommended that BV should be included in the medical school curriculum, and clinicians should be encouraged to use BV data for safe and valid interpretation of test results.

Application and optimization of reference change values for Delta Checks in clinical laboratory

BACKGROUND

Delta check is a patient-based QC tool for detecting errors by comparing current and previous test results of patient. Reference change value (RCV) is adopted in guidelines as method for delta check, but the performance is not verified. We applied RCV-based delta check method to patients' data and modified for application.

MATERIALS AND METHODS

Reference change value were calculated using results of internal QC materials and biological variation data. Test results of 17 analytes in inpatients, outpatients, and health examination recipients were collected. The detection rates of currently used delta check method and those of RCV-based method were compared, and the methods were modified.

RESULTS

Reference change value-based method had higher detection rates compared to conventional method. Applied modifications reduced detection rates. Removing the pairs of results within reference interval reduced detection rates (0.42% ~ 10.92%). When RCV was divided by time interval, the detection rates were similar to prior rates in outpatients (0.19% ~ 1.34%). Using RCV multiplied by twice the upper limit of reference value as cutoff reduced the detection rate (0.07% ~ 1.58%).

CONCLUSIONS

Reference change value is a robust criterion for delta check and included in clinical laboratory practice guideline. However, RCV-based method generates high detection rates which increase workload. It needs modification for use in clinical laboratories.

Biological variation data and analytical specification goal estimates of the thrombin generation assay with and without thrombomodulin in healthy individuals

BACKGROUND

Evaluation of an individual's thrombin-generating capacity enables a global assessment of the coagulation cascade and is therefore thought to better reflect the clotting function of blood. However, the lack of standardization still hampers the use in routine clinical practice.

METHODS

Nineteen healthy subjects were sampled once a week for 5 consecutive weeks. Thrombin generation assay (TGA) was performed in duplicate by calibrated automated thrombogram (CAT) on platelet poor plasma with and without thrombomodulin. After exclusion of outliers, a nested analysis of variance (ANOVA) was performed to evaluate the biological variability (BV) results. Analytical variation (CV_A ), within-individual variation (CV_I ), between-individual variation (CV_G ), index of individuality (II), and reference change value (RCV) were calculated.

RESULTS

All parameters taken together, the CV_A, CV_I , and CV_G without TM, ranged from 2.8% to 6.5%, from 4.1% to 13.3% and from 10.4% to 28.4%, respectively. For TG with TM, CV_I and CV_G were higher and ranged from 5.0% to 18.1% and from 14.9% to 35.3%, respectively. For endogenous thrombin potential (ETP), a CV_I of 4.1% and CV_G of 10.4% were obtained without addition of thrombomodulin (TM). With addition of TM, both CV_I and CV_G were higher: 14.0% and 34.8%, respectively. The II was low and the RCV ranged from 17.2% to 50.4%.

CONCLUSION

CAT parameters are highly individualized and population-based reference values could be called into question. The assessment of BV and RCV for thrombin generation assays could optimize interpretation of serial patient results and guide setting of analytical specification goals.

Current and emerging concepts in biological and analytical variation applied in clinical practice

A single laboratory result actually represents a range of possible values, and a given laboratory result is impacted not just by the presence or absence of disease, but also by biological variation of the measurand in question and analytical variation of the equipment used to make the measurement. Biological variation refers to variability in measurand concentration or activity around a homeostatic set point. Knowledge of biological and analytical variation can be used to facilitate interpretation of patient clinicopathologic data and is particularly useful for interpreting serial patient data and data at or near reference limits or clinical decision thresholds. Understanding how biological and analytical variation impact laboratory results is of increasing importance, because veterinarians evaluate serial data from individual patients, interpret data from multiple testing sites, and use expert consensus guidelines that include decision thresholds for clinicopathologic data interpretation. The purpose of our report is to review current and emerging concepts in biological and analytical variation and discuss how biological and analytical variation data can be used to facilitate clinicopathologic data interpretation. Inclusion of veterinary clinical pathologists having expertise in laboratory quality management and biological variation on research teams and veterinary practice guideline development teams is recommended, to ensure that various considerations for clinicopathologic data interpretation are addressed.

Short-term biological variation of differential count in healthy subjects in a South Asian population

Estimates of Within-Subject and between subject biological variation for the white blood cell differential count (DC) have not been reported in South Asia. Therefore, we attempted to measure the short-term biological variation estimates for DC. The study was conducted on 28 healthy volunteers (15 males and 13 females). Blood from the volunteers was collected in the morning in K3-EDTA vials and analyzed in triplicate on the Sysmex XN-1000 analyzer, for six consecutive days. The Within subject, between subject and analytical coefficient of variation of the DC was calculated from the results by nested repeated measures ANOVA after outlier exclusion. The Reference change values (RCV) were also calculated. The within-subject variation for eosinophil Count and between subject variation for basophils in our study from South Asia was greater than the published European and American studies. Males and females showed similar biological variation for DC. The within-subject variation of other parameters (Neutrophils, Lymphocytes, Monocytes and Basophils) were similar or showed only mild differences to the published studies. The markedly different within-subject variation for Eosinophil counts suggest that the RCV for DC in South Asians need to be different from the published data in order to have clinical relevance. The Within-subject variation values of the other parameters seem transportable from the published European and American studies, but the small differences found mean that further regional estimates need to be reported for robust evidence of the same.

Long-term biological variation estimates of 13 hematological parameters in healthy Chinese subjects

Background The complete blood count (CBC) is a basic test routinely ordered by physicians as a part of initial diagnostic work-up on their patients. To ensure safe clinical application of the CBC, reliable biological variation (BV) data are needed to establish analytical performance specifications. Our aim was to define the BV of CBC parameters using a rigorous protocol that is compliant with the Biological Variation Data Critical Appraisal Checklist (BIVAC) provided by the European Federation of Clinical Chemistry and Laboratory Medicine. Methods Blood samples drawn from 41 healthy Chinese subjects (22 females and 19 males; 23-59 years of age) once monthly for 6 consecutive months were analyzed using an ABX Pentra 80 instrument. The instrument was precisely calibrated. All samples were analyzed in duplicate for 13 CBC parameters. The data were assessed for outliers, normality, and variance homogeneity prior to nested ANOVA. Gender-stratified within-subject (CVI) and between-subject (CVG) BV estimates were calculated. Results The number of remaining data for each subject was 442-484 after removing outliers. No significant differences existed between female/male CVI estimates. Except for leukocytes, neutrophils, and lymphocytes, the mean values of 10 parameters differed significantly between genders, rendering partitioning of CVG data between genders. No significant differences were detected between most BV estimates and recently published estimates representing a Europid population. Conclusions Most BV estimates in BIVAC-compliant studies are similar. The turnover time of blood cells and age distribution of participants should be considered in a CBC BV study. Our study will contribute to global BV estimates and future studies.

Determining biological variation of serum parathyroid hormone in healthy adults

Introduction

Measurement of parathyroid hormone (PTH) is essential in the investigation and management of calcium metabolism disorders. To assess the significance of any assay result when clinical decision making biological variation (BV) of the measurand must be taken into consideration. The aim of the present study is determining the BV parameters for serum PTH.

Materials and methods

Blood samples were taken at weekly intervals from 20 healthy subjects for ten weeks in this prospective BV study. Serum “intact PTH” concentrations were measured with electrochemiluminescence method. Biological variation parameters were estimated using the approach proposed by Fraser.

Results

The values of within-subject biological variation (CV_I), between-subject biological variation (CV_G), analytical variation (CV_A), reference change value (RCV) and individuality index (II) for serum PTH were 21.1%, 24.9%, 3.8%, 59.4% and 0.8%, respectively. Within-subject biological variation and CV_G were also determined according to gender separately; 18.5% and 24.0%; 26.2% and 18.6% for male and female, respectively. Calculated desirable precision and bias goals were < 10.6% and < 6.3%, respectively.

Conclusion

This study may contribute to BV data on serum PTH as it includes a sufficient number of volunteers from both genders over an acceptable period of time. We do not recommend the usage of population-based reference intervals for serum PTH concentrations. Reference change value may be helpful for the evaluation of serial serum PTH results. Nonetheless, evaluation of data according to gender is necessary when setting analytical performance specifications.

Week-to-week biological variation of methylmalonic acid and homocysteine in healthy women

Metylmalonic acid (MMA) and homocysteine (HCY) are important biomarkers in the assessment of cobalamin and folate metabolism. Correct interpretation of patient results benefit from knowledge of biological variation. The aim of the present study was to determine within-subject (CV_I) and between-subject (CV_G) biological variations of serum MMA and HCY in healthy women. We collected blood samples from 12 healthy volunteers (33-61 years) on the same weekday for 10 consecutive weeks. Samples were stored at -80 °C until analysis in duplicate in a single analytical run in random order. The CV_I and CV_G biological variations were estimated by CV-ANOVA after the data were first subjected to outlier and homogeneity analysis. The CV_I (95% CI) for MMA and HCY were 7.2% (6.1-8.5) and 7.4% (6.5-8.5), respectively. The corresponding CV_G were 21.1% (14.0-32.2) and 24.2% (16.2-36.8). The index of individuality (II) was 0.34 for MMA and 0.31 for HCY and the reference change value (RCV) was -17.7; 21.0 (% decrease; increase) for MMA and -16.2; 19.4 for HCY. We provide within- and between-subject biological variation estimates for MMA and HCY in healthy women using an updated protocol. The results will contribute to a better clinical interpretation of these biomarkers and be of aid when setting analytical performance specifications.

Biological variation of routine haematology and biochemistry measurands in the horse

BACKGROUND

Clinical pathology results are typically interpreted by referring to population-based reference intervals. The use of individualised (subject-based) reference intervals is more appropriate for measurands with a high degree of variation between individuals.

OBJECTIVES

To determine the biological variation of routinely analysed equine haematology and biochemistry measurands and calculate indices of individuality and reference change values which enable production of individualised reference intervals, in a group of healthy, privately owned horses.

STUDY DESIGN

In a prospective cohort study, thirty-nine privately owned horses were sampled by jugular venipuncture for analysis of haematology and biochemistry measurands at weekly intervals for 6 weeks.

METHODS

Haematology was analysed on the day of collection. Serum was frozen and biochemistry analyses performed on thawed samples. Duplicate results were obtained and the coefficient of variation was calculated for analytical variation, within-subject variation and between-subject variation. The index of individuality and reference change value were derived for each measurand.

RESULTS

Haematology (red blood cell count, mean corpuscular haemoglobin and mean cell volume) and biochemistry measurands (total protein, globulins, albumin, gamma-glutamyl transferase, aspartate aminotransferase) demonstrated high individuality, indicating that individualised reference intervals are more appropriate for evaluation of these measurands. Two haematology (mean corpuscular haemoglobin concentration and platelets) and three biochemistry measurands (chloride, glucose and sodium) had low individuality, indicating that the use of traditional population-based reference intervals is appropriate for these measurands. Remaining measurands had intermediate individuality suggesting interpretation of the reference change value should occur with consideration of the population-based reference interval.

MAIN LIMITATIONS

The use of privately owned horses, variable management and environmental factors.

CONCLUSIONS

The use of individualised reference intervals is justified for many measurands in horses, supporting the use of serial sampling, consideration of biological variation and application of reference change values for improved clinical decision making and patient management in equine practice.

Impact of reference change value (RCV) based autoverification on turnaround time and physician satisfaction

BACKGROUND

For a quicker delivery of laboratory test results to the hospital emergency department (ED), we implemented an autoverification system based on the reference change value (RCV). The aim of this study was to assess how the RCV based autoverification reflected on turnaround time (TAT) and on physician satisfaction.

MATERIALS AND METHODS

The laboratory information system (LIS) was programmed to autoverify the results as long as they were within the range settled by RCV, so that the autoverified results were reported to the physician as soon as the tests were carried out, without any further intervention. We analyzed the same three-month periods' TAT and verification time (VFT) from the years prior to and following the implementation of RCV autoverification. The change in physicians' satisfaction levels was assessed using the hospital's Annual Physician Satisfaction Survey (APSS). Over sixty percent of physicians completed the questionnaire, and the amount of daily ED test requests (nearly three hundred) did not vary throughout the duration of this study.

RESULTS

Mann-Whitney U test showed that the VFT was significantly reduced in all the test but troponin I. There were substantial reductions in TAT medians (haemogram, 75%; fibrinogen, 41%; prothrombin time, 40%; sodium, 27%). The percentage of physicians satisfied with the haematological and biochemical tests´ TAT increased from 84% to 93% and from 86% to 91% respectively.

CONCLUSIONS

Our results reveal that VFT and TAT were severely reduced in most emergency tests, greatly improving physicians' satisfaction with TAT.

Within-day biological variation and hour-to-hour reference change values for hematological parameters

BACKGROUND

Middle- and long-term biological variation data for hematological parameters have been reported in the literature. Within-day 24-h variability profiles for hematological parameters are currently lacking. However, comprehensive hour-to-hour variability data are critical to detect diurnal cyclical rhythms, and to take into account the 'time of sample collection' as a possible determinant of natural fluctuation. In this study, we assessed 24-h variation profiles for 20 hematological parameters.

METHODS

Blood samples were collected under standardized conditions from 24 subjects every hour for 24 h. At each measurement, 20 hematological parameters were determined in duplicate. Analytical variation (CVA), within-subject biological variation (CVI), between-subject biological variation (CVG), index of individuality (II), and reference change values (RCVs) were calculated. For the parameters with a diurnal rhythm, hour-to-hour RCVs were determined.

RESULTS

All parameters showed higher CVG than CVI. Highest CVG was found for eosinophils (46.6%; 95% CI, 34.9%-70.1%) and the lowest value was mean corpuscular hemoglobin concentration (MCHC) (3.2%; 95% CI, 2.4%-4.8%). CVI varied from 0.4% (95% CI, 0.32%-0.42%) to 20.9% (95% CI, 19.4%-22.6%) for red cell distribution width (RDW) and eosinophils, respectively. Six hematological parameters showed a diurnal rhythm.

CONCLUSIONS

We present complete 24-h variability profiles for 20 hematological parameters. Hour-to-hour reference changes values may help to better discriminate between random fluctuations and true changes in parameters with rhythmic diurnal oscillations.

Different percentages of false-positive results obtained using five methods for the calculation of reference change values based on simulated normal and ln-normal distributions of data

Background Reference change values provide objective tools to assess the significance of a change in two consecutive results for a biomarker from an individual. The reference change value calculation is based on the assumption that within-subject biological variation has random fluctuation around a homeostatic set point that follows a normal (Gaussian) distribution. This set point (or baseline in steady-state) should be estimated from a set of previous samples, but, in practice, decisions based on reference change value are often based on only two consecutive results. The original reference change value was based on standard deviations according to the assumption of normality, but was soon changed to coefficients of variation (CV) in the formula (reference change value = ± Z ċ 2^½ ċ CV). Z is being dependent on the desired probability of significance, which also defines the percentages of false-positive results. The aim of this study was to investigate false-positive results using five different published methods for calculation of reference change value. Methods The five reference change value methods were examined using normally and ln-normally distributed simulated data. Results One method performed best in approaching the theoretical false-positive percentages on normally distributed data and another method performed best on ln-normally distributed data. The commonly used reference change value method based on two results (without use of estimated set point) performed worst both on normally distributed and ln-normally distributed data. Conclusions The optimal choice of method to calculate reference change value limits requires knowledge of the distribution of data (normal or ln-normal) and, if possible, knowledge of the homeostatic set point.

Biological variation of 20 analytes measured in serum from clinically healthy domestic cats

The applications of data on biological variation include assessment of the utility of population-based reference intervals, evaluation of the significance of change in serial results, and setting of analytical quality specifications. We investigated the biological variation of 19 biochemistry analytes and total T4, measured in serum from 7 clinically healthy domestic cats sampled once weekly for 5 weeks. Samples were frozen and analyzed in random order in the same analytical run. Results were analyzed for outliers, and the components of variance, subsequently generated by restricted maximum likelihood, were used to determine within-subject and between-subject variation (CV_I and CV_G, respectively), as well as analytical variation (CV_A) for each analyte. Indices of individuality, reference change values, and analytical performance goals were calculated. The smallest CV_I and CV_G were found for calcium, chloride, and sodium, whereas the largest values were calculated for bile acids. Nine analytes (albumin, alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, cholesterol, creatinine, phosphate [phosphorus], total protein, total T4) demonstrated high individuality, indicating limited utility of population-based reference intervals. Individuality was low, and population-based reference intervals were thereby considered appropriate for 5 analytes (bile acids, calcium, fructosamine, glucose, potassium). The intermediate individuality observed for 4 analytes (creatine kinase, iron, magnesium, urea) indicated that population-based reference intervals should be used with caution.

Biological Variation and Reference Change Value Data for Serum Neuron-Specific Enolase in a Turkish Population

BACKGROUND

Neuron-specific enolase (NSE) is a recognized biomarker for the assessment of cerebral injury in neurological disorders. This study aims to report a definitive assessment of the biological variation (BV) components of this biomarker, including within-subject BV (CVI), between-subject BV (CVG), index of individuality (II), and reference change value (RCV), in a cohort of Turkish participants using an experimental protocol.

METHODS

Six blood specimens were collected from each of the 13 apparently healthy volunteers (seven women, six men; ranging in age from 23 to 36) on the same day, every 2 weeks for 2 months. Serum specimens were stored at -20°C until analysis. Neuron-specific enolase levels were evaluated in serum samples using an electrochemiluminescence (ECLIA) immunoassay kit with a Roche Cobas e 411 auto-analyser. ANOVA test was used to calculate the variations.

RESULTS

The CVI and CVG for NSE were 21.5% and 28.8%, respectively. Analytical variation (CVA) was calculated as 10.2%. Additionally, II and RCV were calculated as 0.74 and 66% (95% confident interval, CI), respectively.

CONCLUSION

As the performance index (PI) was found to be less than 2 (PI = 0.95), it is concluded that the NSE measurements have a desirable performance for analytical imprecision. Since the II was found to be less than 1 (II: 0.74), the reference values will be of little use. Thus, RCV would provide better information for deciding whether a significant change has occurred.

Unnecessary repeated total cholesterol tests in biochemistry laboratory

INTRODUCTION

We aimed to determine the number of repeated cholesterol (RC) tests and the ratio of unnecessary-repeated cholesterol (URC) tests among patients admitted to Pamukkale University Hospital (Denizli, Turkey) and provide solutions to avoid URC testing.

MATERIALS AND METHODS

Total cholesterol (T-cholesterol) tests (N = 86,817) between June 2014 and May 2015 were evaluated. The tests performed more than once per patient were determined as RC test (N = 28,811). RC test with an interval shorter than 4 weeks were determined as URC test (N = 3968) according to the shortest retest interval stated in ACC/AHA blood cholesterol guideline. RC testing included internal medicine, surgery and paediatric outpatients and inpatients. Reference change value (RCV) of total cholesterol was calculated.

RESULTS

The 33.1% of the T-cholesterol tests were RC tests (N = 28,811), 13.7% of them were URC tests (N = 3968). Our RCV value was 25%. The percentage change between consecutive tests was less than RCV in 86.1% (N = 3418) of URC tests. URC tests were performed more frequently in patients with desirable total cholesterol value (P < 0.001).

CONCLUSION

There is a significant part of repeated T-cholesterol tests requested in our hospital. URC test requests can be evaluated by laboratories and the obtained data should be shared with clinicians. Laboratories can calculate RCV for the tests they performed and report this value with the test result. To prevent from URC tests, a warning plug-in can be added to hospital information software in accordance with guidelines to prevent from URC test requests.

Biological variation of thromboelastrography variables in 10 clinically healthy horses

OBJECTIVE

To assess the utility of population-based reference intervals (PRIs) for interpreting thromboelastography (TEG) variables in horses using biological variation data.

DESIGN

Prospective cohort biologic variation study conducted over a 5-week period.

SETTING

Veterinary teaching hospital and research facility.

ANIMALS

Ten clinically healthy horses randomly selected from a veterinary school research and teaching herd.

INTERVENTIONS

Horse health was determined using physical examination, CBC, and biochemical and coagulation profiles prior to the start of the study. Subsequently, once weekly blood sampling for TEG testing was performed for 5 weeks.

MEASUREMENTS AND MAIN RESULTS

The 4 TEG variables reaction time (R), clot formation time (K), angle, and maximum amplitude (MA) were measured, and coefficient of variation representing within- and between-horse biological variation (CVi and CVg , respectively) and coefficient of variation representing analytical variation (CVa ) were calculated using a nested ANOVA after removing outlier data. The CVi , CVg , and CVa for R were 26.8%, 5.2%, and 5.9%; for K were 31.0%, 0.0%, and 5.9%; for angle were 9.4%, 6.2%, and 21.7%; and for MA were 3.4%, 4.1%, and 4.4%, respectively. Index of individuality (IOI) was then calculated for each variable using the formula {( CVi² + CVa²/CVg²)}¹/². IOI for R was 5.3, for angle was 3.8, and for MA was 1.4; IOI was not assessed for K.

CONCLUSIONS

PRIs are appropriate for TEG variables, R, angle, and MA when interpreting results from individual horses based on calculated IOI values equal to or greater than 1.4. PRIs are likely appropriate when interpreting K, but IOI could not be calculated for this variable.

Decision limits and the reporting of cardiac troponin: Meeting the needs of both the cardiologist and the ED physician

Cardiac troponin is the preferred biomarker for defining the acute coronary syndrome and acute myocardial infarction. Currently, the only decision limit formally endorsed with regard to the cardiac troponins is the 99th percentile. This is a "rule-in" criterion, intended to ensure that only persons with the acute coronary syndrome are reviewed. The 99th percentile is an arbitrary cut point and there are many problems associated with its application, including defining a truly healthy population, the difficulty of standardisation of cardiac troponin assays, especially but not only cardiac troponin I, and the effects of age and sex on this parameter. The Emergency Department (ED) screens many more persons for possible acute coronary syndromes than actually have the condition and their needs are best met by a "rule-out" test that enables them to clear their busy departments of the many persons who do not actually have the condition. The needs of the ED are not optimally met using the 99th percentile. The index of individuality for the cardiac troponins is small and significant changes consistent with an acute coronary syndrome can occur without the 99th percentile being exceeded. It appears that the ED may be better served by use of delta troponin changes rather than the 99th percentile, but there are problems with this approach, particularly in persons who present late when troponin release has plateaued. In addition, there are many non-acute coronary syndrome causes for cardiac troponin release. The needs of the cardiologist and the ED physician are so different that it may be inappropriate for both groups to use the same diagnostic criteria for cardiac troponin, and it is of great importance that cardiac troponin measurement be used as only one part of the assessment of the person presenting with possible acute coronary syndrome.

Variation of betaine, N,N-dimethylglycine, choline, glycerophosphorylcholine, taurine and trimethylamine-N-oxide in the plasma and urine of overweight people with type 2 diabetes over a two-year period

BACKGROUND

Plasma betaine concentrations and urinary betaine excretions have high test-retest reliability. Abnormal betaine excretion is common in diabetes. We aimed to confirm the individuality of plasma betaine and urinary betaine excretion in an overweight population with type 2 diabetes and compare this with the individuality of other osmolytes, one-carbon metabolites and trimethylamine-N-oxide (TMAO), thus assessing their potential usefulness as disease markers.

METHODS

Urine and plasma were collected from overweight subjects with type 2 diabetes at four time points over a two-year period. We measured the concentrations of the osmolytes: betaine, glycerophosphorylcholine (GPC) and taurine, as well as TMAO, and the one-carbon metabolites, N,N-dimethylglycine (DMG) and free choline. Samples were measured using tandem mass spectrometry (LC-MS/MS).

RESULTS

Betaine showed a high degree of individuality (or test-retest reliability) in the plasma (index of individuality = 0.52) and urine (index of individuality = 0.45). Betaine in the plasma had positive and negative log-normal reference change values (RCVs) of 54% and -35%, respectively. The other osmolytes, taurine and GPC were more variable in the plasma of individuals compared to the urine. DMG and choline showed high individuality in the plasma and urine. TMAO was highly variable in the plasma and urine (log-normal RCVs ranging from 403% to -80% in plasma).

CONCLUSIONS

Betaine is highly individual in overweight people with diabetes. Betaine, its metabolite DMG, and precursor choline showed more reliability than the osmolytes, GPC and taurine. The low reliability of TMAO suggests that a single TMAO measurement has low diagnostic value.

Biological variations of some analytes in renal posttransplant patients: a different way to assess routine parameters

BACKGROUND

Biological variation (BV) data of analytes have been used to evaluate the significant changes in serial results (reference change value, RCV) of healthy individuals in clinical laboratories. However, BV data of healthy subjects may not be identical to the analytes of patients with ongoing clinical condition. The aim of this study was to calculate intra-(CVw) (coefficient of variation for intra-individual BV) and inter-individual (CVg) BV, index of individuality, and RCV of nine serum analytes of renal posttransplant patients.

METHODS

Six serum specimens were obtained in an interval of two months in a one-year period from 70 transplant patients who had been stable for three years. Each time creatinine, uric acid, urea, sodium, potassium, calcium, inorganic phosphate, total protein, and albumin of these patients were analyzed with an integrated clinical chemistry/immunoassay auto-analyzer. ANOVA tests were used to calculate the variations. Results were compared with the data of healthy subjects obtained from BV database.

RESULTS

CVw of all nine analytes of the renal transplant patients were higher than the healthy subjects. RCVs of these analytes were calculated as 14.5% for creatinine, 16.5% for urea, 13.7% for urate, 12.57% for albumin, 8.26% for total protein, 3.25% for sodium, 12.81% for potassium, 5.88% for calcium, and 21.57% for inorganic phosphate.

CONCLUSION

RCV concept for predicting the clinical status in posttransplant population represents an optimization of laboratory reporting and could be a valuable tool for clinical decision.

Is serum human epididymis protein 4 ready for prime time?

The National Institute for Health and Clinical Excellence (NICE) guidelines have sparked hot debate regarding the role of carbohydrate antigen 125 (CA-125) for ovarian cancer (OC) detection. Recent literature and evidence calls into question the use of CA-125 in diagnostic algorithms, given the better performance of human epididymis protein 4 (HE4) vs. CA-125 to rule OC. This is an important consideration since combined measurements are not cost-effective. The quality of this evidence is, however, threatened by important gaps related to study design, enrolled populations and analytical issues. For instance, despite the clinical need to prioritize the evaluation of biomarker performance in early stage tumours, sound evidence on this cannot be provided. In addition, results should be cautiously interpreted due to wide differences in the type of employed assays and in adopted diagnostic thresholds for HE4. Comparability among results obtained by different commercially available HE4 assays, together with an objective establishment of analytical goals is essential for the optimal clinical application of this marker.

Analytical characterization and clinical evaluation of an enzyme-linked immunosorbent assay for measurement of afamin in human plasma

Background

Comparative proteomics has recently identified afamin, the newest member of the albumin gene family, as a potential biomarker for ovarian cancer. The aim of this study was the analytical and clinical evaluation of a sandwich enzyme-linked immunosorbent assay for the determination of afamin in human plasma.

Methods

We evaluated precision, linearity, and detection limit of the assay, analyte stability and biological variability, determined reference values and quantified afamin concentrations in various diseases.

Results

Within-run and total coefficients of variation were < 10%. The method was linear across the tested measurement range. Detection limit was 7 mg/L for the assay. The analyte was stable for 24 h at room temperature, for 48 h at 4 °C, and for at least one year at − 20 °C and − 80 °C. The reference change value for healthy individuals was 24%. Age- and sex-independent reference values in healthy blood donors were 45–99 mg/L (median 68 mg/L). In the clinical assay evaluation afamin plasma concentrations were modestly decreased in patients with heart failure. Patients with pneumonia or sepsis exhibited markedly decreased afamin plasma concentrations. However, patients with chronic renal disease or chronic obstructive pulmonary disease showed no difference in afamin plasma concentrations as compared to healthy individuals. Correlation analyses revealed an inverse association between afamin and inflammatory biomarkers.

Conclusions

The afamin assay meets quality specifications for laboratory medicine. The results of the clinical assay evaluation revealed novel insights with respect to afamin as a potential negative acute phase protein and should encourage further studies.

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The aim of this study was the analytical and clinical evaluation of a human afamin ELISA.

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The afamin assay meets the needs of quality specifications of laboratory medicine.

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Afamin exerts a good in vitro stability which is important of preanalytical issues.

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The RCV was 24% and reference values in healthy blood donors were 45–99 mg/L.

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We revealed novel insights with respect to afamin as a potential negative acute phase protein.