Analytical Performance Specifications Research Articles

Biological variation and analytical performance specification study of erythrocyte and related parameters

Objective: To study biological variation (BV) and analytical performance specifications (APS) of 15 erythrocyte and related parameters. Methods: Sixty healthy participants from Beijing Hospital were prospectively recruited for the study, from July to December, 2023, including 30 males [aged (41.3±11.9) years] and 30 females [aged (40.3±12.4) years]. The study was designed based on the Biological Variation Data Critical Appraisal Checklist and the characteristics of erythrocyte detection. Whole blood samples were collected 10 times at 2-week intervals during 20 weeks. All samples were tested in duplicate using Mindray BC-7500 hematology analyzer and its accompanying reagents. Within-subject biological variation (CVI) and between-subject biological variation (CVG) were estimated by the nested ANOVA method. The BV data in this study were compared with the BV data of the European Database of Biological Variation. APS, reference change value (RCV) and index of individuality (II) were calculated. Results: The CVI for 15 parameters ranged from 0.49% to 86.46%, and the CVI data for red cell distribution width (RDW)-CV, RDW-SD, reticulocyte (RET) percentage, RET count, reticulocyte hemoglobin content (RHE), medium fluorescence reticulocyte (MFR) and high fluorescence reticulocyte (HFR) were significantly higher in females than in males (all P<0.05). The CVG ranged from 1.27% to 100.79%, and the CVG data for HFR were significantly different between genders (P<0.05). The CVI data for red blood cell (RBC), hemoglobin (Hb), mean corpuscular volume (MCV), mean corpuscular hemoglobin concentration (MCHC) and RHE in this study were lower than those recommended by the European database. The derived indicators of most parameters were influenced by gender. The II values for all parameters were<1.4, where the II values of MCHC and HFR were between 0.6 and 1.4; the II values for the remaining 13 parameters were<0.6. Conclusions: A BV study protocol for erythrocyte parameters is designed. When the reference intervals differ between genders, BV data should be calculated separately and the lower BV data are recommended for calculating APS; the RCV should be set separately for each gender.

Toward Analytical Performance Specifications for Immunosuppressive Drug Quantification in Transplantation: An Opinion Article.

Analytical methods require performance that meets the clinical needs. Different approaches for setting up permissible analytical imprecision goals (pCVA%) for drug analyses have been reported. The aim of this study was to calculate the pCVA% for cyclosporine, tacrolimus, everolimus, sirolimus, and mycophenolic acid using 4 alternative approaches, to compare the results and to critically discuss advantages and disadvantages of each model. The approaches to evaluate pCVA% were (A) based on biological variation observed in routine measurement results between 2022 and 2023 in the authors' laboratory, (B) derived from the terminal elimination half-life and dosing interval of the drugs, and (C and D) explored from the width of the therapeutic ranges (TR) by the 2 methods. For approach A, routine measurement data for cyclosporine and tacrolimus, obtained through liquid chromatography-tandem mass spectrometry and electrochemiluminescence immunoassays, were evaluated separately. The 4 alternative approaches for deriving pCVA% yielded similar results, for cyclosporine and tacrolimus in an analytical method dependent manner. The average pCVA% was 5.2%, 5.6%, 5.1%, 4.8%, and 7.7% for cyclosporine, tacrolimus, everolimus, sirolimus, and mycophenolic acid, respectively. The most challenging goals were those using TR-related approaches, while those using the biological variation approach were most easily achievable. Approach B resulted in more stringent goals for drugs with longer elimination half-lives (eg, everolimus and sirolimus). There is no single ideal approach for setting goals of drug analysis. However, the pCVA% values derived from the various approaches are similar and confirm that a <6% target proposed by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology is adequate and realistic in combination with state-of-the-art measurement technologies. In the authors' opinion, approaches based on the width of the TR are preferable, as they represent a common basis for clinical decisions and reflect elements of biological variation and analytics used to establish the TR.

Feasibility of Metrological Traceability Implementation Using the Joint Committee on Traceability in Laboratory Medicine Database Entries Including the Fulfillment of "Fit-for-Purpose" Maximum Allowable Measurement Uncertainty.

In previous publications, the Task Force on Reference Measurement System Implementation proposed a procedural approach combining a critical review of entries available in the Joint Committee on Traceability in Laboratory Medicine (JCTLM) database with a comparison of this information against analytical performance specifications for measurement uncertainty (MU) and applied it to a group of 13 measurands. Here we applied this approach to 17 additional measurands, of which measurements are frequently requested. The aims of the study were (a) to describe the main characteristics for implementing traceability and the potential to fulfill the maximum allowable MU (MAU) at the clinical sample level of certified reference materials and reference measurement procedures listed in the JCTLM database; (b) to discuss limitations and obstacles, if any, to the achievement of the required quality of laboratory measurements; and (c) to provide a gap analysis by highlighting what is still missing in the database. Results were integrated with those obtained in the previous study, therefore offering an overview of where we are and what is still missing in the practical application of the metrological traceability concept to 30 common biochemical tests employed in laboratory medicine. Our analysis shows that for 28 out of 30 measurands, conditions exist to correctly implement metrological traceability to the International System of units and fulfill at least the MAU of the minimum quality level derived according to internationally recommended models. For 2 measurands (serum albumin and chloride), further improvements in MU of higher-order references would be necessary.

Biological variation of methylmalonic acid in urine in spanish population

Background-AimMethylmalonic acid (MMA) is currently the best biomarker of functional vitamin B12 deficiency. However, for a correct interpretation of the patient’s results it is necessary to know its biological variation (BV).No BV data are available for urine MMA values, as measured by mass spectrometry. Hence, the aim of this study was to estimate the within- and between-person coefficients of variation (CVw, CVg) for MMA in a healthy population, and the associated index of individuality (II), as well as to define quality specifications based on BV and the reference change value (RCV). MethodsRandom urine samples from 34 healthy volunteers were collected over four consecutive weeks. Samples were stored at –80 °C until analysis in a single analytical run. MMA excretion was quantified by tandem liquid chromatography coupled to mass spectrometry (HPLC-MS/MS). Results were normalized to urine creatinine.The coefficients of variation were estimated by CV-ANOVA. Confidence intervals (95 %) were calculated. Quality specifications were defined according to international recommendations. ResultsA total of 128 samples were included. The coefficients of variation were CVw = 35.7 % (26.1–45.3) and CVg = 67.7 % (58.3–77.0). The associated II was 0.5 and the RCV was 88.1 %. ConclusionConsidering the II obtained, MMA in urine has high individuality, therefore, RCV is better to evaluate serial clinical results. Our results will contribute to a better clinical interpretation of this biomarker and will represent a great aid when defining analytical performance specifications for this magnitude.

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

This article defines analytical performance specifications (APS) for evaluating laboratory proficiency through an external quality assessment scheme. Standard deviations for proficiency assessment were derived from Thompson's characteristic function applied to robust data calculated from participants' submissions in the Occupational and Environmental Laboratory Medicine (OELM) external quality assurance scheme for trace elements in serum, whole blood and urine. Characteristic function was based on two parameters: (1) β- the average coefficient of variation (CV) at high sample concentrations; (2) α- the average standard deviation (SD) at low sample concentrations. APSs weredefined as 1.65 standard deviations calculated by Thompson's approach. Comparison between OELM robust data and characteristic function were used to validate the model. Application of the characteristic function allowed calculated APS for 18 elements across three matrices. Some limitations were noted, particularly for elements (1) with no sample concentrations near analytical technique limit of detection; (2) exhibiting high robust CV at high concentration; (3) exhibiting high analytical variability such as whole blood Tl and urine Pb; (4) with an unbalanced number of robust SD above and under the characteristic function such as whole blood Mn and serum Al and Zn. The characteristic function was a useful means of deriving APS for trace elements in biological fluids where biological variation data or outcome studies were not available. However, OELM external quality assurance scheme data suggests that the characteristic functions are not appropriate for all elements.

Outcome-based analytical performance specifications: current status and future challenges.

Analytical performance specifications (APS) based on outcomes refer to how 'good' the analytical performance of a test needs to be to do more good than harm to the patient. Analytical performance of a measurand affects its clinical performance. Without first setting clinical performance requirements, it is difficult to define how good analytically the test needs to be to meet medical needs. As testing is indirectly linked to health outcomes through clinical decisions on patient management, often simulation-based studies are used to assess the impact of analytical performance on the probability of clinical outcomes which is then translated to Model 1b APS according to the Milan consensus. This paper discusses the related key definitions, concepts and considerations that should assist in finding the most appropriate methods for deriving Model 1b APS. We review the advantages and limitations of published methods and discuss the criteria for transferability of Model 1b APS to different settings. We consider that the definition of the clinically acceptable misclassification rate is central to Model 1b APS. We provide some examples and guidance on a more systematic approach for first defining the clinical performance requirements for tests and we also highlight a few ideas to tackle the future challenges associated with providing outcome-based APS for laboratory testing.

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

This study aimed to deliver biological variation (BV) estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping (memory T/B cells, regulatory T cells, etc.) and classical, intermediate, and nonclassical monocyte subsets based on the full spectrum flow cytometry (FS-FCM) and a Biological Variation Data Critical Appraisal Checklist (BIVAC) design. Samples were collected biweekly from 60 healthy Chinese adults over 10 consecutive two-week periods. Each sample was measured in duplicate within a single run for lymphocyte deep immunophenotyping and monocyte subset determination using FS-FCM, including the percentage (%) and absolute count (cells/μL). After trend adjustment, a Bayesian model was applied to deliver the within-subject BV (CVI) and between-subject BV (CVG) estimates with 95 % credibility intervals. Enumeration (% and cells/μL) for 25 types of lymphocyte deep immunophenotyping and three types of monocyte subset percentages showed considerable variability in terms of CVI and CVG. CVI ranged from 4.23 to 47.47 %. Additionally, CVG ranged between 10.32 and 101.30 %, except for CD4+ effector memory T cells re-expressing CD45RA. No significant differences were found between males and females for CVI and CVG estimates. Nevertheless, the CVGs of PD-1+ T cells (%) may be higher in females than males. Based on the desired analytical performance specification, the maximum allowable imprecision immune parameter was the CD8+PD-1+ T cell (cells/μL), with 23.7 %. This is the first study delivering BV estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping, along with classical, intermediate, and nonclassical monocyte subsets, using FS-FCM and adhering to the BIVAC design.

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

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

Evaluation of measurement uncertainty for 19 clinical chemistry analytes and comparison with analytical performance specifications

Evaluation of measurement uncertainty for 19 clinical chemistry analytes and comparison with analytical performance specifications

Analytical performance specifications for plasma copeptin based on outcome and biological variation data

Analytical performance specifications for plasma copeptin based on outcome and biological variation data

Outcome-based analytical performance specifications – Current status and future directions

Outcome-based analytical performance specifications – Current status and future directions

Using analytical performance specifications in a medical laboratory.

Analytical performance specifications (APS) are used for the quantitative assessment of assay analytical performance, with the aim of providing information appropriate for clinical care of patients. One of the major locations where APS are used is in the routine clinical laboratory. These may be used to assess and monitor assays in a range of settings including method selection, method verification or validation, external quality assurance, internal quality control and assessment of measurement uncertainty. The aspects of assays that may be assessed include imprecision, bias, selectivity, sample type, analyte stability and interferences. This paper reviews the practical use of APS in a routine clinical laboratory, using the laboratory I supervise as an example.

The role of analytical performance specifications in international guidelines and standards dealing with metrological traceability in laboratory medicine.

The goal of metrological traceability is to have equivalent results for a measurand in clinical samples (CSs) irrespective of the in-vitro diagnostic medical device (IVD-MD) used for measurements. The International Standards Organization standard 17511 defines requirements for establishing metrological traceability of values assigned to calibrators, trueness control materials and human samples used with IVD-MDs. Each step in metrological traceability has an uncertainty associated with the value assigned to a material. The uncertainty at each step adds to the uncertainty from preceding steps such that the combined uncertainty gets larger at each step. The combined uncertainty fora CS result must fulfil an analytical performance specification (APS) for the maximum allowable uncertainty (umax CS). The umax CS can be partitioned among the steps in a metrological traceability calibration hierarachy to derive the APS for maximum allowable uncertainty at each step. Similarly, the criterion for maximum acceptable noncommutability bias can be derived from the umax CS. One of the challenges in determining if umax CS is fulfilled is determining the repeatability uncertainty (u Rw) from operating an IVD-MD within a clinical laboratory. Most of the current recommendations for estimating u Rw from internal quality control data do not use a sufficiently representative time interval to capture all relevant sources of variability in measurement results. Consequently, underestimation of u Rw is common and may compromise assessment of how well current IVD-MDs and their supporting calibration hierarchies meet the needs of clinical care providers.

Biological Variation Estimates for Plasma Copeptin and Clinical Implications.

Plasma copeptin measurement is useful for the differential diagnoses of polyuria-polydipsia syndrome. It has also been proposed as a prognostic marker for cardiovascular diseases. However, limited information is available about the within- (CVI) and between-subject (CVG) biological variation (BV). This study presents BV estimates for copeptin in healthy individuals. Samples were collected weekly from 41 healthy subjects over 5 weeks and analyzed using the BRAHMS Copeptin proAVP KRYPTOR assay after at least 8 h of food and fluid abstinence. Outlier detection, variance homogeneity, and trend analysis were performed followed by CV-ANOVA for BV and analytical variation (CVA) estimation with 95% confidence intervals. Reference change values (RCVs), index of individuality (II), and analytical performance specification (APS) were also calculated. The analysis included 178 results from 20 males and 202 values from 21 females. Copeptin concentrations were significantly higher in males than in females (mean 8.5 vs 5.2 pmol/L, P < 0.0001). CVI estimates were 18.0% (95% CI, 15.4%-21.6%) and 19.0% (95% CI, 16.4%-22.6%), for males and females, respectively; RCVs were -35% (decreasing value) and 54% (increasing value). There was marked individuality for copeptin. No result exceeded the diagnostic threshold (>21.4 pmol/L) for arginine vasopressin resistance. The availability of BV data allows for refined APS and associated II, and RCVs applicable as aids in the serial monitoring of patients with specific diseases such as heart failure. The BV estimates are only applicable in subjects who abstained from oral intake due to the rapid and marked effects of fluids on copeptin physiology.

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

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

Pre-analytical stability of the CEA, CYFRA 21.1, NSE, CA125 and HE4 tumor markers.

For lung cancer, circulating tumor markers (TM) are available to guide clinical treatment decisions. To ensure adequate accuracy, pre-analytical instabilities need to be known and addressed in the pre-analytical laboratory protocols. This study investigates the pre-analytical stability of CA125, CEA, CYFRA 21.1, HE4 and NSE for the following pre-analytical variables and procedures; i) whole blood stability, ii) serum freeze-thaw cycles, iii) electric vibration mixing and iv) serum storage at different temperatures. Left-over patient samples were used and for every investigated variable six patient samples were used and analysed in duplicate. Acceptance criteria were based on analytical performance specifications based on biological variation and significant differences with baseline. Whole blood was stable for at least 6 hours for all TM except for NSE. Two freeze-thaw cycles were acceptable for all TM except CYFRA 21.1. Electric vibration mixing was allowed for all TM except for CYFRA 21.1. Serum stability at 4°C was 7 days for CEA, CA125, CYFRA 21.1 and HE4 and 4 hours for NSE. Critical pre-analytical processing step conditions were identified that, if not taken into account, will result in reporting of erroneous TM results.

How clinical laboratories select and use Analytical Performance Specifications (APS) in Italy.

Even if the topic of the analytical quality required to provide laboratory results "fit for purpose" exists since the beginning of the modern medical laboratory, there is the suspect that the expression "Analytical Performance Specifications" (APS) is not well-known. To investigate this aspect a survey was conducted. A questionnaire with seven questions related to the knowledge about the topic, the sources of information and the criteria used by the laboratories to set the APS and their applications was prepared. It was distributed to all the clinical pathology laboratories of Lombardy Region (143) and to the members of SIBioC Laboratory Medicine (excluding Lombardy). We received 201 replies: 127 from Lombardy and 74 from the rest of Italy. Fifteen percent declared to ignore the meaning ofAPS and only 64 % of those knowing the meaning of theterm declared to use them in the daily practice. The state-of-the-art was the principle used more frequently to setAPS(about 48 %) followed by biological variation (41 %), and APS were typically applied to set goals for Internal Quality Control for selected measurands. Usually imprecision or total error APS were used, much less frequently uncertainty APS. In fact only 27 % of the laboratories declared to have calculated the measurement uncertainty for part or the majority of their measurands. Even considering the limits of a survey that relies upon self-declarations, it appears clearly that, at list in Italy, there is some work to be done to promote the concept and the use of APS.

Analytical Performance Specifications for Input Variables: Investigation of the Model of End-Stage Liver Disease.

Artificial intelligence models constitute specific uses of analysis results and, therefore, necessitate evaluation of analytical performance specifications (APS) for this context specifically. The Model of End-stage Liver Disease (MELD) is a clinical prediction model based on measurements of bilirubin, creatinine, and the international normalized ratio (INR). This study evaluates the propagation of error through the MELD, to inform choice of APS for the MELD input variables. A total of 6093 consecutive MELD scores and underlying analysis results were retrospectively collected. "Desirable analytical variation" based on biological variation as well as current local analytical variation was simulated onto the data set as well as onto a constructed data set, representing a worst-case scenario. Resulting changes in MELD score and risk classification were calculated. Biological variation-based APS in the worst-case scenario resulted in 3.26% of scores changing by ≥1 MELD point. In the patient-derived data set, the same variation resulted in 0.92% of samples changing by ≥1 MELD point, and 5.5% of samples changing risk category. Local analytical performance resulted in lower reclassification rates. Error propagation through MELD is complex and includes population-dependent mechanisms. Biological variation-derived APS were acceptable for all uses of the MELD score. Other combinations of APS can yield equally acceptable results. This analysis exemplifies how error propagation through artificial intelligence models can become highly complex. This complexity will necessitate that both model suppliers and clinical laboratories address analytical performance specifications for the specific use case, as these may differ from performance specifications for traditional use of the analyses.

Issues in assessing analytical performance specifications in healthcare systems assembling multiple laboratories and measuring systems.

Analytical performance specifications (APS) are usually compared to the intermediate reproducibility uncertainty of measuring a particular measurand using a single invitro diagnostic medical device (IVD MD). Healthcare systems assembling multiple laboratories that include several IVD MDs and cater to patients suffering from long-term disease conditions mean that samples from apatient are analyzed using a few IVD MDs, sometimes fromdifferent manufacturers, but rarely all IVD MDs in thehealthcare system. The reproducibility uncertainty for results of a measurand measured within a healthcare system and the components of this measurement uncertainty is useful in strategies to minimize bias and overall measurement uncertainty within the healthcare system. The root mean squares deviation (RMSD) calculated as the sample standard deviation (SD) and relative SD includes both imprecision and bias and is appropriate for expressing such uncertainties. Results from commutable stabilized internal and external control samples, from measuring split natural patient samples or using big-data techniques, are essential in monitoring bias and measurement uncertainties in healthcare systems. Variance component analysis (VCA) can be employed to quantify the relative contributions of the most influential factors causing measurement uncertainty. Such results represent invaluable information for minimizing measurement uncertainty in the interest of the healthcare system's patients.

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

BackgroundKnowledge of biological variation (BV) of hormones is essential for interpretation of laboratory tests and for diagnostics of endocrinological and reproductive diseases. There is a lack of robust BV data for many hormones in men. MethodsWe used serum samples collected weekly over 10 weeks from the European Biological Variation Study (EuBIVAS) to determine BV of testosterone, follicle-stimulating hormone (FSH), prolactin, luteinizing hormone (LH) and dehydroepiandrosterone sulfate (DHEA-S) in 38 men. We derived within-subject (CVI) and between-subject (CVG) BV estimates by CV-ANOVA after trend, outlier, and homogeneity analysis and calculated reference change values, index of individuality (II), and analytical performance specifications. ResultsThe CVI estimates were 10 % for testosterone, 8 % for FSH, 13 % for prolactin, 22 % for LH, and 9 % for DHEA-S, respectively. The IIs ranged between 0.14 for FSH to 0.66 for LH, indicating high individuality. ConclusionsIn this study, we have used samples from the highly powered EuBIVAS study to derive BV estimates for testosterone, FSH, prolactin, LH and DHEA-S in men. Our data confirm previously published BV estimates of testosterone, FSH and LH. For prolactin and DHEA-S BV data for men are reported for the first time.