Harmonization and Standardization: Where Are We Now?

Synthetic abnormal mast cell particles successfully mimic neoplastic mast cells by flow cytometry

Clinical flow cytometry laboratories require quality control materials for assay development, validation, and performance monitoring, including new reagent lot qualification. However, finding suitable controls for populations with uncommonly expressed antigens or for rare populations, such as mast cells, can be difficult. To that end, we evaluated synthetic abnormal mast cell particles (SAMCP), developed together with, and manufactured by, Slingshot Biosciences. The SAMCP's were designed to phenotypically mimic abnormal neoplastic mast cells: they were customized to have the same light scatter and autofluorescence properties of mast cells, along with surface antigen levels of CD45, CD33, CD117, CD2, CD25, and CD30 consistent with that seen in mast cell disease. We evaluated several performance characteristics of these particles using ARUP's high sensitivity clinical mast cell assay, including limit of detection, off-target activity and FMO controls, precision, scatter properties of the particles utilizing several different cytometer platforms, and particle antigen stability. The phenotype of the SAMCP mimicked abnormal mast cells, and they could be distinguished from normal native mast cells. FMO controls demonstrated specificity of each of the markers, and no off-target binding was detected. The limit of detection of the particles spiked into normal bone marrow was found to be ≤0.003% in a limiting dilution assay. The mast cell particles were found to perform similarly on Becton Dickinson Lyric, Cytek Aurora, and Beckman Coulter Navios and CytoFLEX platforms. Within run and between run precision were less than 10% CV. SAMCP were stable up to 13 days with minimal loss of antigen fluorescence intensity. The SAMCP's were able to successfully mimic neoplastic mast cells based on the results of our high sensitivity mast cell flow cytometry panel. These synthetic cell particles represent an exciting and innovative technology, which can fulfill vital needs in clinical flow cytometry such as serving as standardized control materials for assay development and performance monitoring.

Nanomaterial-Driven Precision Immunomodulation: A New Paradigm in Therapeutic Interventions

Immunotherapy is a rapidly advancing field of research in the treatment of conditions such as cancer and autoimmunity. Nanomaterials can be designed for immune system manipulation, with precise targeted delivery and improved immunomodulatory efficacy. Here, we elaborate on various strategies using nanomaterials, including liposomes, polymers, and inorganic NPs, and discuss their detailed design intricacies, mechanisms, and applications, including the current regulatory issues. This type of nanomaterial design for targeting specific immune cells or tissues and controlling release kinetics could push current technological frontiers and provide new and innovative solutions for immune-related disorders and diseases without off-target effects. These materials enable targeted interactions with immune cells, thereby enhancing the effectiveness of checkpoint inhibitors, cancer vaccines, and adoptive cell therapies. Moreover, they allow for fine-tuning of immune responses while minimizing side effects. At the intersection of nanotechnology and immunology, nanomaterial-based platforms have immense potential to revolutionize patient-centered immunotherapy and reshape disease management. By prioritizing safety, customization, and compliance with regulatory standards, these systems can make significant contributions to precision medicine, thereby significantly impacting the healthcare landscape.

Establishment of dry-chemistry-based reference intervals of routine liver function tests for the adult population of Gandaki Province, Nepal

Every clinical laboratory should ideally establish its own population-specific reference intervals (RIs) to promote precision and evidence-based medicine. However, clinical laboratories in Nepal find it easier to follow external RIs than establish their own, leading to a lack of RIs specific to the local population. This study thus aimed to establish RIs of routine LFTs for the adult population of Gandaki Province, Nepal, and compare them with the current RIs used by our laboratory. We established the dry-chemistry-based reference intervals of 11 common LFT parameters for the adult population of Gandaki Province, Nepal using the direct priori-based method. The combined and sex-specific 95% double-sided RIs of total protein, albumin, globulin, A/G ratio, bilirubin, aspartate aminotransaminase (AST), alanine aminotransaminase (ALT), AST/ALT ratio, and alkaline phosphatase (ALP) were established using non-parametric percentile method. The new RIs were also compared with the currently used RIs that were adopted from the reagent kit inserts. The newly established RIs for each LFT were: Total proteins: 68.0-69.0g/L, albumin: 39.0-52.0g/L; globulin: 27.0-42.0g/L; A/G ratio: 1.1-1.8; total bilirubin: 5.13-25.65μmol/L (0.30-1.50mg/dl); unconjugated bilirubin: 1.71-17.10μmol/L (0.10-1.00mg/dl); conjugated bilirubin: 0.00-10.26 μmol/L (0.00-0.60mg/dl); AST: 20.0-43.2U/L; ALT: 11.0-53.0 U/L; AST/ALT ratio: 0.7-2.1; ALP: 42.0-135.4U/L. The RIs of albumin, globulin, A/G ratio, AST, ALT, and AST/ALT ratio differed significantly (p < 0.05) between males and females. Moreover, calculated out-of-range values showed that up to 4-40% of apparently healthy adults were classified as having abnormal test results based on current RIs. The newly established RIs fulfil the need for population and platform-specific RIs for the adult population of Gandaki Province of Nepal and bring more conformity and accuracy in interpreting the LFT results, diagnosis of hepatobiliary diseases, clinical decision-making, monitoring the success of therapy and future liver specific biomedical researches within the Gandaki Province of Nepal.

An improved implementation of metrological traceability concepts is needed to benefit from standardization of laboratory results

Non-harmonization of laboratory results represents a concrete risk for patient safety. To avoid harms, it is agreed that measurements by in vitro diagnostic medical devices (IVD-MD) on clinical samples should be traceable to higher-order references and adjusted to give the same result. However, metrological traceability is not a formal claim and has to be correctly implemented, which in practice does not happen for a non-negligible number of measurands. Stakeholders, such as higher-order reference providers, IVD manufacturers, and External Quality Assessment organizers, have major responsibilities and should improve their contribution by unambiguously and rigorously applying what is described in the International Organization for Standardization 17511:2020 standard and other documents provided by the international scientific bodies, such as Joint Committee on Traceability in Laboratory Medicine and IFCC. For their part, laboratory professionals should take responsibility to abandon non-selective methods and move to IVD-MDs displaying proper selectivity, which is one of the indispensable prerequisites for the correct implementation of metrological traceability. The practicality of metrological traceability concepts is not impossible but relevant education and appropriate training of all involved stakeholders are essential to obtain the expected benefits in terms of standardization.

From autoantibody test results to decision making: Incorporating likelihood ratios within medical practice

Autoantibodies are important laboratory markers to support diagnosis of autoimmune diseases. Interpretation of autoantibodies is classically done in a dichotomous way (positive versus negative). Yet, interpretation of autoantibody test results can be improved by reporting likelihood ratios. Likelihood ratios convey information on how much more/less likely a test result is in individuals with the disease compared to individuals without the disease. It incorporates information on the antibody level (the higher the antibody level, the higher the association with the disease), which is helpful for (differential) diagnosis. Likelihood ratios are unit-independent and allow users to harmonize test result interpretation. When the likelihood ratio is combined with information on the pre-test probability, post-test probability can be appraised. In this review, the applicability of likelihood ratio in autoimmune diagnostics will be reviewed from the perspective of the clinician, the laboratory professional and the in vitro diagnostic industry.

Harmonization of four biomarkers across nine nationally representative studies of older persons

INTRODUCTION

A growing number of international population surveys have included measurement of biomarkers, but differ in the type of specimens collected, sample processing procedures, shipment protocols, and laboratory assay platforms. The purpose of this study is to harmonize biomarker data from nine nationally representative studies of people 50 years of age and over by adjusting for assay platforms and type of specimens for total cholesterol (total-C), high-density lipoprotein cholesterol (HDL-C), glycosylated hemoglobin (HbA1c), and C-reactive protein (CRP).

METHODS

Sets of 24 identical serum, plasma, whole blood, and dried blood spot harmonization samples with known analyte levels were generated at a reference laboratory, shipped at -80°C to the respective study laboratories, and subsequently assayed following the study laboratory's protocol. Both original and harmonized study data were used to calculate mean values and at-risk prevalence.

RESULTS

The correlation coefficients between the biomarker values of the harmonization samples obtained by the study laboratories and the reference laboratory were 0.99 or above for all analytes and laboratories, indicating the high quality of assays at all laboratories. However, using the harmonized data from each study, there were significant differences in the mean values and country ranking of the prevalence of at-risk levels of these four biomarkers.

CONCLUSIONS

While the biomarker data from the different study laboratories were highly correlated, indicating very high correlation of rank order of specimens, absolute values did vary significantly. This can have a major impact on assessment of international differences in estimates of risks for chronic morbidity and mortality.

Investigating the Current Harmonization Status of Tumor Markers Using Global External Quality Assessment Programs: A Feasibility Study

BACKGROUND

The harmonization status of most tumor markers (TMs) is unknown. We report a feasibility study performed to determine whether external quality assessment (EQA) programs can be used to obtain insights into the current harmonization status of the tumor markers α-fetoprotein (AFP), prostate specific antigen (PSA), carcinoembryonic antigen (CEA), cancer antigen (CA)125, CA15-3 and CA19-9.

METHODS

EQA sample results provided by 6 EQA providers (INSTAND [Germany], Korean Association of External Quality Assessment Service [KEQAS, South Korea], National Center for Clinical Laboratories [NCCL, China], United Kingdom National External Quality Assessment Service [UK NEQAS, United Kingdom], Stichting Kwaliteitsbewaking Medische Laboratoriumdiagnostiek [SKML, the Netherlands], and the Royal College of Pathologists of Australasia Quality Assurance Programs [RCPAQAP, Australia]) between 2020 and 2021 were used. The consensus means, calculated from the measurement procedures present in all EQA programs (Abbott Alinity, Beckman Coulter DxI, Roche Cobas, and Siemens Atellica), was used as reference values. Per measurement procedure, the relative difference between consensus mean for each EQA sample and the mean of all patient-pool-based EQA samples were calculated and compared to minimum, desirable, and optimal allowable bias criteria based on biological variation.

RESULTS

Between 19040 (CA15-3) and 25398 (PSA) individual results and 56 (PSA) to 76 (AFP) unique EQA samples were included in the final analysis. The mean differences with the consensus mean of patient-pool-based EQA samples for all measurement procedures were within the optimum bias criterion for AFP, the desirable bias for PSA, and the minimum bias criterion for CEA. However, CEA results <8 µg/L exceeded the minimum bias criterion. For CA125, CA15-3, and CA19-9, the harmonization status was outside the minimum bias criterion, with systematic differences identified.

CONCLUSIONS

This study provides relevant information about the current harmonization status of 6 tumor markers. A pilot harmonization investigation for CEA, CA125, CA15-3, and CA19-9 would be desirable.

Optimizing the data in direct access testing: information technology to support an emerging care model

Direct access testing (DAT) is an emerging care model that provides on-demand laboratory services for certain preventative, diagnostic, and monitoring indications. Unlike conventional testing models where health care providers order tests and where sample collection is performed onsite at the clinic or laboratory, most interactions between DAT consumers and the laboratory are virtual. Tests are ordered and results delivered online, and specimens are frequently self-collected at home with virtual support. Thus, DAT depends on high-quality information technology (IT) tools and optimized data utilization to a greater degree than conventional laboratory testing. This review critically discusses the United States DAT landscape in relation to IT to highlight digital challenges and opportunities for consumers, health care systems, providers, and laboratories. DAT offers consumers increased autonomy over the testing experience, cost, and data sharing, but the current capacity to integrate DAT as a care option into the conventional patient-provider model is lacking and will require innovative approaches to accommodate. Likewise, both consumers and health care providers need transparent information about the quality of DAT laboratories and clinical decision support to optimize appropriate use of DAT as a part of comprehensive care. Interoperability barriers will require intentional approaches to integrating DAT-derived data into the electronic health records of health systems nationally. This includes ensuring the laboratory results are appropriately captured for downstream data analytic pipelines that are used to satisfy population health and research needs. Despite the data- and IT-related challenges for widespread incorporation of DAT into routine health care, DAT has the potential to improve health equity by providing versatile, discreet, and affordable testing options for patients who have been marginalized by the current limitations of health care delivery in the United States.

Advances in minimal residual disease monitoring in multiple myeloma

Multiple myeloma (MM) is characterized by the clonal expansion of plasma cells and the excretion of a monoclonal immunoglobulin (M-protein), or fragments thereof. This biomarker plays a key role in the diagnosis and monitoring of MM. Although there is currently no cure for MM, novel treatment modalities such as bispecific antibodies and CAR T-cell therapies have led to substantial improvement in survival. With the introduction of several classes of effective drugs, an increasing percentage of patients achieve a complete response. This poses new challenges to traditional electrophoretic and immunochemical M-protein diagnostics because these methods lack sensitivity to monitor minimal residual disease (MRD). In 2016, the International Myeloma Working Group (IMWG) expanded their disease response criteria with bone marrow-based MRD assessment using flow cytometry or next-generation sequencing in combination with imaging-based disease monitoring of extramedullary disease. MRD status is an important independent prognostic marker and its potential as a surrogate endpoint for progression-free survival is currently being studied. In addition, numerous clinical trials are investigating the added clinical value of MRD-guided therapy decisions in individual patients. Because of these novel clinical applications, repeated MRD evaluation is becoming common practice in clinical trials as well as in the management of patients outside clinical trials. In response to this, novel mass spectrometric methods that have been developed for blood-based MRD monitoring represent attractive minimally invasive alternatives to bone marrow-based MRD evaluation. This paves the way for dynamic MRD monitoring to allow the detection of early disease relapse, which may prove to be a crucial factor in facilitating future clinical implementation of MRD-guided therapy. This review provides an overview of state-of-the-art of MRD monitoring, describes new developments and applications of blood-based MRD monitoring, and suggests future directions for its successful integration into the clinical management of MM patients.

Establishing metrological traceability for small molecule measurands in laboratory medicine

For molecules that can be well described metrologically in the sense of the definition of measurands, and which can also be recorded analytically as individual substances, reference measurement service traceability to a metrologically sound foundation is a necessity. The establishment of traceability chains must be initiated by National Metrology Institutes (NMIs) according to applicable standards; they are at the top and leading position in this concept. If NMIs are not in the position to take up this task, alternative approaches must be sought. Traceability initiatives established by in vitro device industry or academia must meet the quality standards of NMIs. Adherence to International Organization for Standardization (ISO) procedure 15193 must be a matter of course for the establishment of reference measurement procedures (RMPs). Certified reference material (CRM) characterization must be thorough, e.g., by the application of quantitative nuclear magnetic resonance measurements and by adherence to ISO 15194. Both for RMPs and CRMs Joint Committee for Traceability in Laboratory Medicine (JCTLM) listing must be the ultimate goal. Results must be shared in a transparent manner to allow other stakeholders including NMIs to reproduce and disseminate the reference measurement procedures.

Calibration of cell-free DNA measurements by next-generation sequencing

OBJECTIVES

Accurate monitoring of disease burden depends on accurate disease marker quantification. Although next-generation sequencing (NGS) is a promising technology for noninvasive monitoring, plasma cell-free DNA levels are often reported in misleading units that are confounded by non-disease-related factors. We proposed a novel strategy for calibrating NGS assays using spiked normalizers to improve precision and to promote standardization and harmonization of analyte concentrations.

METHODS

In this study, we refined our NGS protocol to calculate absolute analyte concentrations to (1) adjust for assay efficiency, as judged by recovery of spiked synthetic normalizer DNAs, and (2) calibrate NGS values against droplet digital polymerase chain reaction (ddPCR). As a model target, we chose the Epstein-Barr virus (EBV) genome. In patient (n = 12) and mock (n = 12) plasmas, NGS and 2 EBV ddPCR assays were used to report EBV load in copies per mL of plasma.

RESULTS

Next-generation sequencing was equally sensitive to ddPCR, with improved linearity when NGS values were normalized for spiked DNA read counts (R2 = 0.95 for normalized vs 0.91 for raw read concentrations). Linearity permitted NGS calibration to each ddPCR assay, achieving equivalent concentrations (copies/mL).

CONCLUSIONS

Our novel strategy for calibrating NGS assays suggests potential for a universal reference material to overcome biological and preanalytical variables hindering traditional NGS strategies for quantifying disease burden.

Reference Interval Harmonization: Harnessing the Power of Big Data Analytics to Derive Common Reference Intervals across Populations and Testing Platforms

BACKGROUND

Harmonization in laboratory medicine is essential for consistent and accurate clinical decision-making. There is significant and unwarranted variation in reference intervals (RIs) used by laboratories for assays with established analytical traceability. The Canadian Society of Clinical Chemists (CSCC) Working Group on Reference Interval Harmonization (hRI-WG) aims to establish harmonized RIs (hRIs) for laboratory tests and support implementation.

METHODS

Harnessing the power of big data, laboratory results were collected across populations and testing platforms to derive common adult RIs for 16 biochemical markers. A novel comprehensive approach was established, including: (a) analysis of big data from community laboratories across Canada; (b) statistical evaluation of age, sex, and analytical differences; (c) derivation of hRIs using the refineR method; and (d) verification of proposed hRIs across 9 laboratories with different instrumentation using serum and plasma samples collected from healthy Canadian adults.

RESULTS

Harmonized RIs were calculated for all assays using the refineR method, except free thyroxine. Derived hRIs met proposed verification criterion across 9 laboratories and 5 manufacturers for alkaline phosphatase, albumin (bromocresol green), chloride, lactate dehydrogenase, magnesium, phosphate, potassium (serum), and total protein (serum). Further investigation is needed for some analytes due to failure to meet verification criteria in one or more laboratories (albumin [bromocresol purple], calcium, total carbon dioxide, total bilirubin, and sodium) or concern regarding excessively wide hRIs (alanine aminotransferase, creatinine, and thyroid stimulating hormone).

CONCLUSIONS

We report a novel data-driven approach for RI harmonization. Findings support feasibility of RI harmonization for several analytes; however, some presented challenges, highlighting limitations that need to be considered in harmonization and big data analytics.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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Key Words

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

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MESH Headings

• Humans
• Thyrotropin
• Algorithms

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Grants Collapse

Affiliation(s)

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

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Machine Learning in Laboratory Medicine: Recommendations of the IFCC Working Group

BACKGROUND

Machine learning (ML) has been applied to an increasing number of predictive problems in laboratory medicine, and published work to date suggests that it has tremendous potential for clinical applications. However, a number of groups have noted the potential pitfalls associated with this work, particularly if certain details of the development and validation pipelines are not carefully controlled.

METHODS

To address these pitfalls and other specific challenges when applying machine learning in a laboratory medicine setting, a working group of the International Federation for Clinical Chemistry and Laboratory Medicine was convened to provide a guidance document for this domain.

RESULTS

This manuscript represents consensus recommendations for best practices from that committee, with the goal of improving the quality of developed and published ML models designed for use in clinical laboratories.

CONCLUSIONS

The committee believes that implementation of these best practices will improve the quality and reproducibility of machine learning utilized in laboratory medicine.

SUMMARY

We have provided our consensus assessment of a number of important practices that are required to ensure that valid, reproducible machine learning (ML) models can be applied to address operational and diagnostic questions in the clinical laboratory. These practices span all phases of model development, from problem formulation through predictive implementation. Although it is not possible to exhaustively discuss every potential pitfall in ML workflows, we believe that our current guidelines capture best practices for avoiding the most common and potentially dangerous errors in this important emerging field.

Harmonization and standardization of biofluid-based biomarker measurements for AT(N) classification in Alzheimer's disease

Fluid biomarkers are currently measured in cerebrospinal fluid and blood for Alzheimer's disease diagnosis and are promising targets for drug development and for patients' follow-up in clinical trials. These biomarkers have been grouped in an unbiased research framework, the amyloid (Aβ), tau, and neurodegeneration (AT[N]) biomarker system to aid patients' early diagnosis and stratification. Metrological approaches relying on mass spectrometry have been used for the development of reference materials and reference measurement procedures. Despite their excellent performances as clinical tools, fluid biomarkers often present an important between-laboratory variation. Standardization efforts were carried out on the biomarkers currently included in the AT(N) classification system, involving the collaboration of national metrology institutes, clinicians, researchers, and in vitro diagnostic providers. This article provides an overview of current activities towards standardization. These reference methods and reference materials may be used for recalibration of immunoassays and the establishment of standardized cutoff values allowing a better stratification of Alzheimer's disease patients.

Highlights

The AT(N) biomarker system allows stratifying AD patients on the basis of biomarker profiles.Fluid biomarker measurements often present an important between-laboratory variation preventing the establishment of standardized cutoff values.Overview on the standardization initiatives involving the fluid biomarkers currently included in the AT(N) framework.

Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML

BACKGROUND

Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical director. Unfortunately, there is no tool or concept that enables diagnostic quality assessment for the various potential AI/ML applications. Specifically, we noted that an operational definition of laboratory diagnostic quality - for the specific purpose of assessing AI/ML improvements - is currently missing.

METHODS

A session at the 3rd Strategic Conference of the European Federation of Laboratory Medicine in 2022 on "AI in the Laboratory of the Future" prompted an expert roundtable discussion. Here we present a conceptual diagnostic quality framework for the specific purpose of assessing AI/ML implementations.

RESULTS

The presented framework is termed diagnostic quality model (DQM) and distinguishes AI/ML improvements at the test, procedure, laboratory, or healthcare ecosystem level. The operational definition illustrates the nested relationship among these levels. The model can help to define relevant objectives for implementation and how levels come together to form coherent diagnostics. The affected levels are referred to as scope and we provide a rubric to quantify AI/ML improvements while complying with existing, mandated regulatory standards. We present 4 relevant clinical scenarios including multi-modal diagnostics and compare the model to existing quality management systems.

CONCLUSIONS

A diagnostic quality model is essential to navigate the complexities of clinical AI/ML implementations. The presented diagnostic quality framework can help to specify and communicate the key implications of AI/ML solutions in laboratory diagnostics.

Control externo de la calidad en medicina del laboratorio. Avances y futuro

Objectivos

Un programa de control externo distribuye las mismas muestras control entre varios laboratorios y evalúa los resultados obtenidos con un criterio común. El objetivo de este trabajo es resumir la evolución de los programas externos, poner de manifiesto los progresos conseguidos y deducir consecuencias prácticas para el laboratorio participante.

Métodos

El material es una breve revisión de los diferentes tipos de programas externos utilizados a lo largo de cuarenta años. El método es el análisis crítico de las ventajas e inconvenientes de cada modelo, a la luz de nuestra experiencia.

Resultados

A mitad del siglo XX se iniciaron los programas EQA, detectándose gran discrepancia entre resultados emitidos por distintos laboratorios. Se desarrollaron EQA en muchos países y se propusieron mecanismos para armonizarlos, como: establecer especificaciones derivadas de la variación biológica, promover el uso de métodos analíticos homogéneos, usar el EQA como herramienta educacional. A partir del 2000 se hacen importantes avances: asegurar el adecuado uso clínico de las pruebas del laboratorio, utilizar material control conmutable con el espécimen humano, armonizar los distintos modelos de EQA, promover una organización de cooperación entre organizadores de programas EQA.

Conclusiones

Participar en un EQA con controles conmutables y valores asignados por método de referencia certificado permite conocer la inexactitud real de los resultados y el impacto en las muestras de pacientes. Si se participa en programas con controles no conmutables solo se conoce si la prestación del laboratorio es similar a la de otros usuarios del mismo método analítico.

External quality control in laboratory medicine. Progresses and future

Objectives

An external quality control program distributes same control samples to various laboratories and evaluates results obtained with a common criterion. The aim of this work is to summarize the evolution of various types of external programs, to point out the progresses ant to preclude practical consequences of the participant laboratories.

Content

The material consists on a brief revision of the different types of external programs that have been used for the last forty years. The method is the critical analysis of the strong and weak points of each program model, from the light of our experience. External quality assessment (EQA) programs were initiated at half the XX century, evidencing big discrepancies among laboratory results. EQA were developed in various countries and some mechanisms to harmonize them were proposed: to establish common performance specifications derived from biological variation, to use EQS as educational tool. Since the 2000 important advances were seen: to focus EQA to assure the adequate clinical use of laboratory tests, to use commutable controls, to harmonize the different EQA models, to promote a forum for co-operation and exchange of knowledge on quality-related matters for EQA organizers.

Summary and Outlook

To participate in an EQA with commutable-reference method assigned values controls allows to know the real inaccuracy of results and their impact on patient' samples. To participate in a EQA with non commutable controls allows to know whether the individual laboratory performance agrees with that from other laboratories using same analytical method.

Prediction of SARS-CoV-2-positivity from million-scale complete blood counts using machine learning

Background

The Complete Blood Count (CBC) is a commonly used low-cost test that measures white blood cells, red blood cells, and platelets in a person’s blood. It is a useful tool to support medical decisions, as intrinsic variations of each analyte bring relevant insights regarding potential diseases. In this study, we aimed at developing machine learning models for COVID-19 diagnosis through CBCs, unlocking the predictive power of non-linear relationships between multiple blood analytes.

Methods

We collected 809,254 CBCs and 1,088,385 RT-PCR tests for SARS-Cov-2, of which 21% (234,466) were positive, from 900,220 unique individuals. To properly screen COVID-19, we also collected 120,807 CBCs of 16,940 individuals who tested positive for other respiratory viruses. We proposed an ensemble procedure that combines machine learning models for different respiratory infections and analyzed the results in both the first and second waves of COVID-19 cases in Brazil.

Results

We obtain a high-performance AUROC of 90 + % for validations in both scenarios. We show that models built solely of SARS-Cov-2 data are biased, performing poorly in the presence of infections due to other RNA respiratory viruses.

Conclusions

We demonstrate the potential of a novel machine learning approach for COVID-19 diagnosis based on a CBC and show that aggregating information about other respiratory diseases was essential to guarantee robustness in the results. Given its versatile nature, low cost, and speed, we believe that our tool can be particularly useful in a variety of scenarios—both during the pandemic and after.

The complete blood count (CBC) is a medical laboratory test that provides information about cells in a person’s blood and is extensively used to support medical decisions. This study explored the ability of a computer-based approach to automatically identify active COVID-19 infections by using CBC exams. We collected a large dataset with over one million CBC exams and the matching tests currently used to detect SARS-Cov-2 or other respiratory viruses. Our results demonstrate both the potential of this approach for diagnosing SARS-Cov-2 infection by using only CBC data, and also that considering information about other respiratory diseases in the methodology is essential to guarantee that results can be trusted. This automated computational approach can be useful in a variety of contexts during the COVID-19 pandemic and after since it is fast, low-cost, and versatile.

Zuin et al. use a large dataset of blood count exams to predict SARS-CoV-2 PCR results with machine learning. The model performs well and is superior to those that do not take into account infection with other RNA respiratory viruses.

An approach for determining allowable between reagent lot variation

Clinicians trust medical laboratories to provide reliable results on which they rely for clinical decisions. Laboratories fulfil their responsibility for accurate and consistent results by utilizing an arsenal of approaches, ranging from validation and verification experiments to daily quality control procedures. All these procedures verify, on different moments, that the results of a certain examination procedure have analytical performance characteristics (APC) that meet analytical performance specifications (APS) set for a particular intended use. The APC can in part be determined by estimating the measurement uncertainty component under conditions of within-laboratory precision (uRw), which comprises all components influencing the measurement uncertainty of random sources. To maintain the adequacy of their measurement procedures, laboratories need to distinguish aspects that are manageable vs. those that are not. One of the aspects that may influence uRw is the momentary significant bias caused by shifts in reagent and/or calibrator lots, which, when accepted or unnoticed, become a factor of the APC. In this paper, we postulate a model for allocating a part of allowable uRw to between-reagent lot variation, based on the need for long-term consistency of the measurement variability for that specific measurand. The allocation manages the ratio between short-term and long-term variation and indicates laboratories when to reject or correct certain variations due to reagent lots.

Resolving the Debate: The Future of Using Race in Estimating Kidney Function

Racial and social unrest witnessed during 2020 ignited a national conversation about the appropriateness of the use of race in health care algorithms and in the estimation of kidney function in particular. The growing concerns over the use of race in kidney function-estimating equations prompted the National Kidney Foundation (NKF) and American Society of Nephrology to launch an effort for change by establishing a task force on reassessing the use of race in diagnosing kidney disease. After nearly a year examining the evidence and obtaining testimony from experts and stakeholders, the task force recommended the immediate implementation of the 2020 Chronic Kidney Disease-Epidemiology creatinine equation refit without race in all US laboratories; increased routine use of cystatin C for confirmation of estimated glomerular filtration rate in clinical decision-making and a call for research on glomerular filtration rate estimation with new endogenous filtration markers and on addressing disparities in health and health care. The NKF and American Society of Nephrology strongly encouraged rapid adoption of these new recommendations. Leadership efforts of the NKF have begun to lay the foundation for national implementation through laboratory engagement, clinician awareness, and patient education.

The multicenter European Biological Variation Study (EuBIVAS): a new glance provided by the Principal Component Analysis (PCA), a machine learning unsupervised algorithms, based on the basic metabolic panel linked measurands

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Reassessing the Inclusion of Race in Diagnosing Kidney Diseases: An Interim Report From the NKF-ASN Task Force

For almost 2 decades, equations that use serum creatinine, age, sex, and race to estimate glomerular filtration rate (GFR) have included "race" as Black or non-Black. Given considerable evidence of disparities in health and health care delivery in African American communities, some regard keeping a race term in GFR equations as a practice that differentially influences access to care and kidney transplantation. Others assert that race captures important non-GFR determinants of serum creatinine and its removal from the calculation may perpetuate other disparities. The National Kidney Foundation (NKF) and American Society of Nephrology (ASN) established a task force in 2020 to reassess the inclusion of race in the estimation of GFR in the United States and its implications for diagnosis and subsequent management of patients with, or at risk for, kidney diseases. This interim report details the process, initial assessment of evidence, and values defined regarding the use of race to estimate GFR. We organized activities in phases: (1) clarify the problem and examine evidence, (2) evaluate different approaches to address use of race in GFR estimation, and (3) make recommendations. In phase 1, we constructed statements about the evidence and defined values regarding equity and disparities; race and racism; GFR measurement, estimation, and equation performance; laboratory standardization; and patient perspectives. We also identified several approaches to estimate GFR and a set of attributes to evaluate these approaches. Building on evidence and values, the attributes of alternative approaches to estimate GFR will be evaluated in the next phases and recommendations will be made.

Peter WL, Warfield C, Powe NR. Reassessing the Inclusion of Race in Diagnosing Kidney Diseases: An Interim Report from the NKF-ASN Task Force

For almost two decades, equations that use serum creatinine, age, sex, and race to eGFR have included "race" as Black or non-Black. Given considerable evidence of disparities in health and healthcare delivery in African American communities, some regard keeping a race term in GFR equations as a practice that differentially influences access to care and kidney transplantation. Others assert that race captures important non GFR determinants of serum creatinine and its removal from the calculation may perpetuate other disparities. The National Kidney Foundation (NKF) and American Society of Nephrology (ASN) established a task force in 2020 to reassess the inclusion of race in the estimation of GFR in the United States and its implications for diagnosis and subsequent management of patients with, or at risk for, kidney diseases. This interim report details the process, initial assessment of evidence, and values defined regarding the use of race to estimate GFR. We organized activities in phases: (1) clarify the problem and examine evidence, (2) evaluate different approaches to address use of race in GFR estimation, and (3) make recommendations. In phase one, we constructed statements about the evidence and defined values regarding equity and disparities; race and racism; GFR measurement, estimation, and equation performance; laboratory standardization; and patient perspectives. We also identified several approaches to estimate GFR and a set of attributes to evaluate these approaches. Building on evidence and values, the attributes of alternative approaches to estimate GFR will be evaluated in the next phases and recommendations will be made.

The evolving role of commutability in metrological traceability

Commutability is a property of a reference material (RM) which denotes that the analytical response in measurement procedures (MPs) observed for the measurand is the same for the RM as for clinical samples that contain the same amount of the measurand. Matrix-based secondary calibrators are required to be commutable with clinical samples to achieve metrological traceability of results from a clinical laboratory MP to higher order references. Results for clinical samples may not agree among different end-user MPs if a noncommutable RM is used in the calibration hierarchy for one or more of the MPs. Consequently, a useful RM is one that is commutable with clinical samples for all or most MPs in common use. If a matrix-based RM is noncommutable for one or a few MPs, a correction for the noncommutability bias may be added in the calibration hierarchy to enable the results for clinical samples to be metrologically traceable to the RM. Producing a large batch of matrix-based RM requires pooling single donations and making various modifications of the matrix such as spiking with exogenous substances, freezing or lyophilization. These modifications could potentially affect commutability of the RM and compromise its suitability. Documentation of commutability of matrix-based RMs used as calibrators is required by the International Organization for Standardization and the Joint Committee for Traceability in Laboratory Medicine. We describe how commutability was recognized as a critical requirement for metrological traceability and we present recommendations from the IFCC Working Groups on Commutability and on Commutability in Metrological Traceability.