A vision to the future: value-based laboratory medicine.

The ultimate goal of value-based laboratory medicine is maximizing the effectiveness of laboratory tests in improving patient outcomes, optimizing resources and minimizing unnecessary costs. This approach abandons the oversimplified notion of test volume and cost, in favor of emphasizing the clinical utility and quality of diagnostic tests in the clinical decision-making. Several key elements characterize value-based laboratory medicine, which can be summarized in some basic concepts, such as organization of in vitro diagnostics (including appropriateness, integrated diagnostics, networking, remote patient monitoring, disruptive innovations), translation of laboratory data into clinical information and measurable outcomes, sustainability, reimbursement, ethics (e.g., patient empowerment and safety, data protection, analysis of big data, scientific publishing). Education and training are also crucial, along with considerations for the future of the profession, which will be largely influenced by advances in automation, information technology, artificial intelligence, and regulations concerning in vitro diagnostics. This collective opinion paper, composed of summaries from presentations given at the two-day European Federation of Laboratory Medicine (EFLM) Strategic Conference "A vision to the future: value-based laboratory medicine" (Padova, Italy; September 23-24, 2024), aims to provide a comprehensive overview of value-based laboratory medicine, projecting the profession into a more clinically effective and sustainable future.

Women Suffered More Than Men Both During and After the COVID-19 Pandemic-A Cross-Sectional Study Among 29,079 Patients With Type 2 Diabetes.

OBJECTIVE: To investigate the gender differences and the disparities between infected and noninfected patients with type 2 diabetes (T2D) regarding patient-reported experiences during the COVID-19 pandemic in Norway. METHOD: Register study using questionnaires sent electronically to patients with T2D, June 2022. The questionnaire included 82 questions covering COVID-19 disease, symptoms, medications, comorbidities, hospital care, possibility of working from home and information received from health authorities. Clinical and demographic data were collected from the Norwegian diabetes registry for adults. RESULTS: A total of 29,079 T2D patients participated, of whom 38.1% were women. Patients infected with COVID-19 were younger, had shorter diabetes duration and less comorbidities than noninfected (p < 0.01). Women reported significantly more anxiety, depression and fear of not getting their diabetes medication than men did. Most patients were vaccinated against COVID-19 (98.3%), whereas approximately 60% had received seasonal flu vaccine, and only 27.2% the pneumococcal vaccine. Women described more vaccine adverse effects and long Covid symptoms. Overall, 14% experienced vaccine complications and 27.3% of infected individuals reported long Covid symptoms. 2.4% of the infected patients needed hospital admission. Patients were satisfied with the follow-up of their diabetes, and with information from the government during the pandemic. CONCLUSION: Female patients were more likely to experience a prolonged Covid course, and higher degree of adverse effects from the COVID-19 vaccine than male patients. Also, long Covid symptoms were significantly more often reported among female patients, while men were more prone to be hospitalised when infected. Hospitalised patients, both men and women, had significantly higher HbA1C than those who were not hospitalised. T2D patients had a surprisingly low pneumococcal vaccination coverage, despite recommendations in national guidelines.

Reference Intervals Revisited: A Novel Model for Population-Based Reference Intervals, Using a Small Sample Size and Biological Variation Data.

BACKGROUND: Conventional population-based reference intervals (popRIs) are established on the ranking of single measurement results from at least 120 reference individuals. In this study, we aimed to explore a new model for popRIs, utilizing biological variation (BV) data to define the reference interval (RI) limits and compared BV-based popRI from different sample sizes with previously published conventional popRIs from the same population. METHODS: The model is based on defining the population set point (PSP) from single-measurement results of a group of reference individuals and using the total variation around the PSP, derived from the combination of BV and analytical variation, to define the RI limits. Using data from 143 reference individuals for 48 clinical chemistry and hematology measurands, BV-based popRIs were calculated for different sample sizes (n = 16, n = 30, and n = 120) and considered acceptable if they covered 90% of the population. In addition, simulation studies were performed to estimate the minimum number of required reference individuals. RESULTS: The median ratio of the BV-based to conventional RI ranges was 0.98. The BV-based popRIs calculated from the different samples were similar, and most met the coverage criterion. For 25 measurands ≤16 reference individuals and for 23 measurands >16 reference individuals were required to estimate the PSP. CONCLUSIONS: The BV-based popRI model delivered robust RIs for most of the included measurands. This new model requires a smaller group of reference individuals than the conventional popRI model and can be implemented if reliable BV data are available.

Direct-to-consumer testing as consumer initiated testing: compromises to the testing process and opportunities for quality improvement.

Direct-to-consumer testing (DTCT) refers to commercial laboratory tests initiated by laypersons without the involvement of healthcare professionals. As this market grows in size and variety of products, a clear definition of DTCT to ground the conceptualization of their harms and benefits is needed. We describe how three different modalities of DTCT (home self-testing, self-sampled tests, and direct access tests) present caveats to the traditional testing process ('brain-to-brain loop'), and how this might differ between medical vs. non-medical laboratories. We make recommendations for ways to improve quality and reduce errors with respect to DTCT. The potential benefits and harms of DTCT will invariably depend on the context and situation of individual consumers and the types of tests involved. Importantly, implications for both consumers and the healthcare system should be considered, such as the effects on improving health outcomes and reducing unnecessary testing and use of clinical resources. 'Consumer initiation' must be a central defining characteristic of DTCT, to clearly demarcate the key drawbacks as well as opportunities of this type of testing from a laboratory specialists' perspective. The concept of 'consumer initiated testing' should also help define DTCT regulation, and provide a locus of efforts to support consumers as the main decision-makers in the purchasing and conducting of these tests in the absence of clinician gatekeeping.

The biological variation of insulin resistance markers: data from the European Biological Variation Study (EuBIVAS).

OBJECTIVES: An insulin resistant state is characteristic of patients with type 2 diabetes, polycystic ovary syndrome, and metabolic syndrome. Identification of insulin resistance (IR) is most readily achievable using formulae combining plasma insulin and glucose results. In this study, we have used data from the European Biological Variation Study (EuBIVAS) to examine the biological variability (BV) of IR using the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and the Quantitative Insulin sensitivity Check Index (QUICKI). METHODS: Ninety EuBIVAS non-diabetic subjects (52F, 38M) from five countries had fasting HOMA-IR and QUICKI calculated from plasma glucose and insulin samples collected concurrently on 10 weekly occasions. The within-subject (CVI) and between-subject (CVG) BV estimates with 95 % CIs were obtained by CV-ANOVA after analysis of trends, variance homogeneity and outlier removal. RESULTS: The CVI of HOMA-IR was 26.7 % (95 % CI 25.5-28.3), driven largely by variability in plasma insulin and the CVI for QUICKI was 4.1 % (95 % CI 3.9-4.3), reflecting this formula's logarithmic transformation of glucose and insulin values. No differences in values or BV components were observed between subgroups of men or women below and above 50 years. CONCLUSIONS: The EuBIVAS, by utilising a rigorous experimental protocol, has produced robust BV estimates for two of the most commonly used markers of insulin resistance in non-diabetic subjects. This has shown that HOMA-IR, in particular, is highly variable in the same individual which limits the value of single measurements.

A standard to report biological variation data studies - based on an expert opinion.

There is a need for standards for generation and reporting of Biological Variation (BV) reference data. The absence of standards affects the quality and transportability of BV data, compromising important clinical applications. To address this issue, international expert groups under the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) have developed an online resource (https://tinyurl.com/bvmindmap) in the form of an interactive mind map that serves as a guideline for researchers planning, performing and reporting BV studies. The mind map addresses study design, data analysis, and reporting criteria, providing embedded links to relevant references and resources. It also incorporates a checklist approach, identifying a Minimum Data Set (MDS) to enable the transportability of BV data and incorporates the Biological Variation Data Critical Appraisal Checklist (BIVAC) to assess study quality. The mind map is open to access and is disseminated through the EFLM BV Database website, promoting accessibility and compliance to a reporting standard, thereby providing a tool to be used to ensure data quality, consistency, and comparability of BV data. Thus, comparable to the STARD initiative for diagnostic accuracy studies, the mind map introduces a Standard for Reporting Biological Variation Data Studies (STARBIV), which can enhance the reporting quality of BV studies, foster user confidence, provide better decision support, and be used as a tool for critical appraisal. Ongoing refinement is expected to adapt to emerging methodologies, ensuring a positive trajectory toward improving the validity and applicability of BV data in clinical practice.

Practical recommendations for biochemical and genetic diagnosis of the porphyrias.

The porphyrias are a group of rare inborn errors of metabolism associated with various clinical presentations and long-term complications, making them relevant differential diagnoses to consider for many clinical specialities, especially hepatologists, gastroenterologists and dermatologists. To diagnose a patient with porphyria requires appropriate biochemical investigations, as clinical features alone are not specific enough. Furthermore, it is important to be aware that abnormalities of porphyrin accumulation and excretion occur in many other disorders that are collectively far more common than the porphyrias. In this review, we provide an overview of porphyria-related tests with their strengths and limitations, give recommendations on requesting and diagnostic approaches in non-expert and expert laboratories for different clinical scenarios and discuss the role of genetic testing in the porphyrias. To diagnose porphyria in a currently symptomatic patient requires analysis of biochemical markers to demonstrate typical patterns of haem precursors in urine, faeces and blood. The use of genomic sequencing in diagnostic pathways for porphyrias requires careful consideration, and the demonstration of increased porphyrin-related markers is necessary prior to genomic testing in symptomatic patients. In the acute porphyrias, genomic testing is presently a useful adjunct for genetic counselling of asymptomatic family members and the most common cutaneous porphyria, porphyria cutanea tarda, is usually a sporadic, non-hereditary disease. Getting a correct and timely porphyria diagnosis is essential for delivering appropriate care and ensuring best patient outcome.

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.

From chemistry to genomics: A concise history of the porphyrias.

We describe developments in understanding of the porphyrias associated with each step in the haem biosynthesis pathway and the role of individuals whose contributions led to major advances over the past 150 years. The first case of erythropoietic porphyria was reported in 1870, and the first with acute porphyria in 1889. Photosensitisation by porphyrin was confirmed by Meyer-Betz, who self-injected haematoporphyrin. Günther classified porphyrias into haematoporphyria acuta, acuta toxica, congenita and chronica. This was revised by Waldenström into porphyria congenita, acuta and cutanea tarda, with the latter describing those with late-onset skin lesions. Waldenström was the first to recognise porphobilinogen's association with acute porphyria, although its structure was not solved until 1953. Hans Fischer was awarded the Nobel prize in 1930 for solving the structure of porphyrins and the synthesis of haemin. After 1945, research by several groups elucidated the pathway of haem biosynthesis and its negative feedback regulation by haem. By 1961, following the work of Watson, Schmid, Rimington, Goldberg, Dean, Magnus and others, aided by the availability of modern techniques of porphyrin separation, six of the porphyrias were identified and classified as erythropoietic or hepatic. The seventh, 5-aminolaevulinate dehydratase deficiency porphyria, was described by Doss in 1979. The discovery of increased hepatic 5-aminolaevulinate synthase activity in acute porphyria led to development of haematin as a treatment for acute attacks. By 2000, all the haem biosynthesis genes were cloned, sequenced and assigned to chromosomes and disease-specific mutations identified in all inherited porphyrias. These advances have allowed definitive family studies and development of new treatments.

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.

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

BACKGROUND: Reference change values (RCV) are used to indicate a change in analyte concentration that is unlikely to be due to random variation in the patient or the measurement. Current theory describes RCV relative to a first measurement result (X1). We investigate an alternative view predicting the starting point for RCV calculations from X1 and its location in the reference interval. METHODS: Data for serum sodium, calcium, and total protein from the European Biological Variation study and from routine clinical collections were analyzed for the effect of the position of X1 within the reference interval on the following result from the same patient. A model to describe the effect was determined, and an equation to predict the RCV for a sample in a population was developed. RESULTS: For all data sets, the midpoints of the RCVs were dependent on the position of X1 in the population. Values for X1 below the population mean were more likely to be followed by a higher result, and X1 results above the mean were more likely to be followed by lower results. A model using population mean, reference interval dispersion, and result diagnostic variation provided a good fit with the data sets, and the derived equation predicted the changes seen. CONCLUSIONS: We have demonstrated that the position of X1 within the reference interval creates an asymmetrical RCV. This can be described as a regression to the population mean. Adding this concept to the theory of RCVs will be an important consideration in many cases.

Biological Variation Data in Triathletes for Metabolism and Growth-Related Biomarkers Included in the Athlete Biological Passport.

BACKGROUND: When using biological variation (BV) data, BV estimates need to be robust and representative. High-endurance athletes represent a population under special physiological conditions, which could influence BV estimates. Our study aimed to estimate BV in athletes for metabolism and growth-related biomarkers involved in the Athlete Biological Passport (ABP), by 2 different statistical models. METHODS: Thirty triathletes were sampled monthly for 11 months. The samples were analyzed for human growth hormone (hGH), insulin-like growth factor-1 (IGF-1), insulin-like growth factor binding protein 3 (IGFBP-3), insulin, and N-terminal propeptide of type III procollagen (P-III-NP) by immunoassay. Bayesian and ANOVA methods were applied to estimate within-subject (CVI) and between-subject BV. RESULTS: CVI estimates ranged from 7.8% for IGFBP-3 to 27.0% for insulin, when derived by the Bayesian method. The 2 models gave similar results, except for P-III-NP. Data were heterogeneously distributed for P-III-NP for the overall population and in females for IGF-1 and IGFBP-3. BV components were not estimated for hGH due to lack of steady state. The index of individuality was below 0.6 for all measurands, except for insulin. CONCLUSIONS: In an athlete population, to apply a common CVI for insulin would be appropriate, but for IGF-1 and IGFBP-3 gender-specific estimates should be applied. P-III-NP data were heterogeneously distributed and using a mean CVI may not be representative for the population. The high degree of individuality for IGF-1, IGFBP-3, and P-III-NP makes them good candidates to be interpreted through reference change values and the ABP.

A register-based study describing time trends in risk factor control and serious hypoglycaemia together with the effects of starting continuous glucose monitoring in people with type 1 diabetes in Norway.

AIMS: To describe trends in risk factor control and serious hypoglycaemia in people with type 1 diabetes and to assess the effect of starting continuous glucose monitoring (CGM) in the real-world setting. METHODS: Two cross-sectional surveys including 5746 individuals in 2012 and 18,984 individuals in 2020 based on data recorded in the Norwegian Diabetes Register for Adults (NDR-A) and an analysis of a longitudinal cohort of 2057 individuals where data on CGM and HbA1c were available in the NDR-A in 2012 and 2020. RESULTS: In the cross-sectional surveys mean HbA1c decreased from 66 mmol/mol (99% CI 65, 66) (8.2%) in 2012 to 61 mmol/mol (99% CI 61, 61) (7.7%) in 2020 (p < 0.0001). The proportion reporting serious hypoglycaemia decreased from 16.9 to 6.2% in 2020 (p < 0.0001). Mean LDL-cholesterol decreased from 2.80 (99% CI 2.78, 2.83) to 2.63 (99% CI 2.61, 2.65) mmol/l in 2020 (p < 0.0001). Mean blood pressure increased slightly. In the CGM cohort, we found a 3 mmol/mol (0.3%) greater improvement in mean HbA1c and a greater reduction in serious hypoglycaemia (-12.3% vs. -6.2%) among individuals that had started using CGM between 2013 and 2020 when compared with individuals that had not started using CGM. CONCLUSIONS: Between 2012 and 2020, we found marked improvements in glycaemic control and a considerable decrease in the proportion of individuals reporting serious hypoglycaemia. The proportion of individuals using CGM increased substantially and individuals that had started using CGM by 2020 showed greater improvement in glycaemic control and less serious hypoglycaemia.

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.

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

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

High prevalence of retinopathy in young-onset type 2 diabetes and possible sex differences: insights from Norwegian general practice.

INTRODUCTION: People with young-onset type 2 diabetes (YOD), defined as diabetes diagnosis before age 40, have a high lifetime risk of vascular complications. We aimed to estimate the prevalence of YOD among adults with type 2 diabetes (T2D) in Norwegian general practice and explore associations between age at diabetes diagnosis and retinopathy overall and in men and women. RESEARCH DESIGN AND METHODS: We collected cross-sectional data from general practice electronic medical records of 10 241 adults with T2D in 2014, and repeated measurements of hemoglobin A1c (HbA1c) from 2012 to 2014. Using multivariate logistic regression, we assessed associations between YOD and later-onset T2D, sex and retinopathy. RESULTS: Of all individuals with T2D, 10% were diagnosed before 40 years of age in both sexes. Compared with later-onset T2D, HbA1c increased faster in YOD, and at the time of diagnosis HbA1c was higher in men, particularly in YOD. Retinopathy was found in 25% with YOD, twice as frequently as in later onset. After adjustments for confounders (age, country of origin, education, body mass index), OR of retinopathy was increased in both men with YOD (OR 2.6 (95% CI 2.0 to 3.5)) and women with YOD (OR 2.2 (1.5 to 3.0)). After further adjustments for potential mediators (diabetes duration and HbA1c), the higher OR persisted in men with YOD (OR 1.8 (1.3 to 2.4)) but was attenuated and no longer significant for women with YOD. CONCLUSIONS: Retinopathy prevalence was more than twice as high in YOD as in later-onset T2D. The increased likelihood of retinopathy in YOD was partly mediated by higher HbA1c and longer T2D duration, but after accounting for these factors it remained higher in men with YOD.

Biological variation of inflammatory and iron metabolism markers in high-endurance recreational athletes; are these markers useful for athlete monitoring?

OBJECTIVES: To deliver biological variation (BV) data for serum hepcidin, soluble transferrin receptor (sTfR), erythropoietin (EPO) and interleukin 6 (IL-6) in a population of well-characterized high-endurance athletes, and to evaluate the potential influence of exercise and health-related factors on the BV. METHODS: Thirty triathletes (15 females) were sampled monthly (11 months). All samples were analyzed in duplicate and BV estimates were delivered by Bayesian and ANOVA methods. A linear mixed model was applied to study the effect of factors related to exercise, health, and sampling intervals on the BV estimates. RESULTS: Within-subject BV estimates (CVI) were for hepcidin 51.9 % (95 % credibility interval 46.9-58.1), sTfR 10.3 % (8.8-12) and EPO 27.3 % (24.8-30.3). The mean concentrations were significantly different between sex, but CVI estimates were similar and not influenced by exercise, health-related factors, or sampling intervals. The data were homogeneously distributed for EPO but not for hepcidin or sTfR. IL-6 results were mostly below the limit of detection. Factors related to exercise, health, and sampling intervals did not influence the BV estimates. CONCLUSIONS: This study provides, for the first time, BV data for EPO, derived from a cohort of well-characterized endurance athletes and indicates that EPO is a good candidate for athlete follow-up. The application of the Bayesian method to deliver BV data illustrates that for hepcidin and sTfR, BV data are heterogeneously distributed and using a mean BV estimate may not be appropriate when using BV data for laboratory and clinical applications.

Sex-related differences in within-subject biological variation estimates for 22 essential and non-essential amino acids.

BACKGROUND: Measurement of serum amino acid (AA) concentrations is important in particular for the diagnosis and monitoring of inborn errors of AA metabolism. To ensure optimal clinical interpretation of AAs, reliable biological variation (BV) data are essential. In the present study, we derived BV data for 22 non-essential, conditionally essential, and essential AAs and assessed differences in BV of AAs related to sex. METHODS: Morning blood samples were drawn from 66 subjects (31 males and 35 females) once a week for 10 consecutive weeks. All samples were analyzed in duplicate using liquid chromatography-tandem mass-spectrometry. The data were assessed for outliers, trends, normality and variance homogeneity analysis prior to estimating within-subject (CVI) and between-subject (CVG) BV. RESULTS: CVI estimates ranged from 9.0 % for histidine (male) to 33.0 % for taurine (male). CVI estimates in males and females were significantly different for all AAs except for aspartic acid, citrulline and phenylalanine, in most cases higher in females than in males. Apart from for arginine, CVG estimates in males and females were similar. CONCLUSIONS: In this highly powered BV study, we provide updated BV estimates for 22 AAs and demonstrate that for most AAs, CVI estimates differ between males and females, with implications for interpretation and use of AAs in clinical practice.