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.

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.

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.

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.

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.

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.

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.

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.

Aggregated data from the same laboratories participating in two glucose external quality assessment schemes show that commutability and transfers of values to control materials are decisive for the biases found.

OBJECTIVES: We report the results of glucose measurements performed during one year by the same measurement procedures (MPs) in 58 Norwegian hospital laboratories using control materials provided by external quality assessment (EQA) schemes from two different providers. The providers used materials with presumed vs. verified commutability and transfers of values using reference material vs. using a highest-order reference MP. METHODS: Data from six Labquality and three Noklus glucose EQA surveys were aggregated for each MP (Abbott Alinity, Abbott Architect, Roche Cobas, and Siemens Advia) in each scheme. For each EQA result, percent difference from target value (% bias) was calculated. Median percent bias for each MP per scheme was then calculated. RESULTS: The median % biases observed for each MP in the Labquality scheme were significantly larger than those in the Noklus scheme, which uses verified commutable control materials and highest-order reference MP target values. The difference ranged from 1.2 (Roche Cobas, 2.9 vs. 1.7 %) to 4.4 percentage points (Siemens Advia, 3.2 % vs. -1.2 %). The order of bias size for the various MPs was different in the two schemes. In contrast to the Labquality scheme, the median % biases observed in the Noklus scheme for Abbott Alinity (-0.1 %), Abbott Architect (-0.5 %), and Siemens Advia (-1.2 %) were not significantly different from target value (p>0.756). CONCLUSIONS: This study underlines the importance of using verified commutable EQA materials and target values traceable to reference MPs in EQA schemes designed for assessment of metrological traceability of laboratory results.

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.

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.

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.

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.

Insulin initiation in patients with type 2 diabetes is often delayed, but access to a diabetes nurse may help-insights from Norwegian general practice.

Objective: We opted to study how support staff operational capacity and diabetes competences may impact the timeliness of basal insulin-initiation in general practice patients with type 2 diabetes (T2D).Design/Setting/Outcomes: This was an observational and retrospective study on Norwegian primary care patients with T2D included from the ROSA4-dataset. Exposures were (1) support staff size, (2) staff size relative to number of GPs, (3) clinic access to a diabetes nurse and (4) share of staff with diabetes course (1 and 2 both relate to staff operational capacity, whereas 3 and 4 are both indicatory of staff diabetes competences). Outcomes were 'timely basal insulin-initiation' (primary) and 'attainment of HbA1c<7%' after insulin start-up (secondary). Associations were analyzed using multiple linear regression, and directed acyclic graphs guided statistical adjustments.Subjects: Insulin naïve patients with 'timely' (N = 294), 'postponed' (N = 219) or 'no need of' (N = 3,781) basal insulin-initiation, respectively.Results: HbA1c [median (IQR)] increased to 8.8% (IQR, 8.0, 10.2) prior to basal insulin-initiation, which reduced HbA1c to 7.3 (6.8-8.1) % by which only 35% of the subjects reached HbA1c <7%. Adjusted risk of 'timely basal insulin-initiation' was more than twofold higher if access to a diabetes nurse (OR = 2.40, [95%CI, 1.68, 3.43]), but related only vaguely to staff size (OR = 1.01, [95%CI, 1.00, 1.03]). No other staff factors related significantly to neither the primary nor the secondary outcome.Conclusion: In Norwegian general practice, insulin initiation in people with T2D may be affected by therapeutic inertia but access to a diabetes nurse may help facilitating more timely insulin start-up.

In patients with type 2 diabetes (T2D) cared for by their general practice physician (GP), insulin therapy was susceptible to therapeutic inertia.In Norwegian general practice, chance of timely basal insulin-initiation was found more than two-fold higher if the GP had access to a diabetes nurse.In contrast, the timeliness of basal insulin-initiation in general practice patients with T2D seemed unaffected by share of support staff with diabetes course and by factors indicatory of support staff overall operational capacity.In Norwegian general practice, a diabetes nurse seems to offer unique clinical benefits to the care of insulin treated patients with T2D.

Biological variation estimates of Alzheimer's disease plasma biomarkers in healthy individuals.

INTRODUCTION: Blood biomarkers have proven useful in Alzheimer's disease (AD) research. However, little is known about their biological variation (BV), which improves the interpretation of individual-level data. METHODS: We measured plasma amyloid beta (Aß42, Aß40), phosphorylated tau (p-tau181, p-tau217, p-tau231), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) in plasma samples collected weekly over 10 weeks from 20 participants aged 40 to 60 years from the European Biological Variation Study. We estimated within- (CVI ) and between-subject (CVG ) BV, analytical variation, and reference change values (RCV). RESULTS: Biomarkers presented considerable variability in CVI and CVG . Aß42/Aß40 had the lowest CVI (≈ 3%) and p-tau181 the highest (≈ 16%), while others ranged from 6% to 10%. Most RCVs ranged from 20% to 30% (decrease) and 25% to 40% (increase). DISCUSSION: BV estimates for AD plasma biomarkers can potentially refine their clinical and research interpretation. RCVs might be useful for detecting significant changes between serial measurements when monitoring early disease progression or interventions. Highlights Plasma amyloid beta (Aß42/Aß40) presents the lowest between- and within-subject biological variation, but also changes the least in Alzheimer's disease (AD) patients versus controls. Plasma phosphorylated tau variants significantly vary in their within-subject biological variation, but their substantial fold-changes in AD likely limits the impact of their variability. Plasma neurofilament light chain and glial fibrillary acidic protein demonstrate high between-subject variation, the impact of which will depend on clinical context. Reference change values can potentially be useful in monitoring early disease progression and the safety/efficacy of interventions on an individual level. Serial sampling revealed that unexpectedly high values in heathy individuals can be observed, which urges caution when interpreting AD plasma biomarkers based on a single test result.

Analytical performance specifications and quality assurance of point-of-care testing in primary healthcare.

Point-of-care testing (POCT) is the fastest-growing segment of laboratory medicine. This review focuses on the essential aspects of setting analytical performance specifications (APS) and performing quality assurance for POCT in primary healthcare. In-vitro diagnostic medical devices for POCT are typically small and easy to operate. Users often have little to no laboratory experience and may not necessarily see the value of conducting quality assurance on their devices. Therefore, training, guidance, and motivation should be integral parts of the total quality management system, as they are vital for managing errors and ensuring reliable results. It is common to believe that the analytical quality of POCT should be comparable to that of laboratory testing, and as a result, APS should be the same. This paper challenges this concept. The APS for POCT can often be less stringent compared to those used in a central laboratory because the requester is closer to both the analytical and clinical situation. Point-of-care instruments should be selected based on clinical needs, the required analytical quality and user-friendliness in the intended usage setting.Quality assurance should include both internal quality control (IQC) and external quality assessment (EQA). It is recommended that IQC protocols should be dependent on the complexity of the POCT device. A scoring system to determine how frequent IQC should be analyzed in primary healthcare on different types of POCT devices has been suggested. The main challenge in EQA for POCT involves using suitable control materials that reflect instrument performance on patient samples. Obtaining commutable control materials for POCT is difficult since the matrix often is whole blood. An essential aspect of EQA for POCT is that feedback reports should be easily interpretable. Users should receive advice from the EQA organizer regarding the root causes of deviating results. Quality assurance for POCT is not an easy task and presents numerous challenges. However, there is evidence that quality assurance improves the quality of POCT measurements and, consequently, can enhance patient outcomes.

Personalized and Population-Based Reference Intervals for 48 Common Clinical Chemistry and Hematology Measurands: A Comparative Study.

BACKGROUND: Personalized reference intervals (prRIs) have the potential to improve individual patient follow-up as compared to population-based reference intervals (popRI). In this study, we estimated popRI and prRIs for 48 clinical chemistry and hematology measurands using samples from the same reference individuals and explored the effect of using group-based and individually based biological variation (BV) estimates to derive prRIs. METHODS: 143 individuals (median age 28 years) were included in the study and had fasting blood samples collected once. From this population, 41 randomly selected subjects had samples collected weekly for 5 weeks. PopRIs were estimated according to Clinical Laboratory Standards Institute EP28 and within-subject BV (CVI) were estimated by CV-ANOVA. Data were assessed for trends and outliers prior to calculation of individual prRIs, based on estimates of (a) within-person BV (CVP), (b) CVI derived in this study, and (c) publically available CVI estimates. RESULTS: For most measurands, the individual prRI ranges were smaller than the popRI range, but overall about half the study participants had a prRI wider than the popRI for 5 or more out of 48 measurands. The dispersion of prRIs based on CVP was wider than that of prRIs based on CVI. CONCLUSION: The prRIs derived in our study varied significantly between different individuals, especially if based on CVP. Our results highlight the limitations of popRIs in interpreting test results of individual patients. If sufficient data from a steady-state situation are available, using prRI based on CVP estimates will provide a RI most specific for an individual patient.

Long-Term Within- and Between-Subject Biological Variation Data of Hematological Parameters in Recreational Endurance Athletes.

BACKGROUND: Hematological parameters have many applications in athletes, from monitoring health to uncovering blood doping. This study aimed to deliver biological variation (BV) estimates for 9 hematological parameters by a Biological Variation Data Critical Appraisal Checklist (BIVAC) design in a population of recreational endurance athletes and to assess the effect of self-reported exercise and health-related variables on BV. METHODS: Samples were drawn from 30 triathletes monthly for 11 months and measured in duplicate for hematological measurands on an Advia 2120 analyzer (Siemens Healthineers). After outlier and homogeneity analysis, within-subject (CVI) and between-subject (CVG) BV estimates were delivered (CV-ANOVA and log-ANOVA, respectively) and a linear mixed model was applied to analyze the effect of exercise and other related variables on the BV estimates. RESULTS: CVI estimates ranged from 1.3% (95%CI, 1.2-1.4) for mean corpuscular volume to 23.8% (95%CI, 21.6-26.3) for reticulocytes. Sex differences were observed for platelets and OFF-score. The CVI estimates were higher than those reported for the general population based on meta-analysis of eligible studies in the European Biological Variation Database, but 95%CI overlapped, except for reticulocytes, 23.9% (95%CI, 21.6-26.5) and 9.7% (95%CI, 6.4-11.0), respectively. Factors related to exercise and athletes' state of health did not appear to influence the BV estimates. CONCLUSIONS: This is the first BIVAC-compliant study delivering BV estimates that can be applied to athlete populations performing high-level aerobic exercise. CVI estimates of most parameters were similar to the general population and were not influenced by exercise or athletes' state of health.