The European Biological Variation Study (EuBIVAS): a summary report

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

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

Biological variation in the estimated glomerular filtration rate of healthy individuals within 24 h calculated using 2021CKD-EPI equations

BACKGROUND AND AIMS

Use the MDRD (Modification of Diet in Renal Disease) and 2021 CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation void of race coefficients (CKD-EPICrea, CKD-EPICys-C, and CKD-EPICrea+Cys-C) to estimate the BV (Biological variation) of eGFR (estimated glomerular filtration rate) within 24 h in a healthy population to help explain future studies using eGFR in the context of a known BV.

METHODS

Blood samples were collected from 30 healthy subjects at six time points within 24 h. Serum creatinine (S-Crea) and serum cystatin C (S-Cys-C) were measured, and the BV of eGFR was calculated. Outlier and variance homogeneity analyses were performed, followed by CV-ANOVA on trend-corrected data.

RESULTS

The eGFR CVI for the four equations (MDRD, CKD-EPICrea, CKD-EPICys-C, and CKD-EPICrea+Cys-C) were 8.39% (7.50-9.51%), 3.90% (3.49-4.42%), 6.58% (5.88-7.46%), and 5.03% (4.50-5.71%), respectively. The corresponding II and RCVpos/neg values were 0.69, 0.48, 0.51, and 0.31, and (29.30%, - 22.66%), (12.69%, - 11.2 6%), (20.97%, - 17.33%), and (15.88%, - 13.70%), respectively; RCVpos /neg of eGFR was highest in the MDRD equation and lowest in the CKD-EPI Crea equation. Additionally, the RCVpos/neg values of the individual was highest in the MDRD equation and lowest in the CKD-EPICrea+Cys-C equation; they are (56.51%, - 36.11%) and (5.01%, - 4.77%), respectively.

CONCLUSIONS

We present data on the 24 h BV eGFR of the 2021 CKD-EPI equations. The presence of BV has impact on the interpretation of GFR results, affecting CKD disease grading. The RCVpos/neg differences were large among the individuals. When using eGFRs based on the MDRD and CKD-EPI equations, it is necessary to combine RCVpos/neg values before interpreting the results.

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.

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.

Biological Variation Estimates for Plasma Copeptin and Clinical Implications

BACKGROUND

Plasma copeptin measurement is useful for the differential diagnoses of polyuria-polydipsia syndrome. It has also been proposed as a prognostic marker for cardiovascular diseases. However, limited information is available about the within- (CVI) and between-subject (CVG) biological variation (BV). This study presents BV estimates for copeptin in healthy individuals.

METHODS

Samples were collected weekly from 41 healthy subjects over 5 weeks and analyzed using the BRAHMS Copeptin proAVP KRYPTOR assay after at least 8 h of food and fluid abstinence. Outlier detection, variance homogeneity, and trend analysis were performed followed by CV-ANOVA for BV and analytical variation (CVA) estimation with 95% confidence intervals. Reference change values (RCVs), index of individuality (II), and analytical performance specification (APS) were also calculated.

RESULTS

The analysis included 178 results from 20 males and 202 values from 21 females. Copeptin concentrations were significantly higher in males than in females (mean 8.5 vs 5.2 pmol/L, P < 0.0001). CVI estimates were 18.0% (95% CI, 15.4%-21.6%) and 19.0% (95% CI, 16.4%-22.6%), for males and females, respectively; RCVs were -35% (decreasing value) and 54% (increasing value). There was marked individuality for copeptin. No result exceeded the diagnostic threshold (>21.4 pmol/L) for arginine vasopressin resistance.

CONCLUSIONS

The availability of BV data allows for refined APS and associated II, and RCVs applicable as aids in the serial monitoring of patients with specific diseases such as heart failure. The BV estimates are only applicable in subjects who abstained from oral intake due to the rapid and marked effects of fluids on copeptin physiology.

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.

Using analytical performance specifications in a medical laboratory

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

The 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.

Are analytical performance specifications derived from reference intervals of any use in the veterinary clinical laboratory? A preliminary study on the empirical biological variation model

BACKGROUND

Analytical performance specifications (APS) are vital for method evaluation and quality control validation. However, the limited availability of biological variation (BV) data, regulatory guidelines, and expert opinion (EO) may present challenges in veterinary medicine. The empirical biological variation (EBV) approach, based on population reference intervals (pRI), has emerged as an alternative method to derive APS in human medicine.

OBJECTIVES

This study aimed to assess the practicality and usefulness of the EBV approach in deriving performance limits for various measurands in dogs and cats.

METHODS

Eight hematology and 13 biochemistry measurands were analyzed in dogs and cats. Estimates of combined biologic variation based on traditional biological (CVB ) and EBV-derived (CVE *) formulas were calculated and assessed for evidence of correlation. Performance limits for expanded uncertainty/total error and imprecision were compared among EO, BV, and EBV.

RESULTS

Strong and significant correlations were found between CVB and CVE * for both dogs (r = .86, p < .00001) and cats (r = 0.95, p < .00001). The EBV-derived APS were generally comparable to EO and BV, with a subjective criterion of 1.5% difference for imprecision and 3% for total error/expanded uncertainty.

CONCLUSION

The EBV approach, using pRI, shows promise as a surrogate marker for biological variation and as a practical tool for determining performance limits in dogs and cats. Assuming accurate pRI generated on analyzers with stable analytical performance, this approach could offer benefits when expert recommendations or robust BV studies are lacking or yield conflicting results. Further research is needed to explore the applicability and advantages of the EBV in veterinary medicine.

Bias in Laboratory Medicine: The Dark Side of the Moon

Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.

Biological variation estimates for serum neurofilament light chain in healthy subjects

OBJECTIVES

Neurofilament light chain (NfL) is an emerging biomarker of neurodegeneration disorders. Knowledge of the biological variation (BV) can facilitate proper interpretation between serial measurements. Here BV estimates for serum NfL (sNfL) are provided.

METHODS

Serum samples were collected weekly from 24 apparently healthy subjects for 10 consecutive weeks and analyzed in duplicate using the Siemens Healthineers sNfL assay on the Atellica® IM Analyzer. Outlier detection, variance homogeneity analyses, and trend analysis were performed followed by CV-ANOVA to determine BV and analytical variation (CVA) estimates with 95%CI and the associated reference change values (RCV) and analytical performance specifications (APS).

RESULTS

Despite observed differences in sNfL concentrations between males and females, BV estimates remained consistent across genders. Both within-subject BV (CVI) for males (10.7%, 95%CI; 9.2-12.6) and females (9.1%, 95%CI; 7.8-10.9) and between-subject BV (CVG) for males (26.1%, 95%CI; 18.0-45.6) and females (30.2%, 95%CI; 20.9-53.5) were comparable. An index of individuality value of 0.33 highlights significant individuality, indicating the potential efficacy of personalized reference intervals in patient monitoring.

CONCLUSIONS

The established BV estimates for sNfL underscore its potential as a valuable biomarker for monitoring neurodegenerative diseases, offering a foundation for improved decision-making in clinical settings.

Analytical performance specifications for the measurement uncertainty of 24,25-dihydroxyvitamin D examinations

OBJECTIVES

The exploration of the metabolites in the degradation pathways of vitamin D (VTD) has gained importance in recent years and simultaneous quantitation of twenty-five-hydroxy vitamin D (25(OH)D) mass concentration together with 24,25-dihydroxyvitamin D (24,25(OH)2D) has been proposed as a newer approach to define VTD deficiency. Yet, no data are available on 24,25(OH)2D biological variation (BV). In this study, we evaluated 24,25(OH)2D's BV on the European Biological Variation Study (EuBIVAS) cohort samples to determine if analytical performance specifications (APS) for 24,25(OH)2D could be generated.

METHODS

Six European laboratories recruited 91 healthy participants. 25(OH)D and 24,25(OH)2D concentrations in K3-EDTA plasma were examined weekly for up to 10 weeks in duplicate with a validated LC-MS/MS method. The Vitamin D Metabolite Ratio (24,25(OH)2D divided by 25(OH)D × 100) was also calculated at each time point.

RESULTS

Linear regression of the mean 24,25(OH)2D concentrations at each blood collection showed participants were not in steady state. Variations of 24,25(OH)2D over time were significantly positively associated with the slopes of 25(OH)D concentrations over time and the concentration of 25(OH)D of the participant at inclusion, and negatively associated with body mass index (BMI), but not with age, gender, or location of the participant. The variation of the 24,25(OH)2D concentration in participants over a 10 weeks period was 34.6%. Methods that would detect a significant change linked to the natural production of 24,25(OH)2D over this period at p<0.05 would need a relative measurement uncertainty (u%)<14.9% while at p<0.01, relative measurement uncertainty should be <10.5%.

CONCLUSIONS

We have defined for the first time APS for 24,25(OH)2D examinations. According to the growing interest in this metabolite, several laboratories and manufacturers might aim to develop specific methods for its determination. The results presented in this paper are thus necessary prerequisites for the validation of such methods.

Biological variation of CA 15-3, CA 125 and HE 4 on lithium heparinate plasma in apparently healthy Caucasian volunteers

OBJECTIVES

Tumor markers are well-known for being important tools in the support of diagnosis, monitoring of treatment efficacy and follow-up of cancers. CA 125, CA 15-3 and HE 4 have demonstrated potential efficacy in other clinical indications. The main objective was to evaluate the biological variation of these glycoproteins using two different immunoassays in an apparently healthy Caucasian population.

METHODS

Nineteen healthy volunteers including 11 women and 8 men were sampled weekly for 5 consecutive weeks. Samples were analyzed in duplicate on Lumipulse® G600II (Fujirebio) and on the Cobas e602 (Roche Diagnostics) analyzers. After assessment of normality, exclusion of outliers and analysis of homogeneity of variance, analytical variation (CVA), within-subject biological variation (CVI) and between-subject biological variation (CVG) were determined using a nested ANOVA.

RESULTS

CVA, CVI and CVG were determined on both analyzers and both genders. For CA 125, the CVA ranges from 1.0 to 3.4%, the CVI from 5.7 to 13.8% and the CVG from 32.2 to 42.9%. For CA 15-3, the CVA is between 1.1 and 3.4%, the CVI between 3.9 and 6.5% and the CVG between 43.7 and 196.9%. Lastly, HE 4 has CVA values between 1.4 and 2.4%, CVI between 5.1 and 10.5% and CVG between 7.1 and 12.6%.

CONCLUSIONS

Our study provided updated data on the biological variation of CA 125, HE 4 and CA 15-3. These data allow to improve the clinical interpretation and thus the management of the patient.

Delta checks

Delta check is an electronic error detection tool. It compares the difference in sequential results within a patient against a predefined limit, and when exceeded, the delta check rule is considered triggered. The patient results should be withheld for review and troubleshooting before releasing to the clinical team for patient management. Delta check was initially developed as a tool to detect wrong-blood-in-tube (sample misidentification) errors. It is now applied to detect errors more broadly within the total testing process. Recent advancements in the theoretical understanding of delta check has allowed for more precise application of this tool to achieve the desired clinical performance and operational set up. In this Chapter, we review the different pre-implementation considerations, the foundation concepts of delta check, the process of setting up key delta check parameters, performance verification and troubleshooting of a delta check flag.

Physiological Rhythms and Biological Variation of Biomolecules: The Road to Personalized Laboratory Medicine

The concentration of biomolecules in living systems shows numerous systematic and random variations. Systematic variations can be classified based on the frequency of variations as ultradian (<24 h), circadian (approximately 24 h), and infradian (>24 h), which are partly predictable. Random biological variations are known as between-subject biological variations that are the variations among the set points of an analyte from different individuals and within-subject biological variation, which is the variation of the analyte around individuals’ set points. The random biological variation cannot be predicted but can be estimated using appropriate measurement and statistical procedures. Physiological rhythms and random biological variation of the analytes could be considered the essential elements of predictive, preventive, and particularly personalized laboratory medicine. This systematic review aims to summarize research that have been done about the types of physiological rhythms, biological variations, and their effects on laboratory tests. We have searched the PubMed and Web of Science databases for biological variation and physiological rhythm articles in English without time restrictions with the terms “Biological variation, Within-subject biological variation, Between-subject biological variation, Physiological rhythms, Ultradian rhythms, Circadian rhythm, Infradian rhythms”. It was concluded that, for effective management of predicting, preventing, and personalizing medicine, which is based on the safe and valid interpretation of patients’ laboratory test results, both physiological rhythms and biological variation of the measurands should be considered simultaneously.

Quantitative evaluations of variations using the population mean as a baseline for bioinformatics interpretation

Objective

The purpose of this study were to establish a model of quantitative evaluation that uses the population mean as a baseline of variations and describe variations derived from different types and systems using new concepts.

Methods

The observed datasets, including measurement data and relative data, were transformed to 0-1.0 using the population mean. Datasets derived from different types (same category of dataset, different categories of datasets, and datasets with the same baseline) were transformed using different methods. The 'middle compared index' (MCI) was used to describe the change in magnitude as follows: [a/(a+b)+(1-b)/(2-a-b)-1]^1.7, where 'a' represents the number after the magnitude change and 'b' represents the number before the magnitude change. Actual data were used to observe the MCI's ability to evaluate variations quantitatively.

Results

When the value before the magnitude change was equal to that after the magnitude change, the MCI was equal to 0; when the value before the magnitude change was equal to 0 and that after the magnitude change was equal to 1, the MCI was equal to 1. This implies the MCI is valid. When the value before the magnitude change was 0 and that after the magnitude change was 0.5, or when the value before the magnitude change was 0.5 and that after the magnitude change was 1.0, each MCI was approximately equal to 0.5. The values derived from the absolute, ratio, and MCI methods were different, indicating that the MCI is an independent index.

Conclusion

The MCI perfectly performs as an evaluation model using the population mean as the baseline, and it may be more a reasonable index than the ratio or absolute methods. The MCI increases our understanding of quantitative variations in evaluation measures of association using new concepts.

Biological variation: recent development and future challenges

Biological variation (BV) data have many applications in laboratory medicine. However, these depend on the availability of relevant and robust BV data fit for purpose. BV data can be obtained through different study designs, both by experimental studies and studies utilizing previously analysed routine results derived from laboratory databases. The different BV applications include using BV data for setting analytical performance specifications, to calculate reference change values, to define the index of individuality and to establish personalized reference intervals. In this review, major achievements in the area of BV from last decade will be presented and discussed. These range from new models and approaches to derive BV data, the delivery of high-quality BV data by the highly powered European Biological Variation Study (EuBIVAS), the Biological Variation Data Critical Appraisal Checklist (BIVAC) and other standards for deriving and reporting BV data, the EFLM Biological Variation Database and new applications of BV data including personalized reference intervals and measurement uncertainty.

Cardiac troponins - a paradigm for diagnostic biomarker identification and development

The introduction of cardiac troponins into clinical diagnostics has not only improved diagnostic pathways for myocardial infarction but also profoundly influenced the definition of myocardial infarction. The term troponin appeared in the literature almost 60 years ago, i.e. shortly after this journal was founded. The development of cardiac troponins from proteins involved in muscle contraction, which were in the focus of few specialized research groups from physiology and biochemistry, to one of the most frequently measured protein biomarkers in medicine is a paradigmatic success story which is also reflected in almost 300 publications on the topic in this journal. From the viewpoint of biomarker development the critical success factors were medical need, timely generation of medical evidence, and the rapid development of robust and precise laboratory assays.

Personalized reference intervals: from theory to practice

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

Clinlabomics: leveraging clinical laboratory data by data mining strategies

The recent global focus on big data in medicine has been associated with the rise of artificial intelligence (AI) in diagnosis and decision-making following recent advances in computer technology. Up to now, AI has been applied to various aspects of medicine, including disease diagnosis, surveillance, treatment, predicting future risk, targeted interventions and understanding of the disease. There have been plenty of successful examples in medicine of using big data, such as radiology and pathology, ophthalmology cardiology and surgery. Combining medicine and AI has become a powerful tool to change health care, and even to change the nature of disease screening in clinical diagnosis. As all we know, clinical laboratories produce large amounts of testing data every day and the clinical laboratory data combined with AI may establish a new diagnosis and treatment has attracted wide attention. At present, a new concept of radiomics has been created for imaging data combined with AI, but a new definition of clinical laboratory data combined with AI has lacked so that many studies in this field cannot be accurately classified. Therefore, we propose a new concept of clinical laboratory omics (Clinlabomics) by combining clinical laboratory medicine and AI. Clinlabomics can use high-throughput methods to extract large amounts of feature data from blood, body fluids, secretions, excreta, and cast clinical laboratory test data. Then using the data statistics, machine learning, and other methods to read more undiscovered information. In this review, we have summarized the application of clinical laboratory data combined with AI in medical fields. Undeniable, the application of Clinlabomics is a method that can assist many fields of medicine but still requires further validation in a multi-center environment and laboratory.

A survey on Artificial Intelligence and Big Data utilisation in Italian clinical laboratories

OBJECTIVES

The Italian Society of Clinical Biochemistry and Clinical Molecular Biology (SIBioC) Big Data and Artificial Intelligence (BAI) Working Group promoted a survey to frame the knowledge, skills and technological predisposition in clinical laboratories.

METHODS

A questionnaire, focussing on digitization, information technology (IT) infrastructures, data accessibility, and BAI projects underway was sent to 1,351 SIBioC participants. The responses were evaluated using SurveyMonkey software and Google Sheets.

RESULTS

The 227 respondents (17%) from all over Italy (47% of 484 labs), mainly biologists, laboratory physicians and managers, mostly from laboratories of public hospitals, revealed lack of hardware, software and corporate Wi-Fi, and dearth of PCs. Only 25% work daily on clouds, while 65%-including Laboratory Directors-cannot acquire health data from sources other than laboratories. Only 50% of those with access can review a clinical patient's health record, while the other access only to laboratory information. The integration of laboratory data with other health data is mostly incomplete, which limits BAI-type analysis. Many are unaware of integration platforms. Over 90% report pulling data from the Laboratory Information System, with varying degrees of autonomy. Very few have already undertaken BAI projects, frequently relying on IT partnerships. The majority consider BAI as crucial in helping professional judgements, indicating a growing interest.

CONCLUSIONS

The questionnaire received relevant feedback from SIBioC participants. It highlighted the level of expertise and interest in BAI applications. None of the obstacles stands out more than the others, emphasising the need to all-around work: IT infrastructures, data warehouses, BAI analysis software acquisition, data accessibility and training.

Practical application of European biological variation combined with Westgard Sigma Rules in internal quality control

OBJECTIVES

Westgard Sigma Rules is a statistical tool available for quality control. Biological variation (BV) can be used to set analytical performance specifications (APS). The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) regularly updates BV data. However, few studies have used robust BV data to determine quality goals and design a quality control strategy for tumor markers. The aim of this study was to derive APS for tumor markers from EFLM BV data and apply Westgard Sigma Rules to establish internal quality control (IQC) rules.

METHODS

Precision was calculated from IQC data, and bias was obtained from the relative deviation of the External quality assurance scheme (EQAS) group mean values and laboratory-measured values. Total allowable error (TEa) was derived using EFLM BV data. After calculating sigma metrics, the IQC strategy for each tumor marker was determined according to Westgard Sigma Rules.

RESULTS

Sigma metrics achieved for each analyte varied with the level of TEa. Most of these tumor markers except neuron-specific enolase reached 3σ or better based on TEa_min. With TEa_des and TEa_opt set as the quality goals, almost all analytes had sigma values below 3. Set TEa_min as quality goal, each analyte matched IQC muti rules and numbers of control measurements according to sigma values.

CONCLUSIONS

Quality goals from the EFLM BV database and Westgard Sigma Rules can be used to develop IQC strategy for tumor markers.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Integrating moving average control procedures into the risk-based quality control plan in small-volume medical laboratories

The modern approach to quality control (QC) in medical laboratories implies the development of a risk-based control plan. This paper aims to develop a risk-based QC plan for a laboratory with a small daily testing volume and to integrate the already optimized moving average (MA) control procedures into this plan. A multistage bracketed QC plan for ten clinical chemistry analytes was made using a Westgard QC frequency calculator. Previously, MA procedures were optimized by the bias detection simulation method. Aspartate aminotransferase, HDL-cholesterol and potassium had patient-risk sigma metrics greater than 6, albumin and cholesterol greater than 5, creatinine, chlorides, calcium and total proteins between 4 and 5, and sodium less than 4. Based on the calculated run sizes and characteristics of optimized MA procedures, for 6 tests, it was possible to replace the monitoring QC procedure with an MA procedure. For the remaining 4 tests, it was necessary to keep the monitoring QC procedure and introduce MA control for added security. This study showed that even in a laboratory with a small volume of daily testing, it is possible to make a risk-based QC plan and integrate MA control procedures into that plan.

Sigma Metrics in Laboratory Medicine: A Call for Harmonization

BACKGROUND AND AIM

Sigma metrics are applied in clinical laboratories to assess the quality of analytical processes. A parameter associated to a Sigma >6 is considered "world class" whereas a Sigma <3 is "poor" or "unacceptable". The aim of this retrospective study was to quantify the impact of different approaches for Sigma metrics calculation.

MATERIAL AND METHODS

Two IQC levels of 20 different parameters were evaluated for a 12-month period. Sigma metrics were calculated using the formula: (allowable total error (TEa) (%) - bias (%))/(coefficient of variation (CV) (%)). Method precision was calculated monthly or annually. The bias was obtained from peer comparison program (PCP) or external quality assessment program (EQAP), and 9 different TEa sources were included.

RESULTS

There was a substantial monthly variation of Sigma metrics for all combinations, with a median variation of 32% (IQR, 25.6-41.3%). Variation across multiple analyzers and IQC levels were also observed. Furthermore, TEa source had the highest impact on Sigma calculation with proportions of Sigma >6 ranging from 17.5% to 84.4%. The nature of bias was less decisive.

CONCLUSION

In absence of a clear consensus, we recommend that laboratories calculate Sigma metrics on a sufficiently long period of time (>6 months) and carefully evaluate the choice of TEa source.

Biological variation and reference change values of serum Mac‐2–binding protein glycosylation isomer (M2BPGi)

Background

Limited data are available with regard to biological variations of the Mac‐2–binding protein glycosylation isomer (M2BPGi), a liver fibrosis biomarker.

Methods

Long‐term biological variation of M2BPGi was investigated using longitudinally measured M2BPGi test results from healthy Korean adult subjects. One‐way analysis of variance (ANOVA) tests were used to calculate the reference change value (RCV) of M2BPGi based on biological variation estimates. Furthermore, asymmetric RCV was calculated according to a recent publication of the European Federation of Clinical Chemistry and Laboratory Medicine Working Group on Biological Variation and Task Group for the Biological Variation Database (EFLM TG‐BVD).

Results

A total of 363 test results from 174 Korean subjects undergoing general health checkups were requested from 13 local clinics and hospitals during a 38‐month period. The within‐subjects biological variation (CV_I), between‐subject biological variation (CV_G), analytical variation (CV_A), RCV, and individuality index (II) values for serum M2BPGi were 23.3%, 30.0%, 4.3%, 65.6%, and 0.78, respectively. Asymmetric RCV calculated using formulae by a recent EFLM TG‐BVD publication ranged from −41.9 to 72.0%. Desirable analytical performance specifications for M2BPGi derived from biological variation were as follows: imprecision 11.6%, bias 9.6%, and total allowable error 28.7%.

Conclusions

RCV based on biological estimates may be helpful for evaluating and interpreting serial M2BPGi measurements by physicians and in clinical laboratories.

Moving average procedures as an additional tool for real-time analytical quality control: challenges and opportunities of implementation in small-volume medical laboratories

Introduction

Moving average (MA) is one possible way to use patient results for analytical quality control in medical laboratories. The aims of this study were to: (1) implement previously optimized MA procedures for 10 clinical chemistry analytes into the laboratory information system (LIS); (2) monitor their performance as a real-time quality control tool, and (3) define an algorithm for MA alarm management in a small-volume laboratory to suit the specific laboratory.

Materials and methods

Moving average alarms were monitored and analysed over a period of 6 months on all patient results (total of 73,059) obtained for 10 clinical chemistry parameters. The optimal MA procedures were selected previously using an already described technique called the bias detection simulation method, considering the ability of bias detection the size of total allowable error as the key parameter for optimization.

Results

During 6 months, 17 MA alarms were registered, which is 0.023% of the total number of generated MA values. In 65% of cases, their cause was of pre-analytical origin, in 12% of analytical origin, and in 23% the cause was not found. The highest alarm rate was determined on sodium (0.10%), and the lowest on calcium and chloride.

Conclusions

This paper showed that even in a small-volume laboratory, previously optimized MA procedures could be successfully implemented in the LIS and used for continuous quality control. Review of patient results, re-analysis of samples from the stable period, analysis of internal quality control samples and assessment of the analyser malfunctions and maintenance log have been proposed for the algorithm for managing MA alarms.

Critical review and meta-analysis of biological variation estimates for tumor markers

OBJECTIVES

Biological variation data (BV) can be used for different applications, but this depends on the availability of robust and relevant BV data. In this study, we aimed to summarize and appraise BV studies for tumor markers, to examine the influence of study population characteristics and concentrations on BV estimates and to discuss the applicability of BV data for tumor markers in clinical practice.

METHODS

Studies reporting BV data for tumor markers related to gastrointestinal, prostate, breast, ovarian, haematological, lung, and dermatological cancers were identified by a systematic literature search. Relevant studies were evaluated by the Biological Variation Data Critical Appraisal Checklist (BIVAC) and meta-analyses were performed for BIVAC compliant studies to deliver global estimates of within-subject (CV_I) and between-subject (CV_G) BV with 95% CI.

RESULTS

The systematic review identified 49 studies delivering results for 22 tumor markers; four papers received BIVAC grade A, 3 B, 27 C and 15 D. Out of these, 29 CV_I and 29 CV_G estimates met the criteria to be included in the meta-analysis. Robust data are lacking to conclude on the relationship between BV and different disease states and tumor marker concentrations.

CONCLUSIONS

This review identifies a lack of high-quality BV studies for many tumor markers and a need for delivery of BIVAC compliant studies, including in different, disease states and tumor marker concentrations. As of yet, the state-of-the-art may still be the most appropriate model to establish analytical performance specifications for the majority of tumor markers.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Sigma and Risk in the Quality Control Routine: Analysis in Chilean Clinical Laboratories

BACKGROUND

The Six Sigma methodology is focused toward improvement, based on the Total Quality Management. It has been implemented in analytical procedures for clinical laboratories in the form of Sigma Metrics. This method is used in the evaluation of analytical procedures, providing evidence for risk-based management.

METHODS

A descriptive study was carried using data from 18 Chilean clinical laboratories. The information of their performance and quality specifications used in their routine work was obtained from UNITY, an internal quality comparison program.

RESULTS

A total of 3461 sigma evaluations was gathered, mostly from biyearly controls. The general distribution shows a median of 5.5 with positive asymmetry similar to other publications. The reported quality specifications are based in CLIA for 51.2% of the cases, 30.2% from biological variation, and 10.7% from other programs for the external quality evaluation. Significant differences (P < 0.05) were found between medians against their specification source.

CONCLUSIONS

In the studied series, it would be feasible to implement a risk-based quality control system with simple rules and minimal control materials for 55.5% of the evaluated sigmas. 19.6% of the sigmas require improvement mainly in precision. The variety in specifications reveals a lack of harmonization in the specification's selections.

Determining Plasma Protein Variation Parameters as a Prerequisite for Biomarker Studies-A TMT-Based LC-MSMS Proteome Investigation

Specific plasma proteins serve as valuable markers for various diseases and are in many cases routinely measured in clinical laboratories by fully automated systems. For safe diagnostics and monitoring using these markers, it is important to ensure an analytical quality in line with clinical needs. For this purpose, information on the analytical and the biological variation of the measured plasma protein, also in the context of the discovery and validation of novel, disease protein biomarkers, is important, particularly in relation to for sample size calculations in clinical studies. Nevertheless, information on the biological variation of the majority of medium-to-high abundant plasma proteins is largely absent. In this study, we hypothesized that it is possible to generate data on inter-individual biological variation in combination with analytical variation of several hundred abundant plasma proteins, by applying LC-MS/MS in combination with relative quantification using isobaric tagging (10-plex TMT-labeling) to plasma samples. Using this analytical proteomic approach, we analyzed 42 plasma samples prepared in doublets, and estimated the technical, inter-individual biological, and total variation of 265 of the most abundant proteins present in human plasma thereby creating the prerequisites for power analysis and sample size determination in future clinical proteomics studies. Our results demonstrated that only five samples per group may provide sufficient statistical power for most of the analyzed proteins if relative changes in abundances >1.5-fold are expected. Seventeen of the measured proteins are present in the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database, and demonstrated remarkably similar biological CV’s to the corresponding CV’s listed in the EFLM database suggesting that the generated proteomic determined variation knowledge is useful for large-scale determination of plasma protein variations.

Long-term within- and between-subject biological variation of 29 routine laboratory measurands in athletes

OBJECTIVES

Within- and between-subject biological variation (BV) estimates have many applications in laboratory medicine. However, robust high-quality BV estimates are lacking for many populations, such as athletes. This study aimed to deliver BV estimates of 29 routine laboratory measurands derived from a Biological Variation Data Critical Appraisal Checklist compliant design in a population of high-endurance athletes.

METHODS

Eleven samples per subject were drawn from 30 triathletes monthly, during a whole sport season. Serum samples were measured in duplicate for proteins, liver enzymes, lipids and kidney-related measurands on an Advia2400 (Siemens Healthineers). After outlier and homogeneity analysis, within-subject (CV_I) and between-subject (CV_G) biological variation estimates were delivered (CV-ANOVA and log-ANOVA, respectively) and a linear mixed model was applied to analyze the effect of exercise and health related variables.

RESULTS

Most CV_I estimates were similar or only slightly higher in athletes compared to those reported for the general population, whereas two- to three-fold increases were observed for amylase, ALT, AST and ALP. No effect of exercise and health related variables were observed on the CV_I estimates. For seven measurands, data were not homogeneously distributed and BV estimates were therefore not reported.

CONCLUSIONS

The observation of higher CV_I estimates in athletes than what has been reported for the general population may be related to physiological stress over time caused by the continuous practice of exercise. The BV estimates derived from this study could be applied to athlete populations from disciplines in which they exercise under similar conditions of intensity and duration.

Within- and between-subject biological variation data for serum zinc, copper and selenium obtained from 68 apparently healthy Turkish subjects

OBJECTIVES

Trace elements (TrEL) are nutritionally essential components in maintaining health and preventing diseases. There is a lack of reliable biological variation (BV) data for TrELs, required for the diagnosis and monitoring of TrEL disturbances. In this study, we aimed to provide updated within- and between-subject BV estimates for zinc (Zn), copper (Cu) and selenium (Se).

METHODS

Weekly serum samples were drawn from 68 healthy subjects (36 females and 32 males) for 10 weeks and stored at -80 °C prior to analysis. Serum Zn, Cu and Se levels were measured using inductively-coupled plasma mass spectrometry (ICP-MS). Outlier and variance homogeneity analyses were performed followed by CV-ANOVA (Røraas method) to determine BV and analytical variation estimates with 95% CI and the associated reference change values (RCV) for all subjects, males and females.

RESULTS

Significant differences in mean concentrations between males and females were observed, with absolute and relative (%) differences for Zn at 0.5 μmol/L (3.5%), Cu 2.0 μmol/L (14.1%) and Se 0.06 μmol/L (6.0%). The within-subject BV (CV_I [95% CI]) estimates were 8.8% (8.2-9.3), 7.8% (7.3-8.3) and 7.7% (7.2-8.2) for Zn, Cu and Se, respectively. Within-subject biological variation (CV_I) estimates derived for male and female subgroups were similar for all three TrELs. Marked individuality was observed for Cu and Se.

CONCLUSIONS

The data of this study provides updated BV estimates for serum Zn, Cu and Se derived from a stringent protocol and state of the art methodologies. Furthermore, Cu and Se display marked individuality, highlighting that population based reference limits should not be used in the monitoring of patients.

Problems with estimating reference change values (critical differences)

The concept of reference change values (RCVs) for diagnosis and monitoring of diseases has become well established. Several models habe been developed, e. g. one assuming a normal distribution and another one for a log-normal distribution. RCV values calculated for some measurands with both models are compared with each other and led to similar results. A few examples led to RCV values which are not plausible for diagnostic purposes. Although statistical concepts of RCV values are well established, their clinical relevance remains questionable at least for some measurands. Studies with clinicians are required whether RCVs are of practical usefulness.

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.