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

The impact of physiological variations on personalized reference intervals and decision limits: an in-depth analysis

The interpretation of laboratory data is a comparative procedure. Physicians typically need reference values to compare patients' laboratory data for clinical decisions. Therefore, establishing reliable reference data is essential for accurate diagnosis and patient monitoring. Human metabolism is a dynamic process. Various types of systematic and random fluctuations in the concentration/activity of biomolecules are observed in response to internal and external factors. In the human body, several biomolecules are under the influence of physiological rhythms and are therefore subject to ultradian, circadian and infradian fluctuations. In addition, most biomolecules are also characterized by random biological variations, which are referred to as biological fluctuations between subjects and within subjects/individuals. In routine practice, reference intervals based on population data are used, which by nature are not designed to capture physiological rhythms and random biological variations. To ensure safe and appropriate interpretation of patient laboratory data, reference intervals should be personalized and estimated using individual data in accordance with systematic and random variations. In this opinion paper, we outline (i) the main variations that contribute to the generation of personalized reference intervals (prRIs), (ii) the theoretical background of prRIs and (iii) propose new methods on how to harmonize prRIs with the systematic and random variations observed in metabolic activity, based on individuals' demography.

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.

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.

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

BACKGROUND

For lung cancer, circulating tumor markers (TM) are available to guide clinical treatment decisions. To ensure adequate accuracy, pre-analytical instabilities need to be known and addressed in the pre-analytical laboratory protocols.

OBJECTIVE

This study investigates the pre-analytical stability of CA125, CEA, CYFRA 21.1, HE4 and NSE for the following pre-analytical variables and procedures; i) whole blood stability, ii) serum freeze-thaw cycles, iii) electric vibration mixing and iv) serum storage at different temperatures.

METHODS

Left-over patient samples were used and for every investigated variable six patient samples were used and analysed in duplicate. Acceptance criteria were based on analytical performance specifications based on biological variation and significant differences with baseline.

RESULTS

Whole blood was stable for at least 6 hours for all TM except for NSE. Two freeze-thaw cycles were acceptable for all TM except CYFRA 21.1. Electric vibration mixing was allowed for all TM except for CYFRA 21.1. Serum stability at 4°C was 7 days for CEA, CA125, CYFRA 21.1 and HE4 and 4 hours for NSE.

CONCLUSIONS

Critical pre-analytical processing step conditions were identified that, if not taken into account, will result in reporting of erroneous TM results.

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 of plasma 25-Hydroxyvitamin D3, Serum vitamin B12, folate and ferritin in Turkish healthy subject

Biological variation (BV) plays a crucial role in determining analytical performance specifications, assessing serial measurements of individuals, and establishing the use of population-based reference intervals. Our study aimed to calculate the BV and BV-based quality goals of 25-hydroxyvitamin D3 (25-OH D3), ferritin, folate and vitamin B12 tests. We included a total of 22 apparently healthy volunteers (9 women and 13 men) aged 18-55 years in the study that we conducted in Turkey. Blood samples were collected from the participants once a week for five weeks. Serum ferritin, folate and vitamin B12 levels were measured using immunochemical method, while plasma 25-OH D3 levels were determined using the high-performance liquid chromatography method. Analysis of variance (ANOVA) was used to estimate analytical variation(CVA), within-subject BV(CVI) and between-subject BV(CVG). The individuality index (II) and reference change value (RCV) were calculated based on these data. The CVI of 25-OH D3, ferritin, folate, and vitamin B12 were found to be 1.8% (0.6%-2.5%), 16.9% (14.4%-20.2%), 10.7% (9.2%-12.7%), and 8.6% (6.8%-10.5%), respectively. CVG were 44.2% (34.3%-69.9%), 132% (87.7%-238%), 19.4% (14.4%-28.8%), and 39.6% (29.8%-59.0%) for the same biomarkers, while CVA were 3.2% (2.81%-3.71%), 3.5% (3.1%-4.1%), 4.0% (3.5%-4.6%), and 7.5% (6.6%-8.6%), respectively. The II values for 25-OH D3, ferritin, folate, and vitamin B12 were calculated as 0.04, 0.13, 0.55, and 0.22, respectively. The RCV were 10.2%, 47.8%, 31.7%, and 31.6%, respectively. Because the tests analyzed in this study exhibit high individuality, RCV should be preferred rather than population-based reference ranges in clinical interpretation of results.

Practical delta check limits for tumour markers in different clinical settings

OBJECTIVES

Few studies have reported on delta checks for tumour markers, even though these markers are often evaluated serially. Therefore, this study aimed to establish a practical delta check limit in different clinical settings for five tumour markers: alpha-fetoprotein, cancer antigen 19-9, cancer antigen 125, carcinoembryonic antigen, and prostate-specific antigen.

METHODS

Pairs of patients' results (current and previous) for five tumour markers between 2020 and 2021 were retrospectively collected from three university hospitals. The data were classified into three subgroups, namely: health check-up recipient (subgroup H), outpatient (subgroup O), and inpatient (subgroup I) clinics. The check limits of delta percent change (DPC), absolute DPC (absDPC), and reference change value (RCV) for each test were determined using the development set (the first 18 months, n=179,929) and then validated and simulated by applying the validation set (the last 6 months, n=66,332).

RESULTS

The check limits of DPC and absDPC for most tests varied significantly among the subgroups. Likewise, the proportions of samples requiring further evaluation, calculated by excluding samples with both current and previous results within the reference intervals, were 0.2-2.9% (lower limit of DPC), 0.2-2.7% (upper limit of DPC), 0.3-5.6% (absDPC), and 0.8-35.3% (RCV99.9%). Furthermore, high negative predictive values >0.99 were observed in all subgroups in the in silico simulation.

CONCLUSIONS

Using real-world data, we found that DPC was the most appropriate delta-check method for tumour markers. Moreover, Delta-check limits for tumour markers should be applied based on clinical settings.

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.

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.

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.

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

Biological variation (BV) data have many important applications in laboratory medicine. Concerns about quality of published BV data led the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) 1st Strategic Conference to indicate need for new studies to generate BV estimates of required quality. In response, the EFLM Working Group on BV delivered the multicenter European Biological Variation Study (EuBIVAS). This review summarises the EuBIVAS and its outcomes. Serum/plasma samples were taken from 91 ostensibly healthy individuals for 10 consecutive weeks at 6 European centres. Analysis was performed by Siemens ADVIA 2400 (clinical chemistry), Cobas Roche 8000, c702 and e801 (proteins and tumor markers/hormones respectively), ACL Top 750 (coagulation parameters), and IDS iSYS or DiaSorin Liaison (bone biomarkers). A strict preanalytical and analytical protocol was applied. To determine BV estimates with 95% CI, CV-ANOVA after analysis of outliers, homogeneity and trend analysis or a Bayesian model was applied. EuBIVAS has so far delivered BV estimates for 80 different measurands. Estimates for 10 measurands (Non-HDL Cholesterol, S100-β protein, neuron-specific enolase, soluble transferrin receptor, intact fibroblast growth-factor-23, uncarboxylated-unphosphorylated matrix-Gla protein, human epididymis protein-4, free, conjugated and %free prostate-specific antigen), prior to EuBIVAS, have not been available. BV data for creatinine and troponin I were obtained using two analytical methods in each case. The EuBIVAS has delivered high-quality BV data for a wide range of measurands. The BV estimates are for many measurands lower than those previously reported, having an impact on the derived analytical performance specifications and reference change values.

Within- and between-subject biological variation data for tumor markers based on the European Biological Variation Study

OBJECTIVES

Reliable biological variation (BV) data are required for the clinical use of tumor markers in the diagnosis and monitoring of treatment effects in cancer. The European Biological Variation Study (EuBIVAS) was established by the EFLM Biological Variation Working Group to deliver BV data for clinically important measurands. In this study, EuBIVAS-based BV estimates are provided for cancer antigen (CA) 125, CA 15-3, CA 19-9, carcinoembryonic antigen, cytokeratin-19 fragment, alpha-fetoprotein and human epididymis protein 4.

METHODS

Subjects from five European countries were enrolled in the study, and weekly samples were collected from 91 healthy individuals (53 females and 38 males; 21-69 years old) for 10 consecutive weeks. All samples were analyzed in duplicate within a single run. After excluding outliers and homogeneity analysis, the BVs of tumor markers were determined by CV-ANOVA on trend-corrected data, when relevant (Røraas method).

RESULTS

Marked individuality was found for all tumor markers. CYFRA 21-1 was the measurand with the highest index of individuality (II) at 0.67, whereas CA 19-9 had the lowest II at 0.07. The CV _I s of HE4, CYFRA 21-1, CA 19-9, CA 125 and CA 15-3 of pre- and postmenopausal females were significantly different from each other.

CONCLUSIONS

This study provides updated BV estimates for several tumor markers, and the findings indicate that marked individuality is characteristic. The use of reference change values should be considered when monitoring treatment of patients by means of tumor markers.