Personalized reference intervals: From the statistical significance to the clinical usefulness

Clinical utility of personalized reference intervals for CEA in the early detection of oncologic disease

OBJECTIVES

Personalized reference intervals (prRI) have been proposed as a diagnostic tool for assessing measurands with high individuality. Here, we evaluate clinical performance of prRI using carcinoembryonic antigen (CEA) for cancer detection and compare it with that of reference change values (RCV) and other criteria recommended by clinical guidelines (e.g. 25 % of change between consecutive CEA results (RV25) and the cut-off point of 5 μg/L (CP5)).

METHODS

Clinical and analytical data from 2,638 patients collected over 19 years were retrospectively evaluated. A total 15,485 CEA results were studied. For each patient, we calculated prRI and RCV using computer algorithms based on the combination of different strategies to assess the number of CEA results needed, consideration of one or two limits of reference interval and the intraindividual biological variation estimate (CVI) used: (a) publicly available (CVI-EU), (b) CVI calculated using an indirect method (CVI-NOO) and (c) within-person BV (CVP). For each new result identified falling outside the prRI, exceeding the RCV interval, RV25 or CP5, we searched for records identifying the presence of tumour at 3 and 12 months after the test. The sensitivity, specificity and predictive power of each strategy were calculated.

RESULTS

PrRI approaches derived using CVI-EU, and both limits of reference interval achieve the best sensitivity (87.5 %) and NPV (99.3 %) at 3 and 12 months of all evaluated criteria. Only 3 results per patients are enough to calculate prRIs that reach this diagnostic performance.

CONCLUSIONS

PrRI approaches could be an effective tool to rule out new oncological findings during the active surveillance of patients.

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 serum neopterin concentrations in apparently healthy individuals

OBJECTIVES

The aims of this study were to determine the biological variation (BV), reference change value (RCV), index of individuality (II), and quality specifications for serum neopterin concentrations; a measurand provided by clinical laboratories as an indicator of cellular immunity.

METHODS

The study delivered serum samples collected for 10 consecutive weeks from 12 apparently healthy individuals (3 male, 9 female). Serum neopterin concentrations were measured using high-performance liquid chromatography with fluorometric detection. The data analysis was performed using an online statistical tool and addressed published criteria for estimation of biological variation.

RESULTS

The mean neopterin concentration was 5.26 nmol/L. The within-subject biological variation (CVI) with 95 % confidence interval (CI) of neopterin serum concentrations was 11.54 % (9.98-13.59), and the between-subject biological variation (CVG) with 95 % CI was 43.27 % (30.52-73.67). The neopterin asymmetrical RCV was -24.9 %/+33.1 %, and the II was 0.27. The desirable quality specifications for neopterin were <5.77 % for precision, <11.20 % for bias, and <20.72 % for total allowable error (TEa). When analytical variation was used instead of CVI to calculate TEa, the desirable TEa was <18.39.

CONCLUSIONS

This study determined BV data for neopterin, an indicator of cell-mediated immune response. Asymmetric RCV values, of 24.9 % decrease or a 33.1 % increase between consecutive measurements indicate significant change. The II of 0.27 indicates a high degree of individuality, therefore that it is appropriate to consider the use of personal reference data and significance of change rather than the reference interval as points of reference for the evaluation of neopterin serum concentrations.

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 (Aβ42, Aβ40), 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 . Aβ42/Aβ40 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 (Aβ42/Aβ40) 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.

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.

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.

Biological variation estimates for spot urine analytes and analyte/creatinine ratios in 33 healthy subjects

OBJECTIVES

Urine samples are frequently used in the clinical practice. In our study, we aimed to calculate the biological variations (BV) of analytes and analyte/creatinine ratios measured in spot urine.

METHODS

Second-morning spot urine samples were collected from 33 (16 female, 17 male) healthy volunteers once weekly for 10 weeks and analyzed in the Roche Cobas 6,000 instrument. Statistical analyzes were performed using BioVar, an online BV calculation software. The data were evaluated in terms of normality, outliers, steady state, homogeneity of the data, and BV values were obtained by analysis of variance (ANOVA). A strict protocol was established for within-subject (CVI) and between-subject (CVG) estimates for both genders.

RESULTS

There was a significant difference between female/male CVI estimates of all analytes except potassium, calcium and magnesium. No difference was found in CVG estimates. When the analytes that had a significant difference in CVI estimates in spot urine analytes were compared to creatinine, it was observed that the significant difference between the genders disappeared. There was no significant difference between female/male CVI and CVG estimates in all spot urine analyte/creatinine ratios.

CONCLUSIONS

Since the CVI estimates of analyte/creatinine ratios are lower, it would be more reasonable to use them in result reporting. Reference ranges should be used with caution, since II values of almost all parameters are between 0.6 and 1.4. The CVI detection power of our study is 1, which is the highest value.

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.

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.