Reference intervals: past, present, and future

Establishment of reference intervals for plasma IL-2, IL-4, IL-5, and IL-17A in healthy adults from the Jiangsu region of eastern China using flow cytometry: A single-center study

BACKGROUND

Cytokines are of utmost importance in both the physiological and pathological immune responses of the human body. This study utilized flow cytometry to measure the levels of plasma interleukin-2 (IL-2), interleukin-4 (IL-4), interleukin-5 (IL-5) and interleukin-17A (IL-17A) and established their reference intervals, aiming to provide data support for the diagnosis and treatment of clinical diseases.

METHODS

According to the inclusion and exclusion criteria, a total of 728 reference individuals were included in this study from January 2023 to June 2023. The Kolmogorov-Smirnov test was used to analyse the distributions of plasma IL-2, IL-4, IL-5 and IL-17A. The reference intervals of plasma IL-2, IL-4, IL-5 and IL-17A were established by the unilateral percentile method (95th percentile) based on the guidelines of C28-A 3 and WS/T 402-2012.

RESULTS

In this study, the levels of plasma IL-2, IL-4, IL-5 and IL-17A were nonnormally distributed. The concentrations of plasma IL-2, IL-4, IL-5 and IL-17A in healthy adults were not significantly different by sex or age (all P > 0.05). Therefore, all the reference individuals were combined into one group, and the reference intervals of plasma IL-2, IL-4, IL-5 and IL-17 were established by flow cytometry (IL-2 ≤ 10.25 pg/mL, IL-4 ≤ 9.87 pg/mL, IL-5 ≤ 3.36 pg/mL and IL-17A ≤ 9.46 pg/mL).

CONCLUSIONS

We first established the reference intervals of plasma IL-2, IL-4, IL-5 and IL-17A in healthy adults based on a single-center population in the Jiangsu region in eastern China, which will provide an important reference value for evaluating human immune status and the diagnosis and treatment of clinical diseases.

CLSI-based verification and de novo establishment of reference intervals for common biochemical assays in Croatian newborns

Introduction

This study aimed to examine whether the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER) reference intervals for 19 commonly used biochemical assays (potassium, sodium, chloride, calcium, magnesium, inorganic phosphorous, glucose, urea, creatinine, direct and total bilirubin, C-reactive protein (CRP), total protein, albumin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP) and lactate dehydrogenase (LD)) could be applied to the newborn population of one Croatian clinical hospital.

Materials and methods

Reference interval verification was performed according to the CLSI EP28-A3c guidelines. Samples of healthy newborns were selected using the direct a posteriori sampling method and analyzed on the Beckman Coulter AU680 biochemical analyzer. If verification wasn't satisfactory, further procedure included de novo determination of own reference intervals by analyzing 120 samples of healthy newborns.

Results

After the first set of measurements, 14/19 tested reference intervals were adopted for use: calcium, inorganic phosphorous, glucose, urea, creatinine, total bilirubin, CRP, total protein, albumin, AST, ALT, GGT, ALP and LD. A second set of samples was tested for 5 analytes: potassium, sodium, chloride, magnesium and direct bilirubin. The verification results of the additional samples for sodium and chloride were satisfactory, while the results for potassium, magnesium and direct bilirubin remained unsatisfactory and new reference intervals were determined.

Conclusions

The CALIPER reference intervals can be implemented into routine laboratory and clinical practice for the tested newborn population for most of the analyzed assays, while own reference intervals for potassium, magnesium and direct bilirubin have been determined.

Development of next-generation reference interval models to establish reference intervals based on medical data: current status, algorithms and future consideration

Evidence derived from laboratory medicine plays a pivotal role in the diagnosis, treatment monitoring, and prognosis of various diseases. Reference intervals (RIs) are indispensable tools for assessing test results. The accuracy of clinical decision-making relies directly on the appropriateness of RIs. With the increase in real-world studies and advances in computational power, there has been increased interest in establishing RIs using big data. This approach has demonstrated cost-effectiveness and applicability across diverse scenarios, thereby enhancing the overall suitability of the RI to a certain extent. However, challenges persist when tests results are influenced by age and sex. Reliance on a single RI or a grouping of RIs based on age and sex can lead to erroneous interpretation of results with significant implications for clinical decision-making. To address this issue, the development of next generation of reference interval models has arisen at an historic moment. Such models establish a curve relationship to derive continuously changing reference intervals for test results across different age and sex categories. By automatically selecting appropriate RIs based on the age and sex of patients during result interpretation, this approach facilitates clinical decision-making and enhances disease diagnosis/treatment as well as health management practices. Development of next-generation reference interval models use direct or indirect sampling techniques to select reference individuals and then employed curve fitting methods such as splines, polynomial regression and others to establish continuous models. In light of these studies, several observations can be made: Firstly, to date, limited interest has been shown in developing next-generation reference interval models, with only a few models currently available. Secondly, there are a wide range of methods and algorithms for constructing such models, and their diversity may lead to confusion. Thirdly, the process of constructing next-generation reference interval models can be complex, particularly when employing indirect sampling techniques. At present, normative documents pertaining to the development of next-generation reference interval models are lacking. In summary, this review aims to provide an overview of the current state of development of next-generation reference interval models by defining them, highlighting inherent advantages, and addressing existing challenges. It also describes the process, advanced algorithms for model building, the tools required and the diagnosis and validation of models. Additionally, a discussion on the prospects of utilizing big data for developing next-generation reference interval models is presented. The ultimate objective is to equip clinical laboratories with the theoretical framework and practical tools necessary for developing and optimizing next-generation reference interval models to establish next-generation reference intervals while enhancing the use of medical data resources to facilitate precision medicine.

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.

An Approach to Re-evaluate the Reference Cutoff of the Parameters of Newborn Screening: An Observational Study

Background Unless a cutoff level of the parameters of newborn screening (NBS) is defined, a screening test's results would end in high recall rates and apprehensive parents. The study aimed to establish a cutoff level of the healthy term newborns. Materials and methods The study was a retrospective observational data analysis on a cohort of 1158 term newborns who underwent NBS in our institute. The percentile distribution of the NBS parameters was computed and the 99th percentile value was considered the new cutoff. For lower values, such as neonatal glucose 6-phosphate dehydrogenase (nG6PD) and neonatal biotinidase (nBIOT), low percentile values were regarded as new cutoff value. Results Neonatal thyroid stimulating hormone (nTSH), nG6PD, neonatal immunoreactive trypsinogen (nIRT), and nBIOT showed a wide variation in the distribution. Most newborns had neonatal galactose (nGAL), nIRT, and nBIOT values above the median. The 99th percentile value of nTSH was 14.5 mIU/L, and that of neonatal 17-hydroxyprogesterone (n17-OHP) was 43.7 nmol/L. The 1.0th percentile value for nG6PD was decreased to 2.18 IU/gHb. The new cutoff values for nBIOT, nIRT, neonatal phenylketonuria (nPKU) and nGAL were 48.59 U, 95.3 µg/L, 2.3 mg/dL and 15.9 mg/dL. The mean and median nTSH values did not significantly differ (p=0.99) in the first five days of birth. On the contrary, the study population depicted considerably raised levels of n17-OHP on day 3, followed by a sharp decrease (p=0.029). Similarly, nIRT displayed significant differences in the first five days (p=0.017). Conclusion Using the 99th percentile values of the NBS parameters as the new cutoff levels might be beneficial in terms of the recall rates and cost burden.

Contexts of care for people with differences of sex development: Diversity is still missing in the laboratory routine

The 2006 Chicago consensus statement of management of disorders/difference of sex development (DSD) has achieved advantages in clinical care and diagnosis for patients and families affect by DSD. This article provides a brief overview of contexts of care for physicians, and points out specific challenges in clinical practice that have arisen from the transformations of the sex/gender system in recent years. We focus on the impact of diagnosis and laboratory measurements. Both laboratory measurements and hormonal therapies still depend on the binary system. One problem is the lack of reference intervals for the different forms of DSD, which means that diversity is often neglected. In the following, we will give a brief insight into this complex topic.