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BackgroundDefining mean and reference intervals for glomerular filtration rate (GFR) has been the subject of only a limited number of studies and review articles, with contradicting statements about the mean. Normal measured GFR (mGFR) values of ∼120–130 mL/min/1.73 m2 have long been the referenced values for young adults but seem to be too high according to recent studies. Reference intervals are difficult to define because of the age decline of GFR, which is also observed in healthy subjects. Little data are available for subjects >70 years of age.MethodsBased on the reference intervals for serum creatinine (SCr) and the recently published full-age spectrum (FAS) equation, we define simple age-dependent equations for the reference limits of GFR. The mGFR of 633 living potential kidney donors was used to validate the new formulae that define the reference interval.ResultsThe reference limits for estimated GFR (eGFR), calculated by entering the reference limits for SCr into the FAS equation closely correspond with published reference limits for mGFR. Of the mGFRs of potential living kidney donors, 97.2% lie between the newly defined reference limits for GFR.ConclusionSCr reference limits may serve to define age-dependent reference limits for eGFR and mGFR.

Background Serum pro-gastrin-releasing peptide (ProGRP) levels significantly vary with age. However, conventional partitioned reference intervals (RIs) fail to reflect the continuous and subtle physiological changes associated with aging. To address this limitation, we investigated the age- and sex-specific dynamics of ProGRP levels and innovatively developed next-generation RIs for adults and elderly individuals in North China. Methods A total of 4136 rigorously screened individuals (aged 20–80 years) were analyzed. The effects of age and sex on ProGRP levels were assessed using Mann–Whitney U tests, Spearman’s correlation, and Kruskal–Wallis tests. The Harris–Boyd method was used to evaluate the necessity of sex or age partitioning. Age-partitioned RIs were established using a nonparametric method. Next-generation RI models were developed using generalized additive models for location, scale, and shape, with age-related dynamics visualized. The clinical applicability of both RI types was evaluated by comparing upper reference limit flagging rates across age subgroups; rates closer to the theoretical 2.5% were considered indicative of superior RI accuracy. Results Sex had a minimal and clinically insignificant effect on ProGRP levels. In contrast, age had a significant effect: concentrations remained stable until approximately 40 years of age, followed by progressive increases. The partitioned RIs were defined as 0–52.77 pg/mL (20–49 years) and 0–63.68 pg/mL (≥ 50 years). Next-generation RIs were quantified and visualized. Compared with partitioned RIs, next-generation RIs yielded reference limit flagging rates more closely aligned with the theoretical 2.5% across most age groups and demonstrated significantly greater stability (1.83%–3.74% vs. 1.07%–7.10%). Conclusions Compared with partitioned RIs, next-generation RIs for ProGRP improve the accuracy of laboratory result interpretation. With advances in laboratory informatics, broader clinical implementation of next-generation RIs is anticipated.

Glycosaminoglycan (GAG) and sialic acid (total and free) assays are used as first‐line screening tests for the diagnosis of mucopolysaccharidoses and glycoproteinoses, respectively. There is a pronounced age‐dependent variation in the urinary concentrations of these metabolites in the normal population, and the stratification of the reference values into discrete age ranges may lead to an undesirably high number of false‐positive or false‐negative results. The aim of this study was to design a method for calculating continuous reference intervals as a function of age and its application to the analysis of GAGs and sialic acid (total, free, and conjugated) in urine. In the postpubertal period, concentrations of urinary GAGs and sialic acid have reached a plateau, so a traditional calculation of the reference range in this specific age group was considered appropriate. In the prepubertal period, a nonlinear regression performed with the Excel add‐in Solver was used to fit the logarithmized concentrations of the controls to a curve that represents the mean values as a function of age. A uniform distribution of the residuals was obtained, which allowed the calculation of the reference intervals by adding the values of their 2.5 and 97.5 percentiles to the independent variable of the regression curve to calculate the upper and lower reference curves. The main advantages of the developed method are (1) a reduction in the number of control samples needed to obtain adequate reference intervals and (2) an improvement in the reliability of diagnostic screening by reducing the uncertainty generated by the gaps in the traditional age‐stratified method.

Abstract Objectives Biochemical testing of urinary metanephrines is useful in the diagnosis and monitoring of pheochromocytoma and paragangliomas. We investigated the feasibility of mixture decomposition (ie, indirect) methods in verifying clinically derived reference intervals for urinary deconjugated metanephrine metabolites. Methods Urinary 24-hour metanephrine and normetanephrine excretion results were extracted from our data warehouse and intervals were estimated by the modern variant of the Hoffmann method, maximum likelihood estimation (MLE), and gamma mixture model using R software. Results Hoffmann, MLE, and gamma mixture models provided metanephrine and normetanephrine intervals that closely matched those derived from clinical studies. However, three-component MLE and gamma models were required for normetanephrine in adult women because the Hoffmann method was not suitable. Some data transformations caused blending of the mixed distributions and subsequent widening of the reference interval estimation, emphasizing the importance of careful data transformation for Hoffmann and MLE analyses. Gamma mixture models gave overall good agreement without the need for data transformation. Conclusions Indirect methods have utility in verifying reference intervals in 24-hour urine specimens collected by patients. We emphasize the benefits of applying multiple decomposition methods to corroborate findings and careful application of data transformation when using Gaussian-based models.

ABSTRACT Background In clinical practice, reference intervals play a pivotal role in interpreting laboratory test results. Yet, when several tests are taken into consideration simultaneously, the traditional univariate intervals might not suffice due to the elevated risk of Type 1 errors. Methods This study introduces and evaluates two multivariate reference interval techniques: one based on Mahalanobis distance and the other an adaptation of the multivariate confidence interval (MCI). Using Monte Carlo simulations, we focused our assessments on the interplay between “serum ferritin and transferrin saturation” values. Results Upon evaluation, it became evident that the multivariate methods significantly reduced false positives. They presented enhanced accuracy over traditional univariate intervals. Notably, the method involving Mahalanobis distance stood out in terms of efficacy. Contributions Beyond presenting novel techniques, our research underscores the importance and potential of using multivariate approaches in clinical lab settings. The findings can guide better medical decision making, ensuring optimized allocation of healthcare resources. In this study, we present two alternative multivariate reference interval methods that consider the relationship between two analytes simultaneously. One method is based on the Mahalanobis distance, while the other adapts the multivariate confidence interval formulation to the multivariate reference interval setting. Through simulation studies, we demonstrate that the proposed methods reduce the probability of false‐positive findings and address challenges in interpreting reference limits corresponding to the elliptical region.

Objective: This study aimed to establish baseline biochemical reference values for apparently healthy Pacific parrotlets in Thailand. Materials and Methods: Blood samples were obtained from 30 healthy individuals, and analyses were conducted using the VETSCAN® VS2 with Avian/Reptilian Profile Plus rotors. Results: The results showed the measured biochemical parameters with the reference intervals included aspartate aminotransferase (73–274 U/L), glucose (235–324 mg/dl), total protein (2–3.3 gm/dl), albumin (1.5–2.5 gm/dl), globulin (0.1–1.2 gm/dl), phosphorus (1.2–5 mg/dl), calcium (7.9–9.7 mg/dl), sodium (147–157 μmol/l), potassium (2.2–3.8 μmol/l), bile acids (0–34 μmol/l), creatine kinase (44–543 U/L), uric acid (1.4–9.7 mg/dl), plasma protein (3–5 gm/dl), and packed cell volume (49%–55%). Conclusion: This study offers the first reference biochemistry values specific to Pacific parrotlets in Thailand, contributing to improved veterinary clinical practices, accurate diagnosis, and effective care for this species.

Background and Aims The reference interval (RI) for a tumor marker may vary between populations, detection systems, and the methods used to obtain their values. The aims of this study were to establish age‐ and sex‐specific RIs for the following nine common tumor markers and to validate the established RIs in Korean adults: alpha fetoprotein (AFP), carcinoembryonic antigen (CEA), cancer antigen (CA) 19‐9, CA15‐3, CA125, Human epididymis protein 4 (HE4), total prostate specific antigen, cytokeratin fragment (Cyfra) 21‐1, and progastrin‐releasing peptide (ProGRP). Methods This cross‐sectional study consecutively selected 214,159 individuals (aged 18–98 years) who underwent health checkups at 16 health‐promotion centers in 13 Korean cities. Finally, 62,752 examinees were used to establish the RIs after removing outliers. RIs were established using an indirect method according to the CLSI EP28‐A3C guideline. The established RIs were validated by calculating the proportion of individuals outside each RI. Results Sex‐related differences were observed for AFP, CEA, CA19‐9, Cyfra 21‐1, and ProGRP (p < 0.05): AFP, CEA and Cyfra 21‐1 were higher in males, and CA19‐9 and proGRP were higher in females. Most of the tumor markers except CA15‐3 and CA125 increased with age: CA125 decreased at ≥50 years of age (p < 0.05), while CA15‐3 did not vary with age. Less than 5% of subjects were outside all RIs (the 2.5th and 97.5th percentiles) established in the present study. Meanwhile, less than 3% of the healthy reference subjects fell outside the current and manufacturers' RIs of all tumor markers except Cyfra 21‐1. Conclusion This study has determined age‐ and sex‐specific RIs for nine common tumor markers in the healthy Korean population, which could be useful for clinicians making clinical decisions and assessments.

The interpretation of individual laboratory test results requires the availability of population-based reference intervals. In children, reference interval estimation has to consider frequently the strong age-dependency. Generally, for the construction of reference intervals, a sufficiently large number of independent measurement values is required. Data selections from hospitals or cohort studies often comprise dependencies violating the independence assumption. In this article, we propose a combination of LMS-like (mean, M; coefficient of variation, S; skewness, λ or L) and resampling methods to overcome this drawback. The former is recommended by the World Health Organization (WHO) for the construction of continuous reference intervals of anthropometric measurements in children. The approach allows the inclusion of dependent measurements, for example, repeated measurements per subject. It also provides pointwise confidence envelopes as a measure of reliability. The combination of LMS-type methods and resampling provides a feasible approach to estimate age-dependent percentiles and reference intervals using unbalanced longitudinal data.

The diagnosis of diseases is a complex process involving the integration of multiple parameters obtained from various sources, including laboratory findings. The interpretation of laboratory data is inherently comparative, necessitating reliable references for accurate assessment. Different types of references, such as reference intervals, decision limits, action limits, and reference change values, are essential tools in the interpretation of laboratory data. Although these references are used to interpret individual laboratory data, they are typically derived from population data, which raises concerns about their reliability and consequently the accuracy of interpretation of individuals’ laboratory data. The accuracy of diagnosis is critical to all subsequent steps in medical practice, making the estimate of reliable references a priority. For more precise interpretation, references should ideally be derived from an individual’s own data rather than from population averages. This manuscript summarizes the current sources of references used in laboratory data interpretation, examines the references themselves, and discusses the transition from population-based laboratory medicine to personalized laboratory medicine.

This study compared the reference intervals (RI) of B12 vitamin concentration in blood found in the literature with RIs extracted from data accumulated from a large number of patients by E. Gulbis Laboratory in Latvia. This paper investigated and demonstrated the possibility of using large amounts of random patient data to establish the RI for clinical laboratory tests. The blood level of B12 vitamin was selected as the model system for this study. The study used blind data for B12 blood level measurements from 132 379 patients accumulated in E. Gulbis Laboratory over a period of 15 years. In order to establish the reference intervals, the frequency distribution of log transformed B12 values was fit to a Gaussian distribution. The established B12 reference interval of 196 pg/ml and 942 pg/ml was found to be in good agreement with RIs reported elsewhere.