Indirect methods for reference interval determination - review and recommendations

Accounting for differences between serum and plasma: An indirect approach to derive reference intervals for total protein, albumin, and globulin

BACKGROUND

Observable quantitative variations exist between plasma and serum in routine protein measurements, often not reflected in standard reference intervals. In this study, we describe an indirect approach for estimating a combined reference interval (RI) (i.e., serum and plasma), for commonly ordered protein measurands: total protein, albumin, and globulin.

METHODS

We applied an indirect reference interval estimation for protein measurements in serum and plasma using data from July 2018 to February 2024. The data were divided into three Epochs based on a period of plasma separator tube shortage during the COVID-19 pandemic. Bootstrap resampling was used to calculate RIs and corresponding 95% confidence intervals for each month.

RESULTS

Our results demonstrate notable changes in RI limits for total protein, albumin, and globulin between Epochs, reflecting the influence of changing sample matrix. A combined RI was identified for all components and verified using plasma and serum samples from 20 healthy individuals and retrospective analysis of flagging rates on our outpatient population using new and historical RIs.

CONCLUSION

The study demonstrates notable differences in the RIs for total protein, albumin, and globulin when container type changes. In addition, the results demonstrate the effectiveness of big data analytics in deriving RIs and highlights the necessity of continuous RI assessment and adjustment based on the patient population and acceptable specimen types.

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.

Plasma lipids paediatric reference intervals: Indirect estimation using a large 14-year database

OBJECTIVES

Establishing direct reference intervals (RIs) for paediatric patients is a very challenging endeavour. Indirect RIs can address this problem, using existing clinical laboratory databases from real-world data research. Compared to the traditional direct method, the indirect approach is highly practical, widely applicable, and low-cost. Considering the relevance of dyslipidemia in the paediatric age, to provide better laboratory services to the local paediatric population, we established population-specific lipid RIs via data mining.

METHODS

Our laboratory information system was searched for cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL) and high-density lipoprotein (HDL) of patients aged less than 18 years, performed from January 2009 until December 2022. RIs were estimated using RefineR algorithm.

RESULTS

Values from 215,594 patients were initially collected. After refining data on the basis of specific exclusion criteria that left 17,933 patients, we determined the RIs for each analyte, including corresponding 95% confidence interval (95% CI). Age and sex partitions were required for proper stratification of the heterogenous subpopulations. Age-related variations in TC and TG values were observed mainly in children until 5 years. RIs were defined for children less than 3 years and for those of 3-18 years. In our population, the obtained RIs were comparable with those of the literature, but the upper TG limit in subjects under the age of 3 (2.03 mmol/L with 95% CI: 1.45-2.86) was lower than that previously reported.

CONCLUSIONS

Our RIs, necessary for paediatric lipid monitoring, are tailored to the serviced patient population as should be done whenever possible.

A Novel Tool for the Rapid and Transparent Verification of Reference Intervals in Clinical Laboratories

Background/Objectives: We present a software package called reflimR (Version 1.0.6), which enables rapid and transparent verification of reference intervals from routine laboratory measurements. Our method makes it easy to compare the results with specified target values and facilitates the interpretation of deviations using traffic light colors. Methods: The algorithm includes three procedural steps: (a) definition of an appropriate distribution model, based on Bowley's quartile skewness, (b) iterative truncation, based on a modified boxplot method to obtain the central 95% of presumably inconspicuous results, and (c) extrapolation of reference limits from a truncated normal quantile-quantile plot. Results: All algorithms have been combined into one consolidated library, which can be called in the R environment with a single command reflim (x). Using an example dataset included in the package, we demonstrate that our method can be applied to mixed data containing a substantial proportion of pathological values. It leads to similar results as the direct guideline approach as well as the more sophisticated indirect refineR software package. As compared to the latter, reflimR works much faster and needs smaller datasets for robust estimates. For the interpretation of the results, we present an intuitive color scheme based on tolerance ranges (permissible uncertainty of laboratory results). We show that a relatively high number of published reference limits require careful reevaluation. Conclusions: The reflimR package closes the gap between direct guideline methods and the more sophisticated indirect refineR method. We recommend reflimR for the rapid routine verification of large amounts of reference limits and refineR for a careful analysis of unclear or doubtful results from this check.

Calculation of reference intervals for the concentrations of α-tocopherol and retinol in serum using indirect data-mining procedures

BACKGROUND

Establishing adequate reference intervals (RIs) for vitamins A and E is essential for diagnosing and preventing deficiencies. Due to the current boom in data mining and its easy applicability, more laboratories are establishing RIs using indirect methods. Our study aims to obtain RIs using four indirect data-mining procedures (Bhattacharya, Hoffmann, Kosmic, and RefineR) for vitamins A and E.

MATERIAL AND METHODS

8943 individuals were collected to establish the RIs. After using different data cleaning steps and checking whether these data should be divided according to age and gender based on multiple linear regression and variance component analyses, indirect RIs were calculated using specific Excel spreadsheets or R-packages software.

RESULTS

A total of 2004 records were eligible. For vitamin A, the RIs obtained were (1.11 - 2.68) µmol/L, (1.13 - 2.70) µmol/L, (1.13 - 2.71) µmol/L, and (1.17 - 2.66) µmol/L using the Bhattacharya, Hoffmann, Kosmic and RefineR approaches, respectively. For vitamin E, these intervals were (17.3 - 49.9) µmol/L (Bhattacharya), (17.3 - 48.9) µmol/L (Hoffmann), (19.6 - 50.3) µmol/L (Kosmic), and (19.4 - 50.9) µmol/L (RefineR). In all cases, the RIs were comparable.

CONCLUSIONS

Suitable RIs for vitamins A and E were calculated using four indirect methods that are suitable and adapted to our population's demographic characteristics.

Calculation of a Reference Interval for Rectal Temperature in Adult Dogs Presenting for Veterinary Care Using an Algorithm for Mixed Data

Veterinarians rely on the measurement of canine body temperature to define the health status of dogs, but no studies exist defining a reference range for rectal temperature on a large group of dogs. The aim of this study was to define the rectal body temperature of dogs based on a large data set of diseased and healthy animals and to evaluate the capability of the employed algorithm to calculate reference intervals of numerical clinical data. Out of 24,013 recorded measurements, statistical analysis was applied to data from 9782 adult dogs that underwent clinical examination at a university clinic between 2008 and 2017. The reference interval was calculated using an algorithm developed by the Deutsche Gesellschaft für Klinische Chemie und Laboratoriumsmedizin e.V. as part of its Reference Limit Estimator software (version 1.40.36.07). The following values were excluded: multiple measurements in a given dog, samples without assigned age or dogs younger than one year, and values <30.0 °C and >43.0 °C. Out of 9782 adult dogs, 665 temperature measurements were identified as outliers, and 9117 were used for further statistical analysis. The mean rectal temperature was 38.6 °C (90% CI: 38.6-38.6 °C) with a reference interval of 37.7 °C (90% CI: 37.7-37.7 °C) to 39.5 °C (90% CI: 39.5-39.5 °C). Validation according to CLSI guidelines showed the results to be valid. The determination of a reference interval for rectal temperatures in dogs using an algorithm for mixed datasets yielded results comparable to the existing reference intervals. This demonstrates that the calculation of reference intervals from mixed datasets of clinical numerical data can be used to confirm existing reference intervals or establish such de novo.

The origin of ferritin reference intervals: a systematic review

Iron deficiency is a highly prevalent condition, which contributes to unnecessary morbidity, mortality, and health inequity. A serum ferritin concentration of less than 30 μg/L has a high specificity and sensitivity for diagnosing iron deficiency in adults, but the laboratory reported lower limit of normal (LLN) is typically lower. These LLNs might not be rooted in rigorous scientific evidence and might be contributing to structural underdiagnosis of iron deficiency. A systematic review was done per systematic reviews and meta-analysis guidelines with the use of medical literature databases from inception of each database to Nov 30, 2021, to identify studies that determined ferritin reference intervals in healthy adults and grey literature search for the five most common ferritin assays (registration number CRD42022268844). The objectives were to systematically summarise the ferritin reference intervals and to do a methodological quality assessment of the included studies. 2306 studies were screened and 61 full texts were included. 37 studies were eligible for analysis of the ferritin LLN in the general population. The population the sample was comprised of was a total of 21 882 females and 23 650 males participants. The ferritin LLN was a median of 8 μg/L (IQR 5-15) and mean of 9 μg/L (SD 11) in females and a median of 25 μg/L (IQR 16-44) and mean of 25 μg/L (SD 29) in males. 30 (49%) of 61 studies did not explicitly screen for patients at risk of iron deficiency, and 32 (52%) did not refer to a reference interval establishment guideline (eg, guideline recommended by Clinical and Laboratory Standards Institute). The five most used commercial ferritin laboratory assays reported reference intervals with a median LLN of 11 (IQR 9-12) and mean of 9 μg/L (SD 4) for females and median of 22 (IQR 22-24) and mean of 23 μg/L (SD 4) for males. In the literature, serum ferritin reference intervals in healthy adults consistently report a LLN of less than 30 μg/L. Data driving these ferritin reference intervals are at high risk of bias, given no exclusion of individuals at risk for iron deficiency in the presumed normal population sample and no adherence to reference interval establishment standards. We suggest the use of evidence-based laboratory clinical decision limits to diagnose iron deficiency.

Lipid levels in a cohort of healthy Danish schoolchildren ages 5 to 17 years

It is internationally recognized to use clinical decision limits (CDL) when interpreting the lipid levels in both adults and children, even though the evidence for children is scarce. The purpose of this study is to describe how lipid levels progress in healthy Danish children ages 5 to 17 years. This study is based on the Childhood Health, Activity, and Motor Performance School Study Denmark (CHAMPS-study DK) consisting of 1456 observations of schoolchildren aged 5 to 17 years. Participants have been tested for blood levels of total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, and remnant cholesterol levels are calculated. Finally, sex-specific percentile reference curves are presented. Percentile reference curves stratified by sex were generated for all cholesterols and showed that the total cholesterol level peaks at 4.32 mmol/l in 10-year-old boys and 4.46 mmol/l in nine-year-old girls. HDL levels in boys peak at 1.72 mmol/l in nine-year-old boys. HDL levels in girls and LDL levels in both sexes are nearly constant. Triglycerides kept rising to the age of 17 years in both sexes and remnant cholesterol decreased from age 5 to 17 years in both sexes. BMI z-score adjustment revealed no significant association with total cholesterol in both sexes but a significant association between HDL, LDL, triglycerides, and remnant cholesterol. This study is the first to generate percentile reference curves for blood levels of total cholesterol, LDL, HDL, triglycerides, and remnant cholesterol in a cohort of healthy Danish children aged 5 to 17 years.

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 preliminary study on the reference intervals of serum tumor marker in apparently healthy elderly population in southwestern China using real-world data

BACKGROUND

The aim is to establish and verify reference intervals (RIs) for serum tumor markers for an apparently healthy elderly population in Southwestern China using an indirect method.

METHODS

Data from 35,635 apparently healthy elderly individuals aged 60 years and above were obtained in West China Hospital from April 2020 to December 2021. We utilized the Box-Cox conversion combined with the Tukey method to normalize the data and eliminate outliers. Subgroups are divided according to gender and age to examine the division of RIs. The Z-test was used to compare differences between groups, and 95% distribution RIs were calculated using a nonparametric method.

RESULTS

In the study, we observed that the RIs for serum ferritin and Des-γ-carboxy prothrombin (DCP) were wider for men, ranging from 64.18 to 865.80 ng/ml and 14.00 to 33.00 mAU/ml, respectively, compared to women, whose ranges were 52.58 to 585.88 ng/ml and 13.00 to 29.00 mAU/ml. For other biomarkers, the overall RIs were established as follows: alpha-fetoprotein (AFP) 0-6.75 ng/ml, carcinoembryonic antigen (CEA) 0-4.85 ng/ml, carbohydrate antigen15-3 (CA15-3) for females 0-22.00 U/ml, carbohydrate antigen19-9 (CA19-9) 0-28.10 U/ml, carbohydrate antigen125 (CA125) 0-20.96 U/ml, cytokeratin 19 fragment (CYFRA21-1) 0-4.66 U/ml, neuron-specific enolase (NSE) 0-19.41 ng/ml, total and free prostate-specific antigens (tPSA and fPSA) for males 0-5.26 ng/ml and 0-1.09 ng/ml. The RIs for all these biomarkers have been validated through our rigorous processes.

CONCLUSION

This study preliminarily established 95% RIs for an apparently healthy elderly population in Southwestern China. Using real-world data and an indirect method, simple and reliable RIs for an elderly population can be both established and verified, which are suitable for application in various clinical laboratories.

Age- and sex-specific 99th percentile upper reference limits for high-sensitivity cardiac troponin T in Chinese older people: Real-world data mining

BACKGROUND

This study aims to investigate the impact of age and sex on high-sensitivity cardiac troponin T (hs-cTnT) and establish 99th percentile upper reference limits (URLs) in older individuals utilizing large-scale real-world data.

METHODS

40,530 outpatient hs-cTnT results were obtained from the laboratory database from January 1, 2018, to December 31, 2023. Our study included 4,199 elderly outpatients (aged ≥ 60) without cardiovascular disease or other heart-related chronic conditions. Nested analysis of variance was used to explore the necessity of partitioning reference intervals (RIs) by sex and age groups. RIs were established by the refineR algorithm and assessed based on ≤ 10% test results of validation data set outside the new RIs.

RESULTS

RIs for hs-cTnT in the older population needed to be partitioned by sex and age groups ([standard deviation ratio] SDRage = 0.75; SDRsex = 0.49). URLs in older Chinese adults were 21.8 ng/L for males, 16.5 ng/L for females, and 20.7 ng/L for the overall participant group. URLs for males aged 60-69, 70-79, and ≥ 80 were 13.7, 19.4, and 31.0 ng/L, respectively. Female values were 10.1, 17.2, and 22.0 ng/L. Importantly, manufacturer-reported RIs do not suffice for Chinese individuals aged ≥ 70. Validation data showed that 2.7-5.2% of test results fell outside the new RIs, confirming the validity of the results.

CONCLUSION

This study establishes age- and sex-specific 99th percentile URLs for hs-cTnT in Chinese older individuals, thereby enhancing the accuracy of clinical assessments.

Harnessing historical data to derive reference limits - A comparison of e-norms to traditionally derived reference limits

Objective

Nerve conduction studies (NCS) require valid reference limits for meaningful interpretation. We aimed to further develop the extrapolated norms (e-norms) method for obtaining NCS reference limits from historical laboratory datasets for children and adults, and to validate it against traditionally derived reference limits.

Methods

We compared reference limits obtained by applying a further developed e-norms with reference limits from healthy controls for the age strata's 9-18, 20-44 and 45-60 years old. The control data consisted of 65 healthy children and 578 healthy adults, matched with 1294 and 5628 patients respectively. Five commonly investigated nerves were chosen: The tibial and peroneal motor nerves (amplitudes, conduction velocities, F-waves), and the sural, superficial peroneal and medial plantar sensory nerves (amplitudes, conduction velocities). The datasets were matched by hospital to ensure identical equipment and protocols. The e-norms method was adapted, and reference limit calculation using both ±2 SD (original method) and ±2.5 SD (to compensate for predicted underestimation of population SD by the e-norms method) was compared to control data using ±2 SD. Percentage agreement between e-norms and the traditional method was calculated.

Results

On average, the e-norms method (mean ±2 SD) produced slightly stricter reference limits compared to the traditional method. Increasing the e-norms range to mean ±2.5 SD improved the results in children while slightly overcorrecting in the adult group. The average agreement between the two methods was 95 % (±2 SD) and 96 % (±2.5 SD).

Conclusions

The e-norms method yielded slightly stricter reference limits overall than ones obtained through traditional methods; However, much of the difference can be attributed to a few outlying plots where the raters found it difficult to apply e-norms correctly. The two methods disagreed on classification of 4-5% of cases. Our e-norms software is suited to analyze large amounts of raw NCS data; it should further reduce bias and facilitate more accurate ratings.

Significance

With small adaptations, the e-norms method adequately replicates traditionally derived reference limits, and is a viable method to produce reference limits from historical datasets.

Establishing age and gender-specific serum creatinine reference ranges for Thai pediatric population

Accurate assessment of kidney function in children requires age and gender-specific reference ranges for serum creatinine. Traditional reference values, often derived from adult populations and different ethnic backgrounds, may not be suitable for children. This study aims to establish specific reference ranges for serum creatinine in the Thai pediatric population, addressing the gap in localized and age-appropriate diagnostic criteria. This retrospective study analyzed serum creatinine levels from Thai children aged newborn to 18 years, collected from the Laboratory Information System of the Queen Sirikit National Institute of Child Health from January 2017 to December 2021. The Bhattacharya method was employed to establish reference ranges, considering different age groups and genders. The study compared these newly established reference values with international studies, including those of Schlebusch H., Pottel H., and Chuang GT., to validate their relevance and accuracy. A total of 27,642 data entries (15,396 males and 12,246 females) were analyzed. The study established distinct reference ranges for serum creatinine, which varied significantly across different age groups and between genders. These ranges were found to gradually increase with age from 2 months to 18 years. The study also highlighted notable differences in reference values when compared with other ethnic populations. The study successfully establishes tailored reference ranges for serum creatinine in Thai children, providing a valuable tool for more accurate diagnosis and monitoring of kidney health in this demographic. This initiative marks a significant advancement in pediatric nephrology in Thailand and suggests a need for continuous refinement of these ranges and further research in this area.

Feasibility of using real-world free thyroxine data from the US and Europe to enable fast and efficient transfer of reference intervals from one population to another

Objectives

The direct approach for determining reference intervals (RIs) is not always practical. This study aimed to generate evidence that a real-world data (RWD) approach could be applied to transfer free thyroxine RIs determined in one population to a second population, presenting an alternative to performing multiple RI determinations.

Design and methods

Two datasets (US, n = 10,000; Europe, n = 10,000) were created from existing RWD. Descriptive statistics, density plots and cumulative distributions were produced for each data set and comparisons made. Cumulative probabilities at the lower and upper limits of the RIs were identified using an empirical cumulative distribution function. According to these probabilities, estimated percentiles for each dataset and estimated differences between the two sets of percentiles were obtained by case resampling bootstrapping. The estimated differences were then evaluated against a pre-determined acceptance criterion of ≤7.8% (inter-individual biological variability). The direct approach was used to validate the RWD approach.

Results

The RWD approach provided similar descriptive statistics for both populations (mean: US = 16.1 pmol/L, Europe = 16.4 pmol/L; median: US = 15.4 pmol/L, Europe = 15.8 pmol/L). Differences between the estimated percentiles at the upper and lower limits of the RIs fulfilled the pre-determined acceptance criterion and the density plots and cumulative distributions demonstrated population homogeneity. Similar RI distributions were observed using the direct approach.

Conclusions

This study provides evidence that a RWD approach can be used to transfer RIs determined in one population to another.

Indirect estimation of pediatric reference interval via density graph deep embedded clustering

Establishing reference intervals (RIs) for pediatric patients is crucial in clinical decision-making, and there is a critical gap of pediatric RIs in China. However, the direct sampling technique for establishing RIs is resource-intensive and ethically challenging. Indirect estimation methods, such as unsupervised clustering algorithms, have emerged as potential alternatives for predicting reference intervals. This study introduces deep graph clustering methods into indirect estimation of pediatric reference intervals. Specifically, we propose a Density Graph Deep Embedded Clustering (DGDEC) algorithm, which incorporates a density feature extractor to enhance sample representation and provides additional perspectives for distinguishing different levels of health status among populations. Additionally, we construct an adjacency matrix by computing the similarity between samples after feature enhancement. The DGDEC algorithm leverages the adjacency matrix to capture the interrelationships between patients and divides patients into different groups, thereby estimating reference intervals for the potential healthy population. The experimental results demonstrate that when compared to other indirect estimation techniques, our method ensures the predicted pediatric reference intervals in different age and gender groups are closer to the true values while maintaining good generalization performance. Additionally, through ablation experiments, our study confirms that the similarity between patients and the multi-scale density features of samples can effectively describe the potential health status of patients.

Reference intervals of biochemical parameters in Chilean adults

Background

Establishing reference intervals (RIs) in clinical laboratories is essential, as these can vary due to inter-individual variability as well as the analytical methods used. The purpose of this study was to determine RIs for markers and ratios biochemical in apparently healthy Chilean adults.

Methods

A sample of 1,143 data was selected from the Universidad Católica de Temuco, Clinical Laboratory database, La Araucanía Region, Chile, which were analysed by sex. The Tukey's Fences was used to detect outliers and the RIs were established using the non-parametric method.

Age-specific Reference Intervals of Abbott Intact PTH-Potential Impacts on Clinical Care

Background

PTH assays are not standardized; therefore, method-specific PTH reference intervals are required for interpretation of results. PTH increases with age in adults but age-related reference intervals for the Abbott intact PTH (iPTH) assay are not available.

Methods

Deidentified serum PTH results from September 2015 to November 2022 were retrieved from the laboratory information system of a laboratory serving a cosmopolitan population in central-west England for individuals aged 18 years and older if the estimated glomerular filtration rate was ≥60 mL/min, serum 25-hydroxyvitamin D was >50 nmol/L, and serum albumin-adjusted calcium and serum phosphate were within reference intervals. Age-specific reference intervals for Abbott iPTH were derived by an indirect method using the refineR algorithm.

Results

PTH increased with age and correlated with age when controlled for 25-hydroxyvitamin D, estimated glomerular filtration rate, and adjusted calcium (r = 0.093, P < .001). The iPTH age-specific reference intervals for 4 age partitions of 18 to 45 years, 46 to 60 years, 61 to 80 years, and 81 to 95 years were 1.6 to 8.6 pmol/L, 1.8 to 9.5 pmol/L, 2.0 to 11.3 pmol/L, and 2.3 to 12.3 pmol/L, respectively. PTH was higher in women compared with men (P < .001). Sex-specific age-related reference intervals could not be derived because of the limited sample size.

Conclusion

Age-specific Abbott iPTH reference intervals were derived. Application of age-specific reference intervals will impact the diagnosis and management of normocalcemic hyperparathyroidism, based on current definitions, and secondary hyperparathyroidism. Additional studies are required to clarify the effect of sex and ethnicity on PTH.

Comparison of results and age-related changes in establishing reference intervals for CEA, AFP, CA125, and CA199 using four indirect methods

•The reference intervals calculated using RefineR, Kosmic, TMC, and non-parametric methods are similar.•TMC algorithm is more robust, demonstrates a high pass rate among the four methods and has the ability to automatically isolate outliers.•The reference intervals of CA125 and CA199 showed significant differences between age and sex.

A survey of total IgE reference intervals reported by Scandinavian and British medical laboratories - a need for harmonisation

OBJECTIVES

There appears to be marked discrepancies between total IgE reference intervals (RIs) in use by many laboratories and those recommended by published studies. The aim of this study was therefore to review total IgE RIs currently reported by Scandinavian and British laboratories and to compare these to published RIs identified by a literature review.

METHODS

Relevant laboratories were identified by test directories provided by the national accreditation bodies in Norway, Sweden, Denmark and the UK. Total IgE RIs and their sources were acquired by accessing laboratory user handbooks or by an electronic survey. In addition a literature review of published total IgE RI studies was performed.

RESULTS

From 172 accredited laboratories providing total IgE analysis, data was acquired from 122 laboratories. An adult upper reference limit between 81 to 150 kU/L was reported by 89% of these. Denmark and Sweden reported the most harmonised RIs whilst Norway and the UK exhibited the least degree of harmonisation. Published adult (n = 6) and paediatric (n = 6) RI studies reported markedly higher upper limits than those currently in use by the laboratories included in this study. There were also large variations in the number of age strata in use for paediatric RIs.

CONCLUSION

This study demonstrates large variations in currently utilised IgE RIs by Scandinavian and British accredited laboratories and most report markedly lower RIs than those recommended by recent RI publications. Many laboratories likely utilise outdated RIs and should consider critically reviewing and updating their RIs.

The BioRef Infrastructure, a Framework for Real-Time, Federated, Privacy-Preserving, and Personalized Reference Intervals: Design, Development, and Application

BACKGROUND

Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population.

OBJECTIVE

This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group-specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis.

METHODS

A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified "big data" resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict "no copy, no move" principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged.

RESULTS

The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group-specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly.

CONCLUSIONS

With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine.

Establishment of age- and -gender specific reference intervals for amino acids and acylcarnitines by tandem mass spectrometry in Turkish paediatric population

Introduction

We determined age- and gender-specific reference intervals (RIs) for acylcarnitines and amino acids by tandem mass spectrometry (MS/MS) in the Turkish paediatric population by using laboratory information system (LIS) data.

Materials and methods

A total of 9156 MS/MS results of children between 0-18 years of age, were downloaded from the LIS. Premature infants and newborns followed in the intensive care unit were excluded and only the first result of each patient attending outpatient clinics was included. Children with a known or suspected diagnosis of metabolic disease, malignancy, epilepsy, mental retardation, or genetic disorder were excluded. Laboratory results were evaluated and children with any pathological laboratory finding were excluded, resulting in a final sample size of 3357 (2029 boys and 1328 girls). Blood was collected by capillary puncture and spotted on Whatman 903 filter paper cards and analysed by MS/MS (Shimadzu LCMS-8050, Shimadzu Corporation, Kyoto, Japan). Data were evaluated for age and gender differences and age partitioning was performed according to the literature and visual evaluation of the data. Age subgroups were: ≤ 1 month, 2 months-1 year, 2-5 years, 6-10 years, and 11-18 years.

Results

There were significant age-related differences for the majority of amino acids and acylcarnitines thus age dependent RIs were established. Gender-specific RIs were established for tyrosine, leucine-isoleucine, isovalerylcarnitine (C5) and hexadecanoylcarnitine (C16).

Conclusions

Establishing age-related RIs can enhance the quality of medical care by facilitating early diagnosis and therapy, especially in certain metabolic disorders presenting with mild biochemical abnormalities and subtle clinical manifestations.

Exploring Utilization and Establishing Reference Intervals for the Apolipoprotein B Test in the Korean Population

We investigated the reference intervals for Apolipoprotein B (ApoB), a valuable biomarker for cardiovascular diseases, in Korean adults who had undergone health check-ups and showed normal lipid levels under traditional lipid tests, including total cholesterol, triglyceride, and high-density lipoprotein cholesterol, along with ApoB. We compared the findings with different cutoffs for ApoB from international clinical guidelines. Among a total of 264,105 traditional lipid test sets, only 464 (0.2%) included ApoB tests, indicating underutilization of this test in health check-up clinics in Korea. From these 464 samples, 334 ApoB results (164 men and 170 women) with normal traditional lipid test results were used to establish reference intervals. Using the parametric method (mean ± 2 SD), the reference intervals ranged from 46 to 134 mg/dL for men and 49 to 129 mg/dL for women. Employing the non-parametric method (central 95th percentile value), the reference intervals ranged from 50 to 131 mg/dL for men and 51 to 127 mg/dL for women. The prevalence of high ApoB did not significantly differ by sex when considering the established reference intervals for each sex and the cutoffs recommended by international clinical guidelines. This study enhances knowledge on ApoB reference intervals in the Korean population, and it will in aid test result interpretation for clinicians and laboratories.

Unsupervised machine learning method for indirect estimation of reference intervals for chronic kidney disease in the Puerto Rican population

Reference intervals (RIs) for clinical laboratory values are extremely important for diagnostics and treatment of patients. However, the determination of these ranges is costly and time-consuming. As a result, often different unverified RIs are used in practice for the same analyte and the same range is used for all patients despite evidence that the values are gender, age, and ethnicity dependent. Moreover, the abnormal flags are rudimentary, merely indicating if a value is within the RI. At the same time, clinical lab data generated in the everyday medical practice contains a wealth of information, that given the correct methodology, can help determine the RIs for each specific segment of the population, including populations that suffer from health disparities. In this work, we develop unsupervised machine learning methods, based on Gaussian mixtures, to determine RIs of analytes related to chronic kidney disease, using millions of routine lab results for the Puerto Rican population. We show that the measures are both gender and age dependent and we find evidence for normal age-related organ function deterioration and failure. We also show that the joint distribution of measures improves the diagnostic value of the lab results.

Deciphering reference intervals and clinical decision limits in equine endocrine diagnostic testing

Reference intervals (RIs) and clinical decision limits (CDLs) are frequently established to facilitate interpretation of values of endocrine biomarkers in the diagnosis of disease. Despite their commonplace use in clinical decision-making, these concepts can be confused. Comparing a test result with a RI provides an estimation as to whether or not the individual is healthy, whereas comparison with a CDL facilitates identification of individuals with a particular disease state or at greater risk of adverse clinical outcomes. In practice, there will also be a range of results for which the discriminative ability of the test is insufficient to inform a specific diagnostic decision. Including a range of uncertain test results, or 'grey zone', between positive and negative avoids the constraint of a binary decision in classifying an individual with a test value above (or below) a single cut-off value as diseased. This review will detail the application of both RIs and CDLs, including defining the range of uncertain test results, in the context of equine endocrinology.

Establishment of a reference interval for total carbon dioxide using indirect methods in Chinese populations living in high-altitude areas: A retrospective real-world analysis

BACKGROUND

Hypoxia leads to different concentrations of the bicarbonate buffer system in Tibetan people. Indirect methods were used to establish the reference interval (RI) for total carbon dioxide (tCO2) based on big data from the adult population of Tibet, a high-altitude area in Western China.

METHODS

Anonymous tCO2 test data (n = 442,714) were collected from the People's Hospital of the Tibet Autonomous Region from January 2018, to December 2021. Multiple linear regression and variance component analyses were performed to assess the effects of sex, age, and race on tCO2 levels. Indirect methods, including Hoffmann, Bhattacharya, expectation maximization (EM), kosmic and refineR, were used to calculate the total RI and ethnicity-partitioned RI.

RESULTS

A total of 230,821 real-world tCO2 test results were eligible. Sex, age, and race were significantly associated with the tCO2 levels. The total and ethnically-partitioned RIs estimated using the five indirect methods were comparable. The total RI of tCO2 was 14-24 mmol/L (calculated using Hoffmann and refineR) and 15-24 mmol/L (Bhattacharya, EM and kosmic). For Han nationality, the RIs were 14-25 mmol/L (calculated using Hoffmann and Bhattacharya), 16-23 mmol/L (EM), 15-24 mmol/L (kosmic), and 14.2-24.5 mmol/L (refineR). For the Tibetan population, the RIs were 14-24 mmol/L (calculated using Hoffmann and refineR), 15-24 mmol/L (Bhattacharya and kosmic), and 15-23 mmol/L (EM). The established RIs were significantly lower than those living at lower altitudes area (22-29 mmol/L) that was provided by the manufacturer.

CONCLUSION

The tCO2 RI of the populations living on the Tibetan Plateau was significantly lower than those at the lower altitudes. The RIs established using indirect methods are suitable for clinical applications in Tibet.

Reference intervals: past, present, and future

Clinical laboratory test results alone are of little value in diagnosing, treating, and monitoring health conditions; as such, a clinically actionable cutoff or reference interval is required to provide context for result interpretation. Healthcare practitioners base their diagnoses, follow-up treatments, and subsequent testing on these reference points. However, they may not be aware of inherent limitations related to the definition and derivation of reference intervals. Laboratorians are responsible for providing the reference intervals they report with results. Yet, the establishment and verification of reference intervals using conventional direct methods are complicated by resource constraints or unique patient demographics. To facilitate standardized reference interval best practices, multiple global scientific societies are actively drafting guidelines and seeking funding to promote these initiatives. Numerous national and international multicenter collaborations demonstrate the ability to leverage combined resources to conduct large reference interval studies by direct methods. However, not all demographics are equally accessible. Novel indirect methods are attractive solutions that utilize computational methods to define reference distributions and reference intervals from mixed data sets of pathologic and non-pathologic patient test results. In an effort to make reference intervals more accurate and personalized, individual-based reference intervals are shown to be more useful than population-based reference intervals in detecting clinically significant analyte changes in a patient that might otherwise go unrecognized when using wider, population-based reference intervals. Additionally, continuous reference intervals can provide more accurate ranges as compared to age-based partitions for individuals that are near the ends of the age partition. The advantages and disadvantages of different reference interval approaches as well as the advancement of non-conventional reference interval studies are discussed in this review.

Current status and challenges in establishing reference intervals based on real-world data

Reference intervals (RIs) are the cornerstone for evaluation of test results in clinical practice and are invaluable in judging patient health and making clinical decisions. Establishing RIs based on clinical laboratory data is a branch of real-world data mining research. Compared to the traditional direct method, this indirect approach is highly practical, widely applicable, and low-cost. Improving the accuracy of RIs requires not only the collection of sufficient data and the use of correct statistical methods, but also proper stratification of heterogeneous subpopulations. This includes the establishment of age-specific RIs and taking into account other characteristics of reference individuals. Although there are many studies on establishing RIs by indirect methods, it is still very difficult for laboratories to select appropriate statistical methods due to the lack of formal guidelines. This review describes the application of real-world data and an approach for establishing indirect reference intervals (iRIs). We summarize the processes for establishing iRIs using real-world data and analyze the principle and applicable scope of the indirect method model in detail. Moreover, we compare different methods for constructing growth curves to establish age-specific RIs, in hopes of providing laboratories with a reference for establishing specific iRIs and giving new insight into clinical laboratory RI research. (201 words).

Personalized and Population-Based Reference Intervals for 48 Common Clinical Chemistry and Hematology Measurands: A Comparative Study

BACKGROUND

Personalized reference intervals (prRIs) have the potential to improve individual patient follow-up as compared to population-based reference intervals (popRI). In this study, we estimated popRI and prRIs for 48 clinical chemistry and hematology measurands using samples from the same reference individuals and explored the effect of using group-based and individually based biological variation (BV) estimates to derive prRIs.

METHODS

143 individuals (median age 28 years) were included in the study and had fasting blood samples collected once. From this population, 41 randomly selected subjects had samples collected weekly for 5 weeks. PopRIs were estimated according to Clinical Laboratory Standards Institute EP28 and within-subject BV (CVI) were estimated by CV-ANOVA. Data were assessed for trends and outliers prior to calculation of individual prRIs, based on estimates of (a) within-person BV (CVP), (b) CVI derived in this study, and (c) publically available CVI estimates.

RESULTS

For most measurands, the individual prRI ranges were smaller than the popRI range, but overall about half the study participants had a prRI wider than the popRI for 5 or more out of 48 measurands. The dispersion of prRIs based on CVP was wider than that of prRIs based on CVI.

CONCLUSION

The prRIs derived in our study varied significantly between different individuals, especially if based on CVP. Our results highlight the limitations of popRIs in interpreting test results of individual patients. If sufficient data from a steady-state situation are available, using prRI based on CVP estimates will provide a RI most specific for an individual patient.

Reference Interval Harmonization: Harnessing the Power of Big Data Analytics to Derive Common Reference Intervals across Populations and Testing Platforms

BACKGROUND

Harmonization in laboratory medicine is essential for consistent and accurate clinical decision-making. There is significant and unwarranted variation in reference intervals (RIs) used by laboratories for assays with established analytical traceability. The Canadian Society of Clinical Chemists (CSCC) Working Group on Reference Interval Harmonization (hRI-WG) aims to establish harmonized RIs (hRIs) for laboratory tests and support implementation.

METHODS

Harnessing the power of big data, laboratory results were collected across populations and testing platforms to derive common adult RIs for 16 biochemical markers. A novel comprehensive approach was established, including: (a) analysis of big data from community laboratories across Canada; (b) statistical evaluation of age, sex, and analytical differences; (c) derivation of hRIs using the refineR method; and (d) verification of proposed hRIs across 9 laboratories with different instrumentation using serum and plasma samples collected from healthy Canadian adults.

RESULTS

Harmonized RIs were calculated for all assays using the refineR method, except free thyroxine. Derived hRIs met proposed verification criterion across 9 laboratories and 5 manufacturers for alkaline phosphatase, albumin (bromocresol green), chloride, lactate dehydrogenase, magnesium, phosphate, potassium (serum), and total protein (serum). Further investigation is needed for some analytes due to failure to meet verification criteria in one or more laboratories (albumin [bromocresol purple], calcium, total carbon dioxide, total bilirubin, and sodium) or concern regarding excessively wide hRIs (alanine aminotransferase, creatinine, and thyroid stimulating hormone).

CONCLUSIONS

We report a novel data-driven approach for RI harmonization. Findings support feasibility of RI harmonization for several analytes; however, some presented challenges, highlighting limitations that need to be considered in harmonization and big data analytics.

A pipeline for the fully automated estimation of continuous reference intervals using real-world data

Reference intervals are essential for interpreting laboratory test results. Continuous reference intervals precisely capture physiological age-specific dynamics that occur throughout life, and thus have the potential to improve clinical decision-making. However, established approaches for estimating continuous reference intervals require samples from healthy individuals, and are therefore substantially restricted. Indirect methods operating on routine measurements enable the estimation of one-dimensional reference intervals, however, no automated approach exists that integrates the dependency on a continuous covariate like age. We propose an integrated pipeline for the fully automated estimation of continuous reference intervals expressed as a generalized additive model for location, scale and shape based on discrete model estimates using an indirect method (refineR). The results are free of subjective user-input, enable conversion of test results into z-scores and can be integrated into laboratory information systems. Comparison of our results to established and validated reference intervals from the CALIPER and PEDREF studies and manufacturers' package inserts shows good agreement of reference limits, indicating that the proposed pipeline generates high-quality results. In conclusion, the developed pipeline enables the generation of high-precision percentile charts and continuous reference intervals. It represents the first parameter-less and fully automated solution for the indirect estimation of continuous reference intervals.

Effects of Using Different Indirect Techniques on the Calculation of Reference Intervals: Observational Study

BACKGROUND

Reference intervals (RIs) play an important role in clinical decision-making. However, due to the time, labor, and financial costs involved in establishing RIs using direct means, the use of indirect methods, based on big data previously obtained from clinical laboratories, is getting increasing attention. Different indirect techniques combined with different data transformation methods and outlier removal might cause differences in the calculation of RIs. However, there are few systematic evaluations of this.

OBJECTIVE

This study used data derived from direct methods as reference standards and evaluated the accuracy of combinations of different data transformation, outlier removal, and indirect techniques in establishing complete blood count (CBC) RIs for large-scale data.

METHODS

The CBC data of populations aged ≥18 years undergoing physical examination from January 2010 to December 2011 were retrieved from the First Affiliated Hospital of China Medical University in northern China. After exclusion of repeated individuals, we performed parametric, nonparametric, Hoffmann, Bhattacharya, and truncation points and Kolmogorov-Smirnov distance (kosmic) indirect methods, combined with log or BoxCox transformation, and Reed-Dixon, Tukey, and iterative mean (3SD) outlier removal methods in order to derive the RIs of 8 CBC parameters and compared the results with those directly and previously established. Furthermore, bias ratios (BRs) were calculated to assess which combination of indirect technique, data transformation pattern, and outlier removal method is preferrable.

RESULTS

Raw data showed that the degrees of skewness of the white blood cell (WBC) count, platelet (PLT) count, mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and mean corpuscular volume (MCV) were much more obvious than those of other CBC parameters. After log or BoxCox transformation combined with Tukey or iterative mean (3SD) processing, the distribution types of these data were close to Gaussian distribution. Tukey-based outlier removal yielded the maximum number of outliers. The lower-limit bias of WBC (male), PLT (male), hemoglobin (HGB; male), MCH (male/female), and MCV (female) was greater than that of the corresponding upper limit for more than half of 30 indirect methods. Computational indirect choices of CBC parameters for males and females were inconsistent. The RIs of MCHC established by the direct method for females were narrow. For this, the kosmic method was markedly superior, which contrasted with the RI calculation of CBC parameters with high |BR| qualification rates for males. Among the top 10 methodologies for the WBC count, PLT count, HGB, MCV, and MCHC with a high-BR qualification rate among males, the Bhattacharya, Hoffmann, and parametric methods were superior to the other 2 indirect methods.

CONCLUSIONS

Compared to results derived by the direct method, outlier removal methods and indirect techniques markedly influence the final RIs, whereas data transformation has negligible effects, except for obviously skewed data. Specifically, the outlier removal efficiency of Tukey and iterative mean (3SD) methods is almost equivalent. Furthermore, the choice of indirect techniques depends more on the characteristics of the studied analyte itself. This study provides scientific evidence for clinical laboratories to use their previous data sets to establish RIs.

Androgens in women: Establishing reference intervals for dehydroepiandrostenedione sulphate and androstenedione on the Roche Cobas

Introduction

Immunoassays are the most common method in routine practice for measuring androgens in women. Study's aim was to establish new population specific indirect reference intervals (RI) for dehydroepiandrostenedione sulphate (DHEAS) and for new androstenedione test available on automated Roche Cobas electrochemiluminescent immunoassay method.

Materials and methods

From extracted laboratory records, testosterone, sex hormone binding globulin and follicle-stimulating hormone were used as reference tests to exclude possibly diseased women. After the data selection steps, the study included 3500 subjects for DHEAS and 520 for androstenedione aged 20-45 years. To evaluate the need for age partitioning, we calculated standard deviation ratio and bias ratio. For each hormone, 90% and 95% RIs were calculated with appropriate statistical method.

Results

Total age group (20-45 years) 95% RIs were: 2.77-11.50 µmol/L for DHEAS and 2.48-8.89 nmol/L for androstenedione. Age-stratified 95% RIs for DHEAS were: 3.65-12.76 µmol/L (20-25 years); 2.97-11.50 µmol/L (25-35 years) and 2.30-9.83 µmol/L (35-45 years). Age-stratified 95% RIs for androstenedione were: 3.02-9.43 nmol/L (20-30 years) and 2.23-7.75 nmol/L (30-45 years).

Conclusion

New RIs for DHEAS were slightly wider for age group 20-25 and 35-45, while the differences in the age group 25-35 years were more pronounced. Androstenedione RI showed significantly higher concentrations than the manufacturer's. Age-related decrease of androgens should be considered when calculating RIs. We propose population specific, age-stratified RIs for DHEAS and androstenedione on electrochemiluminescent method, which should improve test interpretation in women of reproductive age.

Neonatal reference intervals for thyroid stimulating hormone and free thyroxine assayed on a Siemens Atellica® IM analyzer: a cross sectional study

BACKGROUND

Deriving population specific reference intervals (RIs) or at the very least verifying any RI before adoption is good laboratory practice. Siemens has provided RIs for thyroid stimulating hormone (TSH) and free thyroxine (FT4) determined on their Atellica® IM analyzer for all age groups except the neonatal age group which provides a challenge for laboratories that intend to use it to screen for congenital hypothyroidism (CH) and other thyroid disorders in neonates. We set out to determine RIs for TSH and FT4 using data obtained from neonates undergoing routine screening for CH at the Aga Khan University Hospital, Nairobi, Kenya.

METHODOLOGY

TSH and FT4 data for neonates aged 30 days and below were extracted from the hospital management information system for the period March 2020 to June 2021. A single episode of testing for the same neonate was included provided both TSH and FT4 were done on the same sample. RI determination was performed using a non-parametric approach.

RESULTS

A total of 1243 testing episodes from 1218 neonates had both TSH and FT4 results. A single set of test results from each neonate was used to derive RIs. Both TSH and FT4 declined with increase in age with a more marked decline seen in the first 7 days of life. There was a positive correlation between logFT4 and logTSH (r_s (1216) = 0.189, p = < 0.001). We derived TSH RIs for the age groups 2-4 days (0.403-7.942 µIU/mL) and 5-7 days (0.418-6.319 µIU/mL), and sex specific RIs for males (0.609-7.557 µIU/mL) and females (0.420-6.189 µIU/mL) aged 8-30 days. For FT4, separate RIs were derived for the age groups 2-4 days (1.19-2.59 ng/dL), 5-7 days (1.21-2.29 ng/dL) and 8-30 days (1.02-2.01 ng/dL).

CONCLUSION

Our neonatal RIs for TSH and FT4 are different from those published or recommended by Siemens. The RIs will serve as a guide for the interpretation of thyroid function tests in neonates from sub-Saharan Africa where routine screening for congenital hypothyroidism using serum samples is done on the Siemens Atellica® IM analyzer.

Pediatric Reference Value Profiling of Essential Trace and Toxic Elements in Healthy Children and Adolescents Using High-Resolution and Triple Quadrupole Inductively Coupled Plasma Mass Spectrometry

BACKGROUND

Assessment of trace and toxic element status is important for the diagnosis and monitoring of several pediatric conditions. Elemental deficiency and toxicity have serious implications, particularly in pediatrics wherein risk is higher. Pediatric reference intervals (RIs) for trace elements and normal exposure limits for toxic elements are lacking on modern analytical systems. Herein, reference values were established for 13 plasma and 22 whole blood trace elements in the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER) cohort of healthy children and adolescents.

METHODS

Approximately 320 healthy children and adolescents were recruited with informed consent. Trace elements were measured in whole blood and plasma samples using 2 technologies: (a) triple quadrupole inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) (n = 172) and (b) high-resolution sector field ICPMS (HR-SF-ICPMS) (n =161). RIs and normal exposure limits were then established according to Clinical and Laboratory Standards Institute guidelines.

RESULTS

Of all elements assessed, none required sex partitioning and 8 required age partitioning (e.g., copper, manganese, and cadmium). Reference value distributions determined via ICP-MS/MS and HR-SF-ICPMS demonstrated excellent concordance, with few exceptions (e.g., molybdenum, cobalt, and nickel).

CONCLUSIONS

These data represent the first study wherein pediatric RIs and normal exposure limits were derived simultaneously on 2 different clinically validated MS platforms which provide urgently needed data to inform clinical decision-making for trace elements in pediatrics. Study findings suggest some trace elements require age-specific consideration for appropriate interpretation. Highly concordant observations across the 2 analytical methods also demonstrate the comparability and reliability of results obtained on both platforms.

Utilization of five data mining algorithms combined with simplified preprocessing to establish reference intervals of thyroid-related hormones for non-elderly adults

BACKGROUND

Despite the extensive research on data mining algorithms, there is still a lack of a standard protocol to evaluate the performance of the existing algorithms. Therefore, the study aims to provide a novel procedure that combines data mining algorithms and simplified preprocessing to establish reference intervals (RIs), with the performance of five algorithms assessed objectively as well.

METHODS

Two data sets were derived from the population undergoing a physical examination. Hoffmann, Bhattacharya, Expectation Maximum (EM), kosmic, and refineR algorithms combined with two-step data preprocessing respectively were implemented in the Test data set to establish RIs for thyroid-related hormones. Algorithm-calculated RIs were compared with the standard RIs calculated from the Reference data set in which reference individuals were selected following strict inclusion and exclusion criteria. Objective assessment of the methods is implemented by the bias ratio (BR) matrix.

RESULTS

RIs of thyroid-related hormones are established. There is a high consistency between TSH RIs established by the EM algorithm and the standard TSH RIs (BR = 0.063), although EM algorithms seems to perform poor on other hormones. RIs calculated by Hoffmann, Bhattacharya, and refineR methods for free and total triiodo-thyronine, free and total thyroxine respectively are close and match the standard RIs.

CONCLUSION

An effective approach for objectively evaluating the performance of the algorithm based on the BR matrix is established. EM algorithm combined with simplified preprocessing can handle data with significant skewness, but its performance is limited in other scenarios. The other four algorithms perform well for data with Gaussian or near-Gaussian distribution. Using the appropriate algorithm based on the data distribution characteristics is recommended.

Verification of Reference Interval of Thyroid Hormones With Manual and Automated Indirect Approaches: Comparison of Hoffman, KOSMIC and refineR Methods

INTRODUCTION

The interpretation of quantitative test results requires the availability of appropriate reference intervals (RIs). Every laboratory has been advised by scientific literature and reagent manufacturers to establish RIs for all analytes. Measuring RIs using direct methods is very costly, and it poses ethical and practical challenges. To overcome these challenges, indirect methods, such as Hoffman, and newer automated approaches, such as KOSMIC and refineR, are used to verify RIs for thyroid hormones.

OBJECTIVE

To verify RIs for thyroid hormones in adult patients using Hoffman, KOSMIC and refineR methods and to compare these with reference ranges given in kit literature or standard textbooks.

MATERIALS AND METHODS

The observed values (results) of thyroid hormone were collected from the LIS (Laboratory Information System) of the Biochemistry Department at the B. J. Medical College and Civil Hospital in Ahmedabad between 1 January 2021 and 31 May 2022. Hoffman, KOSMIC and refineR methods were used to verify the RIs. The computerised Hoffman approach, which Katayev et al. describe, is a simple method for determining RI from hospital data. Zierk et al. pre-validated and suggested the KOSMIC method based on Python programming, whereas refineR was proposed by Tatjana et al. based on R programming language.

RESULTS

Hoffman, KOSMIC and refineR's indirect RI techniques revealed comparable results with kit literature in free T3 and T4, whereas higher upper reference limits of thyroid-stimulating hormone (TSH) compared to kit literature were observed with KOSMIC and refineR methods. However, the computerised Hoffman method revealed comparable results with TSH also.

CONCLUSION

Indirect approaches, such as Hoffman, KOSMIC and refineR, provide reliable RI verification for free T3 and T4 utilising patient samples obtained from LIS. However, the manual Hoffman method provides reliable RI verification for TSH data derived from the hospital population as compared to automated approaches, such as KOSMIC and refineR.

Comparison of reference intervals for biochemical and hematology markers derived by direct and indirect procedures based on the Isfahan cohort study

INTRODUCTION

Indirect methods for reference interval (RI) establishment apply statistical techniques to generate RIs for test result interpretation using stored laboratory data. They present unique advantages relative to traditional direct approaches such as fewer resource requirements; however, there is debate regarding their performance. Herein, we aimed to compare indirect and direct approaches for RI establishment by harnessing data from the Isfahan Cohort Study (ICS). This cohort includes both healthy individuals and those with a history of disease, enabling a direct comparison.

METHODS

Participants were recruited as part of ICS, including 6504 adults aged 34 years and older. Sociodemographic characteristics, anthropometry, blood pressure, various biochemical indices, and hematology parameters were collected. The refineR method was used to establish indirect RIs (before applying exclusion criteria). Direct RIs were calculated using nonparametric methods per CLSI EP28-A3 guidelines (after applying exclusion criteria). Bias ratios were calculated for each parameter to assess significant differences in estimations.

RESULTS

Direct and indirect RI estimations for most hematological and biochemical parameters were comparable. Statistically significant bias ratios between methods were observed for the upper limits of total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), hemoglobin (female), and platelet count as well as the lower limits of mean corpuscular hemoglobin (female), mean corpuscular volume, hemoglobin, and hematocrit (female).

CONCLUSION

Data presented indicate RIs derived from direct and indirect approaches are similar, but not identical. Further work should focus on the clinical significance of such differences as well as the investigation of necessary data-cleaning criteria before indirect method application.

Identifying elevated plasma free triiodothyronine levels: age-adapted reference intervals for pediatrics

OBJECTIVES

Elevated free T3 (FT3) is an important feature for the early diagnosis of several diseases among which Grave's disease or Allan-Hernon-Dudley syndrome. However, there is a lack of age-adapted reference intervals for plasma thyroid hormones in children. We conducted a study to define reference values of peripheral FT3 in children using a commonly used automated immunoassay.

METHODS

All thyroid function test (TFT) results from our lab collected during 9 months were extracted anonymously, and reference intervals establishment followed recommendations validated by International Federation of Clinical Chemistry (IFCC).

RESULTS

We defined five reference intervals covering the whole pediatric period. Overall, 26.1% of peripheral FT3 measured in children with normal TSH are out of the adult reference range, and 22.2% are upper it leading to misinterpretation. In a 9-month old patient with severe neurodevelopmental disorders, a pathological elevated FT3 has been securely interpreted using the newly established interval.

CONCLUSIONS

The study highlights the poor relevance of adult intervals in pediatric cares, as it confirms that plasmatic FT3 is higher during the whole pediatric period. This work reports useful age-adapted reference intervals for free T3 in pediatrics using a widely used electrochemiluminescent Immunoassay (ECLIA) kit.

Clinical interpretation of thyroid tests: considerations for reference intervals

none.

Artificial Intelligence Applications in Clinical Chemistry

Artificial intelligence (AI) applications are an area of active investigation in clinical chemistry. Numerous publications have demonstrated the promise of AI across all phases of testing including preanalytic, analytic, and postanalytic phases; this includes novel methods for detecting common specimen collection errors, predicting laboratory results and diagnoses, and enhancing autoverification workflows. Although AI applications pose several ethical and operational challenges, these technologies are expected to transform the practice of the clinical chemistry laboratory in the near future.

Application of the Hoffmann, Bhattacharya, nonparametric test, and Q-Q plot methods for establishing reference intervals from laboratory databases

BACKGROUND

Reference intervals (RIs) are vital for interpreting laboratory biomarkers and enabling clinical decision-making. Among various RI-estimation methods, we explored the application value of Hoffmann, Bhattacharya, nonparametric test, and Q-Q plot methods for estimating the RI of urea, creatinine, and uric acid (UA).

METHOD

This cross-sectional study collected patient data recorded between January 2020 and April 2022 at the Chongqing University Central Hospital Laboratory Information System. The RIs of urea, creatinine, and UA levels were established using the Hoffmann, Bhattacharya, nonparametric, and Q-Q plot methods, and RI differences with different computational methods were verified using the reference change value (RCV%) of biological variability.

RESULTS

We included 16,474 and 123,570 patients in the physical examination and clinical groups, respectively. In the clinical group, differences in the RI upper limit of analytes with the four methods (excluding the Q-Q plot method) were within the permissible RCV% range; only the nonparametric test produced an RI of urea with the lower limit within the permissible RCV% range. In the physical examination group, the relative RI differences among the four methods (excluding the lower limit of RI obtained using the Q-Q plot) were all within the acceptable RCV% range; the relative deviation of the RI of UA with the four methods was within the acceptable RCV% range (excluding the lower RI limit obtained using the Q-Q plot and nonparametric test).

CONCLUSION

The Hoffmann and Bhattacharya methods may provide reliable RIs for indirect estimations of urea, creatinine, and UA based on laboratory datasets.

Free thyroxine measurement in clinical practice: how to optimize indications, analytical procedures, and interpretation criteria while waiting for global standardization

Thyroid dysfunctions are among the most common endocrine disorders and accurate biochemical testing is needed to confirm or rule out a diagnosis. Notably, true hyperthyroidism and hypothyroidism in the setting of a normal thyroid-stimulating hormone level are highly unlikely, making the assessment of free thyroxine (FT4) inappropriate in most new cases. However, FT4 measurement is integral in both the diagnosis and management of relevant central dysfunctions (central hypothyroidism and central hyperthyroidism) as well as for monitoring therapy in hyperthyroid patients treated with anti-thyroid drugs or radioiodine. In such settings, accurate FT4 quantification is required. Global standardization will improve the comparability of the results across laboratories and allow the development of common clinical decision limits in evidence-based guidelines. The International Federation of Clinical Chemistry and Laboratory Medicine Committee for Standardization of Thyroid Function Tests has undertaken FT4 immunoassay method comparison and recalibration studies and developed a reference measurement procedure that is currently being validated. However, technical and implementation challenges, including the establishment of different clinical decision limits for distinct patient groups, still remain. Accordingly, different assays and reference values cannot be interchanged. Two-way communication between the laboratory and clinical specialists is pivotal to properly select a reliable FT4 assay, establish reference intervals, investigate discordant results, and monitor the analytical and clinical performance of the method over time.

Verification of sex- and age-specific reference intervals for 13 serum steroids determined by mass spectrometry: evaluation of an indirect statistical approach

OBJECTIVES

Conventionally, reference intervals are established by direct methods, which require a well-characterized, obviously healthy study population. This elaborate approach is time consuming, costly and has rarely been applied to steroid hormones measured by mass spectrometry. In this feasibility study, we investigate whether indirect methods based on routine laboratory results can be used to verify reference intervals from external sources.

METHODS

A total of 11,259 serum samples were used to quantify 13 steroid hormones by mass spectrometry. For indirect estimation of reference intervals, we applied a "modified Hoffmann approach", and verified the results with a more sophisticated statistical method (refineR). We compared our results with those of four recent studies using direct approaches.

RESULTS

We evaluated a total of 81 sex- and age-specific reference intervals, for which at least 120 measurements were available. The overall agreement between indirectly and directly determined reference intervals was surprisingly good as nearly every fourth reference limit could be confirmed by narrow tolerance limits. Furthermore, lower reference limits could be provided for some low concentrated hormones by the indirect method. In cases of substantial deviations, our results matched the underlying data better than reference intervals from external studies.

CONCLUSIONS

Our study shows for the first time that indirect methods are a valuable tool to verify existing reference intervals for steroid hormones. A simple "modified Hoffmann approach" based on the general assumption of a normal or lognormal distribution model is sufficient for screening purposes, while the refineR algorithm may be used for a more detailed analysis.

Definitions and major prerequisites of direct and indirect approaches for estimating reference limits

Reference intervals are established either by direct or indirect approaches. Whereas the definition of direct is well established, the definition of indirect is still a matter of debate. In this paper, a general definition that covers all indirect models presently in use is proposed. With the upcoming popularity of indirect models, it has become evident that further partitioning strategies are required to minimize the risk of patients' false classifications. With indirect methods, such partitions are much easier to execute than with direct methods. The authors believe that the future of reference interval estimation belongs to indirect models with big data pools either from one laboratory or combined from several regional centres (if necessary). Independent of the approach applied, the quality assurance of the pre-analytical and analytical phase, considering biological variables and other confounding factors, is essential.

Reference intervals for thyroid disorders calculated by indirect method and comparison with reference change values

Introduction

The aim of the study was to calculate reference intervals (RIs) for thyroid stimulating hormone (TSH), free thyroxine (fT4) and free triiodothyronine (fT3) and evaluate the clinical significance of these intervals by use of reference change values (RCV) of the analytes.

Materials and methods

Laboratory patient data between August and December 2021 were evaluated for the study. A total of 188,912 patients with TSH, fT4, fT3, anti-thyroid peroxidase antibodies (Anti-TPO) and anti-thyroglobulin antibodies (Anti-Tg) results were evaluated. All measurements were performed on Cobas c801 (Roche Diagnostics, Penzberg, Germany) using electrochemiluminescence immunoassay technology. Estimated RIs were compared with manufacturer's by means of RCVs of analytes.

Results

Thyroid stimulating hormone values didn't differ significantly by gender and age. The combined RIs for whole group (N = 28,437) was found as 0.41-4.37 mIU/mL. Free T4 values (11.6-20.1 pmol/L, N = 13,479 in male; 10.5-19.5 pmol/L, N = 17,634 female) and fT3 values (3.38-6.35 pmol/L, N = 2,516 in male; 3.39-5.99 pmol/L, N = 3,348 pmol/L in female) significantly differed by gender (P < 0.050). Both fT4 and fT3 values also showed significant differences in age subgroups comparisons. So, male and female RIs were represented separately for age subgroups. When compared with manufacturer's RIs, TSH whole group and fT4 subgroups RIs didn't exceed the analytes' RCVs, but this difference was greater for fT3.

Conclusions

Reference interval estimation by use of indirect method out of laboratory data may be more accurate than manufacturer provided RIs. This population based RIs evaluated using RCV of analytes may provide useful information in clinical interpretation of laboratory results.

Harmonization of indirect reference intervals calculation by the Bhattacharya method

OBJECTIVES

The aim of this study was to harmonize the criteria for the Bhattacharya indirect method Microsoft Excel Spreadsheet for reference intervals calculation to reduce between-user variability and use these criteria to calculate and evaluate reference intervals for eight analytes in two different years.

METHODS

Anonymized laboratory test results from outpatients were extracted from January 1st 2018 to December 31st 2019. To assure data quality, we examined the monthly results from an external quality control program. Reference intervals were determined by the Bhattacharya method with the St Vincent's hospital Spreadsheet firstly using original criteria and then using additional harmonized criteria defined in this study. Consensus reference intervals using the additional harmonized criteria were calculated as the mean of four users' lower and upper reference interval results. To further test the operation criteria and robustness of the obtained reference intervals, an external user validated the Spreadsheet procedure.

RESULTS

The extracted test results for all selected laboratory tests fulfilled the quality criteria and were included in the present study. Differences between users in calculated reference intervals were frequent when using the Spreadsheet. Therefore, additional criteria for the Spreadsheet were proposed and applied by independent users, such as: to set central bin as the mean of all the data, bin size as small as possible, at least three consecutive bins and a high proportion of bins within the curve.

CONCLUSIONS

The proposed criteria contributed to the harmonization of reference interval calculation between users of the Bhattacharya indirect method Spreadsheet.

Indirectly determined reference intervals for automated white blood cell differentials of pediatric patients in Berlin and Brandenburg

OBJECTIVES

Establishing direct reference intervals for pediatric patients is a costly, challenging, and time-consuming enterprise. Indirectly established reference intervals can help to ameliorate this situation. It was our objective to establish population-specific reference intervals for automated white blood cell differentials via data mining and non-parametric percentile method.

METHODS

Blood counts and automated white blood cell differentials of patients aged 0 days to 18 years, performed from the 1st of January 2018 until the 30th of June 2022, were identified in our laboratory information system. Reference intervals were established in accordance with IFCC and CLSI recommendations as well as the propositions by Haeckel et al.

RESULTS

Initially, 47,173 blood counts on our SYSMEX XN-9000 were identified. 11,707 data sets were excluded, leaving 35,466 sample sets for analysis. Of these, 17,616 contained automated white blood cell differentials. Due to insufficient patient numbers, no reference intervals for automated white blood cell differentials could be established for children aged <7 months. In comparison to the corresponding reference intervals published by Herklotz et al., reference intervals determined by us showed relevant differences throughout all age groups.

CONCLUSIONS

The combination of non-parametric percentile method and the propositions by Haeckel et al. utilizing conscientious data mining appears to be potent alternative to direct reference interval determination.