refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data

Should we depend on reference intervals from manufacturer package inserts? Comparing TSH and FT4 reference intervals from four manufacturers with results from modern indirect methods and the direct method

OBJECTIVES

Correct interpretation of thyroid function tests relies on correct reference intervals (RIs) for thyroid-stimulating hormone (TSH) and free thyroxine (FT4). ISO15189 mandates periodic verification of RIs, but laboratories struggle with cost-effective approaches. We investigated whether indirect methods (utilizing historical laboratory data) could replace the direct approach (utilizing healthy reference individuals) and compared results with manufacturer-provided RIs for TSH and FT4.

METHODS

We collected historical data (2008-2022) from 13 Dutch laboratories to re-establish RIs by employing indirect methods, TMC (for TSH) and refineR (for FT4). Laboratories used common automated platforms (Roche, Abbott, Beckman or Siemens). Indirect RIs (IRIs) were determined per laboratory per year and clustered per manufacturer (>1.000.000 data points per manufacturer). Direct RIs (DRIs) were established in 125 healthy individuals per platform.

RESULTS

TSH IRIs remained robust over the years for all manufacturers. FT4 IRIs proved robust for three manufacturers (Roche, Beckman and Siemens), but the IRI upper reference limit (URL) of Abbott showed a decrease of 2 pmol/L from 2015. Comparison of the IRIs and DRIs for TSH and FT4 showed close agreement using adequate age-stratification. Manufacturer-provided RIs, notably Abbott, Roche and Beckman exhibited inappropriate URLs (overall difference of 0.5-1.0 µIU/mL) for TSH. For FT4, the URLs provided by Roche, Abbott and Siemens were overestimated by 1.5-3.5 pmol/L.

CONCLUSIONS

These results underscore the importance of RI verification as manufacturer-provided RIs are often incorrect and RIs may not be robust. Indirect methods offer cost-effective alternatives for laboratory-specific or platform-specific verification of RIs.

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.

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.

Data mining of reference intervals for serum creatinine: an improvement in glomerular filtration rate estimating equations based on Q-values

OBJECTIVES

Glomerular filtration rate (GFR) estimating equations based on rescaled serum creatinine (SCr/Q) have shown better performance, where Q represents the median SCr for age- and sex-specific healthy populations. However, there remains a scarcity of investigations in China to determine this value. We aimed to develop Chinese age- and sex-specific reference intervals (RIs) and Q-values for SCr and to validate the equations incorporating new Q-values.

METHODS

We included 117,345 adults from five centers for establishing RIs and Q-values, and 3,692 participants with reference GFR (rGFR, 99mTc-DTPA renal dynamic imaging measurement) for validation. Appropriate age partitioning was determined using the decision tree method. Lower and upper reference limits and medians were calculated using the refineR algorithm, and Q-values were determined accordingly. We evaluated the full age spectrum (FAS) and European Kidney Function Consortium (EKFC) equations incorporating different Q-values considering bias, precision (interquartile range, IQR), and accuracy (percentage of estimates within ±20 % [P20] and ±30 % [P30] of rGFR).

RESULTS

RIs for males were: 18-79 years, 55.53-92.50 μmol/L; ≥80 years, 54.41-96.43 μmol/L. RIs for females were: 18-59 years, 40.42-69.73 μmol/L; 60-79 years, 41.16-73.69 μmol/L; ≥80 years, 46.50-73.20 μmol/L. Q-values were set at 73.82 μmol/L (0.84 mg/dL) for males and 53.80 μmol/L (0.61 mg/dL) for females. After validation, we found that the adjusted equations exhibit less bias, improved precision and accuracy, and increased agreement of GFR categories.

CONCLUSIONS

We determined Chinese age- and sex-specific RIs and Q-values for SCr. The adjustable Q-values provide an effective alternative to obtain valid equations for estimating GFR.

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.

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.

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.

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.

Mining the Gap: Deriving Pregnancy Reference Intervals for Hematology Parameters Using Clinical Datasets

BACKGROUND

Physiological changes during pregnancy invalidate use of general population reference intervals (RIs) for pregnant people. The complete blood count (CBC) is commonly ordered during pregnancy, but few studies have established pregnancy RIs suitable for contemporary Canadian mothers. Prospective RI studies are challenging to perform during pregnancy while retrospective techniques fall short as pregnancy and health status are not readily available in the laboratory information system (LIS). This study derived pregnancy RIs retrospectively using LIS data linked to provincial perinatal registry data.

METHODS

A 5-year healthy pregnancy cohort was defined from the British Columbia Perinatal Data Registry and linked to laboratory data from two laboratories. CBC and differential RIs were calculated using direct and indirect approaches. Impacts of maternal and pregnancy characteristics, such as age, body mass index, and ethnicity, on laboratory values were also assessed.

RESULTS

The cohort contained 143 106 unique term singleton pregnancies, linked to >972 000 CBC results. RIs were calculated by trimester and gestational week. Result trends throughout gestation aligned with previous reports in the literature, although differences in exact RI limits were seen for many tests. Trimester-specific bins may not be appropriate for several CBC parameters that change rapidly within trimesters, including red blood cells (RBCs), some leukocyte parameters, and platelet counts.

CONCLUSIONS

Combining information from comprehensive clinical databases with LIS data provides a robust and reliable means for deriving pregnancy RIs. The present analysis also illustrates limitations of using conventional trimester bins during pregnancy, supporting use of gestational age or empirically derived bins for defining CBC normal values during pregnancy.

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.

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).

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.

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.

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.

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.

Estimation of Reference Intervals from Routine Data Using the refineR Algorithm-A Practical Guide

BACKGROUND

Accurate reference intervals are essential for the interpretation of laboratory test results. Typically, they are determined by the central 95% range of test results from a predefined reference population. As these direct studies can face practical and ethical challenges, indirect methods using routine measurements offer an alternative approach.

METHODS

We provide step-by-step guidance on how to apply an indirect method in practice using refineR, the most recently published indirect method, and showcase the application by evaluating real-world data of 12 prespecified analytes. Measurements were retrieved from ARUP Laboratories' data warehouse, and were obtained from routine patient testing on cobas c502 or e602 analyzers. Test results were prefiltered and cleaned and, if necessary, physiologically partitioned prior to estimating reference intervals using refineR. Estimated reference intervals were then compared to established intervals provided by the manufacturer.

RESULTS

For most analytes, the reference intervals estimated by refineR were comparable to those provided by the manufacturer, shown by overlapping confidence intervals at both reference limits, or only the upper or lower limit. For thyroid-stimulating hormone, refineR estimated higher reference limits, while estimates for prealbumin were lower compared to the established reference interval.

CONCLUSIONS

We applied the refineR algorithm to a variety of real-world data sets resulting in reference intervals similar to intervals previously established by direct methods. We further provide practical guidance and a code example on how to apply an indirect method in a real-world scenario facilitating their access and thus their use in laboratory settings.

A novel fully-automated method to measure steroids in serum by liquid chromatography-tandem mass spectrometry

Background

Steroids play a key role in numerous physiological processes. Steroid determination is a useful tool to explore various endocrine diseases. Because of its specificity, mass spectrometry is considered to be a reference method for the determination of steroids in serum compared to radioimmunoassay. This technology could progress towards more automation for the optimal organization of clinical laboratories and ultimately for the benefit of patients.

Methods

A fully automated ultra-high-performance liquid chromatography-tandem mass spectrometry method was developed and fully validated to determine five steroids in serum. Sample preparation was based on protein precipitation with filtration followed by online solid phase extraction. Chromatographic separation was performed using a biphenyl stationary phase.

Results

The method was successfully validated according to European Medicine Agency guidelines. Coefficients of variation did not exceed, respectively, 8.4% and 8.1% for intra- and inter-assay precision. Method comparison with radioimmunoassay showed a proportional bias for all compounds, except for testosterone in men. Comparison with another LC-MS/MS method demonstrated acceptable concordance for all steroids, although a small bias was observed for androstenedione.

Conclusion

The novelty of this method is that it has been fully automated. Automation provides benefits in traceability and allows significant savings in cost and time.

Oxidative Potential in Exhaled Air (OPEA) as a Tool for Predicting Certain Respiratory Disorders in the General Adult Population: Cross-Sectional Analysis Nested in the Swiss Health Study

In a pilot clinical study, OPEA allowed for distinguishing participants with and without chronic obstructive pulmonary disease. This study aimed to assess whether abnormal spirometry parameters and immunity against SARS-CoV-2 are associated with increased OPEA and estimating the OPEA reference interval. Swiss adult residents of the Vaud Canton aged 20–69 years randomly selected from the Federal Statistical Office’s registries, speaking French or German, were included and examined between 1 October 2020 and 31 December 2021. General health status and presence of respiratory diseases were assessed by questionnaire and spirometry. Spirometric results were compared with the predicted values and their lower limits of norms of the Global Lung Function Initiative. SARS-CoV-2-seroprevalence was assessed using the Luminex-based test of IgG. Statistical analysis consisted of unilateral t-tests and ANOVA. Lower and upper limit of OPEA reference interval with associated 90%-confidence interval (90%CI) were estimated for the sub-sample of healthy adults by bootstrap, after excluding outliers. The study sample included 247 participants. SARS-CoV-2-seropositive participants and those with an obstructive syndrome had a significantly higher OPEA than seronegative and healthy participants. The estimated reference interval was: −0.0516 (90%CI = −0.0735; −0.0316); −0.0044 (90%CI = −0.0224; 0.0153). OPEA could predict inflammatory-based respiratory disorders, but needs further validation in different settings and for other pathologies.

RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation

BACKGROUND

Indirect methods leverage real-world data for the estimation of reference intervals. These constitute an active field of research, and several methods have been developed recently. So far, no standardized tool for evaluation and comparison of indirect methods exists.

METHODS

We provide RIbench, a benchmarking suite for quantitative evaluation of any existing or novel indirect method. The benchmark contains simulated test sets for 10 biomarkers mimicking routine measurements of a mixed distribution of non-pathological (reference) values and pathological values. The non-pathological distributions represent 4 common distribution types: normal, skewed, heavily skewed, and skewed-and-shifted. To identify strengths and weaknesses of indirect methods, test sets have varying sample sizes and pathological distributions differ in location, extent of overlap, and fraction. For performance evaluation, we use an overall benchmark score and sub-scores derived from absolute z-score deviations between estimated and true reference limits. We illustrate the application of RIbench by evaluating and comparing the Hoffmann method and 4 modern indirect methods -TML (Truncated-Maximum-Likelihood), kosmic, TMC (Truncated-Minimum-Chi-Square), and refineR- against one another and against a nonparametric direct method (n = 120).

RESULTS

For the modern indirect methods, pathological fraction and sample size had a strong influence on the results: With a pathological fraction up to 20% and a minimum sample size of 5000, most methods achieved results comparable or superior to the direct method.

CONCLUSIONS

We present RIbench, an open-source R-package, for the systematic evaluation of existing and novel indirect methods. RIbench can serve as a tool for enhancement of indirect methods, improving the estimation of reference intervals.

How to approach clinically discordant FT4 results when changing testing platforms: real-world evidence

PURPOSE

Measurement of thyroid-stimulating hormone (TSH) and free thyroxine (FT4) is important for assessing thyroid dysfunction. After changing assay manufacturer, high FT4 versus TSH levels were reported at Ente Ospedaliero Cantonale (EOC; Bellinzona, Switzerland).

METHODS

Exploratory analysis used existing TSH and FT4 measurements taken at EOC during routine clinical practice (February 2018-April 2020) using Elecsys® TSH and Elecsys FT4 III immunoassays on cobas® 6000 and cobas 8000 analyzers (Roche Diagnostics). Reference intervals (RIs) were estimated using both direct and indirect (refineR algorithm) methods.

RESULTS

In samples with normal TSH levels, 90.9% of FT4 measurements were within the normal range provided by Roche (12-22 pmol/L). For FT4 measurements, confidence intervals (CIs) for the lower end of the RI obtained using direct and indirect methods were lower than estimated values in the method sheet; the estimated value of the upper end of the RI (UEoRI) in the method sheet was within the CI for the UEoRI using the direct method but not the indirect method. CIs for the direct and indirect methods overlapped at both ends of the RI. The most common cause of increased FT4 with normal TSH was identified in a subset of patients as use of thyroxine therapy (72.6%).

CONCLUSIONS

It is important to verify RIs for FT4 in the laboratory population when changing testing platforms; indirect methods may constitute a convenient tool for this. Applying specific RIs for selected subpopulations should be considered to avoid misinterpretations and inappropriate clinical actions.

Mixture density networks for the indirect estimation of reference intervals

BACKGROUND

Reference intervals represent the expected range of physiological test results in a healthy population and are essential to support medical decision making. Particularly in the context of pediatric reference intervals, where recruitment regulations make prospective studies challenging to conduct, indirect estimation strategies are becoming increasingly important. Established indirect methods enable robust identification of the distribution of "healthy" samples from laboratory databases, which include unlabeled pathologic cases, but are currently severely limited when adjusting for essential patient characteristics such as age. Here, we propose the use of mixture density networks (MDN) to overcome this problem and model all parameters of the mixture distribution in a single step.

RESULTS

Estimated reference intervals from varying settings with simulated data demonstrate the ability to accurately estimate latent distributions from unlabeled data using different implementations of MDNs. Comparing the performance with alternative estimation approaches further highlights the importance of modeling the mixture component weights as a function of the input in order to avoid biased estimates for all other parameters and the resulting reference intervals. We also provide a strategy to generate partially customized starting weights to improve proper identification of the latent components. Finally, the application on real-world hemoglobin samples provides results in line with current gold standard approaches, but also suggests further investigations with respect to adequate regularization strategies in order to prevent overfitting the data.

CONCLUSIONS

Mixture density networks provide a promising approach capable of extracting the distribution of healthy samples from unlabeled laboratory databases while simultaneously and explicitly estimating all parameters and component weights as non-linear functions of the covariate(s), thereby allowing the estimation of age-dependent reference intervals in a single step. Further studies on model regularization and asymmetric component distributions are warranted to consolidate our findings and expand the scope of applications.

Parametric and non-parametric estimation of reference intervals for routine laboratory tests: an analysis of health check-up data for 260 889 young men in the South Korean military

OBJECTIVES

Determination of reference intervals (RIs) using big data faces several obstacles due to heterogeneity in analysers, period and ethnicity. The present study aimed to establish the RIs for routine common blood count (CBC) and biochemistry laboratory tests in homogeneous, healthy, male Korean soldiers in their 20s using a large health check-up data set, comparing parametric and non-parametric estimation.

DESIGN

A multicentre, cross-sectional study.

SETTING

Seven armed forces hospitals in South Korea.

PARTICIPANTS

A total of 609 649 men underwent health examination when promoted to corporal between January 2015 and September 2021. 260 889 eligible individuals aged 20-25 were included in the analysis.

MAIN OUTCOMES AND MEASURES

The RIs were established by parametric and non-parametric methods. In the parametric approach, maximum likelihood estimation was applied to measure the Box-Cox transformation parameter and the values at the 2.5th and 97.5th percentiles were recalculated. The non-parametric approach adopted the Tukey's exclusion test and the values at the 2.5th and 97.5th percentiles were obtained. Classification by body mass index was also performed.

RESULTS

The obtained RIs for haematology parameters were comparable between devices. If the values followed a Gaussian distribution, parametric and non-parametric methods were well matched for haematology and biochemical markers. When the values were right-skewed, the upper limits were higher with parametric than with non-parametric methods. Participants with obesity showed higher RIs for CBC, some liver function tests and some lipid profiles than participants without obesity.

CONCLUSIONS

Using data from healthy, male Korean soldiers in their 20s, we proposed the RIs for CBC and biochemical parameters, comparing parametric and non-parametric estimation. As such approaches based on large data sets become more prevalent, further studies are needed to discriminate eligible individuals and determine RIs in an extrapolated sample.

Establishment of Reference Interval and Aging Model of Homocysteine Using Real-World Data

Objective

The level of Homocysteine (Hcy) in males is generally higher than that of females, but the same reference interval (RI) is often used in clinical practice. This study aims to establish a sex-specific RI of Hcy using five data mining algorithms and compare these results. Furthermore, age-related continuous RI was established in order to show the relationship between Hcy concentration distribution and age.

Methods

A total of 20,801 individuals were included in the study and Tukey method was used to identify outliers in subgroups by sex and age. Multiple linear regression and standard deviation ratio (SDR) was used to determine whether the RI for Hcy needs to be divided by sex and age. Five algorithms including Hoffmann, Bhattacharya, expectation maximization (EM), kosmic and refineR were utilized to establish the RI of Hcy. Generalized Additive Models for Location Scale and Shape (GAMLSS) algorithm was used to determine the aging model of Hcy and calculate the age-related continuous RI.

Results

RI of Hcy needed to be partitioned by sex (SDR = 0.735 > 0.375). RIs established by Hoffmann, Bhattacharya, EM (for females) and kosmic are all within the 95% CI of reference limits established by refine R. The Sex-specific aging model of Hcy showed that the upper limits of the RI of Hcy declined with age beginning at age of 18 and began to rise approximately after age of 40 for females and increased with age for males.

Conclusion

The RI of Hcy needs to be partitioned by sex. The RIs established by the five data mining algorithms showed good consistency. The dynamic sex and age-specific model of Hcy showed the pattern of Hcy concentration with age and provide more personalized tools for clinical decisions.

Comparison of 8 methods for univariate statistical exclusion of pathological subpopulations for indirect reference intervals and biological variation studies

BACKGROUND

Indirect reference intervals and biological variation studies heavily rely on statistical methods to separate pathological and non-pathological subpopulations within the same dataset. In recognition of this, we compare the performance of eight univariate statistical methods for identification and exclusion of values originating from pathological subpopulations.

METHODS

The eight approaches examined were: Tukey's rule with and without Box-Cox transformation; median absolute deviation; double median absolute deviation; Gaussian mixture models; van der Loo (Vdl) methods 1 and 2; and the Kosmic approach. Using four scenarios including lognormal distributions and varying the conditions through the number of pathological populations, central location, spread and proportion for a total of 256 simulated mixed populations. A performance criterion of ±0.05 fractional error from the true underlying lower and upper reference interval was chosen.

RESULTS

Overall, the Kosmic method was a standout with the highest number of scenarios lying within the acceptable error, followed by Vdl method 1 and Tukey's rule. Kosmic and Vdl method 1 appears to discriminate better the non-pathological reference population in the case of log-normal distributed data. When the proportion and spread of pathological subpopulations is high, the performance of statistical exclusion deteriorated considerably.

DISCUSSIONS

It is important that laboratories use a priori defined clinical criteria to minimise the proportion of pathological subpopulation in a dataset prior to analysis. The curated dataset should then be carefully examined so that the appropriate statistical method can be applied.

Establishment of Reference Intervals for Thyroid-Associated Hormones Using refineR Algorithm in Chinese Population at High-Altitude Areas

Objectives

Diagnosis of thyroid disease among individuals dwelling at high altitude remains a challenge. Reference intervals (RIs) for thyroid-associated hormones among Tibetans living at various high altitudes were established to improve diagnosis.

Methods

One thousand two hundred eighty-one subjects were randomly recruited from Nyingchi, Shigatse/Lhasa, and Ali of Tibet. Thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4) were measured by the Cobas e601 electrochemiluminescence analyzer. We used multiple linear regression and variance component analysis to assess the effect of sex, age, and altitude on hormones. RIs were established by refineR algorithm and compared with those provided by the manufacturer.

Results

Serum TSH was significantly lower in males than in females, while FT3 and FT4 were higher in males. Both FT3 and FT4 decreased with increasing age. FT3 increased with altitude, while TSH and FT4 were less influenced by altitude. The RI for TSH was 0.764–5.784 μIU/ml, while for FT4, the RIs were 12.36–19.38 pmol/L in females and 14.84–20.18 pmol/L in males. The RIs for FT3 at Nyingchi, Shigatse/Lhasa, and Ali in females were 4.09–4.98, 4.31–5.45, and 4.82–5.58 pmol/L, while in males, the values were 4.82–5.41, 4.88–5.95, and 5.26–6.06 pmol/L, respectively. The obtained RIs for TSH and FT4 were generally higher, while that for FT3 was narrower than the RIs provided by Cobas.

Conclusions

Specific RIs were established for thyroid-associated hormones among Tibetans, which were significantly different from those provided by the manufacturer.

Age- and sex-specific pediatric reference intervals for neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and platelet-to-lymphocyte ratio

INTRODUCTION

Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) are emerging biomarkers for systemic inflammation and have been shown to predict morbidity and mortality for several diseases. However, lack of pediatric reference intervals (RIs) prevents their comprehensive use in patient care and medical research.

MATERIAL AND METHODS

We calculated reference intervals and corresponding confidence intervals for NLR, PLR, and LMR from birth to 18 years using a data-mining approach: We analyzed 232 746 blood counts from 60 685 patients performed during patient care and excluded patients with elevated C-reactive protein and procalcitonin. Test results were separated according to age and sex, and the distribution of physiological ratios was estimated using an indirect approach (refineR). Additionally, we investigated the ratios' diagnostic benefit for different inflammatory diseases (acute appendicitis, asthma, Bell's palsy, Henoch-Schonlein purpura, and cystic fibrosis) using the newly obtained reference intervals.

RESULTS

We estimated age- and sex-specific reference intervals from birth to adulthood for NLR, PLR, and LMR. Analyses in pediatric inflammatory diseases showed that PLR and LMR were poor markers to detect the examined inflammatory diseases, while NLR was significantly increased in patients with appendicitis and asthma.

CONCLUSION

We provide pediatric reference intervals for NLR, PLR, and LMR to improve the interpretation of these biomarkers in children.

Data mining of pediatric reference intervals

Laboratory tests are essential to assess the health status and to guide patient care in individuals of all ages. The interpretation of quantitative test results requires availability of appropriate reference intervals, and reference intervals in children have to account for the extensive physiological dynamics with age in many biomarkers. Creation of reference intervals using conventional approaches requires the sampling of healthy individuals, which is opposed by ethical and practical considerations in children, due to the need for a large number of blood samples from healthy children of all ages, including neonates and young infants. This limits the availability and quality of pediatric reference intervals, and ultimately negatively impacts pediatric clinical decision-making. Data mining approaches use laboratory test results and clinical information from hospital information systems to create reference intervals. The extensive number of available test results from laboratory information systems and advanced statistical methods enable the creation of pediatric reference intervals with an unprecedented age-related accuracy for children of all ages. Ongoing developments regarding the availability and standardization of electronic medical records and of indirect statistical methods will further improve the benefit of data mining for pediatric reference intervals.