Threshold of 25(OH)D and consequently adjusted parathyroid hormone reference intervals: data mining for relationship between vitamin D and parathyroid hormone

PURPOSE

By recruiting reference population, we aimed to (1): estimate the 25(OH)D threshold that maximally inhibits the PTH, which can be defined as the cutoff value for vitamin D sufficiency; (2) establish the PTH reference interval (RI) in population with sufficient vitamin D.

METHODS

Study data were retrieved from LIS (Laboratory Information Management System) under literature suggested criteria, and outliers were excluded using Tukey fence method. Locally weighted regression (LOESS) and segmented regression (SR) were conducted to estimate the threshold of 25(OH)D. Multivariate linear regression was performed to evaluate the associations between PTH concentration and variables including 25(OH)D, gender, age, estimated glomerular filtration rate (EGFR), body mass index (BMI), albumin-adjusted serum calcium (aCa), serum phosphate(P), serum magnesium(Mg), and blood collection season. Z test was adopted to evaluate whether the reference interval should be stratified by determinants such as age and gender.

RESULTS

A total of 64,979 apparently healthy subjects were recruited in this study, with median (Q1, Q3) 25(OH)D of 45.33 (36.15, 57.50) nmol/L and median (Q1, Q3) PTH of 42.19 (34.24, 52.20) ng/L. The segmented regression determined the 25(OH)D threshold of 55 nmol/L above which PTH would somewhat plateau and of 22 nmol/L below which PTH would rise steeply. Multivariate linear regression suggested that gender, EGFR, and BMI were independently associated with PTH concentrations. The PTH RI was calculated as 22.17-72.72 ng/L for subjects with 25(OH)D ≥ 55 nmol/L with no necessity of stratification according to gender, age, menopausal status nor season.

CONCLUSION

This study reported 25(OH)D thresholds of vitamin D sufficiency at 55 nmol/L and vitamin D deficiency at 22 nmol/L, and consequently established PTH RIs in subjects with sufficient vitamin D for northern China population for the first time.

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).

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.

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.

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.

The harmonization issue in laboratory medicine: the commitment of CCLM

The analytical quality of the clinical laboratory results has shown a significant improvement over the past decades, thanks to the joint efforts of different stakeholders, while the comparability among the results produced by different laboratories and methods still presents some critical issues. During these years, Clinical Chemistry and Laboratory Medicine (CCLM) published several papers on the harmonization issue over all steps in the Total Testing Process, training an important number of laboratory professionals in evaluating and monitoring all the criticisms inherent to the pre-analytical, as well as analytical and post analytical phases: from the consensus statement on the most informative testing in emergency setting, to the prevention and detection of hemolysis or to patients identification and tube labeling procedures, as far as to different approaches to harmonize hormones measurements or to describe new reference methods or to harmonize the laboratory report. During these years the commitment of the journal, devoted to the harmonization processes has allowed to improve the awareness on the topic and to provide specific instruments to monitor the rate of errors and to improve patients safety.

Personalized reference intervals: from theory to practice

Using laboratory test results for diagnosis and monitoring requires a reliable reference to which the results can be compared. Currently, most reference data is derived from the population, and patients in this context are considered members of a population group rather than individuals. However, such reference data has limitations when used as the reference for an individual. A patient's test results preferably should be compared with their own, individualized reference intervals (RI), i.e. a personalized RI (prRI).The prRI is based on the homeostatic model and can be calculated using an individual's previous test results obtained in a steady-state situation and estimates of analytical (CV_A) and biological variation (BV). BV used to calculate the prRI can be obtained from the population (within-subject biological variation, CV_I) or an individual's own data (within-person biological variation, CV_P). Statistically, the prediction interval provides a useful tool to calculate the interval (i.e. prRI) for future observation based on previous measurements. With the development of information technology, the data of millions of patients is stored and processed in medical laboratories, allowing the implementation of personalized laboratory medicine. PrRI for each individual should be made available as part of the laboratory information system and should be continually updated as new test results become available.In this review, we summarize the limitations of population-based RI for the diagnosis and monitoring of disease, provide an outline of the prRI concept and different approaches to its determination, including statistical considerations for deriving prRI, and discuss aspects which must be further investigated prior to implementation of prRI in clinical practice.

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.

Long-Term Tubular Dysfunction in Childhood Cancer Survivors; DCCSS-LATER 2 Renal Study

The aim of this nationwide cross-sectional cohort study was to determine the prevalence of and risk factors for tubular dysfunction in childhood cancer survivors (CCS). In the DCCSS-LATER 2 Renal study, 1024 CCS (≥5 years after diagnosis), aged ≥ 18 years at study, treated between 1963 and 2001 with potentially nephrotoxic therapy (i.e., nephrectomy, abdominal radiotherapy, total body irradiation, cisplatin, carboplatin, ifosfamide, high-dose cyclophosphamide, or hematopoietic stem cell transplantation) participated, and 500 age- and sex-matched participants from Lifelines acted as controls. Tubular electrolyte loss was defined as low serum levels (magnesium < 0.7 mmol/L, phosphate < 0.7 mmol/L and potassium < 3.6 mmol/L) with increased renal excretion or supplementation. A α1-microglobulin:creatinine ratio > 1.7 mg/mmol was considered as low-molecular weight proteinuria (LMWP). Multivariable risk analyses were performed. After median 25.5 years follow-up, overall prevalence of electrolyte losses in CCS (magnesium 5.6%, potassium 4.5%, phosphate 5.5%) was not higher compared to controls. LMWP was more prevalent (CCS 20.1% versus controls 0.4%). LMWP and magnesium loss were associated with glomerular dysfunction. Ifosfamide was associated with potassium loss, phosphate loss (with cumulative dose > 42 g/m2) and LMWP. Cisplatin was associated with magnesium loss and a cumulative dose > 500 mg/m2 with potassium and phosphate loss. Carboplatin cumulative dose > 2800 mg/m2 was associated with potassium loss. In conclusion, long-term tubular dysfunction is infrequent. Yet, ifosfamide, cisplatin and carboplatin are risk factors.

Indirect determination of biochemistry reference intervals using outpatient data

The aim of this study was to determine reference intervals in an outpatient population from Vall d'Hebron laboratory using an indirect approach previously described in a Dutch population (NUMBER project). We used anonymized test results from individuals visiting general practitioners and analysed during 2018. Analytical quality was assured by EQA performance, daily average monitoring and by assessing longitudinal accuracy between 2018 and 2020 (using trueness verifiers from Dutch EQA). Per test, outliers by biochemically related tests were excluded, data were transformed to a normal distribution (if necessary) and means and standard deviations were calculated, stratified by age and sex. In addition, the reference limit estimator method was also used to calculate reference intervals using the same dataset. Finally, for standardized tests reference intervals obtained were compared with the published NUMBER results. Reference intervals were calculated using data from 509,408 clinical requests. For biochemical tests following a normal distribution, similar reference intervals were found between Vall d'Hebron and the Dutch study. For creatinine and urea, reference intervals increased with age in both populations. The upper limits of Gamma-glutamyl transferase were markedly higher in the Dutch study compared to Vall d'Hebron results. Creatine kinase and uric acid reference intervals were higher in both populations compared to conventional reference intervals. Medical test results following a normal distribution showed comparable and consistent reference intervals between studies. Therefore a simple indirect method is a feasible and cost-efficient approach for calculating reference intervals. Yet, for generating standardized calculated reference intervals that are traceable to higher order materials and methods, efforts should also focus on test standardization and bias assessment using commutable trueness verifiers.

Reference intervals for Sysmex XN hematological parameters as assessed in the Dutch Lifelines cohort

OBJECTIVES

Our aim was to derive reference intervals for all Sysmex XN hematology analyzer parameters. The rationale behind the study was the lack of reference intervals for the XN analyzer cell population data (CPD) and functional parameters.

METHODS

Fresh fasting blood samples from 18,484 participants in the Dutch Lifelines study were analyzed using two automated XN analyzers. Structured health questionnaire data were used to select a subgroup of 15,803 apparently healthy individuals for inclusion in the reference population. The Latent Abnormal Values Exclusion (LAVE) approach was used to reduce the influence of latent diseases in the reference population on the resulting reference intervals. We applied analysis of variance to judge the need for partitioning of the reference intervals by sex or age.

RESULTS

We report reference intervals for 105 XN analyzer hematological parameters with and without applying LAVE. Sex-related partitioning was required for red blood cells, (RBC, RBC-O), hemoglobin (HGB, HGB-O), hematocrit (HCT), mean corpuscular hemoglobin concentration (MCHC), reticulocyte production index (RPI), and side scattered light intensity of the red blood cell population in the RET channel (RBC-Z). Partitioning for age was not warranted. Body mass index (BMI) and smoking had moderate influence on a minority of the parameters.

CONCLUSIONS

We provide reference intervals for all Sysmex XN analyzer routine, CPD and functional parameters, using a direct approach in a large cohort in the Netherlands.

Age-adjusted interpretation of biomarkers of renal function and homeostasis, inflammation, and circulation in Emergency Department patients

Appropriate interpretation of blood tests is important for risk stratification and guidelines used in the Emergency Department (ED) (such as SIRS or CURB-65). The impact of abnormal blood test values on mortality may change with increasing age due to (patho)-physiologic changes. The aim of this study was therefore to assess the effect of age on the case-mix adjusted association between biomarkers of renal function and homeostasis, inflammation and circulation and in-hospital mortality. This observational multi-center cohort study has used the Netherlands Emergency department Evaluation Database (NEED), including all consecutive ED patients ≥ 18 years of three hospitals. A generalized additive logistic regression model was used to visualize the association between in-hospital mortality, age and five blood tests (creatinine, sodium, leukocytes, C-reactive Protein, and hemoglobin). Multivariable logistic regression analyses were used to assess the association between the number of abnormal blood test values and mortality per age category (18-50; 51-65; 66-80; > 80 years). Of the 94,974 included patients, 2550 (2.7%) patients died in-hospital. Mortality increased gradually for C-reactive Protein (CRP), and had a U-shaped association for creatinine, sodium, leukocytes, and hemoglobin. Age significantly affected the associations of all studied blood tests except in leukocytes. In addition, with increasing age categories, case-mix adjusted mortality increased with the number of abnormal blood tests. In summary, the association between blood tests and (adjusted) mortality depends on age. Mortality increases gradually or in a U-shaped manner with increasing blood test values. Age-adjusted numerical scores may improve risk stratification. Our results have implications for interpretation of blood tests and their use in risk stratification tools and acute care guidelines.Trial registration number Netherlands Trial Register (NTR) NL8422, 03/2020.

Development of nation-wide reference intervals using an indirect method and harmonized assays

OBJECTIVES

For many years, clinical laboratories have either verified or estimated reference intervals (RI) for laboratory tests. Those calculations have largely been performed by direct sampling analysis of ostensibly healthy individuals or by post-analysis biochemical screening. Recently however, indirect calculations have come to the forefront as an IFCC endorsed method by using normal and abnormal patient data.

DESIGN AND METHODS

Using a large database of patient test results from Laboratory Corporation of America, age and gender based RIs, inclusive of neonatal, pediatric, and geriatric populations, were determined using a modified indirect method of Hoffmann, and represent a diverse population distributed across the United States from a nation-wide system of laboratories and is unbiased with respect to age, gender, race or geography.

RESULTS

The tabulation of RIs using big data by an indirect method represent 72 M patient test results. The table includes 266 individual analytes consisting of approximately 2,700 age categories, including tests across multiple medical disciplines.

CONCLUSIONS

To our knowledge, this is the largest collection of RIs that were calculated by an indirect method representing clinical chemistry, endocrinology, coagulation, and hematology analytes that have been derived with very powerful "Ns" for each age bracket. This process provides more robust RIs and allows for the determination of pediatric and geriatric RIs that would otherwise be difficult to obtain using traditional direct RI determinations.

Serum iPTH range in a reference population: From an integrated approach to vitamin D prevalence impact evaluation

BACKGROUND

The iPTH upper reference limit (URL) reported by our laboratory provider (Abbott Laboratories) at Tor Vergata University Hospital was evaluated by internal verification procedures as not representative of our population and resulting as underestimated. In this study, a new reference interval has been investigated and established by comparing a direct and an indirect method based on a statistical reduction from results stored in the laboratory database.

METHODS

For reference interval calculation from the healthy population, we analyzed a cohort of 100 blood donors (84% males and 16% females) screened with no bone-related and malabsorption diseases. We analyzed a cohort of 495 patients retrieved from more than 800 iPTH results by excluding subjects with pathological measurement for calcium, phosphorus, and creatinine for the reference interval evaluation. Patients with vitamin D results were included in the analysis. Vitamin D sufficiency status during the period from January to September 2020 was also evaluated by investigating 3,050 patients.

RESULTS

The iPTH reference interval of a healthy blood donor population was measured as 25.2-109.1 pg/mL (2.7-11.6 pmol/L) at 2.5 and 97.5 distribution percentile. The iPTH reference interval from data stored in the laboratory database was 19.3-112.5 pg/mL (2.0-11.9 pmol/L). Furthermore, 60% of the whole population had prevalently insufficient vitamin D concentration (<30 ng/dL; <75 nmol/L). The impact of vitamin D concentration on the iPTH reference interval was measured for insufficient vitamin D (<30 ng/dL; <75 nmol/L) as 15.2-127.7 pg/mL (1.6-13.5 pmol/L), desirable vitamin D (30-40 ng/ml; 75-100 nmol/L) as 25.6-105 pg/mL (2.7-10.7 pmol/L) and optimal vitamin D (>40 ng/ml; >100 nmol/L) as 26.2-89.2 pg/mL (2.8-9.4 pmol/L), respectively.

CONCLUSIONS

The URL reported in manufacturer datasheets likely refers to a normal population with non-pathological vitamin D levels. On the contrary, the considered population was mostly vitamin D insufficient, resulting in a URL shift. On this basis, we suggest describing in medical reports the iPTH range for vitamin D deficiency for diagnosis of primary hyperparathyroidism even when a specific vitamin D request is lacking. On the other hand, reporting optimal vitamin D-based iPTH reference interval could be clinically relevant in supplemented patients as a marker of treatment efficacy.

The importance of correct stratifications when comparing directly and indirectly estimated reference intervals

OBJECTIVES

There are generally two major reasons for the comparison of reference intervals (RIs): when externally determined RIs (from the literature or provided by a manufacturer) are compared with presently used intra-laboratory RIs and when indirectly estimated RIs are compared with directly established RIs. Discrepancies within these comparisons may occur for two reasons: 1. the pre-analytical and/or analytical conditions do not agree and/or 2. biological variables influencing the establishment of RIs have not been considered adequately. If directly and indirectly estimated reference intervals (RIs) are compared with each other, they very often agree. Sometimes, however, a comparison may differ, with the reason for any discrepancy not being further studied. A major reason for differences in the comparison of RIs is that the requirement for stratification has been neglected.

METHODS

The present report outlines the consequences to RI comparison if stratification is neglected during RI determination with the main variables affecting RIs being sex and age. Alanine aminotransferase was chosen as an example in which the RIs depend on both these factors.

RESULTS

Both direct and indirect approaches lead to erroneous RIs if stratification for variables which are known to affect the estimation of RIs is not performed adequately. However, failing to include a required stratification in procedures for directly determined RIs affects the outcome in a different way to indirectly determined RIs.

CONCLUSIONS

The resulting difference between direct and indirect RIs is often misinterpreted as an incorrect RI estimation of the indirect method.

Big data and reference intervals: rationale, current practices, harmonization and standardization prerequisites and future perspectives of indirect determination of reference intervals using routine data

Reference intervals are commonly used as a decision-making tool. In this review, we provide an overview on "big data" and reference intervals, describing the rationale, current practices including statistical methods, essential prerequisites concerning data quality, including harmonization and standardization, and future perspectives of the indirect determination of reference intervals using routine laboratory data.

Reference intervals for routine biochemical markers and body mass index: A study based on healthcare center database in northeastern Iran

Age- and sex-specific reference intervals (RIs) for some biochemical tests may be useful for their interpretation, due to the variations in lifestyle and genetic, or ethnic factors. The aim of this study was to obtain RIs for some routine biochemical markers including a serum lipid profile, fasting blood glucose (FBG), aspartate and alanine aminotransferase (AST and ALT), uric acid, and body mass index (BMI) in subjects who attended primary healthcare centers. The large database of primary healthcare centers uses RIs to report results for children, adolescents, and young and old adults. RIs were obtained by using the indirect method, recommended by the CLSI Ep28-A3 guidelines. RIs for FBG, BMI, and serum lipid profile, including triglyceride, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol in people aged 18 to 120 years, were obtained without age/sex segmentation. RIs for serum AST, ALT, and uric acid were obtained without age segmentation, though these RIs were higher in males than females. The RIs for AST, ALT, and uric acid were higher in men, while the RIs for the other variables were similar in both sexes. This is the first study reporting the use of indirect RIs for BMI.

Albumin and calcium reference interval using healthy individuals and a data-mining approach

BACKGROUND

Harmonization of reference intervals for analytes that have a sound calibration and metrological traceability is a widely recommended practice. The UK Pathology Harmony has recently harmonized reference intervals for calcium and albumin. In this study, we have determined the reference intervals for calcium and albumin on the UK's most commonly used analytical platforms.

METHOD

A prospective reference population of healthy individuals was recruited according to the IFCC CRIDL criteria. A second indirect population was collected from 14 primary care setting and measured in laboratories using various analytical platforms and methods (Roche, Abbott, Beckman and Siemens analytical platforms).

RESULTS

In total, 299 subjects were recruited; the central 95th centile values for calcium for three out of four analytical platforms were in a close agreement with UK Pathology Harmony reference intervals of 2.2-2.6 mmol/L. Reference intervals of BCG methods from both cohorts and irrespective of analytical platforms were higher for both lower and upper reference limits than those for BCP. In comparison, the indirect study showed an age-related variation. The younger population reference intervals varied by up to 5.7% at the lower reference limit and up to 12% at the upper reference limit compared with Pathology Harmony reference intervals, and the older population showed a variation of up to 14% at both limits.

CONCLUSION

While calcium reference intervals can be a subject for harmonization, albumin reference intervals studied showed large variation which is unsupportive of embracing a common reference interval for albumin.

A new indirect estimation of reference intervals: truncated minimum chi-square (TMC) approach

All known direct and indirect approaches for the estimation of reference intervals (RIs) have difficulties in processing very skewed data with a high percentage of values at or below the detection limit. A new model for the indirect estimation of RIs is proposed, which can be applied even to extremely skewed data distributions with a relatively high percentage of data at or below the detection limit. Furthermore, it fits better to some simulated data files than other indirect methods. The approach starts with a quantile-quantile plot providing preliminary estimates for the parameters (λ, μ, σ) of the assumed power normal distribution. These are iteratively refined by a truncated minimum chi-square (TMC) estimation. The finally estimated parameters are used to calculate the 95% reference interval. Confidence intervals for the interval limits are calculated by the asymptotic formula for quantiles, and tolerance limits are determined via bootstrapping. If age intervals are given, the procedure is applied per age interval and a spline function describes the age dependency of the reference limits by a continuous function. The approach can be performed in the statistical package R and on the Excel platform.