Estimation Of Reference Intervals Research Articles

Estimation of Gestational Age-Specific Reference Intervals for Coagulation Assays in a Neonatal Intensive Care Unit Using Real-World Clinical Data

Abstract Interpretation of coagulation testing in neonates currently relies on reference intervals defined from adult cohorts. These likely do not reflect the normative values for neonates, in whom immature synthesis of vitamin K-dependent factors may contribute to this divergence. Direct reference interval estimation studies are difficult, particularly in neonatal populations; however, advances in indirect estimation may allow us to infer normative distributions from routinely collected clinical data. To accomplish this, initial coagulation testing results from all patients admitted to a level IV neonatal intensive care unit between 1/1/2018 - 1/1/2023 were extracted from the electronic health record and analyzed. Results prior to June 2021 were performed on the viscosity-based STA Compact Max (Diagnostica Stago. Parsippany, NJ, USA). Afterward, results were performed on a nephelometry-based ACL Top (Instrumentation Laboratory. Bedford, MA, USA). Specimens collected after transfusion of blood products were excluded. Prothrombin times (PT) and international normalized ratios (INR) were available for 1,117 neonates, while activated partial thromboplastin times (aPTT) were available for 790 neonates. The 95% confidence intervals (CI) of the median PT for extremely preterm, very preterm, preterm and term neonates were [19 - 21s], [18 - 22s], [17 - 19s] and [17 - 18s]. For INR, the CI were [1.6 - 1.8], [1.5 - 1.8], [1.4 - 1.6], and [1.4 - 1.5]. For aPTT, the CI were [51 - 61s], [47 - 58s], [45 - 50s], and [42 - 46s]. Indirect reference intervals (IRIs) were then estimated across gestational age groups (binning all patients delivered at less than 37 weeks into one preterm category) using refineR. The IRI for PT was 10 - 24s for preterm,10 - 22s for term, and 10 - 24s for all neonates. The IRI for INR was 0.7 - 2.1 for preterm, 0.8 - 1.8 for term, and 0.8 - 1.9 for all neonates. The IRI for aPTT was 24.5 - 68.4s for preterm, 24.7 - 58.2s for term, and 25 - 62.3s for all neonates. When compared to currently reported reference intervals, the lower limits of the IRI were similar to currently reported reference intervals; however, the upper limits of the IRIs were 1.3 - 1.8 times higher than current reference intervals. Theoretically, implementation of these IRIs would lead to a 3-fold decrease in the number of results flagged as abnormal. In conclusion, IRIs for coagulation assays estimated from neonates using routine clinical data show substantial divergence from current reference intervals in their upper limits, possibly due to the decreased factor concentrations seen prior to liver maturation. If implemented, these IRIs would substantially reduce the proportion of abnormal results. Future work will explore whether switching to these reference intervals could lead to a reduction in unnecessary transfusions or improved clinical outcomes.

B-114 Reference interval (RI) estimation for TSH in adults using an indirect method with unsupervised machine learning tool and R programming language

Abstract Background Thyroid-stimulating hormone (TSH) plays a fundamental role in regulating thyroid hormones T3 (triiodothyronine) and T4 (thyroxine). Understanding TSH test results is essential for effective management of thyroid disorders and depends, among other factors, on establishing specific reference intervals (RI) for the population being served. We aimed to estimate the reference intervals for TSH in the adult population served by a large diagnostic network in Brazil. Methods We employed data mining followed by estimation using an indirect method through the LabRI™ algorithm with parametric, non-parametric, and robust statistical treatments for outlier exclusion, a series of algorithms programmed in R Language. Included were TSH values from outpatients, aged 20 to 60 years, both sexes, from two analytical platforms. Used as a comparative RI recommended by the American Thyroid Association (ATA, 2014). Data were extracted from the Data Lake, from January to December 2022, from two laboratories in Brazil. Exclusion criteria: samples from hospital sources and referral laboratories; pregnant; some laboratory tests correlated with de TSH test; samples from patients using thyroid hormone and medications that may interfere with hormonal function and TSH laboratory analysis. Results After exclusion criteria, the sample totaled 26,909 adults of both sexes. After excluding 2.24% outliers, the estimated RI was 0.67-4.26 mIU/L, with the comparative RI chosen for this study being the one recommended by ATA of 0.40-4.00 mIU/L. The RI obtained in our study was similar to the Canadian RI harmonization study for TSH (0.60-4.48 mIU/L). Conclusions We estimated the RI, harmonized between Roche and Siemens platforms, to be 0.67-4.26 mIU/L. The RI obtained by the LabRI™ tool is equivalent to the Adeli study (2023) and is under discussion by the company's endocrinology committee for whether to use the estimated RI from our internal study or the one recommended by ATA (2014) in reports.

Estimation of gestational age-specific reference intervals for coagulation assays in a neonatal intensive care unit using real-world data

BackgroundInterpretation of coagulation testing in neonates currently relies on reference intervals (RIs) defined from older patient cohorts. Direct RI studies are difficult, but indirect estimation may allow us to infer normative neonatal distributions from routinely collected clinical data. ObjectiveAssess the utility of indirect reference interval methods in estimating coagulation reference intervals in critically ill neonates. MethodsWe analyzed first-in-life coagulation testing results from all patients admitted to a level IV neonatal intensive care unit between January 1, 2018, and January 1, 2024. Results obtained after transfusion of any blood product were excluded. Indirect RIs were estimated across gestational age groups using refineR and compared with currently reported intervals for patients less than 1 year of age. ResultsProthrombin times (PTs) and international normalized ratios (INRs) were available for 1128 neonates, while activated partial thromboplastin times (APTTs) were available for 790 neonates. The indirect RI was 10 to 25 seconds in preterm, 10 to 22 seconds in term, and 10 to 24 seconds in all neonates for PT; 0.7 to 2.1 in preterm, 0.8 to 1.8 in term, and 0.8 to 1.9 in all neonates for INR; and 25 to 68 seconds in preterm, 25 to 58 seconds in term, and 25 to 62 seconds in all neonates for APTT. Compared with our current intervals, the indirect RIs would flag 58% fewer PT, 43% fewer INR, and 17% fewer APTT results as abnormal. ConclusionIndirectly estimated RIs in neonates admitted to intensive care show substantial divergence from current, first-year-of-life RIs, leading to an abundance of abnormal flags. The associations between these flags and provider behavior, transfusion practice, or clinical outcomes are areas of future exploration.

Indirect reference interval estimation using a convolutional neural network with application to cancer antigen 125

Indirect methods for reference interval (RI) estimation, which use data acquired from routine pathology testing, have the potential to accelerate the establishment of RIs to account for variables such as gender and age to improve clinical assessments. However, they require more sophisticated methods of analysis due to the potential influence of pathological patients in raw clinical datasets. In this paper we develop a novel convolutional neural network (CNN) model trained on synthetic data to identify underlying healthy distributions within pathological admixtures. We present both the methodology to generate synthetic data and the CNN model. We evaluate the CNN using two synthetic test datasets, including samples from a proposed benchmark for indirect methods (RIBench) and show significant improvements compared to the reported state-of-the-art method based on the benchmark (refineR). We also demonstrate a real-world application of the model, estimating age-specific RIs for cancer antigen 125 (CA-125), a crucial biomarker for ovarian cancer diagnostics. Our results show that CA-125 RIs are strongly age-dependent, which could have important diagnostic consequences.

How Gaussian mixture modelling can help to verify reference intervals from laboratory data with a high proportion of pathological values

Abstract Objectives Although there are several indirect methods that can be used to verify reference limits, they have a common weakness in that they assume a low proportion of pathological values. This paper investigates whether a Gaussian decomposition algorithm can identify the non-pathological fraction even if it is not the main subset of mixed data. Methods All investigations are carried out in the R programming environment. The mclust package is used for Gaussian mixture modelling via the expectation maximization (EM) algorithm. For right-skewed distributions, logarithms of the original values are taken to approximate the Gaussian model. We use the Bayesian information criterion (BIC) for evaluation of the results. The reflimR and refineR packages serve as comparison procedures. Results We generate synthetic data mixtures with known normal distributions to demonstrate the feasibility and reliability of our approach. Application of the algorithm to real data from a Nigerian and a German population produces results, which help to interpret reference intervals of reflimR and refineR that are obviously too wide. In the first example, the mclust analysis of hemoglobin in Nigerian women supports the medical hypothesis that an anemia rate of more than 50 % leads to falsely low reference limits. Our algorithm proposes various scenarios based on the BIC values, one of which suggests reference limits that are close to published data for Nigeria but significantly lower than those established for the Caucasian population. In the second example, the standard statistical analysis of creatine kinase in German patients with predominantly cardiac diseases yields a reference interval that is clearly too wide. With mclust we identify overlapping fractions that explain this false result. Conclusions Gaussian mixture modelling does not replace standard methods for reference interval estimation but is a valuable adjunct when these methods produce discrepant or implausible results.

Automated sex and age partitioning for the estimation of reference intervals using a regression tree model

Abstract Objectives Reference intervals (RI) play a decisive role in the interpretation of medical laboratory results. An important step in the determination of RI is age- and sex specific partitioning, which is usually based on an empirical approach by graphical representation. In this study, we evaluate an automated machine learning approach. Methods This study uses pediatric data from the CALIPER RI (Canadian Laboratory Initiative on Pediatric Reference Intervals) study. The calculation of potential partitions is carried out using a regression tree model included in the rpart package of the statistical programming language R. The Harris & Boyd method is used to compare the corresponding partitions suggested by rpart and CALIPER. For better comparability, the reference ranges of the partitions of both approaches are then calculated using reflimR. Results Most of the partitions suggested by rpart or CALIPER show sufficient heterogeneity among themselves to justify age- and/or sex-specific RI partitioning. With only few individual exceptions, both methods yield comparable results. The partitions of both approaches for albumin and γ-glutamyltransferase are very similar to each other. For creatinine rpart suggests a slightly earlier distinction between the sexes. Alkaline phosphatase shows the most pronounced differences. In addition to a considerable earlier sex split, rpart suggests different age intervals for both sexes, resulting in three partitions for females and four partitions for males. Conclusions Our findings indicate that the automated analysis provided by rpart yields results that comparable to traditional methods. Nevertheless, the medical plausibility of the automatic suggestions needs to be validated by human experts.

Direct, age- and gender-specific reference intervals: applying a modified M-estimator of the Yeo-Johnson transformation to clinical real-world data

Abstract Objectives Reference intervals for the general clinical practice are expected to cover non-pathological values, but also reflect the underlying biological variation present in age- and gender-specific patient populations. Reference intervals can be inferred from routine patient data measured in high capacity using parametric approaches. Stratified reference distributions are obtained which may be transformed to normality via e.g. a Yeo-Johnson transformation. The estimation of the optimal transformation parameter for Yeo-Johnson through maximum likelihood can be highly influenced by the presence of outlying observations, resulting in biased reference interval estimates. Methods To reduce the influence of outlying observations on parametric reference interval estimation, a reweighted M-estimator approach for the Yeo-Johnson (YJ) transformation was utilised to achieve central normality in stratified reference populations for a variety of laboratory test results. The reweighted M-estimator for the YJ transformation offers a robust parametric approach to infer relevant reference intervals. Results The proposed method showcases robustness up to 15 % of outliers present in routine patient data, highlighting the applicability of the reweighted M-estimator in laboratory medicine. Furthermore, reference intervals are personalised based on the patients’ age and gender for a variety of analytes from routine patient data collected in a tertiary hospital, robustly reducing the dimensionality of the data for more data-driven approaches. Conclusions The method shows the advantages for estimating reference intervals directly and parametrically from routine patient data in order to provide expected reference ranges. This approach to locally inferred reference intervals allows a more nuanced comparison of patients’ test results.

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

BackgroundObservable quantitative variations exist between plasma and serum in routine protein measurements, often not reflected in standard reference intervals. In this study, we describe an indirect approach for estimating a combined reference interval (RI) (i.e., serum and plasma), for commonly ordered protein measurands: total protein, albumin, and globulin. MethodsWe applied an indirect reference interval estimation for protein measurements in serum and plasma using data from July 2018 to February 2024. The data were divided into three Epochs based on a period of plasma separator tube shortage during the COVID-19 pandemic. Bootstrap resampling was used to calculate RIs and corresponding 95% confidence intervals for each month. ResultsOur results demonstrate notable changes in RI limits for total protein, albumin, and globulin between Epochs, reflecting the influence of changing sample matrix. A combined RI was identified for all components and verified using plasma and serum samples from 20 healthy individuals and retrospective analysis of flagging rates on our outpatient population using new and historical RIs. ConclusionThe study demonstrates notable differences in the RIs for total protein, albumin, and globulin when container type changes. In addition, the results demonstrate the effectiveness of big data analytics in deriving RIs and highlights the necessity of continuous RI assessment and adjustment based on the patient population and acceptable specimen types.

The performance of unsupervised machine learning method in the indirect reference interval estimation of selected biochemical analytes

The performance of unsupervised machine learning method in the indirect reference interval estimation of selected biochemical analytes

An Objective Function-Based Clustering Algorithm with a Closed-Form Solution and Application to Reference Interval Estimation in Laboratory Medicine

Clustering algorithms are usually iterative procedures. In particular, when the clustering algorithm aims to optimise an objective function like in k-means clustering or Gaussian mixture models, iterative heuristics are required due to the high non-linearity of the objective function. This implies higher computational costs and the risk of finding only a local optimum and not the global optimum of the objective function. In this paper, we demonstrate that in the case of one-dimensional clustering with one main and one noise cluster, one can formulate an objective function, which permits a closed-form solution with no need for an iteration scheme and the guarantee of finding the global optimum. We demonstrate how such an algorithm can be applied in the context of laboratory medicine as a method to estimate reference intervals that represent the range of “normal” values.

Estimation of reference interval for neutrophil activity evaluation systems: a interim report.

Neutrophils play an important role in innate immunity and produce reactive oxygen species, but they can also cause inflammation and oxidative stress that can damage their own tissues. We have developed neutrophil activity evaluation systems that simultaneously monitors superoxide radicals and hypochlorite ions secreted by stimulated neutrophils in a few microliters of whole blood and have conducted clinical studies in humans. Here, we report normal reference intervals with our systems based on the results of 3,082 persons who underwent comprehensive cancer screening between February 2020 and March 2022. A total of 344 were extracted as reference individuals based on the results of the cancer screening and the reference intervals of the two systems were interim estimated considering gender and age. Reference intervals can be used as a marker of sub-clinical inflam-mation, which is difficult to detect with other blood markers.

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.

Estimation of reference intervals for physical fitness indicators in healthy community-dwelling older adults

Estimation of reference intervals for physical fitness indicators in healthy community-dwelling older adults

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.

B-152 Estimation of Creatinine Reference Interval (RI) by the Indirect Method using an Unsupervised Machine Learning Tool and R Programming Language

Abstract Background Creatinine is widely used in diagnostic medicine as a primary renal marker. Clinical laboratory measurement of serum creatinine is not standardized, and the RI varies slightly between different manufacturers. Roche brings as reference ranges 0.5–0.9 mg/DL for women and 0.7–1.2 mg/dL for men. After clinical manifestations of elevated female results without clinical correspondence, the laboratory proceeded with reevaluation of RI. Methods We retrospectively studied the creatinine results of the laboratory database of samples run from January to October 2022 in two different technical areas in Brazil (São Paulo and Rio de Janeiro). The laboratory platform was Roche® 502/702, Jaffe method to creatinine. The LabRI tool was used with parametric, non-parametric, and robust statistical treatments to exclude outliers and algorithms in the R Language and latent abnormal values of outpatient patients. The partitions proposed were gender (male and female), age (18–60 years old and over 60 years old). The proposed RI for verification were those proposed by Rosenfeld, LG et al in REV BRAS EPIDEMIOL 2019; 22 (SUPPL2):E190002.SUPL.2 - F: 0.5–0.9 and M: 0.7–1.2. Exclusion criteria were patients with urea >48.5 mg/dL, glycemia >200 mg/dL, 24 h proteinuria >150 mg/24 h, LDL > 130 mg/dL, uric acid > 130 mg/dL, anti-nucleus positive factor, as well as clinical data compatible with chronic renal failure or dialysis, hypertension and diabetes. Results After exclusion, retrieved 225 181 individuals 52% female. Between the different sites, the results of the partitions did not undergo significant changes. For women in both age partitions, the values did not vary significantly. For men, there was no significant variation in the lower limit of IR, and we had a variation of 0.20 (18–60: 0.69–1.31 and >60: 0.69–1.41) in the upper limit, mainly for the age partition >60 years old, below the 95% confidence interval of the proposed references. Conclusions The adoption of the Roche IR and the Brazilian article was approved for the female gender (0.5–0.9) with 118 100 individuals, and a new IR was defined for the male gender (0.7–1.2) with 107 081 adult individuals, including the elderly over 60 years old, for both genders.

B-151 Estimation of the Uric Acid Reference Interval by the Indirect Method Using an Unsupervised Machine Learning Tool and R Programming Language

Abstract Background Determining the Reference Intervals (RI) of quantitative clinical laboratory tests is crucial for patient diagnosis, treatment, and monitoring. It is also a concern for laboratories seeking to meet accreditation requirements. Our study aimed to evaluate the manufacturer’s RI for uric acid (3.7–9.2 mg/dL) through indirect methods using a programming tool in R language in a Brazilian laboratory. Methods We performed a data mining study for uric acid using the laboratory’s LIS database from January to December 2021. Outpatients >18 years old, analyzed on the Siemens® platform with results within linearity were included. Exclusion criteria were total cholesterol >200 mg/dL, triglycerides >150 mg/dL, glycemia >126 mg/dL, ferritin >300 ng/mL and creatinine >1.5 mg/dL at any time in the patient history and CRP >10 mg/dL in the same month. We used a tool that integrates different algorithms including preliminary treating, grouping, and statistical reduction of test results. The Expectation-Maximization (EM) algorithm calculates the mean, standard deviation, and weight of the distribution for each cluster. Additionally, it integrates algorithms that determine the global mean/median, modes, antimodes, kurtosis, and skewness, to find values that best represent the reference population’s distribution (Figure). Manufacturer's and estimated RI for Male and Female partitions. Results The final database included 49.489 subjects, partitioned in 26.439 women and 23.050 men. Statistically estimated RI were 2.8–6.7 mg/dL for women and 3.8–8.6 mg/dL for men. Considering the RI proposed by the manufacturer, 23.8% of women were outside the RI provided by the manufacturer, all of them below the lower reference value. Conclusions Our results confirm the importance of verification/establishment of reference intervals by clinical laboratories and suggest that estimated RI using this software is appropriate for the assisted population, demonstrating consistency with physicians’ concerns. After specialist analysis, the manufacturer´s RI has been replaced by the calculated population-specific RI.

Critical appraisal of two Box-Cox formulae for their utility in determining reference intervals by realistic simulation and extensive real-world data analyses

BackgroundThe reference interval (RI) is defined as the central 95 % range of reference values (RVs) from healthy individuals. The ideal method for determining RIs is to transform RV distribution into Gaussian and estimate its 95 % range parametrically. One-parameter Box-Cox formula (1pBC) is widely used for correcting skewness (Sk) or kurtosis (Kt) in data distribution. However, 1pBC is not popular for computing RIs due to its unreliability in Gaussian transformation. While its two-parameter version (2pBC) is not used due to a challenge in fitting power (λ) and shift (α) parameters simultaneously. In this study, technical issues in fitting both formulae are assessed, and an alternative algorithm for successful use of 2pBC is proposed. MethodsFor fitting 1pBC, optimal λ was determined by stepwise linear search. For 2pBC, optimal [λ, α] combination was pursued in two ways: by grid search of λ and α (2pBCgrid) or by using the grid search but keeping α-range close to the reference distribution (2pBCopt). Their accuracy and precision in determining RIs were compared by generating power-normal distributions simulating RVs of 23 major chemistry analytes. Additionally, their practical utilities were compared by analyzing 776 real-world datasets comprising test results of 25 analytes that were obtained from the global multicenter RV study of IFCC. For comparison, the performance of nonparametric method was evaluated in both settings. ResultsFor analytes with not-much-skewed distributions, unbiased estimation of RIs was accomplished by all methods. Nevertheless, when reference distributions are located far from zero, λ estimated by1pBC and 2pBCgrid fluctuated widely, which was attributable to virtually flat goodness-of-fit profile for [λ, α]. For highly skewed distributions, 1pBC caused bias in estimating RI and λ due to remotely peaked goodness-of-fit profile. Real-world data analyses revealed that 2pBCopt and 1pBC achieved Gaussian transformation (|Sk|<0.1 and |Kt|<0.3) in 82.4 % and 66.9 % among 776 datasets, respectively. Fitting bias signified by Kt<−0.4 was more common to 1pBC. The practical utility of 2pBCopt was unbiased prediction of analyte-specific distribution-shape (λ). Whereas nonparametric method gave highly variable RIs for both simulated and real-world datasets. Conclusions2pBCopt is suitable for calculating RIs and grasping distribution-shape from the estimated λ.

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.

Comparison of three indirect methods for verification and validation of reference intervals at eight medical laboratories: a European multicenter study

Abstract Objectives Indirect methods for the indirect estimation of reference intervals are increasingly being used, especially for validation of reference intervals, as they can be applied to routine patient data. In this study, we compare three statistically different indirect methods for the verification and validation of reference intervals in eight laboratories distributed throughout Europe. Methods The RefLim method is a fast and simple approach which calculates the reference intervals by extrapolating the theoretical 95 % of non-pathological values from the central linear part of a quantile-quantile plot. The Truncated Maximum Likelihood (TML) method estimates a smoothed kernel density function for the distribution of the mixed data, for which it is assumed that the ‘‘central’’ part of the distribution represents the healthy population. The refineR utilizes an inverse modelling approach. This algorithm identifies a model that best explains the observed data before transforming the data with the Box-Cox transformation. Results We show that the different indirect methods each have their advantages but can also lead to inaccurate or ambiguous results depending on the approximation of the mathematical model to real-world data. A combination of different methodologies can improve the informative value and thus the reliability of results. Conclusions Based on routine measurements of four enzymes alkaline phosphatase (ALP), total amylase (AMY), cholinesterase (CHE) and gamma-glutamyl transferase (GGT) in adult women and men, we demonstrate that some reference limits taken from the literature need to be adapted to the laboratory’s particular local and population characteristics.

Age-and sex-specific reference intervals of total cholesterol, LDL cholesterol, HDL cholesterol and non-HDL cholesterol. Comparison of two algorithms for the indirect estimation of reference intervals

Abstract Objectives Reference intervals of total cholesterol concentrations in plasma and of their fractions low-density lipoprotein (LDL)-, high-density lipoprotein (HDL)-and non-HDL concentrations are seldom studied with respect to the relevance of age and sex. Therefore, the effect of age and sex on the reference intervals was reinvestigated with 2 indirect procedures. Methods As an indirect approach, the truncated minimum chi-square method was applied. All analyses were performed by computer programs available. The script published on the homepage of the German Society of Clinical Chemistry and Laboratory Medicine (DGKL) allows to derive a continuous age dependency of reference intervals together with their confidence and equivalence limits. The results of this approach were compared with those obtained by an indirect method developed more recently, the refineR algorithm. Results In the present study, the upper reference limits of total cholesterol varied from 5.1 to 7.8 mmol/L (197–302 mg/dL) depending on various biological variables (as age, sex, inpatients versus outpatients). These upper limits increased with age. Differences between sexes can be neglected except for the age above 80 years. The pattern of reference limits of LDL cholesterol and non-HDL cholesterol paralleled those of total cholesterol. The reference limits of HDL cholesterol were higher in women than in men but were independent of age. Conclusions Reference limits for the concentrations of total cholesterol and their fractions LDL-, HDL-and non-HDL concentrations should be stratified for age and sex.