Indirect methods for reference interval determination – review and recommendations

Abstract Reference intervals are a vital part of the information supplied by clinical laboratories to support interpretation of numerical pathology results such as are produced in clinical chemistry and hematology laboratories. The traditional method for establishing reference intervals, known as the direct approach, is based on collecting samples from members of a preselected reference population, making the measurements and then determining the intervals. An alternative approach is to perform analysis of results generated as part of routine pathology testing and using appropriate statistical techniques to determine reference intervals. This is known as the indirect approach. This paper from a working group of the International Federation of Clinical Chemistry (IFCC) Committee on Reference Intervals and Decision Limits (C-RIDL) aims to summarize current thinking on indirect approaches to reference intervals. The indirect approach has some major potential advantages compared with direct methods. The processes are faster, cheaper and do not involve patient inconvenience, discomfort or the risks associated with generating new patient health information. Indirect methods also use the same preanalytical and analytical techniques used for patient management and can provide very large numbers for assessment. Limitations to the indirect methods include possible effects of diseased subpopulations on the derived interval. The IFCC C-RIDL aims to encourage the use of indirect methods to establish and verify reference intervals, to promote publication of such intervals with clear explanation of the process used and also to support the development of improved statistical techniques for these studies.

Download Full-text

Establishing and using reference intervals

Turkish Journal of Biochemistry ◽

10.1515/tjb-2017-0299 ◽

2020 ◽

Vol 45 (1) ◽

pp. 1-10

Author(s):

Yesim Ozarda

Keyword(s):

Clinical Chemistry ◽

Laboratory Data ◽

Age Groups ◽

Clinical Decision ◽

Reference Population ◽

External Source ◽

Reference Intervals ◽

Multicenter Studies ◽

Indirect Methods ◽

Laboratory Test Results

AbstractReference intervals (RIs) and clinical decision limits (CDLs) are fundamental tools used by healthcare and laboratory professionals to interpret patient laboratory test results. The traditional method for establishing RIs, known as the direct approach, is based on collecting samples from members of a preselected reference population, making the measurements and then determining the intervals. For challenging groups such as pediatric and geriatric age groups, indirect methods are appointed for the derivation of RIs in the EP28-A3c guideline. However, there has been an increasing demand to use the indirect methods of deriving RIs by the use of routine laboratory data stored in the laboratory information system. International Federation of Clinical Chemistry (IFCC), Committee on Reference Intervals and Decision Limits (C-RIDL) is currently working on the study for the comparison of the conventional (direct) and alternative (indirect) approaches for the determination of reference intervals. As a matter of fact that, the process of developing RIs is often beyond the capabilities of an individual laboratory due to the complex, expensive and time-consuming process to develop them. Therefore, a laboratory can alternatively transfer and verify RIs established by an external source (i.e. manufacturers’ package inserts, publications). IFCC, C-RIDL has focused primarily on RIs and has performed multicenter studies to obtain common RIs in recent years. However, as the broader responsibility of the Committee, from its name, includes “decision limits”, the C-RIDL also emphasizes the importance of the correct use of both RIs and CDLs and to encourage laboratories to specify the appropriate information to clinicians as needed.

Download Full-text

Application of the TML method to big data analytics and reference interval harmonization

LaboratoriumsMedizin ◽

10.1515/labmed-2020-0133 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Mary Kathryn Bohn ◽

Khosrow Adeli

Keyword(s):

Clinical Chemistry ◽

A Priori ◽

Big Data Analytics ◽

Reference Interval ◽

German Society ◽

Reference Intervals ◽

Indirect Methods ◽

Advantages And Disadvantages ◽

Result Interpretation ◽

Indirect Reference

Abstract Significant variation in reported reference intervals across healthcare centers and networks for many well-standardized laboratory tests continues to exist, negatively impacting patient outcomes by increasing the risk of inappropriate and inconsistent test result interpretation. Reference interval harmonization has been limited by challenges associated with direct reference interval establishment as well as hesitancies to apply currently available indirect methodologies. The Truncated Maximum Likelihood (TML) method for indirect reference interval establishment developed by the German Society of Clinical Chemistry and Laboratory Medicine (DGKL) presents unique clinical and statistical advantages compared to traditional indirect methods (Hoffmann and Bhattacharya), increasing the feasibility of developing indirect reference intervals that are comparable to those determined using a direct a priori approach based on healthy reference populations. Here, we review the application of indirect methods, particularly the TML method, to reference interval harmonization and discuss their associated advantages and disadvantages. We also describe the CSCC Reference Interval Harmonization Working Group’s experience with the application of the TML method in harmonization of adult reference intervals in Canada.

Download Full-text

Review of potentials and limitations of indirect approaches for estimating reference limits/intervals of quantitative procedures in laboratory medicine

Journal of Laboratory Medicine ◽

10.1515/labmed-2020-0131 ◽

2021 ◽

Vol 45 (2) ◽

pp. 35-53

Author(s):

Rainer Haeckel ◽

Werner Wosniok ◽

Thomas Streichert

Keyword(s):

Direct Methods ◽

Reference Population ◽

Reference Intervals ◽

Simulation Studies ◽

Indirect Methods ◽

Medical Laboratories ◽

Pathological Conditions ◽

Crucial Difference ◽

External Sources ◽

Direct And Indirect Methods

Abstract Reference intervals (RIs) can be determined by direct and indirect procedures. Both approaches identify a reference population from which the RIs are defined. The crucial difference between direct and indirect methods is that direct methods select particular individuals after individual anamnesis and medical examination have confirmed the absence of pathological conditions. These individuals form a reference subpopulation. Indirect methods select a reference subpopulation in which the individuals are not identified. They isolate a reference population from a mixed population of patients with pathological and non-pathological conditions by statistical reasoning. At present, the direct procedure internationally recommended is the “gold standard”. It has, however, the disadvantage of high expenses which cannot easily be afforded by most medical laboratories. Therefore, laboratories adopt RIs established by direct methods from external sources requiring a high responsibility for transference problems which are usually neglected by most laboratories. These difficulties can be overcome by indirect procedures which can easily be performed by most laboratories without causing economic problems. The present review focuses on indirect approaches. Various procedures are presented with their benefits and limitations. Preliminary simulation studies indicate that more recently developed concepts are superior to older approaches.

Download Full-text

Calculation of indirect reference intervals of plasma lipase activity of adults from existing laboratory data based on the Reference Limit Estimator integrated in the OPUS::L information system

Journal of Laboratory Medicine ◽

10.1515/labmed-2021-0008 ◽

2021 ◽

Vol 45 (2) ◽

pp. 131-134

Author(s):

Britta Amodeo ◽

Aline Schindler ◽

Ulrike Schacht ◽

Hans Günther Wahl

Keyword(s):

Information System ◽

Lipase Activity ◽

Clinical Chemistry ◽

Laboratory Data ◽

Direct Methods ◽

Reference Interval ◽

Reference Intervals ◽

Specific Reference ◽

Reference Limit ◽

Reference Limits

Abstract Objectives Most laboratories have difficulties to determine their own reference intervals for the diagnostic evaluation of patient results by direct methods. Therefore, data is often just taken from the literature or package inserts of the analytical tests. Methods The section on Reference Limits of the German Society for Clinical Chemistry and Laboratory Medicine (DGKL) first uploaded the Reference Limit Estimator (RLE) as an R-program with MS Excel-interface on the DGKL home page and now this tool is implemented in the commercial Laboratory Information System OPUS::L (OSM AG Essen, Germany). We used this OPUS::L “Population specific Reference Limits” tool online with our laboratory database. First calculations were done using the example of lipase. Results The manufacturer’s original reference interval for lipase 12–53 U/L (adults) was changed to age dependent upper reference limits of <41 U/L (<20 years), <60 U/L (20–80 years) and <70 U/L (>80 years). Conclusions By means of the OPUS::L “Population specific Reference Limits” tool we were able to establish our laborarotry specific reference interval for plasma lipase activity. The new reference limits helped to solve an old problem of implausible low elevated lipase values.

Download Full-text

Diurnal variation of leukocyte counts affects the indirect estimation of reference intervals

LaboratoriumsMedizin ◽

10.1515/labmed-2020-0132 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Antje Torge ◽

Rainer Haeckel ◽

Mustafa Özcürümez ◽

Alexander Krebs ◽

Ralf Junker

Keyword(s):

Diurnal Variation ◽

Indirect Method ◽

Venous Blood ◽

Direct Methods ◽

Reference Intervals ◽

Indirect Methods ◽

Indirect Estimation ◽

Late Afternoon ◽

Reference Limits ◽

Leukocyte Counts

Abstract It has been observed that the estimation of reference intervals of leukocytes in whole venous blood leads to higher upper reference limits (uRLs) with indirect methods than has been reported in the literature determined by direct approaches. This phenomenon was reinvestigated with a newer, more advanced indirect method, and could be confirmed. Furthermore, a diurnal variation was observed with lower values during the morning and higher values in the late afternoon and at night. This observation can explain why indirect approaches using samples collected during 24 h lead to higher uRLs than direct methods applied on samples collected presumably in the morning.

Download Full-text

Personalized Reference Intervals in Laboratory Medicine: A New Model Based on Within-Subject Biological Variation

Clinical Chemistry ◽

10.1093/clinchem/hvaa233 ◽

2020 ◽

Author(s):

Abdurrahman Coşkun ◽

Sverre Sandberg ◽

Ibrahim Unsal ◽

Coskun Cavusoglu ◽

Mustafa Serteser ◽

...

Keyword(s):

Steady State ◽

Personalized Medicine ◽

Clinical Chemistry ◽

Reference Interval ◽

Reference Intervals ◽

Biological Variation ◽

Test Results ◽

Steady State Condition ◽

Laboratory Test Results ◽

Measurement Results

Abstract Background The concept of personalized medicine has received widespread attention in the last decade. However, personalized medicine depends on correct diagnosis and monitoring of patients, for which personalized reference intervals for laboratory tests may be beneficial. In this study, we propose a simple model to generate personalized reference intervals based on historical, previously analyzed results, and data on analytical and within-subject biological variation. Methods A model using estimates of analytical and within-subject biological variation and previous test results was developed. We modeled the effect of adding an increasing number of measurement results on the estimation of the personal reference interval. We then used laboratory test results from 784 adult patients (>18 years) considered to be in a steady-state condition to calculate personalized reference intervals for 27 commonly requested clinical chemistry and hematology measurands. Results Increasing the number of measurements had little impact on the total variation around the true homeostatic set point and using ≥3 previous measurement results delivered robust personalized reference intervals. The personalized reference intervals of the study participants were different from one another and, as expected, located within the common reference interval. However, in general they made up only a small proportion of the population-based reference interval. Conclusions Our study shows that, if using results from patients in steady state, only a few previous test results and reliable estimates of within-subject biological variation are required to calculate personalized reference intervals. This may be highly valuable for diagnosing patients as well as for follow-up and treatment.

Download Full-text

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

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2018-1341 ◽

2019 ◽

Vol 57 (12) ◽

pp. 1933-1947 ◽

Cited By ~ 1

Author(s):

Werner Wosniok ◽

Rainer Haeckel

Keyword(s):

Detection Limit ◽

Simulated Data ◽

Reference Interval ◽

Reference Intervals ◽

Skewed Data ◽

Chi Square ◽

Indirect Methods ◽

Indirect Estimation ◽

Estimated Parameters ◽

Data Files

Abstract 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.

Download Full-text

Reference intervals for 21 clinical chemistry analytes in arterial and venous umbilical cord blood

Clinical Chemistry ◽

10.1093/clinchem/39.6.1041 ◽

1993 ◽

Vol 39 (6) ◽

pp. 1041-1044 ◽

Cited By ~ 16

Author(s):

S L Perkins ◽

J F Livesey ◽

J Belcher

Keyword(s):

Alkaline Phosphatase ◽

Umbilical Cord ◽

Clinical Chemistry ◽

Venous Blood ◽

Reference Interval ◽

Reference Intervals ◽

Nonparametric Analysis ◽

Term Infants ◽

Male And Female ◽

Gamma Glutamyltransferase

Abstract Reference intervals were determined for 21 clinical chemistry analytes in umbilical cord arterial and venous blood from healthy term infants. Nonparametric analysis (rank number) was used to determine the central 95% reference interval. No significant differences were observed between male and female infants. Reference intervals for glucose, urea, creatinine, urate, phosphate, calcium, albumin, total protein, cholesterol, triglycerides, alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, creatine kinase, lactate dehydrogenase, gamma-glutamyltransferase, and magnesium all were significantly different from adult values.

Download Full-text

DETERMINING REFERENCE RANGES FOR TREC AND KREC ASSAYS IN IMMUNE DEFICIENCY SCREENING OF NEWBORNS IN RUSSIAN FEDERATION

Medical Immunology (Russia) ◽

10.15789/1563-0625-2019-3-527-538 ◽

2019 ◽

Vol 21 (3) ◽

pp. 527-538

Author(s):

M. A. Gordukova ◽

I. A. Korsunsky ◽

Yu. V. Chursinova ◽

M. M. Byakhova ◽

I. P. Oscorbin ◽

...

Keyword(s):

Reference Interval ◽

Reference Population ◽

Dried Blood Spots ◽

Reference Intervals ◽

Asymmetric Distribution ◽

Reference Ranges ◽

Individual Values ◽

Experimental Values ◽

Deficiency Screening ◽

Guthrie Cards

In this work, we used a reference population of newborns and sampled dried blood spots on Guthrie cards of 2,739 individual samples to determine the reference intervals for TRECs and KRECs values, in order to diagnose primary immunodeficiency by means of neonatal screening. The median absolute values for TRECs and KRECs were 195 (CI95%: 185-206) and 185 (CI95%: 176-197) copies per μl, respectively; the normalized value for TRECs was 2780 (CI95%: 2690-2840), and for KRECs, 2790 (CI95%: 2700-2900) copies per 2 × 105 copies of the albumin gene or 105 cells. The reference interval was calculated for 99 and 99.9 percentiles of total TRECs and KRECs individual values. Due to asymmetric distribution of data, the outliers were filtered off, using the Tukey’s criterion applied after logarithmic transformation of the data. When analyzing absolute values for TREC/KREC (per μL of blood), no “drop-down” TRECs values were identified; for KRECs, 18 experimental values were excluded from further analysis (from 9.8 to 13.5). The outlying values were not identified among the normalized values of TRECs/KRECs. The obtained reference values for TRECs and KRECs (at the 0.1 percentile level) were, respectively, 458 and 32 per 105 cells, or 23 and 17 per μl of blood samples from neonates.

Download Full-text

Impact of Subgroup Prevalences on Partitioning of Gaussian-distributed Reference Values

Clinical Chemistry ◽

10.1093/clinchem/48.11.1987 ◽

2002 ◽

Vol 48 (11) ◽

pp. 1987-1999 ◽

Cited By ~ 49

Author(s):

Ari Lahti ◽

Per Hyltoft Petersen ◽

James C Boyd

Keyword(s):

Reference Interval ◽

Reference Population ◽

Reference Intervals ◽

Source Population ◽

Distributed Data ◽

New Model ◽

Common Reference ◽

Reference Limits ◽

Partitioning Problem ◽

Analytical Expressions

Abstract Background: The aims of this report were to examine how unequal subgroup prevalences in the source population may affect reference interval partitioning decisions and to develop generally applicable guidelines for partitioning gaussian-distributed data. Methods: We recently proposed a new model for partitioning reference intervals when the underlying data distribution is gaussian. This model is based on controlling the proportions of the subgroup distributions that fall outside each of the common reference limits, using the distances between the reference limits of the subgroup distributions as functions to these proportions. We examine the significance of the unequal prevalence effect for the partitioning problem and quantify it for distance partitioning criteria by deriving analytical expressions to express these criteria as a function of the ratio of prevalences. An application example, illustrating various aspects of the importance of the prevalence effect, is also presented. Results: Dramatic shrinkage of the critical distances between reference limits of the subgroups needed for partitioning was observed as the ratio of prevalences, the larger one divided by the smaller one, was increased from unity. Because of this shrinkage, the same critical distances are not valid for all ratios of prevalences, but specific critical distances should be used for each particular value of this ratio. Although proportion criteria used in determining the need for reference interval partitioning are not dependent on the prevalence effect, this effect should be accounted for when these criteria are being applied by adjusting the sample sizes of the subgroups to make them correspond to the ratio of prevalences. Conclusions: The prevalences of subgroups in the reference population should be known and observed in the calculations for every reference interval study, irrespective of whether distance or proportion criteria are being used to determine the need for reference interval partitioning. We present detailed methods to account for the prevalences when applying each of these types of criteria. Analytical expressions for the distance criteria, to be used when high precision is needed, and approximate distances, to be used in practical work, are derived. General guidelines for partitioning gaussian distributed data are presented. Following these guidelines and using the new model, we suggest that partitioning can be performed more reliably than with any of the earlier models because the new model not only offers an improved correspondence between the critical distances and the critical proportions, but also accounts for the prevalence effect.

Download Full-text