Separating disease and health for indirect reference intervals

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.

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Reference intervals for hematology test parameters from apparently healthy individuals in southwest Ethiopia

SAGE Open Medicine ◽

10.1177/2050312118807626 ◽

2018 ◽

Vol 6 ◽

pp. 205031211880762 ◽

Cited By ~ 6

Author(s):

Lealem Gedefaw Bimerew ◽

Tesfaye Demie ◽

Kaleab Eskinder ◽

Aklilu Getachew ◽

Shiferaw Bekele ◽

...

Keyword(s):

Platelet Count ◽

Blood Cell ◽

White Blood Cell ◽

Red Blood Cell ◽

Hematological Parameters ◽

Age Groups ◽

Reference Interval ◽

Reference Intervals ◽

Southwest Ethiopia ◽

Apparently Healthy

Background: Clinical laboratory reference intervals are an important tool to identify abnormal laboratory test results. The generating of hematological parameters reference intervals for local population is very crucial to improve quality of health care, which otherwise may lead to unnecessary expenditure or denying care for the needy. There are no well-established reference intervals for hematological parameters in southwest Ethiopia. Objective: To generate hematological parameters reference intervals for apparently healthy individuals in southwest Ethiopia. Methods: A community-based cross-sectional study was conducted involving 883 individuals from March to May 2017. Four milliliter of blood sample was collected and transported to Jimma University Medical Center Laboratory for hematological analysis and screening tests. A hematological parameters were measured by Sysmex XS-500i hematology analyzer (Sysmex Corporation Kobe, Japan). The data were analyzed by SPSS version 20 statistical software. The non-parametric independent Kruskal–Wallis test and Wilcoxon rank-sum test (Mann–Whitney U test) were used to compare the parameters between age groups and genders. The 97.5 percentile and 2.5 percentile were the upper and lower reference limit for the population. Results: The reference interval of red blood cell, white blood cell, and platelet count in children were 4.99 × 1012/L (4.26–5.99 × 1012/L), 7.04 × 109/L (4.00–11.67 × 109/L), and 324.00 × 109/L (188.00–463.50 × 109/L), respectively. The reference interval of red blood cell, white blood cell, and platelet count in adults was 5.19 × 1012/L (4.08–6.33 × 1012/L), 6.35 × 109/L (3.28–11.22 × 109/L), and 282.00 × 109/L (172.50–415.25 × 109/L), respectively. The reference interval of red blood cell, white blood cell, and platelet count in geriatrics were 5.02 × 1012/L (4.21–5.87 × 1012/L), 6.21 × 109/L (3.33–10.03 × 109/L), and 265.50 × 109/L (165.53–418.80 × 109/L), respectively. Most of the hematological parameters showed significant differences across all age groups. Conclusion: Most of the hematological parameters in this study showed differences from similar studies done in the country. This study provided population-specific hematological reference interval for southwest Ethiopians. Reference intervals should also be established in the other regions of the country.

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A multi-stage Gaussian transformation algorithm for clinical laboratory data.

Clinical Chemistry ◽

10.1093/clinchem/28.8.1735 ◽

1982 ◽

Vol 28 (8) ◽

pp. 1735-1741 ◽

Cited By ~ 26

Author(s):

J C Boyd ◽

D A Lacher

Keyword(s):

Mean Squared Error ◽

Clinical Laboratory ◽

Statistical Tests ◽

Random Noise ◽

Laboratory Data ◽

Reference Interval ◽

Reference Intervals ◽

Distributed Data ◽

Multi Stage ◽

Positive Coefficients

Abstract We have developed a multi-stage computer algorithm to transform non-normally distributed data to a normal distribution. This transformation is of value for calculation of laboratory reference intervals and for normalization of clinical laboratory variates before applying statistical procedures in which underlying data normality is assumed. The algorithm is able to normalize most laboratory data distributions with either negative or positive coefficients of skewness or kurtosis. Stepwise, a logarithmic transform removes asymmetry (skewness), then a Z-score transform and power function transform remove residual peakedness or flatness (kurtosis). Powerful statistical tests of data normality in the procedure help the user evaluate both the necessity for and the success of the data transformation. Erroneous assessments of data normality caused by rounded laboratory test values have been minimized by introducing computer-generated random noise into the data values. Reference interval endpoints that were estimated parametrically (mean +/- 2 SD) by using successfully transformed data were found to have a smaller root-mean-squared error than those estimated by the non-parametric percentile technique.

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Indirect reference intervals of plasma and serum thyrotropin (TSH) concentrations from intra-laboratory data bases from several German and Italian medical centres

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm.2011.114 ◽

2011 ◽

Vol 49 (4) ◽

Cited By ~ 18

Author(s):

Farhad Arzideh ◽

Werner Wosniok ◽

Rainer Haeckel

Keyword(s):

Laboratory Data ◽

Reference Intervals ◽

Data Bases ◽

Indirect Reference

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Blood sampling frequency as a proxy for comorbidity indices when identifying patient samples for review of reference intervals

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2021-0987 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Simon Lykkeboe ◽

Stine Linding Andersen ◽

Claus Gyrup Nielsen ◽

Peter Vestergaard ◽

Peter Astrup Christensen

Keyword(s):

Laboratory Data ◽

Low Frequency ◽

Reference Interval ◽

Reference Intervals ◽

Sampling Frequency ◽

Blood Sampling ◽

Test Results ◽

Healthy Individuals ◽

Simple Method ◽

Blood Samples

Abstract Objectives Indirect data mining methods have been proposed for review of published reference intervals (RIs), but methods for identifying patients with a low likelihood of disease are needed. Many indirect methods extract test results on patients with a low frequency blood sampling history to identify putative healthy individuals. Although it is implied there has been no attempt to validate if patients with a low frequency blood sampling history are healthy and if test results from these patients are suitable for RI review. Methods Danish nationwide health registers were linked with a blood sample database, recording a population of 316,337 adults over a ten-year period. Comorbidity indexes were defined from registrations of hospital diagnoses and redeemed prescriptions of drugs. Test results from patients identified as having a low disease burden were used for review of RIs from the Nordic Reference Interval Project (NORIP). Results Blood sampling frequency correlated with comorbidity Indexes and the proportion of patients without disease conditions were enriched among patients with a low number of blood samples. RIs based on test results from patients with only 1–3 blood samples per decade were for many analytes identical compared to NORIP RIs. Some analytes showed expected incongruences and gave conclusive insights into how well RIs from a more than 10 years old multi-center study (NORIP) performed on current pre-analytical and analytical methods. Conclusions Blood sampling frequency enhance the selection of healthy individuals for reviewing reference intervals, providing a simple method solely based on laboratory data without the addition of clinical information.

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Increase in dialyzable calcium associated with therapy with lithium.

Clinical Chemistry ◽

10.1093/clinchem/25.10.1806 ◽

1979 ◽

Vol 25 (10) ◽

pp. 1806-1809 ◽

Cited By ~ 14

Author(s):

J Toffaletti ◽

R B McComb ◽

G N Bowers

Keyword(s):

Sex Differences ◽

Healthy Volunteers ◽

Calcium Concentration ◽

Reference Interval ◽

Reference Intervals ◽

Lithium Salts ◽

Total Calcium ◽

Apparently Healthy

Abstract We measured total and dialyzable calcium concentrations in consecutive sera submitted for routine lithium analysis. Of 98 samples from 61 different individuals, six (6.1%) total calcium results and 32 (33%) dialyzable calcium results were above the respective reference intervals. By comparison, when both total and dialyzable calcium were measured on 50 different apparently healthy volunteers, no results were outside either reference interval (2.20--2.58 mmol/L for total and 1.30--1.47 mmol/L for dialyzable calcium). These increases were not due to age or sex differences between the patients and controls. From the dialyzable calcium data, there appears to be an even higher incidence of mild hypercalcemia in patients receiving oral lithium salts than is indicated by the total calcium concentration alone.

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Indirect Reference Intervals: Harnessing the Power of Stored Laboratory Data.

10.33176/aacb-19-00022 ◽

2019 ◽

Vol 40 (2) ◽

pp. 99-111 ◽

Cited By ~ 1

Keyword(s):

Data Analysis ◽

Traditional Approach ◽

Laboratory Data ◽

Age Groups ◽

Reference Interval ◽

Reference Intervals ◽

Indirect Approach ◽

Distribution Of Values ◽

Alternative Approach ◽

Indirect Technique

Reference intervals are relied upon by clinicians when interpreting their patients’ test results. Therefore, laboratorians directly contribute to patient care when they report accurate reference intervals. The traditional approach to establishing reference intervals is to perform a study on healthy volunteers. However, the practical aspects of the staff time and cost required to perform these studies make this approach difficult for clinical laboratories to routinely use. Indirect methods for deriving reference intervals, which utilise patient results stored in the laboratory’s database, provide an alternative approach that is quick and inexpensive to perform. Additionally, because large amounts of patient data can be used, the approach can provide more detailed reference interval information when multiple partitions are required, such as with different age-groups. However, if the indirect approach is to be used to derive accurate reference intervals, several considerations need to be addressed. The laboratorian must assess whether the assay and patient population were stable over the study period, whether data ‘clean-up’ steps should be used prior to data analysis and, often, how the distribution of values from healthy individuals should be modelled. The assumptions and potential pitfalls of the particular indirect technique chosen for data analysis also need to be considered. A comprehensive understanding of all aspects of the indirect approach to establishing reference intervals allows the laboratorian to harness the power of the data stored in their laboratory database and ensure the reference intervals they report are accurate.

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The establishment of serum 25-hydroxyvitamin D reference intervals in children aged 0–14 years in Zigong area, China

Journal of PHYSIOLOGICAL ANTHROPOLOGY ◽

10.1186/s40101-021-00265-x ◽

2021 ◽

Vol 40 (1) ◽

Author(s):

Jianhong Yu ◽

Xiaoping He ◽

Shengwei Huang

Keyword(s):

Age Groups ◽

Reference Interval ◽

Reference Intervals ◽

Healthy Children ◽

Hydroxyvitamin D ◽

Significant Difference ◽

Different Seasons ◽

25 Hydroxyvitamin D ◽

Apparently Healthy ◽

Normally Distributed

Abstract Objective To establish the reference interval of serum 25-hydroxyvitamin D (25(OH)D) in apparently healthy children in Zigong, China, using an indirect method to provide a basis for proper clinical diagnosis and treatment. Methods A total of 1851 apparently healthy children of the Children’s Health Care Department in Zigong First People’s Hospital between January 2016 and December 2020 were included in the study. The Kolmogorov–Smirnov test was used to analyze the data for normality, and the non-normally distributed data were transformed into approximately normal distribution by Blom's normal rank transformation, and the transformed data were excluded from outliers by the quartile spacing method, and the data were stratified and analyzed according to sex, age, and season. The data were stratified according to sex, age, and season, and the area between the 2.5% and 97.5% percentile points was used as the reference interval. Results The serum 25(OH)D data were non-normally distributed. The data were normally distributed after Blom’s normality rank transformation, and 92 cases of outliers were excluded from the transformed data according to the interquartile spacing method. The differences in serum 25(OH)D levels between sex were not statistically significant (P > 0.05), and there was no need to establish reference intervals based on sex. There was no statistically significant difference in serum 25-hydroxyvitamin D levels between winter and spring, and also no difference between summer and autumn (P > 0.05), and the levels were lower in winter-spring than in summer-autumn. Comparison between age groups showed that there was no statistically significant difference in serum 25(OH)D levels between the < 6 months group and the 6 ~ 11 months group, and between the 6 ~ 9 years group and the 10 ~ 14 years group (P > 0.05); serum 25(OH)D levels decreased with increasing age. There was an interactive effect of season and age group on 25(OH)D levels, and the corresponding reference intervals were established according to different seasons and age groups. In summer and autumn, the reference intervals of serum 25(OH)D for < 1 year, 1 ~ 2 years, 3 ~ 5 years, and 6 ~ 14 years were 39.86 ~ 151.43, 31.54 ~ 131.65, 22.05 ~ 103.75, and 15.36 ~ 85.53 ng/ml and 24.42 ~ 144.20, 31.54 ~ 131.65, 16.80 ~ 165.68, and 15.46 ~ 85.54 ng/ml in winter and spring, respectively. Conclusion Reference intervals for serum 25(OH)D in children of different seasons and ages in Zigong, China, were established to provide a reference for clinical disease diagnosis, treatment, and prognosis determination.

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Simple estimation of reference intervals from routine laboratory data

LaboratoriumsMedizin ◽

10.1515/labmed-2015-0104 ◽

2016 ◽

Vol 39 (6) ◽

Author(s):

Georg Hoffmann ◽

Ralf Lichtinghagen ◽

Werner Wosniok

Keyword(s):

Standard Deviation ◽

Linear Part ◽

Laboratory Data ◽

Real Data ◽

Reference Interval ◽

Reference Intervals ◽

Standard Normal Distribution ◽

Routine Laboratory ◽

Data Set ◽

Straight Line

Abstract:According to the recommendations of the IFCC and other organizations, medical laboratories should establish or at least adapt their own reference intervals, to make sure that they reflect the peculiar characteristics of the respective methods and patient collectives. In practice, however, this postulate is hard to fulfill. Therefore, two task forces of the DGKL (“AG Richtwerte” and “AG Bioinformatik”) have developed methods for the estimation of reference intervals from routine laboratory data. Here we describe a visual procedure, which can be performed on an Excel sheet without any programming knowledge. Patient values are plotted against the quantiles of the standard normal distribution (so-called QQ plot) using the NORM. INV function of Excel. If the examined population contains mainly non-diseased persons with approximately normally distributed values, the respective dots form a straight line. Very often the values are rather lognormally distributed; in this case the straight line can be detected after logarithmic transformation of the original values. Values, which do not match with the assumed theoretical distribution, deviate from the linear shape and can easily be identified and eliminated. Using the reduced data set, the mean value and standard deviation are calculated and the reference interval (μ±2σ) is estimated. The method yields plausible results with simulated and real data. With the increasing number of results, which do not match with the model, it tends to underestimate the standard deviation. In all cases, where the QQ plot does not yield a substantial linear part, the proposed method is not applicable.

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Indirect Reference Intervals: Harnessing the Power of Stored Laboratory Data

10.33176/test ◽

2019 ◽

Vol 40 (2) ◽

pp. 99-111

Keyword(s):

Data Analysis ◽

Traditional Approach ◽

Laboratory Data ◽

Age Groups ◽

Reference Interval ◽

Reference Intervals ◽

Indirect Approach ◽

Distribution Of Values ◽

Alternative Approach ◽

Indirect Technique

Reference intervals are relied upon by clinicians when interpreting their patients’ test results. Therefore, laboratorians directly contribute to patient care when they report accurate reference intervals. The traditional approach to establishing reference intervals is to perform a study on healthy volunteers. However, the practical aspects of the staff time and cost required to perform these studies make this approach difficult for clinical laboratories to routinely use. Indirect methods for deriving reference intervals, which utilise patient results stored in the laboratory’s database, provide an alternative approach that is quick and inexpensive to perform. Additionally, because large amounts of patient data can be used, the approach can provide more detailed reference interval information when multiple partitions are required, such as with different age-groups. However, if the indirect approach is to be used to derive accurate reference intervals, several considerations need to be addressed. The laboratorian must assess whether the assay and patient population were stable over the study period, whether data ‘clean-up’ steps should be used prior to data analysis and, often, how the distribution of values from healthy individuals should be modelled. The assumptions and potential pitfalls of the particular indirect technique chosen for data analysis also need to be considered. A comprehensive understanding of all aspects of the indirect approach to establishing reference intervals allows the laboratorian to harness the power of the data stored in their laboratory database and ensure the reference intervals they report are accurate.

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