NUMBER: standardized reference intervals in the Netherlands using a ‘big data’ approach

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Wendy P.J. den Elzen ◽  
Nannette Brouwer ◽  
Marc H. Thelen ◽  
Saskia Le Cessie ◽  
Inez-Anne Haagen ◽  
...  
Keyword(s):  
General Chemistry ◽  
Big Data ◽  
The Netherlands ◽  
Clinical Laboratory ◽  
Reference Interval ◽  
Clinical Decision ◽  
Reference Intervals ◽  
Test Results ◽  
Study Population ◽  
Set Up

AbstractBackgroundExternal quality assessment (EQA) programs for general chemistry tests have evolved from between laboratory comparison programs to trueness verification surveys. In the Netherlands, the implementation of such programs has reduced inter-laboratory variation for electrolytes, substrates and enzymes. This allows for national and metrological traceable reference intervals, but these are still lacking. We have initiated a national endeavor named NUMBER (Nederlandse UniforMe Beslisgrenzen En Referentie-intervallen) to set up a sustainable system for the determination of standardized reference intervals in the Netherlands.MethodsWe used an evidence-based ‘big-data’ approach to deduce reference intervals using millions of test results from patients visiting general practitioners from clinical laboratory databases. We selected 21 medical tests which are either traceable to SI or have Joint Committee for Traceability in Laboratory Medicine (JCTLM)-listed reference materials and/or reference methods. Per laboratory, per test, outliers were excluded, data were transformed to a normal distribution (if necessary), and means and standard deviations (SDs) were calculated. Then, average means and SDs per test were calculated to generate pooled (mean±2 SD) reference intervals. Results were discussed in expert meetings.ResultsSixteen carefully selected clinical laboratories across the country provided anonymous test results (n=7,574,327). During three expert meetings, participants found consensus about calculated reference intervals for 18 tests and necessary partitioning in subcategories, based on sex, age, matrix and/or method. For two tests further evaluation of the reference interval and the study population were considered necessary. For glucose, the working group advised to adopt the clinical decision limit.ConclusionsUsing a ‘big-data’ approach we were able to determine traceable reference intervals for 18 general chemistry tests. Nationwide implementation of these established reference intervals has the potential to improve unequivocal interpretation of test results, thereby reducing patient harm.

Pediatric reference interval verification for endocrine and fertility hormone assays on the Abbott Alinity system

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Mary Kathryn Bohn ◽  
Siobhan Wilson ◽  
Alexandra Hall ◽  
Khosrow Adeli
Keyword(s):  
Clinical Decision Making ◽  
De Novo ◽  
Reference Interval ◽  
Clinical Decision ◽  
Reference Intervals ◽  
Healthy Children ◽  
Confidence Limits ◽  
Test Results ◽  
Serum Samples ◽  
Abbott Architect

Abstract Objectives The Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER) has developed an extensive database of reference intervals (RIs) for several biomarkers on various analytical systems. In this study, pediatric RIs were verified for key immunoassays on the Abbott Alinity system based on the analysis of healthy children samples and comparison to comprehensive RIs previously established for Abbott ARCHITECT assays. Methods Analytical performance of Alinity immunoassays was first assessed. Subsequently, 100 serum samples from healthy children recruited with informed consent were analyzed for 16 Alinity immunoassays. The percentage of test results falling within published CALIPER ARCHITECT reference and confidence limits was determined. If ≥ 90% of test results fell within the confidence limits, they were considered verified based on CLSI guidelines. If <90% of test results fell within the confidence limits, additional samples were analyzed and new Alinity RIs were established. Results Of the 16 immunoassays assessed, 13 met the criteria for verification with test results from ≥ 90% of healthy serum samples falling within the published ARCHITECT confidence limits. New CALIPER RIs were established for free thyroxine and prolactin on the Alinity system. Estradiol required special considerations in early life. Conclusions Our data demonstrate excellent concordance between ARCHITECT and Alinity immunoassays, as well as the robustness of previously established CALIPER RIs for most immunoassays, eliminating the need for de novo RI studies for most parameters. Availability of pediatric RIs for immunoassays on the Alinity system will assist clinical laboratories using this new platform and contribute to improved clinical decision-making.

scholarly journals Establishment of variation source and age-related reference interval models for 22 common biochemical analytes in older people using real-world big data mining

2020 ◽  
Vol 49 (6) ◽  
pp. 1062-1070
Author(s):  
Chaochao Ma ◽  
Liangyu Xia ◽  
Xinqi Chen ◽  
Jie Wu ◽  
Yicong Yin ◽  
...  
Keyword(s):  
Big Data ◽  
Older People ◽  
Real World ◽  
Reference Interval ◽  
Reference Intervals ◽  
Ageing Population ◽  
Factors Affecting ◽  
Age Related ◽  
The Status ◽  
Source Models

Abstract Background the ageing population has increased in many countries, including China. However, reference intervals (RIs) for older people are rarely established because of difficulties in selecting reference individuals. Here, we aimed to analyse the factors affecting biochemical analytes and establish RI and age-related RI models for biochemical analytes through mining real-world big data. Methods data for 97,220 individuals downloaded from electronic health records were included. Three derived databases were established. The first database included 97,220 individuals and was used to build age-related RI models after identifying outliers by the Tukey method. The second database consisted of older people and was used to establish variation source models and RIs for biochemical analytes. Differences between older and younger people were compared using the third database. Results sex was the main source of variation of biochemical analytes for older people in the variation source models. The distributions of creatinine and uric acid were significantly different in the RIs of biochemical analytes for older people established according to sex. Age-related RI models for biochemical analytes that were most affected by age were built and visualized, revealing various patterns of changes from the younger to older people. Conclusion the study analysed the factors affecting biochemical analytes in older people. Moreover, RI and age-related RI models of biochemical analytes for older people were established to provide important insight into biological processes and to assist clinical use of various biochemical analytes to monitor the status of various diseases for older people.

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

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 (&gt;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.

scholarly journals Reference Intervals (RIs) of Lipid Parameters for Nepalese Population

2018 ◽  
Vol 6 (4) ◽  
pp. 366-372
Author(s):  
R.V. Mahato ◽  
R.K. Singh ◽  
A. M. Dutta ◽  
K. Ichihara ◽  
M. Lamsal
Keyword(s):  
Total Cholesterol ◽  
Reference Values ◽  
Clinical Laboratory ◽  
Local Population ◽  
Density Lipoprotein ◽  
Physical Exertion ◽  
Hdl Cholesterol ◽  
Reference Interval ◽  
Reference Intervals ◽  
Lipoprotein Cholesterol

Introduction: Reference interval (RIs) is the range of values provided by laboratory scientists in a convenient and practical form to support clinician in interpreting observed values for diagnosis, treatment and monitoring of a disease. Laboratories in Nepal uses RIs, provided in the kit inserts by the manufacturers or from the scientific literature, established for western/European population. It is well known that population across the globe differs physiologically, genetically; race, ethnically, lifestyle, food habits and diet which have great impact on the reference values. Thus, it is inappropriate to use RIs that do not represent the local population. This approach highlights for establishing reference values in Nepalese population using the IFCC-CRIDL guidelines published in (C28-A3). Objectives: The objective of this study is to analyze blood lipids concentration in apparently healthy Nepalese population to set up reference values for total cholesterol (TC), triglycerides (TG), High Density Lipoprotein-cholesterol (HDL-C) and Low Density Lipoprotein-cholesterol (LDL-C) and compare with the internationally recommended values. Methods: Reference individuals selected from healthy volunteers according to the IFCC/C-RIDL protocol in (C28 –A3). Volunteers were requested to avoid excessive physical exertion/exercise/excessive eating and drinking and fast overnight for 10-12 hour. Blood samples were collected from 120 subjects from each five centers of the country between 7:00-10:00 am, serum were separated and refrigerated at -20 in a cryo-vials. Finally, 617 samples were transported to Yamaguchi University, Graduate School of Medicine, Ube, Japan for analysis in dry Ice and 30 parameters were measured by fully automated biochemistry analyzer, Beckman Coulter (BC480) in the clinical laboratory. Results: A reference interval for each parameter was calculated from the 95% reference intervals ranging from 2.5% and 97.5% percentiles and, arithmetic mean + 2 SD were also calculated. The 95% reference range for total cholesterol (2.53-6.14), triglyceride was(0.42-3.32),for HDL Cholesterol was (0.28-1.46), for LDL was(1.05-4.00) and for VLDL was (0.054-0.92) for Nepalese population. Conclusion: Nepalese clinicians can take into consideration of reference lipid values of this study for diagnosis, treatment and monitoring of disease. Int. J. Appl. Sci. Biotechnol. Vol 6(4): 366-372

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

1982 ◽  
Vol 28 (8) ◽  
pp. 1735-1741 ◽  
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.

scholarly journals Partitioning Reference Intervals by Use of Genetic Information

2011 ◽  
Vol 57 (3) ◽  
pp. 475-481 ◽  
Author(s):  
Brian H Shirts ◽  
Andrew R Wilson ◽  
Brian R Jackson
Keyword(s):  
Genetic Information ◽  
Clinical Information ◽  
Reference Interval ◽  
Reference Intervals ◽  
Genetic Effects ◽  
Test Results ◽  
Simple Method ◽  
Asian Populations ◽  
Gilbert Syndrome ◽  
Recessive Effects

BACKGROUND Reference intervals that incorporate genetic information could reduce the misidentification of unusual test results caused by non–disease-associated genetic variation and increase the detection of results indicating underlying pathology. Subdividing reference groups by genetic effects, however, may lead to increased uncertainty around reference interval endpoints (because of the smaller subgroup sample sizes), thus offsetting any benefits. METHODS We evaluated CLSI guidelines to develop a method appropriate for partitioning reference intervals on the basis of genetic variants with dominant or recessive effects. This method uses information available before reference samples are recruited, thus allowing a preliminary decision regarding partitioning to be made before sampling. We used this method to evaluate the example of Gilbert syndrome. RESULTS The decision point for partitioning occurs when the percentage of total variance attributable to a dominant or recessive genetic polymorphism exceeds 4%. Similarly, partitioning decision curves are presented based on difference in means between 2 subgroups, sample SD, and subgroup or allele frequency. Laboratory-specific partitioned reference intervals for Gilbert syndrome appear to be statistically warranted for white and African-American populations, but not for Asian populations. CONCLUSIONS We present a simple method to evaluate whether partitioning based on dominant or recessive genetic effects is statistically justified. Important limitations remain that, in many situations, will preclude integration of genetic, laboratory, and clinical information. As society moves toward personalized medicine, additional research is needed on how to evaluate patient normality while accounting for additive genetic, multigenic, and other multifactorial effects.

scholarly journals Verification of reference intervals in routine clinical laboratories: practical challenges and recommendations

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Yesim Ozarda ◽  
Victoria Higgins ◽  
Khosrow Adeli
Keyword(s):  
Clinical Laboratory ◽  
Local Population ◽  
Age Groups ◽  
External Source ◽  
Reference Intervals ◽  
Laboratory Method ◽  
Test Results ◽  
Multicenter Studies ◽  
Clinical Laboratories ◽  
Laboratory Test Results

Abstract Reference intervals (RIs) are fundamental tools used by healthcare and laboratory professionals to interpret patient laboratory test results, ideally enabling differentiation of healthy and unhealthy individuals. Under optimal conditions, a laboratory should perform its own RI study to establish RIs specific for its method and local population. However, 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 verify RIs established by an external source. Common RIs can be established by large, multicenter studies and can subsequently be received by local laboratories using various verification procedures. The standard approach to verify RIs recommended by the Clinical Laboratory Standards Institute (CLSI) EP28-A3c guideline for routine clinical laboratories is to collect and analyze a minimum of 20 samples from healthy subjects from the local population. Alternatively, “data mining” techniques using large amounts of patient test results can be used to verify RIs, considering both the laboratory method and local population. Although procedures for verifying RIs in the literature and guidelines are clear in theory, gaps remain for the implementation of these procedures in routine clinical laboratories. Pediatric and geriatric age-groups also continue to pose additional challenges in respect of acquiring and verifying RIs. In this article, we review the current guidelines/approaches and challenges to RI verification and provide a practical guide for routine implementation in clinical laboratories.

Use of a Hospital Database to Determine the Characteristics of Diagnostic Tests

1990 ◽  
Vol 29 (03) ◽  
pp. 205-212 ◽  
Author(s):  
Johanna Zwetsloot-Schonk
Keyword(s):  
Clinical Practice ◽  
Diagnostic Tests ◽  
Patient Population ◽  
Clinical Laboratory ◽  
University Hospital ◽  
Test Results ◽  
Hospital Database ◽  
Routinely Collected Data ◽  
Study Population ◽  
Using Data

AbstractTest indices are often determined by comparing test results of healthy persons with test results of patients known to have the disease. However, the patient population for which the test is ordered in clinical practice often differs from the study population on which the test indices are based. Hence, these indices are not applicable to clinical practice and should be recalculated using data from daily clinical practice. Two major problems of using routinely collected data are discussed: the assessment of the final health status and tracing the reason for ordering the test. Prior considerations are given to the use of hospital information systems (HIS) to sample the patient population that is desired and to collect the necessary data for calculating test indices. We investigated whether the HIS of Leiden University Hospital (which is presented as an example) can be used to calculate the indices of clinical laboratory tests, histopathologic examinations and radiodiagnostic investigations. The results indicate that the registration of diagnoses must be improved and that a way must be found to capture the implicit reasoning for ordering diagnostic tests.

Evaluation of biological variations in glucose and glycated hemoglobin levels in healthy individuals

2017 ◽  
Vol 43 (5) ◽  
pp. 495-501
Author(s):  
Cihan Coskun ◽  
Berrin Bercik Inal ◽  
Humeyra Ozturk Emre ◽  
Sehide Baz ◽  
Alper Gumus ◽  
...  
Keyword(s):  
International Organizations ◽  
Glycated Hemoglobin ◽  
Reference Interval ◽  
Reference Intervals ◽  
Study Group ◽  
Test Results ◽  
Healthy Individuals ◽  
Reference Change Value ◽  
Performance Specifications ◽  
Index Of Individuality

Abstract Objective: In this study, we firstly aimed to determine components of biological variations (BVCs) in levels of glucose and glycated hemoglobin (HbA1c) in detail based on guidance from relevant organizations and experts. We also investigated whether reference intervals for both analytes were useful for evaluations, particularly consecutive test results. Methods: The study group consisted of 36 healthy volunteers. Samples were collected from each individual 4 times every 2 weeks for 45 days. All samples were assayed in duplicate within a single run. Finally, we estimated BVCs and the analytical performance specifications of both analytes. Results: Our results were fairly compatible with current biological variations (BVs) in both analytes reported in a database. It was calculated as within biological variation (CVI)=4.2% and between-subject variation (CVG)=5.3% for glucose while calculating as CVI=1.7% and CVG=4.5% for HbA1c. According to these results, the index of individuality (II) of glucose was higher than 0.6 while HbA1c’s II was lower than this value. Conclusion: We thought that guidelines from relevant international organizations should be followed to standardize the study design and to appropriately calculate BVCs for any analyte in BV studies. Finally, reference change value should be used to evaluate meaningful differences in HbA1c levels instead of reference interval.

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