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

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.

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.

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.

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 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.

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.

Reference intervals for thyreotropin and thyroid hormones for healthy adults based on the NOBIDA material and determined using a Modular E170®

2008 ◽  
Vol 46 (9) ◽  
Author(s):  
Lennart Friis-Hansen ◽  
Linda Hilsted
Keyword(s):  
Thyroid Hormones ◽  
Reference Interval ◽  
Reference Intervals ◽  
Healthy Adults ◽  
Free Triiodothyronine ◽  
Thyroglobulin Antibodies ◽  
Men And Women ◽  
Blood Samples ◽  
Upper Limit

Abstract: The aim of the present study was to establish Nordic reference intervals for thyreotropin (TSH) and the thyroid hormones in heparinized plasma.: We used 489 heparinized blood samples, collected in the morning, from the Nordic NOBIDA reference material, from healthy adults without medication. TSH, thyroxine, free thyroxine, triiodothyronine, free triiodothyronine, and thyroglobulin antibodies (Tg-ab) were measured using assays for Roche Modular E170: The measured concentrations for the thyroid hormones, but not TSH, followed a Gaussian distribution. There were more TPO-ab and Tg-ab positive women than men. After exclusion of the TPO-ab and the Tg-ab positive individuals, the reference interval TSH was 0.64 (0.61–0.72) to 4.7 (4.4–5.0) mIU/L. The exclusion of these ab-positive samples also minimized the differences in TSH concentrations between the sexes and the different Nordic countries. For the thyroid hormones, there were only minor differences between the reference intervals between the Nordic populations and between men and women. These reference intervals were unaffected by removal of the TPO-ab and TG-ab positive samples.: The upper limit of the TSH reference interval in our study is high compared to some other recent reports. This could be due to blood sampling in the morning. Furthermore, the Roche platform gives slightly higher results than other platforms. The number and distribution of the samples in the NOBIDA material makes it suitable for the determination of hormone Nordic reference intervals.Clin Chem Lab Med 2008;46:1305–12.

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.

Reference interval for immature platelet fraction on Sysmex XN hematology analyzer: a comparison study with Sysmex XE-2100

2015 ◽  
Vol 53 (7) ◽  
Author(s):  
Young Jin Ko ◽  
Mina Hur ◽  
Hanah Kim ◽  
Sang Gyeu Choi ◽  
Hee-Won Moon ◽  
...  
Keyword(s):  
Large Scale ◽  
Reference Interval ◽  
Reference Intervals ◽  
Modular System ◽  
Future Research ◽  
Healthy Individuals ◽  
Platelet Counts ◽  
Immature Platelet Fraction ◽  
Hematology Analyzer ◽  
Non Parametric

AbstractRecently introduced hematology analyzer, the Sysmex XN modular system (Sysmex, Kobe, Japan), has newly adopted a florescent channel to detect platelets and immature platelet fraction (IPF). This study aimed to establish new reference intervals for %-IPF and absolute number of IPF (A-IPF) on Sysmex XN. Platelet counts, %-IPF, and A-IPF were also compared between Sysmex XN and XE-2100 systems (Sysmex).Except outliers, blood samples from 2104 healthy individuals and 140 umbilical cord blood were analyzed using both Sysmex XN and XE-2100. The results of two systems were compared using Bland-Altman plot. The reference intervals for %-IPF and A-IPF were defined using non-parametric percentile methods according to the Clinical and Laboratory Standard Institute guideline (C28-A3).The platelet counts, %-IPF, and A-IPF showed non-parametric distributions. The mean difference between Sysmex XN and XE-2100 in healthy individuals revealed a positive bias in platelets (+8.0×10This large-scale study demonstrates a clear difference of platelet counts and IPF between Sysmex XN and XE-2100. The new reference intervals for IPF on Sysmex XN would provide fundamental data for clinical practice and future research.

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

2020 ◽  
Vol 57 (5) ◽  
pp. 373-381 ◽  
Author(s):  
N Jassam ◽  
A Luvai ◽  
D Narayanan ◽  
D Turnock ◽  
G Lee ◽  
...  
Keyword(s):  
Metrological Traceability ◽  
Reference Interval ◽  
Reference Population ◽  
Reference Intervals ◽  
Healthy Individuals ◽  
Reference Limit ◽  
Common Reference ◽  
Age Related ◽  
Upper Reference Limit ◽  
Analytical Platforms

Background Harmonization of reference intervals for analytes that have a sound calibration and metrological traceability is a widely recommended practice. The UK Pathology Harmony has recently harmonized reference intervals for calcium and albumin. In this study, we have determined the reference intervals for calcium and albumin on the UK’s most commonly used analytical platforms. Method A prospective reference population of healthy individuals was recruited according to the IFCC CRIDL criteria. A second indirect population was collected from 14 primary care setting and measured in laboratories using various analytical platforms and methods (Roche, Abbott, Beckman and Siemens analytical platforms). Results In total, 299 subjects were recruited; the central 95th centile values for calcium for three out of four analytical platforms were in a close agreement with UK Pathology Harmony reference intervals of 2.2–2.6 mmol/L. Reference intervals of BCG methods from both cohorts and irrespective of analytical platforms were higher for both lower and upper reference limits than those for BCP. In comparison, the indirect study showed an age-related variation. The younger population reference intervals varied by up to 5.7% at the lower reference limit and up to 12% at the upper reference limit compared with Pathology Harmony reference intervals, and the older population showed a variation of up to 14% at both limits. Conclusion While calcium reference intervals can be a subject for harmonization, albumin reference intervals studied showed large variation which is unsupportive of embracing a common reference interval for albumin.

scholarly journals Separating disease and health for indirect reference intervals

2021 ◽  
Vol 45 (2) ◽  
pp. 55-68 ◽  
Author(s):  
Kenneth A. Sikaris
Keyword(s):  
A Priori ◽  
Laboratory Data ◽  
Reference Interval ◽  
Statistical Technique ◽  
Reference Intervals ◽  
Large Databases ◽  
Statistical Issues ◽  
Physiological Variations ◽  
Indirect Reference ◽  
Apparently Healthy

Abstract The indirect approach to defining reference intervals operates ‘a posteriori’, on stored laboratory data. It relies on being able to separate healthy and diseased populations using one or both of clinical techniques or statistical techniques. These techniques are also fundamental in a priori, direct reference interval approaches. The clinical techniques rely on using clinical data that is stored either in the electronic health record or within the laboratory database, to exclude patients with possible disease. It depends on the investigators understanding of the data and the pathological impacts on tests. The statistical technique relies on identifying a dominant, apparently healthy, typically Gaussian distribution, which is unaffected by the overlapping populations with higher (or lower) results. It depends on having large databases to give confidence in the extrapolation of the narrow portion of overall distribution representing unaffected individuals. The statistical issues involved can be complex, and can result in unintended bias, particularly when the impacts of disease and the physiological variations in the data are under appreciated.

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