scholarly journals RIA for Serum Holo-Transcobalamin: Method Evaluation in the Clinical Laboratory and Reference Interval

2003 ◽  
Vol 49 (3) ◽  
pp. 455-462 ◽  
Author(s):  
Saila Loikas ◽  
Minna Löppönen ◽  
Pauli Suominen ◽  
Jan Møller ◽  
Kerttu Irjala ◽  
...  
Keyword(s):  
Biochemical Markers ◽  
Methylmalonic Acid ◽  
Clinical Laboratory ◽  
Reference Interval ◽  
Reference Population ◽  
Reference Intervals ◽  
Method Evaluation ◽  
Elderly Individuals ◽  
Reference Limit ◽  
Total Homocysteine

Abstract Background: Decreased serum holo-transcobalamin (holoTC) could be the earliest marker of cobalamin (Cbl) deficiency, but there has been no method suitable for routine use. We evaluated a new commercial holoTC RIA, determined reference values, and assessed holoTC concentrations in relation to other biochemical markers of Cbl deficiency. Methods: The reference population consisted of 303 individuals 22–88 years of age, without disease or medication affecting Cbl or homocysteine metabolism. In elderly individuals (≥65 years), normal Cbl status was further confirmed by total homocysteine (tHcy; <19 μmol/L) and methylmalonic acid (MMA; <0.28 μmol/L) concentrations within established reference intervals. HoloTC in Cbl deficiency was studied in a population of 107 elderly individuals with normal renal function. The Cbl deficiency was graded as potential (total Cbl ≤150 pmol/L or tHcy ≥19 μmol/L), possible (total Cbl ≤150 pmol/L and either tHcy ≥19 μmol/L or MMA ≥0.45 μmol/L), and probable (tHcy ≥19 μmol/L and MMA ≥0.45 μmol/L). Results: The intra- and between-assay imprecision (CV) for the holoTC RIA were 4–7% and 6–8%, respectively. A 95% central reference interval for serum holoTC was 37–171 pmol/L. All participants (n = 16) with probable Cbl deficiency, 86% of those with possible, and 30% of those with potential Cbl deficiency had holoTC below the reference limit (<37 pmol/L). The holoTC correlated with total Cbl (rs = 0.80; P <0.0001) and inversely with MMA (rs = −0.52; P <0.0001). HoloTC concentrations were significantly (P = 0.01) higher in women than in men. Conclusions: The new holoTC RIA is precise and simple to perform. Low holoTC is found in individuals with biochemical signs of Cbl deficiency, but the sensitivity and specificity of low holoTC in diagnosis of Cbl deficiency need to be further evaluated.

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 Advances in Pediatric Reference Intervals for Biochemical Markers: Establishment of the Caliper Database in Healthy Children and Adolescents/Napredak U Oblasti Pedijatrijskih Referentnih Intervala Za Biohemijske Markere: Izrada Baze Podataka Caliper Kod Zdrave Dece I Adolescenata

2014 ◽  
Vol 34 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Kimiya Karbasy ◽  
Petra Ariadne ◽  
Stephanie Gaglione ◽  
Michelle Nieuwesteeg ◽  
Khosrow Adeli
Keyword(s):  
Children And Adolescents ◽  
Biochemical Markers ◽  
Clinical Laboratory ◽  
Reference Interval ◽  
Reference Intervals ◽  
Healthy Children ◽  
Medical Practitioners ◽  
Laboratory Test Results ◽  
Laboratory Reference ◽  
And Gender

Summary Clinical laboratory reference intervals provide valuable information to medical practitioners in their interpretation of quantitative laboratory test results, and therefore are critical in the assessment of patient health and in clinical decisionmaking. The reference interval serves as a health-associated benchmark with which to compare an individual test result. Unfortunately, critical gaps currently exist in accurate and upto-date pediatric reference intervals for accurate interpretation of laboratory tests performed in children and adolescents. These critical gaps in the available laboratory reference intervals have the clear potential of contributing to erroneous diagnosis or misdiagnosis of many diseases. To address these important gaps, several initiatives have begun internationally by a number of bodies including the KiGGS initiative in Germany, the Aussie Normals in Australia, the AACC-National Children Study in USA, the NORICHILD Initiative in Scandinavia, and the CALIPER study in Canada. In the present article, we will review the gaps in pediatric reference intervals, challenges in establishing pediatric norms in healthy children and adolescents, and the major contributions of the CALIPER program to closing the gaps in this crucial area of pediatric laboratory medicine. We will also discuss the recently published CALIPER reference interval database (www.caliperdatabase.com) developed to provide comprehensive age and gender specific pediatric reference intervals for a larger number of biochemical markers, based on a large and diverse healthy children cohort. The CALIPER database is based on a multiethnic population examining the influence of ethnicity on laboratory reference intervals. Thus the database has proved to be of global benefit and is being adopted by hospital laboratories worldwide.

scholarly journals Intra- and interindividual variability of carbohydrate-deficient transferrin, γ-glutamyltransferase, and mean corpuscular volume in teetotalers

1998 ◽  
Vol 44 (10) ◽  
pp. 2120-2125 ◽  
Author(s):  
Anders Helander ◽  
Erling Vabö ◽  
Klas Levin ◽  
Stefan Borg
Keyword(s):  
Mean Corpuscular Volume ◽  
Iron Concentration ◽  
Reference Interval ◽  
Reference Intervals ◽  
Mean Value ◽  
Corpuscular Volume ◽  
Mean Values ◽  
Reference Limit ◽  
Carbohydrate Deficient Transferrin ◽  
The Mean

Abstract Blood samples for determination of the biochemical alcohol markers carbohydrate-deficient transferrin (CDT) in serum, γ-glutamyltransferase (GGT) in serum, and erythrocyte mean corpuscular volume (MCV) were collected once every 1–2 weeks over ∼5 months from 10 female and 4 male teetotalers. Mean values for serum CDT (using the CDTectTM assay) ranged from 9.9 to 29.4 units/L (median, 14.2 units/L), and the highest results were obtained in the women. The mean values for serum GGT ranged from 0.15 to 0.49 μkat/L (median, 0.30 μkat/L, or 18 U/L) except for one woman with a very high mean of 3.07 μkat/L. For MCV, the mean values ranged from 79.5 to 91.5 fL. Two women showed several CDT results above the upper reference limit (mean values, 27.6 and 29.4 units/L, respectively); however, their GGT and MCV values fell within the reference intervals. One of these women exhibited an increased total transferrin concentration (mean value, 5.38 g/L), which was possibly related to the use of oral contraceptives and/or a low serum iron concentration. When the CDTect value was expressed relative to total transferrin, a ratio within the reference interval was observed for this woman but not for the other woman with increased CDTect values. The present study demonstrates a considerable variation between individuals in CDT, GGT, and MCV without drinking any alcohol. The results also show that these baseline values are fairly constant over time within the same individual.

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 Choice of Statistical Tools for Outlier Removal Causes Substantial Changes in Analyte Reference Intervals in Healthy Populations

2020 ◽  
Vol 66 (12) ◽  
pp. 1558-1561 ◽  
Author(s):  
Peter E Hickman ◽  
Gus Koerbin ◽  
Julia M Potter ◽  
Nicholas Glasgow ◽  
Juleen A Cavanaugh ◽  
...  
Keyword(s):  
Statistical Methods ◽  
Population Studies ◽  
Large Population ◽  
Large Body ◽  
Reference Interval ◽  
Reference Intervals ◽  
Outlier Removal ◽  
Reference Limit ◽  
Statistical Measures ◽  
Upper Reference Limit

Abstract Background Reference intervals are an important aid in medical practice as they provide clinicians a guide as to whether a patient is healthy or diseased. Outlier results in population studies are removed by any of a variety of statistical measures. We have compared several methods of outlier removal and applied them to a large body of analytes from a large population of healthy persons. Methods We used the outlier exclusion criteria of Reed-Dixon and Tukey and calculated reference intervals using nonparametric and Harrell-Davis statistical methods and applied them to a total of 36 different analytes. Results Nine of 36 analytes had a greater than 20% difference in the upper reference limit, and for some the difference was 100% or more. Conclusions For some analytes, great importance is attached to the reference interval. We have shown that different statistical methods for outlier removal can cause large changes to reported reference intervals. So that population studies can be readily compared, common statistical methods should be used for outlier removal.

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

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.

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.

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

2002 ◽  
Vol 48 (11) ◽  
pp. 1987-1999 ◽  
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.

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