Impact of Individualized Hemolysis Management Based on Biological Variation Cut-offs in a Clinical Laboratory

AbstractBackground:Proficiency testing or external quality assessment schemes (PT/EQASs) are an important method of assessing laboratory performance. As each scheme establishes assigned values and acceptable ranges for the analyte according to its own criteria, monitoring of participant performance varies according to the scheme and can lead to conflicting conclusions.Methods:Standard deviations (SDs) for PT were derived from Thompson’s and biological variation models applied to blood and urine manganese (Mn) robust data from four EQASs from North America and Europe. The fitness for purpose was verified by applying these SDs to individual results.Results:Using Thompson characteristic function the relationship between SD and Mn concentration, expressed in nmol/L was the square root of [19.7Conclusions:The biological variation model can be used to propose quality specifications for blood, however it could not be applied to urine. The Thompson characteristic function model could be applied to derive quality specifications for Mn in urine and, to a lesser extent in blood. The more lenient quality specifications for blood highlight the difficulty of determining Mn in this matrix. Further work is needed to harmonize PT, such as using assigned ranges for the specimens.

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Models to estimate biological variation components and interpretation of serial results: strengths and limitations

Advances in Laboratory Medicine / Avances en Medicina de Laboratorio ◽

10.1515/almed-2020-0063 ◽

2020 ◽

Vol 1 (3) ◽

Author(s):

Jorge Díaz-Garzón Marco ◽

Pilar Fernández-Calle ◽

Carmen Ricós

Keyword(s):

Clinical Laboratory ◽

Direct Methods ◽

Biological Variation ◽

European Federation ◽

Mixed Effect ◽

Mixed Effect Models ◽

Reference Change Value ◽

Large Databases ◽

The One

AbstractBiological variation (BV) has multiple applications in a variety of fields of clinical laboratory. The use of BV in statistical modeling is twofold. On the one hand, some models are used for the generation of BV estimates (within- and between-subject variability). Other models are built based on BV in combination with other factors to establish ranges of normality that will help the clinician interpret serial results for the same subject. There are two types of statistical models for the calculation of BV estimates: A. Direct methods, prospective studies designed to calculate BV estimates; i. Classic model: developed by Harris and Fraser, revised by the Working Group on Biological Variation of the European Federation of Laboratory Medicine. ii. Mixed-effect models. iii. Bayesian model. B. Indirect methods, retrospective studies to derive BV estimates from large databases of results. Big data. Understanding the characteristics of these models is crucial as they determine their applicability in different settings and populations. Models for defining ranges that help in the interpretation of individual serial results include: A. Reference change value and B. Bayesian data network. In summary, this review provides an overview of the models used to define BV components and others for the follow-up of patients. These models should be exploited in the future to personalize and improve the information provided by the clinical laboratory and get the best of the resources available.

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Analytical goals for coagulation tests based on biological variation

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm.2004.015 ◽

2004 ◽

Vol 42 (1) ◽

Cited By ~ 6

Author(s):

Yui Wada ◽

Masako Kurihara ◽

Mitsuko Toyofuku ◽

Minako Kawamura ◽

Hiroko Iida ◽

...

Keyword(s):

Coefficient Of Variation ◽

Clinical Laboratory ◽

Laboratory Data ◽

Biological Variation ◽

University Hospital ◽

Healthy Individuals ◽

Coagulation Tests ◽

Reference Limits ◽

Clinical Chemical ◽

Control Plasma

AbstractAllowable imprecision and bias reference limits for laboratory data can be calculated based on measurements of biological variation. Although biological variation of clinical chemical data has been reported from many laboratories, there have been few reports of biological variation in coagulation tests. In this study, we calculated the biological variation of 13 coagulation tests in the clinical laboratory of Kyushu University Hospital and determined allowable imprecision and bias limits of variation. The participating subjects were 17 healthy individuals: three males and two females in their 20s, two males and two females in their 30s, one male and four females in their 40s, and two males and one female in their 50s. Monthly measurements were performed before breakfast 12 times from June 2001 to May 2002 and allowable imprecision and bias limits were calculated. Taken together with coefficient of variation of control plasma used in daily laboratory work at the hospital, the allowable imprecision limits of intra-laboratory variation determined in this study appear to be in attainable ranges.

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Estimates of Within-Subject Biological Variation Derived from Pathology Databases: An Approach to Allow Assessment of the Effects of Age, Sex, Time between Sample Collections, and Analyte Concentration on Reference Change Values

Clinical Chemistry ◽

10.1373/clinchem.2018.290841 ◽

2019 ◽

Vol 65 (4) ◽

pp. 579-588 ◽

Cited By ~ 4

Author(s):

Graham Ross Dallas Jones

Keyword(s):

Clinical Laboratory ◽

Biological Variation ◽

Clinical Environment ◽

Analyte Concentration ◽

Regression To The Mean ◽

Statistical Process ◽

Reference Change Value ◽

First Results ◽

The Mean ◽

Number Of Individuals

Abstract BACKGROUND Within-subject biological variation data (CVI) are used to establish quality requirements for assays and allow calculation of the reference change value (RCV) for quantitative clinical laboratory tests. The CVI is generally determined using a large number of samples from a small number of individuals under controlled conditions. The approach presented here is to use a small number of samples (n = 2) that have been collected for routine clinical purposes from a large number of individuals. METHODS Pairs of sequential results from adult patients were extracted from a routine pathology database for 29 common chemical and hematological tests. Using a statistical process to identify a central gaussian distribution in the ratios of the result pairs, the total result variation for individual results was determined for 26 tests. The CVI was then calculated by removing the effect of analytical variation. RESULTS This approach produced estimates of CVI that, for most of the analytes in this study, show good agreement with published values. The data demonstrated minimal effect of sex, age, or time between samples. Analyte concentration was shown to affect the distributions with first results more distant from the population mean more likely to be followed by a result closer to the mean. DISCUSSION The process described here has allowed rapid and simple production of CVI data. The technique requires no patient intervention and replicates the clinical environment, although it may not be universally applicable. Additionally, the effect of regression to the mean described here may allow better interpretation of sequential patient results.

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