Evaluation of 20 clinical chemistry and 12 immunoassay analytes in terms of total analytical error and measurement uncertainty

2021 ◽  
pp. 1-6
Author(s):  
Ahmet Rıfat Balık ◽  
Funda Gücel
Keyword(s):  
Measurement Uncertainty ◽  
Clinical Chemistry ◽  
Analytical Error

scholarly journals Evaluation of the clinical chemistry tests analytical performance by using different models and specifications

2019 ◽  
Vol 45 (1) ◽  
pp. 11-18
Author(s):  
Murat Keleş
Keyword(s):  
Quality Management ◽  
Measurement Uncertainty ◽  
Goal Setting ◽  
Clinical Chemistry ◽  
Biological Variation ◽  
Analytical Performance ◽  
Analytical Quality ◽  
Analytical Error ◽  
Circular Letter ◽  
Clinical Chemistry Tests

Abstract Background The importance of managing analytical quality in clinical laboratories is known. Goal-setting models are critical for analytical quality management, along with correctly implemented error models. However, the methods used to determine analytical performance and more importantly, the relevant analytical quality goals are open to discussion. Our aim was to compare the analytical performance characteristics of routine clinical chemistry tests with different goal-setting models which was proposed by various establishments. In addition, to provide a perspective to Turkish total analytical error (TAE) circular letter that compulsory to calculate from 2016. Materials and methods This study was performed by the data obtained from the internal and external quality control of clinical chemistry tests which were measured by Roche Cobas c501 biochemistry analyzer. TAE calculated with TAE% = 1.65 ×(CV%) + Bias% formula. Nordtest uncertainty model was used in the calculation of measurement uncertainty (MU). In this context, total analytical error was evaluated with biological variation (BV), RCPA, CLIA and Turkish allowable total error (ATE) goals. Measurement uncertainty was evaluated with only permissible measurement uncertainty (pU%) goal. Results In our study, RCPA goals are the most stringent, followed by the BVEuBIVAS, BVRicos, pU%, CLIA and finally the ATETurkey goals coming in last. In cumulatively, BVEuBIVAS goals were 18.3% lower than BVRicos for evaluated parameters. Conclusion The balance between applicability and analytical assurance of goals should be well ensured when determining goal-setting models. Circular letter (2016/18) creates awareness to the analytical quality management but still open to development. Biological variation dependent total allowable error model never designed to be used as benchmarks for measurement uncertainty and it is not methodologically appropriate for assessing measurement uncertainty which was estimated by the Nordtest method. Also considered that, the use of “permissible MU” is more methodologically appropriate in the evaluation of measurement uncertainty.

Irregular analytical errors in diagnostic testing – a novel concept

2018 ◽  
Vol 56 (3) ◽  
pp. 386-396 ◽  
Author(s):  
Michael Vogeser ◽  
Christoph Seger
Keyword(s):  
Quality Control ◽  
Measurement Uncertainty ◽  
Measurement System ◽  
Clinical Chemistry ◽  
Individual Sample ◽  
Reference Measurement ◽  
Analytical Error ◽  
The Individual ◽  
Analytical Errors ◽  
Reference Measurement System

AbstractBackground:In laboratory medicine, routine periodic analyses for internal and external quality control measurements interpreted by statistical methods are mandatory for batch clearance. Data analysis of these process-oriented measurements allows for insight into random analytical variation and systematic calibration bias over time. However, in such a setting, any individual sample is not under individual quality control. The quality control measurements act only at the batch level. Quantitative or qualitative data derived for many effects and interferences associated with anindividualdiagnostic sample can compromise any analyte. It is obvious that a process for a quality-control-sample-based approach of quality assurance is not sensitive to such errors.Content:To address the potential causes and nature of such analytical interference in individual samples more systematically, we suggest the introduction of a new term called theirregular(individual)analytical error. Practically, this term can be applied in any analytical assay that is traceable to a reference measurement system. For an individual sample an irregular analytical error is defined as an inaccuracy (which is the deviation from a reference measurement procedure result) of a test result that is so high it cannot be explained by measurement uncertainty of the utilized routine assay operating within the accepted limitations of the associated process quality control measurements.Summary:The deviation can be defined as the linear combination of the process measurement uncertainty and the method bias for the reference measurement system. Such errors should be coinedirregular analytical errorsof the individual sample. The measurement result is compromised either by an irregular effect associated with the individual composition (matrix) of the sample or an individual single sample associated processing error in the analytical process.Outlook:Currently, the availability of reference measurement procedures is still highly limited, but LC-isotope-dilution mass spectrometry methods are increasingly used for pre-market validation of routine diagnostic assays (these tests also involve substantial sets of clinical validation samples). Based on this definition/terminology, we list recognized causes of irregular analytical error as arisk catalogfor clinical chemistry in this article. These issues include reproducible individual analytical errors (e.g. caused by anti-reagent antibodies) and non-reproducible, sporadic errors (e.g. errors due to incorrect pipetting volume due to air bubbles in a sample), which can both lead to inaccurate results and risks for patients.

Evaluation of Measurement Uncertainty of Glucose in Clinical Chemistry

2007 ◽  
Vol 1100 (1) ◽  
pp. 223-226 ◽  
Author(s):  
B. BERCIK INAL ◽  
M. KOLDAS ◽  
H. INAL ◽  
C. COSKUN ◽  
A. GUMUS ◽  
...  
Keyword(s):  
Measurement Uncertainty ◽  
Clinical Chemistry

Is the combination of trueness and precision in one expression meaningful? On the use of total error and uncertainty in clinical chemistry

2016 ◽  
Vol 54 (8) ◽  
Author(s):  
Anders Kallner
Keyword(s):  
Health Care ◽  
Measurement Uncertainty ◽  
Clinical Chemistry ◽  
Total Error ◽  
End User ◽  
Root Cause ◽  
True Value ◽  
Clinical Laboratories ◽  
Best Estimate ◽  
Custom Made

AbstractThe performance of all measurement procedures used in routine clinical laboratories shall be verified; a minimum is to verify the precision and trueness of the results. This is well established and adequate recommendations and procedures are available. Conveying this information in a form that is adequate and understandable for the practical end-user in the health care sector is still a much debated issue. By tradition, since several decades, the “total error” (TE) is presented, a quantity that is the linear sum of an imprecision and bias. Since any combination of the two can yield the same TE it may not be very helpful in finding and correcting a root-cause for an unacceptable value. Also, an acceptable TE may hide an unacceptable level of its constituents. An alternative is the measurement uncertainty (MU), which is recommended by accreditation and standardizing bodies The MU separates the imprecision and bias and expresses an interval around a best estimate within which the true value is expected with a certain probability. We describe the reporting the best estimate of a measurement result and describe how the uncertainty of the result, can be calculated, using simple custom-made software.

scholarly journals Intraperson Variability

1978 ◽  
Vol 15 (1-6) ◽  
pp. 49-54 ◽  
Author(s):  
D. B. Morgan
Keyword(s):  
Steady State ◽  
Total Variation ◽  
Clinical Chemistry ◽  
Single Test ◽  
Analytical Error ◽  
Analytical Technique ◽  
A Value ◽  
The Common

summary In a person in a steady state the set of values obtained for a test carried out on a series of samples will show fluctuation about a value (the setting). In practice a single test value is usually taken as an estimate of the setting. The general consequences of this approximation are discussed in relation to the various uses of data in diagnosis, and illustrated for the common measurements of clinical chemistry. The magnitude of these consequences depends on the proportion of the total variation in a group of persons which is caused by these fluctuations in the person—within-person variation (Varwp). When the proportion is high the aim should be to reduce Varwp. When Varwp is high because of analytical error, then improved analytical technique or replicate analysis is required. Otherwise standardised techniques and conditions for venepuncture or, as a last resort, repeated samplings are necessary. These problems are discussed in relation to the detection of hypokalaemia.

scholarly journals Reagent Carryover Studies: Preventing Analytical Error with Open Clinical Chemistry Systems

2005 ◽  
Vol 36 (11) ◽  
pp. 705-710 ◽  
Author(s):  
Joseph Boneno ◽  
Michelle Fokakis ◽  
Dave Armbruster
Keyword(s):  
Clinical Chemistry ◽  
Analytical Error

Diagnostic efficiency in models for permissible measurement uncertainty

2017 ◽  
Vol 41 (6) ◽  
Author(s):  
Rainer Haeckel ◽  
Werner Wosniok ◽  
Eberhard Gurr
Keyword(s):  
Measurement Uncertainty ◽  
Clinical Chemistry ◽  
Present Report ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
European Federation ◽  
Diagnostic Efficiency ◽  
Common Mean ◽  
Analytical Imprecision

AbstractLimits for measurement uncertainty related to analytical imprecision and bias are most appropriately defined by the magnitude of tolerable diagnostic errors. A common mean to characterize the consequence of these errors is the diagnostic efficiency, which, in the case of data from a non-diseased population, is the rate of true-positive results (specificity). Three models have been identified by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) for defining permissible uncertainty limits. Their model 1 is based on diagnostic requirements whereas models 2 and 3 do not primarily consider diagnostic errors. The present report links tolerable diagnostic error, empirical biological variation and the technical state of the art to derive the limits for measurement uncertainty. This approach combines the essential aspects of all three EFLM models and uses the diagnostic error, the clinically most relevant aspect, as the crucial criterion for the characterization of measurement uncertainty limits. The present approach is designed for the sole purpose of quality assurance.

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