Optimizing the use of the “state-of-the-art” performance criteria

Haeckel, Rainer; Wosniok, Werner; Streichert, Thomas

doi:10.1515/cclm-2014-1201

Cited by 13 publications

(19 citation statements)

References 11 publications

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“…A working group of the German Society of Clinical Chemistry and Laboratory Medicine (DGKL) has developed a model for deriving APS based on a combination of BV and state-of-the-art, with the definition of empiric biologic variation (EBV). [26][27][28] Traditional BV studies assess intra-individual (CV I ) and inter-individual variation (CV G ) through periodically repeated measurements over a specified time period, which can be merged into combined biological variation (CV B ). Conversely, EBV is derived from limits of population-based reference intervals (pRI), which should indirectly reflect biological variability and is expressed as an empirical coefficient of variation (CV E ).…”

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confidence: 99%

“…Conversely, EBV is derived from limits of population-based reference intervals (pRI), which should indirectly reflect biological variability and is expressed as an empirical coefficient of variation (CV E ). 26,27 CV E has been proposed as a surrogate for combined biological variation (CV B ) and has a good correlation with CV B for most human measurands, despite the fact that CV E additionally contains analytical variation (CV A ). 26 CV E can be utilized to determine APS.…”

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confidence: 99%

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Are analytical performance specifications derived from reference intervals of any use in the veterinary clinical laboratory? A preliminary study on the empirical biological variation model

Manzocchi,

van Rooyen

2024

Veterinary Clinical Pathol

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BackgroundAnalytical performance specifications (APS) are vital for method evaluation and quality control validation. However, the limited availability of biological variation (BV) data, regulatory guidelines, and expert opinion (EO) may present challenges in veterinary medicine. The empirical biological variation (EBV) approach, based on population reference intervals (pRI), has emerged as an alternative method to derive APS in human medicine.ObjectivesThis study aimed to assess the practicality and usefulness of the EBV approach in deriving performance limits for various measurands in dogs and cats.MethodsEight hematology and 13 biochemistry measurands were analyzed in dogs and cats. Estimates of combined biologic variation based on traditional biological (CVB) and EBV‐derived (CVE*) formulas were calculated and assessed for evidence of correlation. Performance limits for expanded uncertainty/total error and imprecision were compared among EO, BV, and EBV.ResultsStrong and significant correlations were found between CVB and CVE* for both dogs (r = .86, p < .00001) and cats (r = 0.95, p < .00001). The EBV‐derived APS were generally comparable to EO and BV, with a subjective criterion of 1.5% difference for imprecision and 3% for total error/expanded uncertainty.ConclusionThe EBV approach, using pRI, shows promise as a surrogate marker for biological variation and as a practical tool for determining performance limits in dogs and cats. Assuming accurate pRI generated on analyzers with stable analytical performance, this approach could offer benefits when expert recommendations or robust BV studies are lacking or yield conflicting results. Further research is needed to explore the applicability and advantages of the EBV in veterinary medicine.

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Are analytical performance specifications derived from reference intervals of any use in the veterinary clinical laboratory? A preliminary study on the empirical biological variation model

Manzocchi,

van Rooyen

2024

Veterinary Clinical Pathol

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“…It has been argued that the TE concept only deserves the predicate "total" when all kinds of errors are included [11]. Notably, the permissible TAE is often derived from reference intervals [36,64,65] that are substantially influenced by other types of errors (variation) than the analytical variation.…”

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The use of error and uncertainty methods in the medical laboratory

Oosterhuis

Bayat²,

Armbruster

et al. 2017

Clinical Chemistry and Laboratory Medicine (CCLM)

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Error methods -compared with uncertainty methods -offer simpler, more intuitive and practical procedures for calculating measurement uncertainty and conducting quality assurance in laboratory medicine. However, uncertainty methods are preferred in other fields of science as reflected by the guide to the expression of uncertainty in measurement. When laboratory results are used for supporting medical diagnoses, the total uncertainty consists only partially of analytical variation. Biological variation, pre-and postanalytical variation all need to be included. Furthermore, all components of the measuring procedure need to be taken into account. Performance specifications for diagnostic tests should include the diagnostic uncertainty of the entire testing process. Uncertainty methods may be particularly useful for this purpose but have yet to show their strength in laboratory medicine. The purpose of this paper is to elucidate the pros and cons of error and uncertainty methods as groundwork for future consensus on their use in practical performance specifications. Error and uncertainty methods are complementary when evaluating measurement data.

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“…This is the least preferred method because there may be no relationship between what is technically achievable and what is clinically needed. There is no official agreement on how to set APS based on this model, but a possible way to derive them is from external quality assessment programs or with some empirical method as proposed by Haeckel et al [31]. This model should be used for measurands that cannot be included in models 1 or 2, as described above.…”

Section: Reasons For Choosing Model 3 (State Of the Art)mentioning

confidence: 99%

Criteria for assigning laboratory measurands to models for analytical performance specifications defined in the 1st EFLM Strategic Conference

Ceriotti

Fernández–Calle

Klee

et al. 2016

Clinical Chemistry and Laboratory Medicine (CCLM)

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138

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This paper, prepared by the EFLM Task and Finish Group on Allocation of laboratory tests to different models for performance specifications (TFG-DM), is dealing with criteria for allocating measurands to the different models for analytical performance specifications (APS) recognized in the 1st EFLM Strategic Conference Consensus Statement. Model 1, based on the effect of APS on clinical outcome, is the model of choice for measurands that have a central role in the decision-making of a specific disease or clinical situation and where cutoff/decision limits are established for either diagnosing, screening or monitoring. Total cholesterol, glucose, HbA 1c , serum albumin and cardiac troponins represent practical examples. Model 2 is based on components of biological variation and should be applied to measurands that do not have a central role in a specific disease or clinical situation, but where the concentration of the measurand is in a steady state. This is best achieved for measurands under strict homeostatic control in order to preserve their concentrations in the body fluid of interest, but it can also be applied to other measurands that are in a steady state in biological fluids. In this case, it is expected that the "noise" produced by the measurement procedure will not significantly alter the signal provided by the concentration of the measurand. This model especially applies to electrolytes and minerals in blood plasma (sodium, potassium, chloride, bicarbonate, calcium, magnesium, inorganic phosphate) and to creatinine, cystatin C, uric acid and total protein in plasma. Model 3, based on stateof-the-art of the measurement, should be used for all the measurands that cannot be included in models 1 or 2.

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Optimizing the use of the “state-of-the-art” performance criteria

Cited by 13 publications

References 11 publications

Are analytical performance specifications derived from reference intervals of any use in the veterinary clinical laboratory? A preliminary study on the empirical biological variation model

Are analytical performance specifications derived from reference intervals of any use in the veterinary clinical laboratory? A preliminary study on the empirical biological variation model

The use of error and uncertainty methods in the medical laboratory

Criteria for assigning laboratory measurands to models for analytical performance specifications defined in the 1st EFLM Strategic Conference

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