Application of the Characteristic Function to Evaluate and Compare Analytical Variability in an External Quality Assessment Scheme for Serum Ethanol

Coucke, Wim; Charlier, Corinne; Lambert, Willy E.; Martens, Frank; Neels, Hugo; Tytgat, Jan; Walle, P. Van de; Vanescote, André; Wallemacq, Pierre; Wille, Sarah M. R.; Verstraete, Alain G.

doi:10.1373/clinchem.2015.240176

Cited by 8 publications

(6 citation statements)

References 20 publications

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“…The latter however is rarely applied in EQA schemes for clinical laboratories. If historic data are available, the standard deviation could be derived from the assigned value, for example by means of the characteristic function ( 32 , 33 ), which is a mathematical relation to estimate the standard deviation based on the assigned value:where α and β are to be estimated from the historical data by means of non-linear regression. The coefficients α and β have a different meaning in explaining the standard deviation.…”

Section: Determining the Standard Deviationmentioning

confidence: 99%

Demystifying EQA statistics and reports

Coucke

Soumali

2017

Biochem Med

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Reports act as an important feedback tool in External Quality Assessment (EQA). Their main role is to score laboratories for their performance in an EQA round. The most common scores that apply to quantitative data are Q- and Z-scores. To calculate these scores, EQA providers need to have an assigned value and standard deviation for the sample. Both assigned values and standard deviations can be derived chemically or statistically. When derived statistically, different anomalies against the normal distribution of the data have to be handled. Various procedures for evaluating laboratories are able to handle these anomalies. Formal tests and graphical representation techniques are discussed and suggestions are given to help choosing between the different evaluations techniques. In order to obtain reliable estimates for calculating performance scores, a satisfactory number of data is needed. There is no general agreement about the minimal number that is needed. A solution for very small numbers is proposed by changing the limits of evaluation. Apart from analyte- and sample-specific laboratory evaluation, supplementary information can be obtained by combining results for different analytes and samples. Various techniques are overviewed. It is shown that combining results leads to supplementary information, not only for quantitative, but also for qualitative and semi-quantitative analytes.

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Section: Determining the Standard Deviationmentioning

confidence: 99%

Demystifying EQA statistics and reports

Coucke

Soumali

2017

Biochem Med

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“…Eventually, a supposed estimated sample heterogeneity could be subtracted from the calculated PT standard deviations from the past. Because the PT standard deviation of the reported results varies from sample to sample, an interpolation between standard deviations for different concentrations is recommended, for example, by using the characteristic function, , a function that draws the relation between the target value and standard deviation.…”

Section: Controlling Sample Heterogeneity For Quantitative Resultsmentioning

confidence: 99%

Alternative Sample-Homogeneity Test for Quantitative and Qualitative Proficiency Testing Schemes

et al. 2019

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Proficiency Testing (PT) External Quality Assessment (EQA) schemes are designed to ascertain the ability of individual laboratories to perform satisfactorily with respect to their peer laboratories or to limits imposed by external sources. Observed deviation of a laboratory result for a PT sample must be entirely attributed to the laboratory and not to the PT provider. To minimize the probability that deviations could be attributed to the PT provider, sample homogeneity should be assured. It is generally required that for quantitative parameters, the standard deviation among PT units should be calculated on the basis of duplicate measurements of at least 10 samples chosen at random, and the standard deviation among PT units should not exceed 0.3 times the standard deviation used to evaluate laboratories. Because this approach has important drawbacks, an alternative procedure is proposed by applying the theory of acceptance sampling to the assessment of sample heterogeneity for both quantitative and qualitative data and deriving acceptance limits on the basis of minimizing the probability of falsely evaluating laboratories. For obtaining acceptance limits for quantitative parameters, a distinction is made between laboratory evaluation using fixed limits on the one hand and laboratory evaluation using limits that are based on the variability of the reported results on the other hand. Sequential tests are proposed to evaluate sample heterogeneity by means of a comparison with the χ 2 distribution. For qualitative parameters, acceptancesampling plans are proposed that are based on minimizing the joint probability of rejecting batches that have a satisfactory amount of defective samples and accepting batches unnecessarily. The approach for quantitative parameters is applied on samples for a PT scheme of ethanol quantification and for qualitative parameters on the presence of monoblasts in a blood smear. It was found that five samples could already be enough to prove that the batch was homogeneous for quantitative parameters, although more than 20 samples were needed to prove homogeneity for qualitative parameters. This study describes a direct relation among the objective of an PT round, the criteria for evaluating the results, and the sample heterogeneity. When samples are effectively homogeneous, less measurements are needed than current practices require. A drawback of the proposed approach is that the number of samples to be tested is not known beforehand, and good knowledge of the analytical variability is crucial. The formulas to be applied are relatively simple. Despite the drawbacks, the proposed approach is generally applicable for both quantitative and qualitative data.

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“…Samples have to be sufficiently stable and homogeneous, according to the ISO norm 17043, which also refers to the ISO norm 13528 as model to assess these aspects. [5,7,24] Establishment of multidisciplinary quality manuals for the management of specific diseases [5,25] Financing of quality coordinators Quality coordinator: person responsible for the different aspects of the Quality Management System of the institution (audits, accreditation, participation to EQAs/ring tests, norms/SOPs/DOCs, etc.). [4,26] Healthcare coordination Satisfaction surveys among patients Use of questionnaires and/or interviews between patients and clinicians in order to collect and measure patient's satisfaction regarding some specific aspects of healthcare/services provided by the hospital, based on appropriated scores of satisfaction and quality of life.…”

Section: Methodsmentioning

confidence: 99%

Strategies to improve the quality of reference networks for rare diseases

Vandevelde

2020

TRD

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BACKGROUND: In 2017, 24 European Reference Networks (ERNs) have been recognized in order to improve rare diseases' management. However, a lack of practical information persists regarding the most adequate ways to fulfil ERNs' quality criteria. OBJECTIVE: Identify the most efficient strategies to improve ERNs' quality. METHODS: 67 strategies, related to 8 activity domains (Quality Management System, healthcare coordination, clinicians' training, inter-ERNs' members collaborations, clinical data management, ERNs' external collaborations/supports, information provided to patients, and ERNs' financing) were selected from peer-reviewed scientific publications. Their usefulness, feasibility and adequacy was evaluated by 17 ERNs-related clinicians and 10 Belgian health policy makers. RESULTS: Results showed that the most efficient strategies are the participation of quality controls, satisfaction surveys among patients and external physicians, diffusion of information about the hospitals' research activities, specific trainings addressed to clinicians, exchange of medical samples between hospitals, and development of adequate clinical databases. The poor practical feasibility of 7 highly-useful strategies was also highlighted, such as major clinicians' concerns regarding legal/ethical frameworks for the reimbursement of crosscountry care and exchange of patients' data. CONCLUSIONS: This study enabled identification of efficient strategies to improve and validate ERNs' quality, expertise and functioning, as well as weaknesses that should be addressed.

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Application of the Characteristic Function to Evaluate and Compare Analytical Variability in an External Quality Assessment Scheme for Serum Ethanol

Cited by 8 publications

References 20 publications

Demystifying EQA statistics and reports

Demystifying EQA statistics and reports

Alternative Sample-Homogeneity Test for Quantitative and Qualitative Proficiency Testing Schemes

Strategies to improve the quality of reference networks for rare diseases

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