Comprehensive evaluation of the internal and external quality control to redefine analytical quality goals

Varela, Beatriz Ferreira; Pacheco, Gonzalo Clemente Lara

doi:10.11613/bm.2018.020710

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…TEa represents the quality goals selected by the laboratory, and previous studies have shown that there are significant differences in sigma levels for the quality goals used for the same analytes in the same laboratories. 18,19 Thus, based on the model developed at the Milan Conference, 20 the desirable specification derived from biological variation was selected as the quality goal for cystatin C, and the sigma values were calculated based on the concentrations of two IQC materials. Our study found differences in the analytical performance for cystatin C at different concentrations.…”

Section: Discussionmentioning

confidence: 99%

Application of the sigma metrics to evaluate the analytical performance of cystatin C and design a quality control strategy

et al. 2021

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Background Sigma metrics are commonly used to evaluate laboratory management. In this study, we aimed to evaluate the analytical performance of cystatin C using sigma metrics and to develop an individualized quality control scheme for cystatin C concentrations. Methods Bias was calculated based on the samples used for the external quality assessment. The coefficient of variation was calculated using six months of internal quality control measurements at two levels, and desirable specification derived from biological variation was used as the quality goal. The sigma value for cystatin C was calculated using the above data. The internal quality control scheme and improvement measures were formulated according to the Westgard sigma standards for batch size and quality goal index. Results The sigma values for cystatin C, for quality control levels 1 and 2, were 3.04 and 4.95, respectively. The 13s/22s/R4s/41s/6x multirules ( n = 6, R = 1), with a batch size of 45 patient samples, were selected as the internal quality control schemes for cystatin C. With different concentrations of cystatin C, the power function graph showed a probability for error detection of 94% and 100% and a probability for false rejection of 4% and 2%, respectively. According to the quality goal index of cystatin C, its precision needs to be improved. Conclusions With a ‘desirable’ biological variation of 6.50%, the Westgard rule 13s/22s/R4s/41s/6x ( n = 6, R = 1, batch size of 45) with high efficacy for determining the detection error is recommended for individualized quality control schemes of cystatin C.

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Section: Discussionmentioning

confidence: 99%

Application of the sigma metrics to evaluate the analytical performance of cystatin C and design a quality control strategy

et al. 2021

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“…Bias and precision both influence the Sigma metrics while precision has a considerable impact [19]. A few studies have evaluated both Sigma metrics and QGI levels for the analytic process [20,21]. QGI levels for the BNP test in our emergency laboratory conditions lower than 0.8 are due to high imprecision rather than a bias problem that is difficult to correct.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Sigma value and quality goal index for brain natriuretic peptide test

Üstündağ¹

2020

Int J Med Biochem

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Evaluation of sigma value and quality goal index for brain natriuretic peptide test T he b-type or brain natriuretic peptide (BNP) is a member of the natriuretic peptide family that is involved in many biological events in the body, including regulation of blood pressure and blood volume [1][2][3]. BNP is mainly synthesized in the cardiac ventricle as proBNP, which is a prohormone consisting of 108 amino acids. ProBNP is cleaved in the circulation as an active hormone 32 amino acid structure that corresponds to the C-terminus of proBNP. The N terminal (NT) 76 amino acid NT-proBNP is an inactive protein. Both BNP and NT-proBNP have a wide range of clinical effects that increase cardiac stress and output [1][2][3]. Measurement of BNP levels to differentiate the causes of acute dyspnoea in patients admitted to the emergency de-partment has high diagnostic accuracy [3,4]. Heart failure (HF) is unlikely when the plasma BNP level is <100 pg/ml, and a level higher than 400 pg/ml is appropriate for ruling in acute HF, which makes it a suitable test for patient identification in emergency care settings [1][2][3][4]. However, recent studies suggested that there are significant differences in analytical performance among commercial methods for BNP immunoassays [5] An erroneous result, in which the difference between a test result and the corresponding true value exceeds the allowable total error (Tea), leads to medically significant errors. To improve patient safety, zero errors should be targeted in health services that cannot be possible in a real laboratory setting [6].Objectives: In this study, we aimed to evaluate the analytical process by determining the sigma values and quality goal index (QGI) of the brain natriuretic peptide (BNP) test parameter performed in our emergency laboratory. Methods: BNP levels were tested using a commercially available immunoassay autoanalyser (Advia Centaur XP; Siemens Healthcare Diagnostic Ltd., Muenchen, Germany). Bias was calculated by comparison with the group mean for each external quality assessment report, and an internal quality control-based approach was applied. The internal quality control results of the BNP test levels between September and December 2018 and external quality program reports were used in the coefficient of variation (CV) and bias calculations. The sigma metrics and QGI were calculated. Results: The sigma metric calculated from the external quality control was 2.1 and the QGI was 0.34. In the internal quality control study, the sigma level was 1.92, and the QGI was 0.55, as calculated at the 400 pg/ml level. The QGI level suggests that the problem in the BNP study was imprecision. Conclusion: Unsatisfactory sigma levels for BNP tests were achieved using both methods of calculating sigma metrics.

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“…(3) Quality objectives can perform a motivational function (Bugdol, 2014;Varela and Pacheco, 2018;Whitaker and Levy, 2012) provided that a remuneration system is regarded as just and quality objectives are both objective and measurable.…”

Section: Quality Objectives In Management Systemsmentioning

confidence: 99%

Quality objectives in management systems – their attributes, establishment and motivational function

Bugdol

Jedynak

2021

IJQRM

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PurposeThe aim of this paper is to show the ways of setting quality objectives, their attributes and the conditions under which they can perform a motivational function.Design/methodology/approachCollecting relevant data, the authors used the results of previous research and theoretical assumptions concerning quality objectives. Subsequently, they carried out a survey and exemplification research based on participatory observations, document content analysis and interviews.FindingsGoals are set mainly by top management, but the communication process itself is insufficient; the needs of system users are not taken into account. In the opinion of the employees, quality objectives are measurable and objective, although not very ambitious. For quality objectives to fulfil a motivational function, they should be objective and measurable. Also, the allocation of tasks among employees needs to be fair. Furthermore, quality objectives have to play a greater role in remuneration systems.Originality/valueThis paper is one of the first publications on the role and function of quality objectives. Its advantage is that it defines the conditions under which such objectives can have a motivational effect and encourage employees to pursue the improvement of their products and services.

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Comprehensive evaluation of the internal and external quality control to redefine analytical quality goals

Cited by 14 publications

References 16 publications

Application of the sigma metrics to evaluate the analytical performance of cystatin C and design a quality control strategy

Application of the sigma metrics to evaluate the analytical performance of cystatin C and design a quality control strategy

Evaluation of Sigma value and quality goal index for brain natriuretic peptide test

Quality objectives in management systems – their attributes, establishment and motivational function

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