Determination of Six Sigma Metric in Control of Enzymes Determination in Human Serum

Đido, Vedran; Ćorić, Jozo; Mujić, Jasminka; Panjeta, Mirsad; Bodulović, Aleksandar; Marjanovic, M.

doi:10.7754/clin.lab.2019.190108

Cited by 5 publications

(5 citation statements)

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“…Furthermore, Đido et al. 22 applied sigma metrics to the evaluation of the analytical performance for serum enzymes and showed significant differences in the sigma values for analytes at different concentrations.…”

Section: Discussionmentioning

confidence: 99%

“…Wang et al 21 applied sigma metrics to the evaluation of analytical performance for urinary albumin and revealed significant performance differences when the analyte levels differed. Furthermore, -Dido et al 22 applied sigma metrics to the evaluation of the analytical performance for serum enzymes and showed significant differences in the sigma values for analytes at different concentrations.…”

Section: Discussionmentioning

confidence: 99%

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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%

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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“…O N L I N E F I R S T (17,18,19). We can say that a more detailed evaluation of analytical performances is needed by strengthening quality control to achieve the highest possible level of six sigma for a medical laboratory, as some studies have shown (2,12,(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31). The lowest sigma value was observed for the following parameters: sodium (17,19), potassium (18), chloride (2,17,22), urea (18,19,21,27), creatinine (22,26,27), total protein (2,22), albumin (2,22,23,27), cholesterol (17,18,22,27), total bilirubin (18,22,27), glucose (17,22), some tumor markers (Ca 125, AFP) (20) and some hormones (fT4, prolactin, testosterone, and insulin)…”

Section: Analytical Phase and Six Sigma Metricsmentioning

confidence: 99%

"Six SIGMA" standard as a level of quality of biochemical laboratories

Pašić¹,

Šeherčehajić²

2022

Sanamed

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The principal role of biochemical laboratories is responsibility for reliable, reproducible, accurate, timely, and accurately interpreted analysis results that help in making clinical decisions, while ensuring the desired clinical outcomes. To achieve this goal, the laboratory should introduce and maintain quality control in all phases of work. The importance of applying the Six SIGMA quality model has been analyzed in a large number of scientific studies. The purpose of this review is to highlight the importance of using six SIGMA metrics in biochemical laboratories and the current application of six SIGMA metrics in all laboratory work procedures. It has been shown that the six SIGMA model can be very useful in improving all phases of laboratory work, as well as that a detailed assessment of all procedures of the phases of work and improvement of the laboratory's quality control system is crucial for the laboratory to have the highest level of six SIGMA. Clinical laboratories should use SIGMA metrics to monitor their performance, as it makes it easier to identify gaps in their performance, thereby improving their efficiency and patient safety. Medical laboratory quality managers should provide a systematic methodology for analyzing and correcting quality assurance systems to achieve Six SIGMA quality-level standards.

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“…Currently, the performance of quantitative laboratory tests in clinical chemistry is often expressed as a sigma metric, and control rules for IQC can be obtained using a sigma metric [13][14][15][16][17][18][19][20][21]. Quantitative readout values, such as the reflectance rate, are used to evaluate the performance of a URST [9,[22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Internal Quality Control Data of Urine Reagent Strip Tests and Derivation of Control Rules Based on Sigma Metrics

Park

2021

Ann Lab Med

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Background: Urine reagent strip test (URST) results are semi-quantitative; therefore, the precision of URSTs is evaluated as the proportion of categorical results from repeated measurements of a sample that are concordant with an expected result. However, URSTs have quantitative readout values before ordinal results challenging statistical monitoring for internal quality control (IQC) with control rules. This study aimed to determine the sigma metric of URSTs and derive appropriate control rules for IQC.Methods: The URiSCAN Super Plus fully automated urine analyzer (YD Diagnostics, Yongin, Korea) was used for URSTs. Change in reflectance rate (change %R) data from IQC for URSTs performed between November 2018 and May 2020 were analyzed. Red blood cells, bilirubin, urobilinogen, ketones, protein, glucose, leukocytes, and pH were measured from 2-3 levels of control materials. The total allowable error (TEa) for a grade was the difference in midpoints of a predefined change %R range between two adjacent grades. The sigma metric was calculated as TEa/SD. Sigma metric-based control rules were determined with Westgard EZ Rules 3 software (Westgard QC, Madison, WI, USA).Results: Seven out of the eight analytes had a sigma metric > 4 in the control materials with a negative grade (−), which were closer to the cut-offs. Corresponding control rules ranged from 12.5s to 13.5s. Conclusions:Although the URST is a semi-quantitative test, statistical IQC can be performed using the readout values. According to the sigma metric, control rules recommended for URST IQC in routine clinical practice are 12.5s to 13.5s.

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Determination of Six Sigma Metric in Control of Enzymes Determination in Human Serum

Cited by 5 publications

References 0 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

"Six SIGMA" standard as a level of quality of biochemical laboratories

Internal Quality Control Data of Urine Reagent Strip Tests and Derivation of Control Rules Based on Sigma Metrics

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