Application of Six Sigma for evaluating the analytical quality of tumor marker assays

Liu, Qian; Fu, Mei R.; Yang, Fumeng; Liang, Wei; Yang, Chuanxi; Zhu, Wenjun; Ma, Liming; Zhao, Chenkai

doi:10.1002/jcla.22682

Cited by 9 publications

(13 citation statements)

References 11 publications

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“…[16][17][18]20,21 The chart is divided into six grades by five lines. 22 Based on the sigma level, the performance of the analytes was divided into six grades 23 : worldclass (σ > 6), excellent (5 ≤ σ < 6), good (4 ≤ σ < 5), marginal (3 ≤ σ < 4), poor (2 ≤ σ < 3), and unacceptable (σ < 2) ( Figure 2). The sigma value of the analyte was represented by colored circles marked in certain sigma grades of the chart when the parameters of the analyte's name, TEa, bias, and CV were inputted into the interface.…”

Section: Construction Of the Normalized Qc Performance Decision Chartmentioning

confidence: 99%

Evaluation of the analytical performance of endocrine analytes using sigma metrics

Yanming

Cao

Liu

et al. 2020

Clinical Laboratory Analysis

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Background (a) To evaluate the clinical performance of endocrine analytes using the sigma metrics (σ) model. (b) To redesign quality control strategies for performance improvement. Methods The sigma values of the analytes were initially evaluated based on the allowable total error (TEa), bias, and coefficient of variation (CV) at QC materials level 1 and 2 in March 2018. And then, the normalized QC performance decision charts, personalized QC rules, quality goal index (QGI) analysis, and root causes analysis (RCA) were performed based on the sigma values of the analytes. Finally, the sigma values were re‐evaluated in September 2018 after a series of targeted corrective actions. Results Based on the initial sigma values, two analytes (FT3 and TSH) with σ > 6, only needed one QC rule (13S) with N2 and R500 for QC management. On the other hand, seven analytes (FT4, TT4, CROT, E2, PRL, TESTO, and INS) with σ < 4 at one QC material level or both needed multiple rules (13S/22S/R4S/41S/10X) with N6 and R10‐500 depending on different sigma values for QC management. Subsequently, detailed and comprehensive RCA and timely corrective actions were performed on all the analytes base on the QGI analysis. Compared with the initial sigma values, the re‐evaluated sigma metrics of all the analytes increased significantly. Conclusions It was demonstrated that the combination of sigma metrics, QGI analysis, and RCA provided a useful evaluation system for the analytical performance of endocrine analytes.

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Section: Construction Of the Normalized Qc Performance Decision Chartmentioning

confidence: 99%

Evaluation of the analytical performance of endocrine analytes using sigma metrics

Yanming

Cao

Liu

et al. 2020

Clinical Laboratory Analysis

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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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“…Clinical laboratories should regularly measure detection performance for all parameters and develop individualized quality control plans for all analytes to provide long-term, feasible measures for continuous improvement of assay quality. [11] Verma et al study reveals that if TE of an analyte is within allowable error limits specific for that analyte, bias % and CV % might be more reliable than sigma metrics. [12] TE for levels 1&2 shows the values did not exceed the allowable total error recommended by CLIA.…”

Section: Discussionmentioning

confidence: 99%

A Short-Term Assessment of Routine Chemistry Parameters by Sigma Metrics and Quality Goal Index Ratio in a Tertiary Care Hospital Laboratory

Sayeed¹,

Ganji²,

Mopuri³

2019

jemds

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BACKGROUND Sigma metrics is an important tool to evaluate the errors in quality control of laboratory system. It can easily quantify the exact number of errors by combining bias, precision, and total allowable error (TEa). Quality goal index is a newer parameter to represent the relative extent to which both bias and precision meet their respective quality goals. The aim of the present study is to assess the Internal and External quality control by Sigma Metrics and Quality Goal Index ratio (QGI). METHODSThis observational study was conducted at Clinical Biochemistry Laboratory, Osmania General Hospital, Hyderabad, Telangana. Data of 8 most commonly run analytes: glucose, urea, creatinine, total proteins, total bilirubin, cholesterol, albumin and uric acid were extracted over a period of 3 months. The two levels of controls of these parameters were run on a Beckman Coulter AU5800 autoanalyser. Mean, SD, and coefficient of variance (CV) were calculated for each month from IQC. Bias obtained from EQAS. Sigma, QGI and TE were calculated subsequently. RESULTSThe sigma metrics for level 1 for all eight parameters indicated that 5 failed to meet a minimum sigma quality performance wi th metrics less than 3 and another 2 just met minimal acceptable performance with sigma metrics between 3 and 6. For level 2, none of the parameters achieved six sigma, of which 5 parameters had metrics less than 3 and 3 had metrics between 3 and 6. The calculated TE of all the parameters was less than the specified TEa. QGI ratio indicated that out of five parameters of level 1 and level 2, which failed to meet six sigma quality performances, the main problem was imprecision in all parameters except albumin which showed both imprecision and inaccuracy for both levels. CONCLUSIONSBoth Internal and External quality control for the most routinely run parameters in our hospital need stringent monitoring in order to fulfill the requirements of a standard clinical laboratory. KEY WORDSExternal Quality Assurance Scheme (EQAS), Internal Quality Control (IQC), Quality Goal Index (QGI), Six Sigma HOW TO CITE THIS ARTICLE: Sayeed S, Ganji SB, Mopuri R. A short-term assessment of routine chemistry parameters by sigma metrics and quality goal index ratio in a tertiary care hospital laboratory. BACKGROUND Clinical biochemistry investigations are important in diagnosis and treatment. It is essential to follow a proper quality management system (QMS) to provide accurate and reliable reports in an agreed upon time frame. Sigma metrics is an important tool to evaluate the errors in quality control of laboratory system. Sigma is a metric that quantifies the performance of a process at a rate of defects per million. The sigma value indicates how often errors are likely to occur. The sigma value of one corresponds to 31% of accuracy which is equal to 698,000 defects per million tests.

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Application of Six Sigma for evaluating the analytical quality of tumor marker assays

Cited by 9 publications

References 11 publications

Evaluation of the analytical performance of endocrine analytes using sigma metrics

Evaluation of the analytical performance of endocrine analytes using sigma metrics

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

A Short-Term Assessment of Routine Chemistry Parameters by Sigma Metrics and Quality Goal Index Ratio in a Tertiary Care Hospital Laboratory

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