Planning Risk-Based SQC Schedules for Bracketed Operation of Continuous Production Analyzers

Westgard, James O.; Bayat, Hassan; Westgard, Sten A.

doi:10.1373/clinchem.2017.278291

Cited by 60 publications

(40 citation statements)

References 9 publications

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“…The difference in Urea on the Beckman analyser, however, may have been the result of periodic biases with different signs averaged to an ignorable long-term bias. In practice, laboratories must take caution when implementing “Westgard Sigma Rules” in quality control based on σ values, because these values may change continuously with respect to precision and bias arising, for example, from calibrations, reagents, or personnel ( 4 ). As described previously, σ value between different periods for some analytes may differ, laboratory should monitor the σ level continuously when using individualized quality control rules based on the σ evaluation.…”

Section: Discussionmentioning

confidence: 99%

Sigma metrics for assessing the analytical quality of clinical chemistry assays: a comparison of two approaches

et al. 2018

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IntroductionTwo approaches were compared for the calculation of coefficient of variation (CV) and bias, and their effect on sigma calculation, when different allowable total error (TEa) values were used to determine the optimal method for Six Sigma quality management in the clinical laboratory.Materials and methodsSigma metrics for routine clinical chemistry tests using three systems (Beckman AU5800, Roche C8000, Siemens Dimension) were determined in June 2017 in the laboratory of Peking Union Medical College Hospital. Imprecision (CV%) and bias (bias%) were calculated for ten routine clinical chemistry tests using a proficiency testing (PT)- or an internal quality control (IQC)-based approach. Allowable total error from the Clinical Laboratory Improvement Amendments of 1988 and the Chinese Ministry of Health Clinical Laboratory Center Industry Standard (WS/T403-2012) were used with the formula: Sigma = (TEa − bias) / CV to calculate the Sigma metrics (σCLIA, σWS/T) for each assay for comparative analysis.ResultsFor the PT-based approach, eight assays on the Beckman AU5800 system, seven assays on the Roche C8000 system and six assays on the Siemens Dimension system showed σCLIA > 3. For the IQC-based approach, ten, nine and seven assays, respectively, showed σCLIA > 3. Some differences in σ were therefore observed between the two calculation methods and the different TEa values.ConclusionsBoth methods of calculating σ can be used for Six Sigma quality management. In practice, laboratories should evaluate Sigma multiple times when optimizing a quality control schedule.

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Section: Discussionmentioning

confidence: 99%

Sigma metrics for assessing the analytical quality of clinical chemistry assays: a comparison of two approaches

et al. 2018

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“…When considering continuous laboratory production processes, bracketed internal QC (iQC) is applied for analytical quality assurance [39]. In current practice with short turn-around times, continuous analysis and the release of multiple diagnostic test results, many laboratory results will be released before a confirmatory QC is performed.…”

Section: Ma Qc To Support Statistical Qcmentioning

confidence: 99%

“…When a continuous production process is analytically controlled using scheduled iQC, there is a risk of reporting erroneous results by either rapid onset of (large) error in between the scheduled QC [3] or temporary assay failure in between the scheduled iQC, as demonstrated for a sodium MA alarm case [9]. Furthermore, by design, statistical QC is limited in its ability to detect clinically relevant errors for low sigma processes [39,40]. Interestingly, these low sigma processes (characterized by a low biological variation/analytical variation ratio) generally have the characteristics for optimal MA performance [19].…”

Section: Ma Qc To Support Statistical Qcmentioning

confidence: 99%

“…Interestingly, these low sigma processes (characterized by a low biological variation/analytical variation ratio) generally have the characteristics for optimal MA performance [19]. Furthermore, when riskbased statistical QC schedules are used for high-volume low sigma (<3) assays, statistical QC becomes almost unmanageable [39]. For these reasons, MA QC can further support and extend the analytical quality assurance of statistical QC [39].…”

Section: Ma Qc To Support Statistical Qcmentioning

confidence: 99%

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Moving average quality control: principles, practical application and future perspectives

Rossum

2018

Clinical Chemistry and Laboratory Medicine (CCLM)

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Moving average quality control (MA QC) was described decades ago as an analytical quality control instrument. Although a potentially valuable tool, it is struggling to meet expectations due to its complexity and need for evidence-based guidance. For this review, relevant literature and the world wide web were examined in order to (i) explain the basic concepts and current understanding of MA QC, (ii) discuss moving average (MA) optimization methods, (iii) gain insight into practical aspects related to applying MA in daily practice and (iv) describe future prospects to enable more widespread acceptance and application of MA QC. Each of the MA QC optimization methods currently available has their own advantages and disadvantages. Recently developed simulation methods provide realistic error detecting properties for MA QC and are available for laboratories. Operational MA management issues have been identified that allow developers of MA software to upgrade their packages to support optimal MA QC application and guide laboratories on MA management issues, such as MA alarm workup. The new insights into MA QC characteristics and operational issues, together with supporting online tools, may promote more widespread acceptance and application of MA QC.

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“…The practice of using the σ to improve clinical laboratory quality has been in place for nearly two decades. 2 Sigma metric has become a useful tool to monitor quality indicators, 1 to assess the analytical quality of assays, 3,4 to set quality control rules, [5][6][7][8] to describe assay analytical performance for external quality assessment participants, 9 and to help manufacturers choose product requirements. 10 Analytical quality of assays is quantitatively estimated as a sigma metric based on 3 parameters: allowable total error (TEa), bias, and imprecision.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of calculating sigma metrics by a trueness verification proficiency testing‐based approach and an internal quality control data inter‐laboratory comparison‐based approach

Wang

Gong

et al. 2019

Clinical Laboratory Analysis

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IntroductionTwo methods were compared for evaluating the sigma metrics of clinical biochemistry tests using two different allowable total error (TEa) specifications.Materials and methodsThe imprecision (CV%) and bias (bias%) of 19 clinical biochemistry analytes were calculated using a trueness verification proficiency testing (TPT)‐based approach and an internal quality control data inter‐laboratory comparison (IQC)‐based approach, respectively. Two sources of total allowable error (TEa), the Clinical Laboratory Improvement Amendments of 1988 (CLIA '88) and the People's Republic of China Health Industry Standard (WS/T 403‐2012), were used to calculate the sigma metrics (σCLIA, σWS/T). Sigma metrics were calculated to provide a single value for assessing the quality of each test based on a single concentration level.ResultsFor both approaches, σCLIA > σWS/T in 18 out of 19 assays. For the TPT‐based approach, 16 assays showed σCLIA > 3, and 12 assays showed σWS/T > 3. For the IQC‐based approach, 19 and 16 assays showed σCLIA > 3 and σWS/T > 3, respectively.ConclusionsBoth methods can be used as references for calculating sigma metrics and designing QC schedules in clinical laboratories. Sigma metrics should be evaluated comprehensively by different approaches.

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Planning Risk-Based SQC Schedules for Bracketed Operation of Continuous Production Analyzers

Abstract: Continuous production analyzers that demonstrate high σ performance can be effectively controlled with multistage SQC designs that employ a startup QC event followed by periodic monitoring or bracketing QC events. Such designs can be optimized to minimize the risk of harm to patients.

Cited by 60 publications

References 9 publications

Sigma metrics for assessing the analytical quality of clinical chemistry assays: a comparison of two approaches

Sigma metrics for assessing the analytical quality of clinical chemistry assays: a comparison of two approaches

Moving average quality control: principles, practical application and future perspectives

Comparative analysis of calculating sigma metrics by a trueness verification proficiency testing‐based approach and an internal quality control data inter‐laboratory comparison‐based approach

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