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Article ; Online: How to evaluate fixed clinical QC limits vs. risk-based SQC strategies.

Westgard, James O / Bayat, Hassan / Westgard, Sten A

Clinical chemistry and laboratory medicine

2022 Volume 60, Issue 9, Page(s) e199–e201

MeSH term(s)	Humans ; Laboratories ; Quality Control
Language	English
Publishing date	2022-06-14
Publishing country	Germany
Document type	Letter
ZDB-ID	1418007-8
ISSN	1437-4331 ; 1434-6621 ; 1437-8523
ISSN (online)	1437-4331
ISSN	1434-6621 ; 1437-8523
DOI	10.1515/cclm-2022-0539
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Article ; Online: Multirule procedures vs moving average algorithms for IQC: An appropriate comparison reveals how best to combine their strengths.

Bayat, Hassan / Westgard, Sten A / Westgard, James O

Clinical biochemistry

2022 Volume 102, Page(s) 50–55

Abstract: ... to replace traditional IQC procedures because they "outperform Westgard Rules," which is a current standard ...

Abstract	Background: Moving Average Algorithms (MAA) have been widely recommended for use in Patient Based Real Time Quality Control applications (PBRTQC) to supplement or replace traditional Internal Quality Control (IQC) techniques. A recent "proof of concept" study recommends applying MAAs to IQC data to replace traditional IQC procedures because they "outperform Westgard Rules," which is a current standard of practice for IQC. Methods: We generated power curves for multi-rule procedures with 2 and 4 control measurements per QC event, as well as a Simple Moving Average having block sizes of 5, 10, and 20 control measurements. We also assessed time to detection in terms of the Average Number of QC Events required to detect different sizes of systematic errors. Results: As expected, the more control measurements included in the control technique, the better the error detection. However, when QC performance is considered on the Sigma Scale, high Sigma methods require only 1 or 2 control measurements to detect medically important systematic errors. MAAs have very low ability to detect error at the first few QC events following shift, so they suffer a lag phase in detecting medically important errors. MAAs are most useful for methods having 4.0 Sigma performance or less. Even then, large systematic shifts are more quickly detected by simple single and multirule procedures. Conclusions: Choice of control techniques (rules, means, ranges, etc.) should consider the Sigma-metric of the method. For methods having Sigmas of 4 or greater, traditional single rule and multirule procedures with Ns up to 4 are most effective; below 4 Sigma, a multirule coupled with a Simple Moving Average (SMA) rule with Ns of 4 to 8 can improve error detection.
MeSH term(s)	Algorithms ; Humans ; Quality Control
Language	English
Publishing date	2022-01-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	390372-2
ISSN	1873-2933 ; 0009-9120
ISSN (online)	1873-2933
ISSN	0009-9120
DOI	10.1016/j.clinbiochem.2022.01.001
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Article ; Online: A multi-test planning model for risk based statistical quality control strategies.

Westgard, Sten A / Bayat, Hassan / Westgard, James O

Clinica chimica acta; international journal of clinical chemistry

2021 Volume 523, Page(s) 216–223

Abstract: Background: Efforts to improve QC for multi-test analytic systems should focus on risk-based bracketed SQC strategies, as recommended in the CLSI C24-Ed4 guidance for QC practices. The objective is to limit patient risk by controlling the expected ... ...

Abstract	Background: Efforts to improve QC for multi-test analytic systems should focus on risk-based bracketed SQC strategies, as recommended in the CLSI C24-Ed4 guidance for QC practices. The objective is to limit patient risk by controlling the expected number of erroneous patient test results that would be reported over the period an error condition goes undetected. Methods: A planning model is described to provide a structured process for considering critical variables for the development of SQC strategies for continuous production multi-test analytic systems. The model aligns with the principles of the CLSI C24-Ed4 "roadmap" and calculation of QC frequency, or run size, based on Parvin's patient risk model. Calculations are performed using an electronic spreadsheet to facilitate application of the planning model. Results: Three examples of published validation data are examined to demonstrate the application of the planning model for multi-test chemistry and enzyme analyzers. The ability to assess "what if" conditions is key to identifying the changes and improvements that are necessary to simplify the overall system to a manageable number of SQC procedures. Conclusions: The planning of risk based SQC strategies should align operational requirements for workload and reporting intervals with QC frequency in terms of the run size or the number of patient samples between QC events. Computer tools that support the calculation of run sizes greatly facilitate the planning process and make it practical for medical laboratories to quickly assess the effects of critical variables.
MeSH term(s)	Humans ; Quality Control
Language	English
Publishing date	2021-09-27
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	80228-1
ISSN	1873-3492 ; 0009-8981
ISSN (online)	1873-3492
ISSN	0009-8981
DOI	10.1016/j.cca.2021.09.020
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Article ; Online: Planning SQC strategies and adapting QC frequency for patient risk.

Westgard, James O / Bayat, Hassan / Westgard, Sten A

Clinica chimica acta; international journal of clinical chemistry

2021 Volume 523, Page(s) 1–5

Abstract: Background: Risk-based Statistical QC strategies are recommended by the CLSI guidance for Statistical Quality Control (C24-Ed4). Using Parvin's patient risk model, QC frequency can be determined in terms of run size, i.e., the number of patient samples ... ...

Abstract	Background: Risk-based Statistical QC strategies are recommended by the CLSI guidance for Statistical Quality Control (C24-Ed4). Using Parvin's patient risk model, QC frequency can be determined in terms of run size, i.e., the number of patient samples between QC events. Run size provides a practical goal for planning SQC strategies to achieve desired test reporting intervals. Methods: A QC Frequency calculator is utilized to evaluate critical factors (quality required for test, precision and bias observed for method, rejection characteristics of SQC procedure) and also to consider patient risk as a variable for adjusting run size. Results: We illustrate the planning of SQC strategies for a HbA1c test where two levels of controls show different sigma performance, for three different HbA1c analyzers used to achieve a common quality goal in a network of laboratories, and for an 18 test chemistry analyzer where a common run size is achieved by changes in control rules and adjustments for the patient risk of different tests. Conclusions: Run size provides a practical characteristic for adapting QC frequency to systematize the SQC strategies for multiple levels of controls or multiple tests in a chemistry analyzer. Patient risk can be an important variable for adapting run size to fit the laboratory's desired reporting intervals for high volume continuous production analyzers.
MeSH term(s)	Humans ; Laboratories ; Quality Control
Language	English
Publishing date	2021-08-28
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	80228-1
ISSN	1873-3492 ; 0009-8981
ISSN (online)	1873-3492
ISSN	0009-8981
DOI	10.1016/j.cca.2021.08.028
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Article ; Online: Establishing Evidence-Based Statistical Quality Control Practices.

Westgard, James O / Westgard, Sten A

American journal of clinical pathology

2018 Volume 151, Issue 4, Page(s) 364–370

Abstract: ... of Westgard Sigma Rules with Run Sizes.: Conclusion: Medical laboratories can plan evidence-based SQC ...

Abstract	Objectives: To establish an objective, scientific, evidence-based process for planning statistical quality control (SQC) procedures based on quality required for a test, precision and bias observed for a measurement procedure, probabilities of error detection and false rejection for different control rules and numbers of control measurements, and frequency of QC events (or run size) to minimize patient risk. Methods: A Sigma-Metric Run Size Nomogram and Power Function Graphs have been used to guide the selection of control rules, numbers of control measurements, and frequency of QC events (or patient run size). Results: A tabular summary is provided by a Sigma-Metric Run Size Matrix, with a graphical summary of Westgard Sigma Rules with Run Sizes. Conclusion: Medical laboratories can plan evidence-based SQC practices using simple tools that relate the Sigma-Metric of a testing process to the control rules, number of control measurements, and run size (or frequency of QC events).
MeSH term(s)	Evidence-Based Practice/statistics & numerical data ; Humans ; Laboratories/standards ; Nomograms ; Probability ; Quality Assurance, Health Care ; Quality Control ; Statistics as Topic
Language	English
Publishing date	2018-12-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2944-0
ISSN	1943-7722 ; 0002-9173
ISSN (online)	1943-7722
ISSN	0002-9173
DOI	10.1093/ajcp/aqy158
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Article: Planning Risk-Based Statistical Quality Control Strategies: Graphical Tools to Support the New Clinical and Laboratory Standards Institute C24-Ed4 Guidance.

Bayat, Hassan / Westgard, Sten A / Westgard, James O

The journal of applied laboratory medicine

2020 Volume 2, Issue 2, Page(s) 211–221

Abstract: Background: Clinical and Laboratory Standards Institute (CLSI)'s new guideline for statistical quality control (SQC; C24-Ed4) (CLSI C24-Ed4, 2016; Parvin CA, 2017) recommends the implementation of risk-based SQC strategies. Important changes from ... ...

Abstract	Background: Clinical and Laboratory Standards Institute (CLSI)'s new guideline for statistical quality control (SQC; C24-Ed4) (CLSI C24-Ed4, 2016; Parvin CA, 2017) recommends the implementation of risk-based SQC strategies. Important changes from earlier editions include alignment of principles and concepts with the general patient risk model in CLSI EP23A (CLSI EP23A, 2011) and a recommendation for optimizing the frequency of SQC (number of patients included in a run, or run size) on the basis of the expected number of unreliable final patient results. The guideline outlines a planning process for risk-based SQC strategies and describes 2 applications for examination procedures that provide 9-σ and 4-σ quality. A serious limitation is that there are no practical tools to help laboratories verify the results of these examples or perform their own applications. Methods: Power curves that characterize the rejection characteristics of SQC procedures were used to predict the risk of erroneous patient results based on Parvin's MaxE(Nuf) parameter (Clin Chem 2008;54:2049-54). Run size was calculated from MaxE(Nuf) and related to the probability of error detection for the critical systematic error (Pedc). Results: A plot of run size vs Pedc was prepared to provide a simple nomogram for estimating run size for common single-rule and multirule SQC procedures with Ns of 2 and 4. Conclusions: The "traditional" SQC selection process that uses power function graphs to select control rules and the number of control measurements can be extended to determine SQC frequency by use of a run size nomogram. Such practical tools are needed for planning risk-based SQC strategies.
Language	English
Publishing date	2020-07-02
Publishing country	England
Document type	Journal Article
ISSN	2576-9456
ISSN	2576-9456
DOI	10.1373/jalm.2017.023192
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Article ; Online: Rhetoric Versus Reality? Laboratory Surveys Show Actual Practice Differs Considerably from Proposed Models and Mandated Calculations.

Westgard, Sten A

Clinics in laboratory medicine

2016 Volume 37, Issue 1, Page(s) 35–45

Abstract: The scientific debate on goals, measurement uncertainty, and individualized quality control plans has diverged significantly from the reality of laboratory operation. Academic articles promoting certain approaches are being ignored; laboratories may be ... ...

Abstract	The scientific debate on goals, measurement uncertainty, and individualized quality control plans has diverged significantly from the reality of laboratory operation. Academic articles promoting certain approaches are being ignored; laboratories may be in compliance with new regulations, mandates, and calculations, but most of them still adhere to traditional quality management practices. Despite a considerable effort to enforce measurement uncertainty and eliminate or discredit allowable total error, laboratories continue to use these older, more practical approaches for quality management.
MeSH term(s)	Clinical Laboratory Techniques/methods ; Clinical Laboratory Techniques/standards ; Diagnostic Errors ; Humans ; Laboratories/standards ; Models, Theoretical ; Quality Control ; Surveys and Questionnaires ; Uncertainty
Language	English
Publishing date	2016-12-05
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	604580-7
ISSN	1557-9832 ; 0272-2712
ISSN (online)	1557-9832
ISSN	0272-2712
DOI	10.1016/j.cll.2016.09.004
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Article ; Online: Utilizing global data to estimate analytical performance on the Sigma scale: A global comparative analysis of methods, instruments, and manufacturers through external quality assurance and proficiency testing programs.

Westgard, Sten A

Clinical biochemistry

2016 Volume 49, Issue 9, Page(s) 699–707

Abstract: Objective: To assess the analytical performance of instruments and methods through external quality assessment and proficiency testing data on the Sigma scale.: Design and methods: A representative report from five different EQA/PT programs around ... ...

Abstract	Objective: To assess the analytical performance of instruments and methods through external quality assessment and proficiency testing data on the Sigma scale. Design and methods: A representative report from five different EQA/PT programs around the world (2 US, 1 Canadian, 1 UK, and 1 Australasian) was accessed. The instrument group standard deviations were used as surrogate estimates of instrument imprecision. Performance specifications from the US CLIA proficiency testing criteria were used to establish a common quality goal. Then Sigma-metrics were calculated to grade the analytical performance. Results: Different methods have different Sigma-metrics for each analyte reviewed. Summary Sigma-metrics estimate the percentage of the chemistry analytes that are expected to perform above Five Sigma, which is where optimized QC design can be implemented. The range of performance varies from 37% to 88%, exhibiting significant differentiation between instruments and manufacturers. Median Sigmas for the different manufacturers in three analytes (albumin, glucose, sodium) showed significant differentiation. Conclusions: Chemistry tests are not commodities. Quality varies significantly from manufacturer to manufacturer, instrument to instrument, and method to method. The Sigma-assessments from multiple EQA/PT programs provide more insight into the performance of methods and instruments than any single program by itself. It is possible to produce a ranking of performance by manufacturer, instrument and individual method. Laboratories seeking optimal instrumentation would do well to consult this data as part of their decision-making process. To confirm that these assessments are stable and reliable, a longer term study should be conducted that examines more results over a longer time period.
MeSH term(s)	Bias ; Blood Proteins/analysis ; Humans ; Laboratories/standards ; Laboratory Proficiency Testing/methods ; Quality Assurance, Health Care ; Quality Control ; Reference Standards
Chemical Substances	Blood Proteins
Language	English
Publishing date	2016-06
Publishing country	United States
Document type	Journal Article
ZDB-ID	390372-2
ISSN	1873-2933 ; 0009-9120
ISSN (online)	1873-2933
ISSN	0009-9120
DOI	10.1016/j.clinbiochem.2016.02.013
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Article: Special issue on Six Sigma metrics - experiences and recommendations.

Westgard, Sten / Bayat, Hassan / Westgard, James O

Biochemia medica

2018 Volume 28, Issue 2, Page(s) 20301

MeSH term(s)	Clinical Laboratory Techniques ; Humans ; Total Quality Management
Language	English
Publishing date	2018-06-30
Publishing country	Croatia
Document type	Editorial
ZDB-ID	1208725-7
ISSN	1846-7482 ; 1330-0962
ISSN (online)	1846-7482
ISSN	1330-0962
DOI	10.11613/BM.2018.020301
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Article ; Online: Prioritizing risk analysis quality control plans based on Sigma-metrics.

Westgard, Sten

Clinics in laboratory medicine

2013 Volume 33, Issue 1, Page(s) 41–53

Abstract: Six Sigma provides data-driven techniques that can enhance and improve the EP23 risk management approach for formulating quality control (QC) Plans. Risk analysis has significant drawbacks in its ability to identify and appropriately prioritize hazards ... ...

Abstract	Six Sigma provides data-driven techniques that can enhance and improve the EP23 risk management approach for formulating quality control (QC) Plans. Risk analysis has significant drawbacks in its ability to identify and appropriately prioritize hazards and failure modes for mitigation of risks. Six Sigma quality management is inherently risk oriented on the basis of the required tolerance limits that define defective products. Six Sigma QC tools provide a quantitative assessment of method performance and an objective selection/design of statistical QC procedures. Furthermore, the observed sigma performance of a method is useful for prioritizing the need for development of QC plans.
MeSH term(s)	Humans ; Laboratories/standards ; Medical Informatics Applications ; Medical Laboratory Science/standards ; Quality Assurance, Health Care/methods ; Quality Control ; Risk Assessment/methods ; Risk Management/methods
Language	English
Publishing date	2013-03
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	604580-7
ISSN	1557-9832 ; 0272-2712
ISSN (online)	1557-9832
ISSN	0272-2712
DOI	10.1016/j.cll.2012.11.008
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