How to define a significant deviation from the expected internal quality control result

Ceriotti, Ferruccio; Brugnoni, Duilio; Mattioli, Sonia

doi:10.1515/cclm-2014-1149

Cited by 20 publications

(14 citation statements)

References 3 publications

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“…For example, a new SQC model from the Milan conference employs an acceptance chart having ATE limits that are narrowed by subtracting the observed MU [45]. Such a chart will have rejection properties that are determined by a single statistical control rule (identifiable by dividing the control limit by the observed SD of the method).…”

Section: Discussionmentioning

confidence: 99%

Useful measures and models for analytical quality management in medical laboratories

Westgard¹

2016

Clinical Chemistry and Laboratory Medicine (CCLM)

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Abstract:The 2014 Milan Conference "Defining analytical performance goals 15 years after the Stockholm Conference" initiated a new discussion of issues concerning goals for precision, trueness or bias, total analytical error (TAE), and measurement uncertainty (MU). Goal-setting models are critical for analytical quality management, along with error models, quality-assessment models, quality-planning models, as well as comprehensive models for quality management systems. There are also critical underlying issues, such as an emphasis on MU to the possible exclusion of TAE and a corresponding preference for separate precision and bias goals instead of a combined total error goal. This opinion recommends careful consideration of the differences in the concepts of accuracy and traceability and the appropriateness of different measures, particularly TAE as a measure of accuracy and MU as a measure of traceability. TAE is essential to manage quality within a medical laboratory and MU and trueness are essential to achieve comparability of results across laboratories. With this perspective, laboratory scientists can better understand the many measures and models needed for analytical quality management and assess their usefulness for practical applications in medical laboratories.

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Section: Discussionmentioning

confidence: 99%

Useful measures and models for analytical quality management in medical laboratories

Westgard¹

2016

Clinical Chemistry and Laboratory Medicine (CCLM)

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“…However, it is a complicated diverse approach applicable mainly for metrology institutions and accredited reference laboratories and challenging to be assigned by the medical laboratories [64] . Instead, the »top-down« approach can be easily applied by clinical laboratories using intralaboratory (for imprecision) and inter-laboratory (for bias estimation) quality control data [65] [66] [67] .…”

Section: Systematic Errormentioning

confidence: 99%

Verification of quantitative analytical methods in medical laboratories

Abdel¹,

El-Masry²

2021

J Med Biochemistry

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Background: Globally, all medical laboratories seeking accreditation should meet international quality standards to perform certain specific tests. Quality management program provides disciplines targeted to ensure that quality standards have been implemented by a laboratory in order to generate correct results. The hallmark of the accreditation process is method verification and quality assurance. Before introducing a new method in your laboratory, it is important to assess certain performance characteristics that reflect the concept of method verification. Methods: In this review, we illustrated how to verify the performance characteristics of a new method according to the recent guidelines. It includes an assessment of precision, trueness, analytical sensitivity, detection limits, analytical specificity, interference, measuring range, linearity, and measurement uncertainty. Conclusions: Although the presence of several updated guidelines used to determine the performance characteristics of new methods in clinical chemistry laboratories, the real practice raised several concerns with the application of these guidelines which in need for further consideration in the upcoming updates of these guidelines.

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“…Another approach that was recently recommended by Ceriotti et al 40 is the use of an 'acceptance control chart' where the measurement uncertainty is first characterized, then an acceptance zone established by subtracting the expanded measurement uncertainty from the TEa limits for the maximum permissible errors. The authors state that optimal control is achieved when the relationship between the maximum permissible error (TEa) and the expanded measurement uncertainty (U, 95%) is 2.5 and that when TEa/U is close to 1, it will be impossible to control the measurement procedure.…”

Section: Step 3 Validate Safety Characteristicsmentioning

confidence: 99%

Quality control review: implementing a scientifically based quality control system

Westgard¹,

Westgard²

2015

Ann Clin Biochem

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This review focuses on statistical quality control in the context of a quality management system. It describes the use of a 'Sigma-metric' for validating the performance of a new examination procedure, developing a total quality control strategy, selecting a statistical quality control procedure and monitoring ongoing quality on the sigma scale. Acceptable method performance is a prerequisite to the design and implementation of statistical quality control procedures. Statistical quality control can only monitor performance, and when properly designed, alert analysts to the presence of additional errors that occur because of unstable performance. A new statistical quality control planning tool, called 'Westgard Sigma Rules,' provides a simple and quick way for selecting control rules and the number of control measurements needed to detect medically important errors. The concept of a quality control plan is described, along with alternative adaptations of a total quality control plan and a risk-based individualized quality control plan. Finally, the ongoing monitoring of analytic performance and test quality are discussed, including determination of measurement uncertainty from statistical quality control data collected under intermediate precision conditions and bias determined from proficiency testing/external quality assessment surveys. A new graphical tool, called the Sigma Quality Assessment Chart, is recommended for demonstrating the quality of current examination procedures on the sigma scale.

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How to define a significant deviation from the expected internal quality control result

Cited by 20 publications

References 3 publications

Useful measures and models for analytical quality management in medical laboratories

Useful measures and models for analytical quality management in medical laboratories

Verification of quantitative analytical methods in medical laboratories

Quality control review: implementing a scientifically based quality control system

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