Demystifying Reference Sample Quality Control

Cembrowski, George S.; Cervinski, Mark A.

doi:10.1373/clinchem.2016.258129

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…Thus, Jones stresses the preference for within‐run QC rules against across‐run QC rules. Besides, Westgard himself advocates “[s]imple control rules …over multirule combinations.” [13, p. 598] This fact is reassured by Cembrowski and Cervinski 46 with the remark that “instrument performance will not be improved by the use of more sensitive rules” [46, p. 907] like the across‐run QC rules.…”

Section: Discussionmentioning

confidence: 99%

“…However, the use of replicates leads to increased usage of time and QC material and thus to increased costs. Besides, the aforementioned criticism of Cembrowski and Cervinski 46 is also valid with respect to the use of replicates. These issues are summarized by Schoenmakers et al 10 as follows: “[T]he authors are fully aware of the fact that analyzing in more than two replicates is totally unpractical.…”

Section: Discussionmentioning

confidence: 99%

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Selection of within‐run quality control rules for laboratory biomarkers

Laubender

Geistanger

2021

Statistics in Medicine

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In order to release correct biomarker results of a laboratory test, it is a regulatory requirement to apply quality control standards for controlling analytical errors. Releasing an incorrect test result might lead to wrong diagnosis or treatment of a patient in medical decision‐making. In laboratory medicine, one of the means to control analytical errors is statistical process control procedures proposed by James O. Westgard and his coworkers nowadays known as “Westgard rules.” To judge their performance for discriminating in‐control from out‐of‐control processes, power curves are used. In this article, we describe functions for the power curves of the within‐run Westgard rules. Based on these power curves, we use a benchmark approach for selecting a quality control procedure out of the set of Westgard rules. It is shown that two graphical procedures proposed by Westgard and his coworkers can be reduced to this benchmark approach. Besides, a commonly used measure in laboratory medicine for describing out‐of‐control processes is critically examined revealing the threat of selecting too optimistic quality control rules.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Selection of within‐run quality control rules for laboratory biomarkers

Laubender

Geistanger

2021

Statistics in Medicine

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“…The initial step is to identify weaknesses in the control processes, such as quality control parameters that do not accurately reflect the true variability of the measurements of the analyzers due to small initial study samples and the excessive precision of measurements on control materials. Further efforts should focus on identifying mechanisms to achieve quality control by determining appropriate methods for analyzing data from real patient measurements [21]. Quality control (QC) based on measurements of proprietary control materials covers only the analytical stage of the result generation process.…”

Section: Detection Of the Required Calibration Moment-neural Networkmentioning

confidence: 99%

“…This study proposes the development of the PBRTQC approach [17,21], which stands for Patient-Based Real-Time Quality Control, for a broader spectrum of applications than just quality control itself. Based on data from real-time patient studies, we can draw conclusions regarding quality control, as well as reagent management, analytical device overload, and patient profiles.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Machine Learning Prediction Techniques in the Optimization of Diagnostic Laboratories’ Network Operations

Regulski,

Opaliński,

Swadźba

et al. 2024

Applied Sciences

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The article presents an outline of the concept of a prototype system allowing for the optimization of inventory management in a diagnostic laboratory on the basis of patients results. The effectiveness of laboratory diagnostics depends largely on the appropriate management of resources and the quality of tests. A functional quality management system is an integral element of every diagnostic laboratory, ensuring reliability and appropriate work standards. This system includes maintaining correct and reliable analytical test results as well as the optimal use of the laboratory equipment’s processing capacity and the appropriate organization of the supply chain—both analytical material and reagents. It is extremely important to avoid situations in which tests cannot be performed due to a lack of reagents, the overloading of analyzers, or improper calibration. Therefore, the accurate prediction of the number of orders is crucial to optimize the laboratory’s operations, both in the short term—for the next few hours and minutes—and in the longer term, even monthly, which will allow for the appropriate planning of reagent stock. As part of the research presented in this article, machine learning methods were used to implement the above functionalities, which allowed for the development of a prototype of a laboratory optimization system using patient test results as a basis.

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Six Sigma Quality Management System and Design of Risk-based Statistical Quality Control

Westgard

Westgard²

2017

Clinics in Laboratory Medicine

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Demystifying Reference Sample Quality Control

Cited by 5 publications

References 9 publications

Selection of within‐run quality control rules for laboratory biomarkers

Selection of within‐run quality control rules for laboratory biomarkers

Machine Learning Prediction Techniques in the Optimization of Diagnostic Laboratories’ Network Operations

Six Sigma Quality Management System and Design of Risk-based Statistical Quality Control

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