Lack of randomness of internal quality control data: An alert for thein vitrodiagnostic industry

A modified cumulative sum technique has been applied to radioimmunoassay quality control data. The method is approximately 50% more efficient in detecting systematic changes in the mean and variance of quality control values for plasma samples than more widely used conventional methods. The salient features of the technique have been restricted to changes in the mean quality control value of a plasma pool, but potential applications to changes in variance and as a diagnostic aid to problems in radioimmunoassay have been evaluated. The method is independent of computing facilities and statistical expertise since all computations have been presented in the form of a nomogram and thus can be used by technicians at the bench.

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A novel practical approach to calculate measurement uncertainty in clinical pathology laboratories using quality control data with the use of biological variation where applicable

Journal of Analytical & Pharmaceutical Research ◽

10.15406/japlr.2021.10.00385 ◽

2021 ◽

Vol 10 (5) ◽

pp. 196-210

Author(s):

Ashraf Mina ◽

Shanmugam Banukumar ◽

Santiago Vazquez

Keyword(s):

Health Care ◽

Quality Control ◽

Quality Assurance ◽

Clinical Pathology ◽

Internal Quality ◽

External Quality ◽

Control Data ◽

Iso 15189 ◽

Care Costs ◽

Quality Control Data

Background: Measurement Uncertainty (MU) can assist the interpretation and comparison of the laboratory results against international diagnostic protocols, facilitate a reduction in health care costs and also help protect laboratories against legal challenges. Determination of MU for quantitative testing in clinical pathology laboratories is also a requirement for ISO 15189. Methods: A practical and simple to use statistical model has been designed to make use of data readily available in a clinical laboratory to assess and establish MU for quantitative assays based on internal quality control data to calculate Random Error and external quality assurance scheme results to calculate Systematic Error. The model explained in this article has also been compared and verified against quality specifications based on Biological Variation. Results: Examples that explain and detail MU calculations for the proposed model are given where different components of MU are calculated with tabulated results. Conclusions: The designed model is cost-effective because it utilises readily available data in a clinical pathology laboratory. Data obtained from internal quality control programs and external quality assurance schemes are used to calculate the MU using a practical and convenient approach that will not require resources beyond what is available. Such information can additionally be useful not only in establishing limits for MU to satisfy ISO 15189 but also in selecting and/or improving methods and instruments in use. MU can as well play an important role in reducing health care costs as shown by examples in the article.

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