scholarly journals The importance of regulation (EU) 2017/746 for quality control in medical laboratories

2021 ◽  
Vol 32 (1) ◽  
Author(s):  
Cristiano Ialongo ◽  
Maria Sapio ◽  
Leda Elisabetta Antetomaso ◽  
Antonio Angeloni
Keyword(s):  
Quality Control ◽  
Medical Laboratories

scholarly journals The internal quality control in the traceability era

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Federica Braga ◽  
Sara Pasqualetti ◽  
Elena Aloisio ◽  
Mauro Panteghini
Keyword(s):  
Quality Control ◽  
Internal Quality Control ◽  
Internal Quality ◽  
Clinical Samples ◽  
Test Results ◽  
Clinical Validity ◽  
Measuring Systems ◽  
Medical Laboratories ◽  
Laboratory Results

AbstractTo be accurate and equivalent, laboratory results should be traceable to higher-order references. Furthermore, their quality should fulfill acceptable measurement uncertainty (MU) as defined to fit the intended clinical use. With this aim, in vitro diagnostics (IVD) manufacturers should define a calibration hierarchy to assign traceable values to their system calibrators. Medical laboratories should know and verify how manufacturers have implemented the traceability of their calibrators and estimate the corresponding MU on clinical samples. Accordingly, the internal quality control (IQC) program should be redesigned to permit IVD traceability surveillance through the verification by medical laboratories that control materials, provided by the manufacturer as a part of measuring systems, are in the clinically suitable validation range (IQC component I). Separately, laboratories should also monitor the reliability of employed IVD measuring systems through the IQC component II, devoted to estimation of MU due to random effects and to obtaining MU of provided results, in order to apply prompt corrective actions if the performance is worsening when compared to appropriate analytical specifications, thus jeopardizing the clinical validity of test results.

Benefits, limitations, and controversies on patient-based real-time quality control (PBRTQC) and the evidence behind the practice

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Huub H. van Rossum ◽  
Andreas Bietenbeck ◽  
Mark A. Cervinski ◽  
Alex Katayev ◽  
Tze Ping Loh ◽  
...  
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Cost Efficiency ◽  
Moving Average ◽  
Daily Practice ◽  
Added Value ◽  
Current Status ◽  
Average Quality ◽  
Medical Laboratories ◽  
Recent Developments

Abstract Background In recent years, there has been renewed interest in the “old” average of normals concept, now generally referred to as moving average quality control (MA QC) or patient-based real-time quality control (PBRTQC). However, there are some controversies regarding PBRTQC which this review aims to address while also indicating the current status of PBRTQC. Content This review gives the background of certain newly described optimization and validation methods. It also indicates how QC plans incorporating PBRTQC can be designed for greater effectiveness and/or (cost) efficiency. Furthermore, it discusses controversies regarding the complexity of obtaining PBRTQC settings, the replacement of iQC, and software functionality requirements. Finally, it presents evidence of the added value and practicability of PBRTQC. Outlook Recent developments in, and availability of, simulation methods to optimize and validate laboratory-specific PBRTQC procedures have enabled medical laboratories to implement PBRTQC in their daily practice. Furthermore, these methods have made it possible to demonstrate the practicability and added value of PBRTQC by means of two prospective “clinical” studies and other investigations. Although internal QC will remain an essential part of any QC plan, applying PBRTQC can now significantly improve its performance and (cost) efficiency.

Establishing Evidence-Based Statistical Quality Control Practices

2018 ◽  
Vol 151 (4) ◽  
pp. 364-370 ◽  
Author(s):  
James O Westgard ◽  
Sten A Westgard
Keyword(s):  
Quality Control ◽  
Error Detection ◽  
Scientific Evidence ◽  
Measurement Procedure ◽  
Statistical Quality Control ◽  
Evidence Based ◽  
Statistical Quality ◽  
Medical Laboratories ◽  
Control Rules ◽  
Graphical Summary

AbstractObjectivesTo 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.MethodsA 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).ResultsA tabular summary is provided by a Sigma-Metric Run Size Matrix, with a graphical summary of Westgard Sigma Rules with Run Sizes.ConclusionMedical 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).

scholarly journals An approach for estimating measurement uncertainty in medical laboratories using data from long-term quality control and external quality assessment schemes

2017 ◽  
Vol 55 (11) ◽  
Author(s):  
Andrea Padoan ◽  
Giorgia Antonelli ◽  
Ada Aita ◽  
Laura Sciacovelli ◽  
Mario Plebani
Keyword(s):  
Quality Control ◽  
Measurement Uncertainty ◽  
Quality Assessment ◽  
External Quality Assessment ◽  
Internal Quality ◽  
Test Results ◽  
External Quality ◽  
Iso 15189 ◽  
Medical Laboratories

AbstractBackground:The present study was prompted by the ISO 15189 requirements that medical laboratories should estimate measurement uncertainty (MU).Methods:The method used to estimate MU included the: a) identification of quantitative tests, b) classification of tests in relation to their clinical purpose, and c) identification of criteria to estimate the different MU components. Imprecision was estimated using long-term internal quality control (IQC) results of the year 2016, while external quality assessment schemes (EQAs) results obtained in the period 2015–2016 were used to estimate bias and bias uncertainty.Results:A total of 263 measurement procedures (MPs) were analyzed. On the basis of test purpose, in 51 MPs imprecision only was used to estimate MU; in the remaining MPs, the bias component was not estimable for 22 MPs because EQAs results did not provide reliable statistics. For a total of 28 MPs, two or more MU values were calculated on the basis of analyte concentration levels. Overall, results showed that uncertainty of bias is a minor factor contributing to MU, the bias component being the most relevant contributor to all the studied sample matrices.Conclusions:The model chosen for MU estimation allowed us to derive a standardized approach for bias calculation, with respect to the fitness-for-purpose of test results. Measurement uncertainty estimation could readily be implemented in medical laboratories as a useful tool in monitoring the analytical quality of test results since they are calculated using a combination of both the long-term imprecision IQC results and bias, on the basis of EQAs results.

Regional Quality Control for Medical Laboratories

1970 ◽  
Vol 1 (5) ◽  
pp. 22-23
Author(s):  
Glenna J. Corley
Keyword(s):  
Quality Control ◽  
Medical Laboratories
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