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.

Moving average quality control: principles, practical application and future perspectives

2019 ◽  
Vol 57 (6) ◽  
pp. 773-782 ◽  
Author(s):  
Huub H. van Rossum
Keyword(s):  
Quality Control ◽  
Moving Average ◽  
Relevant Literature ◽  
Daily Practice ◽  
Optimization Methods ◽  
Average Quality ◽  
Advantages And Disadvantages ◽  
Widespread Acceptance ◽  
Operational Issues ◽  
Management Issues

Abstract Moving average quality control (MA QC) was described decades ago as an analytical quality control instrument. Although a potentially valuable tool, it is struggling to meet expectations due to its complexity and need for evidence-based guidance. For this review, relevant literature and the world wide web were examined in order to (i) explain the basic concepts and current understanding of MA QC, (ii) discuss moving average (MA) optimization methods, (iii) gain insight into practical aspects related to applying MA in daily practice and (iv) describe future prospects to enable more widespread acceptance and application of MA QC. Each of the MA QC optimization methods currently available has their own advantages and disadvantages. Recently developed simulation methods provide realistic error detecting properties for MA QC and are available for laboratories. Operational MA management issues have been identified that allow developers of MA software to upgrade their packages to support optimal MA QC application and guide laboratories on MA management issues, such as MA alarm workup. The new insights into MA QC characteristics and operational issues, together with supporting online tools, may promote more widespread acceptance and application of MA QC.

Ten-Month Evaluation of the Routine Application of Patient Moving Average for Real-Time Quality Control in a Hospital Setting

2020 ◽  
Vol 5 (6) ◽  
pp. 1184-1193 ◽  
Author(s):  
Huub H van Rossum ◽  
Daan van den Broek
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Clinical Application ◽  
Moving Average ◽  
Hospital Setting ◽  
Average Quality ◽  
Validation Methods ◽  
Analytical Errors ◽  
Routine Clinical Application ◽  
Hematological Tests

Abstract Background In recent years there has been renewed interest in patient-based real-time quality control (PBRTQC) techniques. This interest has been stimulated by the availability of new optimization and validation methods. Only a limited amount of research has focused on investigating the true operational value of PBRTQC. Therefore, we have evaluated the performance and value of recently implemented patient moving average quality control (MA QC) procedures. Methods The MA QC settings and protocols were as previously described (Clin Chem Lab Med 2019;57:1329–38) and included MA QCs for 10 chemistry and 6 hematological tests, all performed on duplicate analyzer systems. All MA QC alarms that occurred during the first 10 months of routine clinical application were investigated for assay-specific alarm rate and occurrence in time. Furthermore, the causes of these MA QC alarms were investigated, and alarm relevance was determined on the basis of total allowable bias (TBa) and error (TEa) derived from biological variations. Results During the 10-month period, 202 individual MA QC alarms occurred, resulting in an overall MA QC alarm rate of 0.030% and a frequency of 4.67 per week. Most alarms were triggered by sodium MA QC. Based on all available fully executed and documented MA QC alarm work-ups, MA QC detected errors that in 26.0% of the alarms exceeded the TBa and in 13.7% the TEa. In 9.2% of the alarms, MA QC alarming triggered instant (technical) corrections. Conclusions Routine clinical application of MA QC is feasible with maintaining a manageable number of alarms and enabling detection of relevant analytical errors.

Moving average quality control—Practical experience

2019 ◽  
Vol 493 ◽  
pp. S512-S513
Author(s):  
M. Vershinina ◽  
N. Steriopolo ◽  
V. Ibragimova
Keyword(s):  
Quality Control ◽  
Moving Average ◽  
Practical Experience ◽  
Average Quality

Design and implementation of quality control plans that integrate moving average and internal quality control: incorporating the best of both worlds

2019 ◽  
Vol 57 (9) ◽  
pp. 1329-1338 ◽  
Author(s):  
Huub H. van Rossum ◽  
Daan van den Broek
Keyword(s):  
Quality Control ◽  
Systematic Error ◽  
Total Protein ◽  
Moving Average ◽  
Optimization Methods ◽  
Internal Quality ◽  
Average Quality ◽  
Design And Implementation ◽  
Work Up ◽  
Hematological Tests

Abstract Background New moving average quality control (MA QC) optimization methods have been developed and are available for laboratories. Having these methods will require a strategy to integrate MA QC and routine internal QC. Methods MA QC was considered only when the performance of the internal QC was limited. A flowchart was applied to determine, per test, whether MA QC should be considered. Next, MA QC was examined using the MA Generator (www.huvaros.com), and optimized MA QC procedures and corresponding MA validation charts were obtained. When a relevant systematic error was detectable within an average daily run, the MA QC was added to the QC plan. For further implementation of MA QC for continuous QC, MA QC management software was configured based on earlier proposed requirements. Also, protocols for the MA QC alarm work-up were designed to allow the detection of temporary assay failure based on previously described experiences. Results Based on the flowchart, 10 chemistry, two immunochemistry and six hematological tests were considered for MA QC. After obtaining optimal MA QC settings and the corresponding MA validation charts, the MA QC of albumin, bicarbonate, calcium, chloride, creatinine, glucose, magnesium, potassium, sodium, total protein, hematocrit, hemoglobin, MCH, MCHC, MCV and platelets were added to the QC plans. Conclusions The presented method allows the design and implementation of QC plans integrating MA QC for continuous QC when internal QC has limited performance.

A direct comparison of patient-based real-time quality control techniques: The importance of the analyte distribution

2020 ◽  
Vol 57 (3) ◽  
pp. 206-214 ◽  
Author(s):  
Joel D Smith ◽  
Tony Badrick ◽  
Francis Bowling
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Error Detection ◽  
Alanine Aminotransferase ◽  
Clinical Chemistry ◽  
Moving Average ◽  
Moving Averages ◽  
Skewed Distributions ◽  
Real Patient ◽  
Box Cox Transformation

Background Patient-based real-time quality control (PBRTQC) techniques have been described in clinical chemistry for over 50 years. PBRTQC has a number of advantages over traditional quality control including commutability, cost and the opportunity for real-time monitoring. However, there are few systematic investigations assessing how different PBRTQC techniques perform head-to-head. Methods In this study, we compare moving averages with and without truncation and moving medians. For analytes with skewed distributions such as alanine aminotransferase and creatinine, we also investigate the effect of Box–Cox transformation of the data. We assess the ability of each technique to detect simulated analytical bias in real patient data for multiple analytes and to retrospectively detect a real analytical shift in a creatinine and urea assay. Results For analytes with symmetrical distributions, we show that error detection is similar for a moving average with and without four standard deviation truncation limits and for a moving median. In contrast to analytes with symmetrically distributed results, moving averages perform poorly for right skewed distributions such as alanine aminotransferase and creatinine and function only with a tight upper truncation limit. Box–Cox transformation of the data both improves the performance of moving averages and allows all data points to be used. This was also confirmed for retrospective detection of a real analytical shift in creatinine and urea. Conclusions Our study highlights the importance of careful assessment of the distribution of patient results for each analyte in a PBRTQC program with the optimal approaches dependent on whether the patient result distribution is symmetrical or skewed.

scholarly journals Recommendation for performance verification of patient-based real-time quality control

2020 ◽  
Vol 58 (8) ◽  
pp. 1205-1213 ◽  
Author(s):  
Tze Ping Loh ◽  
Andreas Bietenbeck ◽  
Mark A. Cervinski ◽  
Huub H. van Rossum ◽  
Alex Katayev ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Quality Control ◽  
Real Time ◽  
Moving Average ◽  
Quality System ◽  
Performance Verification ◽  
Realistic Representation ◽  
Verification Process ◽  
Laboratory Tool ◽  
Exponentially Weighted

AbstractPatient-based real-time quality control (PBRTQC) is a laboratory tool for monitoring the performance of the testing process. It includes well-established procedures like Bull’s algorithm, average of nomals, moving median, moving average (MA) and exponentially (weighted) MAs. Following the setup and optimization processes, a key step prior to the routine implementation of PBRTQC is the verification and documentation of the performance of the PBRTQC as part of the laboratory quality system. This verification process should provide a realistic representation of the performance of the PBRTQC in the environment it is being implemented in, to allow proper risk assessment by laboratory practitioners. This document focuses on the recommendation on performance verification of PBRTQC prior to implementation.

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