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

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.

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

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2018-0795 ◽

2019 ◽

Vol 57 (6) ◽

pp. 773-782 ◽

Cited By ~ 13

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.

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

The Journal of Applied Laboratory Medicine ◽

10.1093/jalm/jfaa071 ◽

2020 ◽

Vol 5 (6) ◽

pp. 1184-1193 ◽

Cited By ~ 1

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.

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

Clinica Chimica Acta ◽

10.1016/j.cca.2019.03.1068 ◽

2019 ◽

Vol 493 ◽

pp. S507

Author(s):

H. Van Rossum ◽

D. Van Den Broek

Keyword(s):

Quality Control ◽

Moving Average ◽

Internal Quality Control ◽

Internal Quality ◽

Design And Implementation

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Moving average quality control—Practical experience

Clinica Chimica Acta ◽

10.1016/j.cca.2019.03.1080 ◽

2019 ◽

Vol 493 ◽

pp. S512-S513

Author(s):

M. Vershinina ◽

N. Steriopolo ◽

V. Ibragimova

Keyword(s):

Quality Control ◽

Moving Average ◽

Practical Experience ◽

Average Quality

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Design of internal quality control for reference value studies

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm.2004.141 ◽

2004 ◽

Vol 42 (7) ◽

Cited By ~ 5

Author(s):

James O. Westgard

Keyword(s):

Quality Control ◽

Systematic Error ◽

Total Error ◽

Reference Value ◽

Internal Quality Control ◽

Internal Quality ◽

Control Procedure ◽

Graphical Tool ◽

Critical Error ◽

Power Curves

AbstractInternal quality control should assure that the desired quality goals are achieved during reference value studies. Quality goals are often stated in the form of allowable limits of error, such as an allowable total error or an allowable bias. For reference value studies, it may be more appropriate to utilize a goal for allowable bias. In either case, it is possible to calculate a metric in the form of the critical systematic error that can be used to guide selection or design of the internal quality control procedure. A graphical tool, called the critical-error graph, facilitates the selection by superimposing the calculated critical systematic error on the power curves of different control rules and numbers of control measurements. Examples are provided to illustrate the calculation of the critical systematic error from both an allowable total error goal and an allowable bias goal, using figures from an extensive tabulation of available total error and bias goals.

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Performance evaluation of internal quality control rules, EWMA, CUSUM, and the novel machine learning model

Turkish Journal of Biochemistry ◽

10.1515/tjb-2021-0199 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Hikmet Can Çubukçu

Keyword(s):

Machine Learning ◽

Quality Control ◽

Random Forest ◽

Systematic Error ◽

Error Detection ◽

Average Run Length ◽

Learning Model ◽

Internal Quality ◽

Machine Learning Model ◽

Outcome Metrics

Abstract Objectives The present study set out to build a machine learning model to incorporate conventional quality control (QC) rules, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) with random forest (RF) algorithm to achieve better performance and to evaluate the performances the models using computer simulation to aid laboratory professionals in QC procedure planning. Methods Conventional QC rules, EWMA, CUSUM, and RF models were implemented on the simulation data using an in-house algorithm. The models’ performances were evaluated on 170,000 simulated QC results using outcome metrics, including the probability of error detection (Ped), probability of false rejection (Pfr), average run length (ARL), and power graph. Results The highest Pfr (0.0404) belonged to the 1–2s rule. The 1–3s rule could not detect errors with a 0.9 Ped up to 4 SD of systematic error. The random forest model had the highest Ped for systematic errors lower than 1 SD. However, ARLs of the model require the combined utility of the RF model with conventional QC rules having lower ARLs or more than one QC measurement is required. Conclusions The RF model presented in this study showed acceptable Ped for most degrees of systematic error. The outcome metrics established in this study will help laboratory professionals planning internal QC.

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

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2021-0072 ◽

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.

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