Past, present, and future of laboratory quality control: patient- based real-time quality control or when getting more quality at less cost is not wishful thinking

Katayev, Alexander; Fleming, James H.

doi:10.21037/jlpm-2019-qc-03

Cited by 19 publications

(15 citation statements)

References 9 publications

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“…It is quite clear that global efforts to promote the idea of PBRTQC make complete sense only when they are locally accepted in as many laboratories as possible around the world ( 5 , 11 , 19 , 23 ). We believe the paper represents a contribution to this global idea and can assist in changing the paradigm of traditional quality control in our country ( 16 , 27 ).…”

Section: Discussionmentioning

confidence: 96%

“…They state that the optimization and validation of PBRTQC procedures are often based on reliable TEa detection ( 19 , 25 ). In addition, in several papers dealing with control techniques, TEa is considered the cut-off value for detecting clinically significant error ( 2 , 8 , 9 , 16 , 26 ). When we talk about selecting TEa from different sources, we used CLIA data because we have already used it for calculating Sigma metrics in our laboratory.…”

Section: Discussionmentioning

confidence: 99%

“…An optimal combination of the formula, truncation limits, and control limits was chosen by the selection of optimal bias detection performance. The ability of an MA procedure to detect bias the size of the TEa within the daily number of tests performed in the laboratory was considered a key parameter for optimization, based on literature data ( 2 , 8 , 9 , 16 ). Moving average validation charts provide data about the number of results needed to detect a bias of a specific size.…”

Section: Methodsmentioning

confidence: 99%

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Moving average procedures as an additional tool for real-time analytical quality control

Lukić¹,

Ignjatović

2022

Biochem. med. (Online)

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Introduction Moving average (MA) is one possible way to use patient results for analytical quality control in medical laboratories. The aims of this study were to: (1) implement previously optimized MA procedures for 10 clinical chemistry analytes into the laboratory information system (LIS); (2) monitor their performance as a real-time quality control tool, and (3) define an algorithm for MA alarm management in a small-volume laboratory to suit the specific laboratory. Materials and methods Moving average alarms were monitored and analysed over a period of 6 months on all patient results (total of 73,059) obtained for 10 clinical chemistry parameters. The optimal MA procedures were selected previously using an already described technique called the bias detection simulation method, considering the ability of bias detection the size of total allowable error as the key parameter for optimization. Results During 6 months, 17 MA alarms were registered, which is 0.023% of the total number of generated MA values. In 65% of cases, their cause was of pre-analytical origin, in 12% of analytical origin, and in 23% the cause was not found. The highest alarm rate was determined on sodium (0.10%), and the lowest on calcium and chloride. Conclusions This paper showed that even in a small-volume laboratory, previously optimized MA procedures could be successfully implemented in the LIS and used for continuous quality control. Review of patient results, re-analysis of samples from the stable period, analysis of internal quality control samples and assessment of the analyser malfunctions and maintenance log have been proposed for the algorithm for managing MA alarms.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Moving average procedures as an additional tool for real-time analytical quality control

Lukić¹,

Ignjatović

2022

Biochem. med. (Online)

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“…With the demands on laboratories for high turnaround and large patient volumes, how labs may deal with failure and when they could or would repeat samples during the failure period are concerning [ 131 ]. It is now more apparent that there are problems with conventional QC including non-commutability of the sample, non-Gaussian nature of the distribution of QC values, low cost, and insensitivity of the QC rules, particularly for assays with low sigma (≤4) [ [132] , [133] , [134] , [135] , [136] ]. The model that we have used for many years in QC is still based on the fact that there are three types of error systematic, random, and proportional, although the last is usually subsumed into the first [ 72 ].…”

Section: Quality Control and External Quality Assurancementioning

confidence: 99%

“…However, it is now apparent that this was an oversimplification and that errors can be hybrids of these; they don’t follow this model at all [ 137 ]. There are other problems with conventional quality control that go to the core of the approach, including its statistical basis [ 132 ].…”

Section: Quality Control and External Quality Assurancementioning

confidence: 99%

Biological variation: Understanding why it is so important?

Badrick

2021

Practical Laboratory Medicine

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Application of Patient‐Based Real‐Time Quality Control Based on Artificial Intelligence Monitoring Platform in Continuously Quality Risk Monitoring of Down Syndrome Serum Screening

Yang,

Chen,

Pan

et al. 2024

Clinical Laboratory Analysis

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BackgroundPatient‐based real‐time quality control (PBRTQC) has gained attention because of its potential to continuously monitor the analytical quality in situations wherein internal quality control (IQC) is less effective. Therefore, we tried to investigate the application of PBRTQC method based on an artificial intelligence monitoring (AI‐MA) platform in quality risk monitoring of Down syndrome (DS) serum screening.MethodsThe DS serum screening item determination data and relative IQC data from January 4 to September 7 in 2021 were collected. Then, PBRTQC exponentially weighted moving average (EWMA) and moving average (MA) procedures were built and optimized in the AI‐MA platform. The efficiency of the EWMA and MA procedures with intelligent and traditional control rules were compared. Next, the optimal EWMA procedures that contributed to the quality assurance of serum screening were run and generated early warning cases were investigated.ResultsOptimal EWMA and MA procedures on the AI‐MA platform were built. Comparison results showed the EWMA procedure with intelligent QC rules but not traditional quality rules contained the best efficiency. Based on the AI‐MA platform, two early warning cases were generated by using the optimal EWMA procedure, which finally found were caused by instrument failure. Moreover, the EWMA procedure could truly reflect the detection accuracy and quality in situations wherein traditional IQC products were unstable or concentrations were inappropriate.ConclusionsThe EWMA procedure built by the AI‐MA platform could be a good complementary control tool for the DS serum screening by truly and timely reflecting the detection quality risks.

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Past, present, and future of laboratory quality control: patient- based real-time quality control or when getting more quality at less cost is not wishful thinking

Cited by 19 publications

References 9 publications

Moving average procedures as an additional tool for real-time analytical quality control

Moving average procedures as an additional tool for real-time analytical quality control

Biological variation: Understanding why it is so important?

Application of Patient‐Based Real‐Time Quality Control Based on Artificial Intelligence Monitoring Platform in Continuously Quality Risk Monitoring of Down Syndrome Serum Screening

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