Internal quality control: Moving average algorithms outperform Westgard rules

Poh, Daren; Lim, Chun Yee; Tan, Rui Zhen; Markus, C.; Loh, Tze Ping

doi:10.1016/j.clinbiochem.2021.09.007

Cited by 16 publications

(9 citation statements)

References 17 publications

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“…Among the MW methods, Moving Average (MA) is the most common and this has been investigated and recommended in many PBRTQC studies. 9,[14][15][16][17][18][19][20][21][22][23][24][25] Interested readers are also referred to Badrick and Loh 26 and the references therein for a comparison of the advantages and disadvantages of the PBRTQC and the use of conventional QC samples. A successful implementation of a PBRTQC protocol based on the MA and the Moving Median in a nationwide reference laboratory system is reported in Fleming and Katayev 14 and it is indicated that the use of conventional control material was reduced by approximately 75%−85% and repeat analysis was reduced by approximately 50%.…”

Section: Monitoring Individual Patient Test Results For Iqcmentioning

confidence: 99%

“…is calculated and monitored by a control chart. Among the MW methods, Moving Average (MA) is the most common and this has been investigated and recommended in many PBRTQC studies 9,14–25 . Interested readers are also referred to Badrick and Loh 26 and the references therein for a comparison of the advantages and disadvantages of the PBRTQC and the use of conventional QC samples.…”

Section: Introductionmentioning

confidence: 99%

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Patient‐based real‐time quality control in medical laboratories: On the design and robustness of the moving average control chart with truncation limits

Murat,

Testik,

Pinar

2024

Quality & Reliability Eng

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Quality is an indispensable requirement in medical laboratories, where controls are performed at different phases of the laboratory processes. In the following, we consider the internal quality control activities during the analytical phase of medical laboratories for verifying the test results prior to their release. To monitor the measurement process for errors in the test results, one approach that is considerably attracting the health professionals is patient‐based real‐time quality control. In this respect, real‐time patient test results are monitored, commonly by implementing the Moving Average (MA) control chart with truncation limits. A usual practice is the use of truncation limits to exclude abnormal patient test results from the MA calculations. However, an open issue in the literature is how to determine the width of the truncation limits and the window size in the MA calculations. Furthermore, the distributions of the test results are often nonnormal and difficult to model by personnel having insufficient training in statistics. Consequently, robustness of the performance of the MA control chart to non‐normal distributions of test results is another open topic in the literature. In this study, a detailed robustness analysis is performed by considering various distributions having different shapes. Performance of several MA control chart designs constructed by altering the truncation limits and window sizes are investigated. Based on our simulation results with selected distributions, we conclude that the truncation limits should not be used and a window size of 20 is recommended. Under such a setting, the MA control chart performs similar to its intended performance in terms of false alarms and in detecting systematic errors, hence being robust to non‐normality of the test results.

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Section: Monitoring Individual Patient Test Results For Iqcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Patient‐based real‐time quality control in medical laboratories: On the design and robustness of the moving average control chart with truncation limits

Murat,

Testik,

Pinar

2024

Quality & Reliability Eng

View full text Add to dashboard Cite

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“…With the FR rate and type II errors within acceptable limits, as well as simple calculation procedures for implementation at no additional cost, the MR program can automatically analyze data entered into the form. The QC program with multiple parameters can also be selected to ensure the sensitivity to biases in different directions as a supplement to or even outperform the existing conventional QC program 24 …”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…The QC program with multiple parameters can also be selected to ensure the sensitivity to biases in different directions as a supplement to or even outperform the existing conventional QC program. 24 This study could not exclude the possibility of not detecting system errors in the dataset, and the original data were obtained after instrument maintenance and calibration to ensure the reliability of the results. Additionally, the biases in the experimental process may have been more complex and variable.…”

Section: Table 2 (Continued)mentioning

confidence: 99%

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A study of the moving rate of positive results for use in a patient‐based real‐time quality control program on a procalcitonin point‐of‐care testing analyzer

Kong

et al. 2022

Clinical Laboratory Analysis

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Point-of-care testing (POCT) is currently a means for clinical laboratories to provide test results. However, with the rapid development of new methods and technologies, clinicians are paying more attention to the advantages and disadvantages of POCT. 1 Traditional quality control (QC) programs analyze only QC products of two concentrations at a time, 2 which may not be sufficient for the rapid detection of analytical errors affecting patient outcomes. 3,4 Moreover, artificial serum can produce matrix effects. 5 Although some clinicians use patient samples

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