Assessment of patient-based real-time quality control algorithm performance on different types of analytical error

Duan, Xincen; Wang, Beili; Jing, Zhu; Shao, Wenqi; Wang, Hao; Shen, Junfei; Wu, Wenhao; Jiang, Wen-An; Yiu, Kwok Leung; Pan, Baishen; Guo, Wei

doi:10.1016/j.cca.2020.10.006

Cited by 27 publications

(19 citation statements)

References 14 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%

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%

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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“…In the previous research [28], specimens from diseases that had significant impacts on the data were excluded. Moreover, data also would be divided into different groups, such as outpatient and inpatient, to build up the corresponding procedures [23,24].…”

Section: Discussionmentioning

confidence: 99%

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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“… 8 , 18 When setting up the PBRTQC protocols, it is essential to screen out parameters through data training and to select appropriate indicators to evaluate the performance of the model, which typically requires researchers with an academic background in computer science. Researchers have proposed algorithm optimization methods, including simulated annealing 19 and grid searches, 18 , 20 to enable laboratories to practically design PBRTQC procedures. In the present study, we used commercial AI‐MA software to establish PBRTQC models by incorporating multiple parameters through a deep learning strategy, which was developed based on big data processing by artificial intelligence technology.…”

Section: Discussionmentioning

confidence: 99%

Assessment of patient based real‐time quality control on comparative assays for common clinical analytes

Yang

Wen

et al. 2022

Clinical Laboratory Analysis

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Background It is critical for laboratories to conduct multianalyzer comparisons as a part of daily routine work to strengthen the quality management of test systems. Here, we explored the application of patient‐based real‐time quality controls (PBRTQCs) on comparative assays to monitor the consistency among clinical laboratories. Methods The present study included 11 commonly tested analytes that were detected using three analyzers. PBRTQC procedures were set up with exponentially weighted moving average (EWMA) algorithms and evaluated using the AI‐MA artificial intelligence platform. Comparative assays were carried out on serum samples, and patient data were collected. Patients were divided into total patient (TP), inpatient (IP), and outpatient (OP) groups. Results Optimal PBRTQC protocols were evaluated and selected with appropriate truncation limits and smoothing factors. Generally, similar comparative assay performance was achieved using both the EWMA and median methods. Good consistency between the results from patients' data and serum samples was obtained, and unacceptable bias was detected for alkaline phosphatase (ALP) and gamma‐glutamyl transferase (GGT) when using analyzer C. Categorizing patients' data and applying specific groups for comparative assays could significantly improve the performance of PBRTQCs. When monitoring the inter‐ and intraanalyzer stability on a daily basis, EWMA was superior in detecting very small quality‐related changes with lower false‐positive alarms. Conclusions We found that PBRTQCs have the potential to efficiently assess multianalyzer comparability. Laboratories should be aware of population variations concerning both analytes and analyzers to build more suitable PBRTQC protocols.

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Assessment of patient-based real-time quality control algorithm performance on different types of analytical error

Cited by 27 publications

References 14 publications

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

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

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

Assessment of patient based real‐time quality control on comparative assays for common clinical analytes

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