Regression-Adjusted Real-Time Quality Control.

BACKGROUND: Patient-based real-time quality control (PBRTQC) has gained increasing attention in the field of clinical laboratory management in recent years. Despite the many upsides that PBRTQC brings to the laboratory management system, it has been questioned for its performance and practical appli...

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Publicado en:Clinical Chemistry Vol. 67; no. 10; pp. 1342 - 1351
Autores principales: Xincen Duan, Beili Wang, Jing Zhu, Chunyan Zhang, Wenhai Jiang, Jiaye Zhou, Wenqi Shao, Yin Zhao, Qian Yu, Luo Lei, Kwok Leung Yiu, Kim Thiam Chin, Baishen Pan, Wei Guoa
Formato: Journal Article
Publicado: Oxford University Press / USA Oct2021
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Oct2021
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      pub: Oxford University Press / USA
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        152806846
        10.1093/clinchem/hvab115
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        atl: Regression-Adjusted Real-Time Quality Control.
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        au:
          Xincen Duan
          Beili Wang
          Jing Zhu
          Chunyan Zhang
          Wenhai Jiang
          Jiaye Zhou
          Wenqi Shao
          Yin Zhao
          Qian Yu
          Luo Lei
          Kwok Leung Yiu
          Kim Thiam Chin
          Baishen Pan
          Wei Guoa
        affil: Department of Laboratory Medicine, Zhongshan Hospital, Fudan University
      sug:
      ab: BACKGROUND: Patient-based real-time quality control (PBRTQC) has gained increasing attention in the field of clinical laboratory management in recent years. Despite the many upsides that PBRTQC brings to the laboratory management system, it has been questioned for its performance and practical applicability for some analytes. This study introduces an extended method, regression-adjusted real-time quality control (RARTQC), to improve the performance of real-time quality control protocols. METHODS: In contrast to the PBRTQC, RARTQC has an additional regression adjustment step before using a common statistical process control algorithm, such as the moving average, to decide whether an analytical error exists. We used all patient test results of 4 analytes in 2019 from Zhongshan Hospital, Fudan University, to compare the performance of the 2 frameworks. Three types of analytical error were added in the study to compare the performance of PBRTQC and RARTQC protocols: constant, random, and proportional errors. The false alarm rate and error detection charts were used to assess the protocols. RESULTS: The study showed that RARTQC outperformed PBRTQC. RARTQC, compared with the PBRTQC, improved the trimmed average number of patients affected before detection (tANPed) at total allowable error by about 50% for both constant and proportional errors. CONCLUSIONS: The regression step in the RARTQC framework removes autocorrelation in the test results, allows researchers to add additional variables, and improves data transformation. RARTQC is a powerful framework for real-time quality control research.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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