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Optimization and validation of patient‐based real‐time quality control procedure using moving average and average of normals with multi‐rules for TT3, TT4, FT3, FT3, and TSH on three analyzers

BACKGROUND: We have designed a patient‐based real‐time quality control (PBRTQC) procedure to detect analytical shifts and review analytical trends of measurement procedures. METHODS: All the nine months' patient results of total thyroxine (TT4), total triiodothyronine (TT3), free thyroxine (FT4...

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Detalles Bibliográficos
Autores principales: Song, Chao, Zhou, Jun, Xia, Jun, Ye, Deli, Chen, Qian, Li, Weixing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7439426/
https://www.ncbi.nlm.nih.gov/pubmed/32363618
http://dx.doi.org/10.1002/jcla.23314
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author Song, Chao
Zhou, Jun
Xia, Jun
Ye, Deli
Chen, Qian
Li, Weixing
author_facet Song, Chao
Zhou, Jun
Xia, Jun
Ye, Deli
Chen, Qian
Li, Weixing
author_sort Song, Chao
collection PubMed
description BACKGROUND: We have designed a patient‐based real‐time quality control (PBRTQC) procedure to detect analytical shifts and review analytical trends of measurement procedures. METHODS: All the nine months' patient results of total thyroxine (TT4), total triiodothyronine (TT3), free thyroxine (FT4), free triiodothyronine (FT3), and thyrotropin (TSH) measured by three identical analyzers were divided into three groups according to the source of inpatient patients, outpatient patients, and healthy people. The data in each group were truncated by optimized Box‐Plot method and normalized by Box‐Cox method if necessary. The z‐score charts of internal quality control (IQC) samples' results and PBRTQC data were drawn by IQC levels and groups, respectively. The analytical shifts and analytical trends were detected by multi‐rules of 2‐2S rules and moving average rules. The performances of PBRTQC were compared with the BIQC in which IQC samples were measurand only once per day at the beginning of the analytical batch. Twelve quality control cases were listed to validate the performances. RESULTS: All the five analytes presented normal distributions when the parameter n of Box‐Plot method was 1.2. The percentages of excluded data ranged from 2.9% to 11.6%. 31 and 14 rejections triggered in PBRTQC and BIQC, respectively. 96.8% of the shift rejections in PBRTQC were trend‐related shifts and calibration‐related shifts, while the proportion was 85.7% in BIQC but 78.6% of the shift rejections in TSH. 25.7% and 8.6% of 105 calibration events which caused analytical shifts were detected by PBRTQC and BIQC, respectively. However, the performance of PBRTQC was not well in TSH because of its large coefficient of variation. CONCLUSIONS: The optimized PBRTQC is high efficiency than BIQC in detecting analytical shifts, trends, and calibration events. The PBRTQC can be used as a low‐cost supplementary procedure to IQC every day, especially at the end of the analytical batch on that day when the within‐individual biological variation of analyte is not larger than its coefficient of variation in IQC. Further optimization and validation of PBRTQC are still needed.
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spelling pubmed-74394262020-08-21 Optimization and validation of patient‐based real‐time quality control procedure using moving average and average of normals with multi‐rules for TT3, TT4, FT3, FT3, and TSH on three analyzers Song, Chao Zhou, Jun Xia, Jun Ye, Deli Chen, Qian Li, Weixing J Clin Lab Anal Research Articles BACKGROUND: We have designed a patient‐based real‐time quality control (PBRTQC) procedure to detect analytical shifts and review analytical trends of measurement procedures. METHODS: All the nine months' patient results of total thyroxine (TT4), total triiodothyronine (TT3), free thyroxine (FT4), free triiodothyronine (FT3), and thyrotropin (TSH) measured by three identical analyzers were divided into three groups according to the source of inpatient patients, outpatient patients, and healthy people. The data in each group were truncated by optimized Box‐Plot method and normalized by Box‐Cox method if necessary. The z‐score charts of internal quality control (IQC) samples' results and PBRTQC data were drawn by IQC levels and groups, respectively. The analytical shifts and analytical trends were detected by multi‐rules of 2‐2S rules and moving average rules. The performances of PBRTQC were compared with the BIQC in which IQC samples were measurand only once per day at the beginning of the analytical batch. Twelve quality control cases were listed to validate the performances. RESULTS: All the five analytes presented normal distributions when the parameter n of Box‐Plot method was 1.2. The percentages of excluded data ranged from 2.9% to 11.6%. 31 and 14 rejections triggered in PBRTQC and BIQC, respectively. 96.8% of the shift rejections in PBRTQC were trend‐related shifts and calibration‐related shifts, while the proportion was 85.7% in BIQC but 78.6% of the shift rejections in TSH. 25.7% and 8.6% of 105 calibration events which caused analytical shifts were detected by PBRTQC and BIQC, respectively. However, the performance of PBRTQC was not well in TSH because of its large coefficient of variation. CONCLUSIONS: The optimized PBRTQC is high efficiency than BIQC in detecting analytical shifts, trends, and calibration events. The PBRTQC can be used as a low‐cost supplementary procedure to IQC every day, especially at the end of the analytical batch on that day when the within‐individual biological variation of analyte is not larger than its coefficient of variation in IQC. Further optimization and validation of PBRTQC are still needed. John Wiley and Sons Inc. 2020-05-03 /pmc/articles/PMC7439426/ /pubmed/32363618 http://dx.doi.org/10.1002/jcla.23314 Text en © 2020 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals, Inc This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Song, Chao
Zhou, Jun
Xia, Jun
Ye, Deli
Chen, Qian
Li, Weixing
Optimization and validation of patient‐based real‐time quality control procedure using moving average and average of normals with multi‐rules for TT3, TT4, FT3, FT3, and TSH on three analyzers
title Optimization and validation of patient‐based real‐time quality control procedure using moving average and average of normals with multi‐rules for TT3, TT4, FT3, FT3, and TSH on three analyzers
title_full Optimization and validation of patient‐based real‐time quality control procedure using moving average and average of normals with multi‐rules for TT3, TT4, FT3, FT3, and TSH on three analyzers
title_fullStr Optimization and validation of patient‐based real‐time quality control procedure using moving average and average of normals with multi‐rules for TT3, TT4, FT3, FT3, and TSH on three analyzers
title_full_unstemmed Optimization and validation of patient‐based real‐time quality control procedure using moving average and average of normals with multi‐rules for TT3, TT4, FT3, FT3, and TSH on three analyzers
title_short Optimization and validation of patient‐based real‐time quality control procedure using moving average and average of normals with multi‐rules for TT3, TT4, FT3, FT3, and TSH on three analyzers
title_sort optimization and validation of patient‐based real‐time quality control procedure using moving average and average of normals with multi‐rules for tt3, tt4, ft3, ft3, and tsh on three analyzers
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7439426/
https://www.ncbi.nlm.nih.gov/pubmed/32363618
http://dx.doi.org/10.1002/jcla.23314
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