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使用移动平均值和正常平均值以及多规则对 TT3、TT4、FT3、FT4 和 TSH 在三台分析仪上进行基于患者的实时质量控制程序的优化和验证。

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.

机构信息

Zhejiang Center for Clinical Laboratories, Zhejiang Provincial People's Hospital, Hangzhou, China.

Clinical Laboratory, Hangzhou Children's Hospital, Hangzhou, China.

出版信息

J Clin Lab Anal. 2020 Aug;34(8):e23314. doi: 10.1002/jcla.23314. Epub 2020 May 3.

DOI:10.1002/jcla.23314
PMID:32363618
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7439426/
Abstract

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.

摘要

背景

我们设计了一种基于患者的实时质量控制(PBRTQC)程序,用于检测分析偏移并审查测量程序的分析趋势。

方法

根据住院患者、门诊患者和健康人群的来源,将三个月内总甲状腺素(TT4)、总三碘甲状腺原氨酸(TT3)、游离甲状腺素(FT4)、游离三碘甲状腺原氨酸(FT3)和促甲状腺素(TSH)的所有九个月患者结果分别分配到三组。对每组数据进行优化 Box-Plot 方法截断,并根据需要通过 Box-Cox 方法进行归一化。根据内部质量控制(IQC)水平和组分别绘制 IQC 样本结果和 PBRTQC 数据的 z 分数图。通过 2-2S 规则和移动平均规则的多条规则检测分析偏移和分析趋势。与 BIQC 进行了比较,BIQC 每天仅在分析批开始时测量一次 IQC 样本。列出了 12 个质量控制案例来验证性能。

结果

当 Box-Plot 方法的参数 n 为 1.2 时,所有五个分析物均呈正态分布。排除数据的百分比范围为 2.9%至 11.6%。在 PBRTQC 和 BIQC 中分别触发了 31 和 14 次拒绝。PBRTQC 中 96.8%的偏移拒绝与趋势相关的偏移和校准相关的偏移有关,而 BIQC 中的比例为 85.7%,但 TSH 中的比例为 78.6%。通过 PBRTQC 和 BIQC 分别检测到引起分析偏移的 105 次校准事件中的 25.7%和 8.6%。然而,由于变异系数较大,TSH 中的 PBRTQC 性能不佳。

结论

优化后的 PBRTQC 比 BIQC 更有效地检测分析偏移、趋势和校准事件。PBRTQC 可作为每天 IQC 的低成本补充程序使用,尤其是当天分析批内个体内分析物的生物变异不大于其 IQC 变异系数时。仍然需要进一步优化和验证 PBRTQC。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c02d/7439426/d45a50f42870/JCLA-34-e23314-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c02d/7439426/76313cdd7d51/JCLA-34-e23314-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c02d/7439426/b7efc2ad2ec7/JCLA-34-e23314-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c02d/7439426/fb59ec0cf7c7/JCLA-34-e23314-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c02d/7439426/b17014f0f076/JCLA-34-e23314-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c02d/7439426/d45a50f42870/JCLA-34-e23314-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c02d/7439426/76313cdd7d51/JCLA-34-e23314-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c02d/7439426/b7efc2ad2ec7/JCLA-34-e23314-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c02d/7439426/fb59ec0cf7c7/JCLA-34-e23314-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c02d/7439426/b17014f0f076/JCLA-34-e23314-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c02d/7439426/d45a50f42870/JCLA-34-e23314-g005.jpg

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