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建立和验证基于实验室信息系统的癌症患者生化检验结果自动验证系统。

Establishing and validating of an laboratory information system-based auto-verification system for biochemical test results in cancer patients.

机构信息

Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

出版信息

J Clin Lab Anal. 2019 Jun;33(5):e22877. doi: 10.1002/jcla.22877. Epub 2019 Mar 6.

DOI:10.1002/jcla.22877
PMID:30843281
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6595299/
Abstract

BACKGROUND

To establish and validate an laboratory information system (LIS)-based auto-verification (AV) system by using large amounts of biochemical test results in cancer patients.

METHODS

An algorithm of the AV process was designed for pre-analysis, analysis, and post-analysis. The limit range check was adjusted three times, while the delta check criteria were first replaced by the same patients' historical extremum results. AV rules of 51 biochemical test items were tested by using data of 121 123 samples (6 177 273 tests) in 2016 that were manually reviewed through the simulative i-Vertification software of Roche. The improved and optimal AV rules were programed into our LIS and validated by using 140 113 clinical specimens in 2018.

RESULTS

The AV passing rate for samples tested in our laboratory increased from 15.57% to the current overall passing rate of 49.70%. The passing rate of each item for rule 3 was between 71.16% and 99.91%. Different cancer groups had different passing rate, while the disease group of liver, gallbladder, and pancreas always had the lowest passing rate. A total of 9420 reports (6.72%) were not verified by AV but could be verified by MV in 2018, while there were no reports that were verified by AV but not by MV. The TAT of March 2018 decreased with increase in sample size compared with the same time in 2017.

CONCLUSION

We have firstly established an LIS-based AV system and implemented it in actual clinical care for cancer patients.

摘要

背景

利用大量癌症患者的生化检验结果,建立并验证基于实验室信息系统(LIS)的自动验证(AV)系统。

方法

设计了 AV 过程的算法,包括预分析、分析和后分析三个阶段。对限界检查范围进行了三次调整,同时将差值检查标准首次替换为同一患者的历史极值结果。使用罗氏公司的 i-Vertification 模拟软件,对 51 项生化检验项目的 AV 规则进行了测试,共使用了 2016 年 121123 份样本(6177273 次检验)的数据进行人工复查。将改进和优化后的 AV 规则编入我们的 LIS 中,并在 2018 年使用 140113 份临床标本进行验证。

结果

实验室检测样本的 AV 通过率从 15.57%提高到目前的总体通过率 49.70%。规则 3 中每个项目的通过率在 71.16%至 99.91%之间。不同癌症组的通过率不同,而肝、胆、胰疾病组的通过率始终最低。2018 年共有 9420 份报告(6.72%)未通过 AV 验证,但可通过 MV 验证,而无 AV 验证但 MV 验证通过的报告。与 2017 年同期相比,2018 年 3 月的 TAT 随着样本量的增加而降低。

结论

我们首次建立了基于 LIS 的 AV 系统,并将其应用于癌症患者的实际临床护理中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d7e/6595299/f1eee36e4ba3/JCLA-33-e22877-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d7e/6595299/3e7c7a1a4f62/JCLA-33-e22877-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d7e/6595299/7b987ef6864f/JCLA-33-e22877-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d7e/6595299/f1eee36e4ba3/JCLA-33-e22877-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d7e/6595299/3e7c7a1a4f62/JCLA-33-e22877-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d7e/6595299/7b987ef6864f/JCLA-33-e22877-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d7e/6595299/f1eee36e4ba3/JCLA-33-e22877-g003.jpg

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