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使用基于常规血液实验室检测的算法在医护人员筛查环境中排除 COVID-19。

Use of an algorithm based on routine blood laboratory tests to exclude COVID-19 in a screening-setting of healthcare workers.

机构信息

Department of Clinical Chemistry & Hematology, Zuyderland Medical Center, Heerlen-Sittard, The Netherlands.

Department Of Clinical Chemistry & Hematology, Catharina Hospital, Eindhoven, The Netherlands.

出版信息

PLoS One. 2022 Jun 28;17(6):e0270548. doi: 10.1371/journal.pone.0270548. eCollection 2022.

DOI:10.1371/journal.pone.0270548
PMID:35763522
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9239486/
Abstract

BACKGROUND

COVID-19 is an ongoing pandemic leading to exhaustion of the hospital care system. Our health care system has to deal with a high level of sick leave of health care workers (HCWs) with COVID-19 related complaints, in whom an infection with SARS-CoV-2 has to be ruled out before they can return back to work. The aim of the present study is to investigate if the recently described CoLab-algorithm can be used to exclude COVID-19 in a screening setting of HCWs.

METHODS

In the period from January 2021 till March 2021, HCWs with COVID-19-related complaints were prospectively collected and included in this study. Next to the routinely performed SARS-CoV-2 RT-PCR, using a set of naso- and oropharyngeal swab samples, two blood tubes (one EDTA- and one heparin-tube) were drawn for analysing the 10 laboratory parameters required for running the CoLab-algorithm.

RESULTS

In total, 726 HCWs with a complete CoLab-laboratory panel were included in this study. In this group, 684 HCWs were tested SARS-CoV-2 RT-PCR negative and 42 cases RT-PCR positive. ROC curve analysis showed an area under the curve (AUC) of 0.853 (95% CI: 0.801-0.904). At a safe cut-off value for excluding COVID-19 of -6.525, the sensitivity was 100% with a specificity of 34% (95% CI: 21 to 49%). No SARS-CoV-2 RT-PCR cases were missed with this cut-off and COVID-19 could be safely ruled out in more than one third of HCWs.

CONCLUSION

The CoLab-score is an easy and reliable algorithm that can be used for screening HCWs with COVID-19 related complaints. A major advantage of this approach is that the results of the score are available within 1 hour after collecting the samples. This results in a faster return to labour process of a large part of the COVID-19 negative HCWs (34%), next to a reduction in RT-PCR tests (reagents and labour costs) that can be saved.

摘要

背景

COVID-19 是一场持续的大流行,导致医院医疗系统不堪重负。我们的医疗系统必须应对大量因 COVID-19 相关疾病而请病假的医护人员(HCWs),在这些人返回工作岗位之前,必须排除感染 SARS-CoV-2 的可能性。本研究的目的是调查最近描述的 CoLab 算法是否可用于筛查 HCWs 中 COVID-19 的情况。

方法

在 2021 年 1 月至 2021 年 3 月期间,前瞻性收集了因 COVID-19 相关疾病而请病假的 HCWs,并将其纳入本研究。除了常规进行 SARS-CoV-2 RT-PCR 外,还使用一组鼻咽和口咽拭子样本,抽取两管血(一管 EDTA 管,一管肝素管),以分析运行 CoLab 算法所需的 10 项实验室参数。

结果

本研究共纳入 726 名完整 CoLab 实验室参数的 HCWs。在这组患者中,684 名 HCWs 的 SARS-CoV-2 RT-PCR 检测结果为阴性,42 名 HCWs 的 RT-PCR 检测结果为阳性。ROC 曲线分析显示曲线下面积(AUC)为 0.853(95%CI:0.801-0.904)。当排除 COVID-19 的安全截止值为-6.525 时,敏感性为 100%,特异性为 34%(95%CI:21 至 49%)。使用此截止值不会遗漏任何 SARS-CoV-2 RT-PCR 病例,并且可以安全排除超过三分之一的 HCWs 患有 COVID-19。

结论

CoLab 评分是一种简单可靠的算法,可用于筛查因 COVID-19 相关疾病而请病假的 HCWs。该方法的一个主要优势是,在收集样本后 1 小时内即可获得评分结果。这使得大多数 COVID-19 检测结果为阴性的 HCWs(34%)更快地恢复工作,同时还可以减少 RT-PCR 检测(试剂和劳动力成本)的使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0a8/9239486/fc334837f290/pone.0270548.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0a8/9239486/824a39e976f7/pone.0270548.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0a8/9239486/66921122fb74/pone.0270548.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0a8/9239486/624340675d40/pone.0270548.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0a8/9239486/2c658f2c300f/pone.0270548.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0a8/9239486/fc334837f290/pone.0270548.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0a8/9239486/824a39e976f7/pone.0270548.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0a8/9239486/66921122fb74/pone.0270548.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0a8/9239486/624340675d40/pone.0270548.g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0a8/9239486/fc334837f290/pone.0270548.g005.jpg

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