Prognostic value of NT-proBNP in patients with severe COVID-19
Identifieur interne : 000205 ( Pmc/Checkpoint ); précédent : 000204; suivant : 000206Prognostic value of NT-proBNP in patients with severe COVID-19
Auteurs : Lei Gao [République populaire de Chine] ; Dan Jiang [République populaire de Chine] ; Xue-Song Wen [République populaire de Chine] ; Xiao-Cheng Cheng [République populaire de Chine] ; Min Sun [République populaire de Chine] ; Bin He [République populaire de Chine] ; Lin-Na You [République populaire de Chine] ; Peng Lei [République populaire de Chine] ; Xiao-Wei Tan [République populaire de Chine] ; Shu Qin [République populaire de Chine] ; Guo-Qiang Cai [République populaire de Chine] ; Dong-Ying Zhang [République populaire de Chine]Source :
- Respiratory Research [ 1465-9921 ] ; 2020.
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China has been declared a public health emergency of international concern. The cardiac injury is a common condition among the hospitalized patients with COVID-19. However, whether N terminal pro B type natriuretic peptide (NT-proBNP) predicted outcome of severe COVID-19 patients was unknown.
The study initially enrolled 102 patients with severe COVID-19 from a continuous sample. After screening out the ineligible cases, 54 patients were analyzed in this study. The primary outcome was in-hospital death defined as the case fatality rate. Research information and following-up data were obtained from their medical records.
The best cut-off value of NT-proBNP for predicting in-hospital death was 88.64 pg/mL with the sensitivity for 100% and the specificity for 66.67%. Patients with high NT-proBNP values (> 88.64 pg/mL) had a significantly increased risk of death during the days of following-up compared with those with low values (≤88.64 pg/mL). After adjustment for potential risk factors, NT-proBNP was independently correlated with in-hospital death.
NT-proBNP might be an independent risk factor for in-hospital death in patients with severe COVID-19.
ClinicalTrials,
Url:
DOI: 10.1186/s12931-020-01352-w
PubMed: 32293449
PubMed Central: 7156898
Affiliations:
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<author><name sortKey="You, Lin Na" sort="You, Lin Na" uniqKey="You L" first="Lin-Na" last="You">Lin-Na You</name>
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<author><name sortKey="Tan, Xiao Wei" sort="Tan, Xiao Wei" uniqKey="Tan X" first="Xiao-Wei" last="Tan">Xiao-Wei Tan</name>
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Chongqing, 400016 China</nlm:aff>
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<author><name sortKey="Zhang, Dong Ying" sort="Zhang, Dong Ying" uniqKey="Zhang D" first="Dong-Ying" last="Zhang">Dong-Ying Zhang</name>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p id="Par1">The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China has been declared a public health emergency of international concern. The cardiac injury is a common condition among the hospitalized patients with COVID-19. However, whether N terminal pro B type natriuretic peptide (NT-proBNP) predicted outcome of severe COVID-19 patients was unknown.</p>
</sec>
<sec><title>Methods</title>
<p id="Par2">The study initially enrolled 102 patients with severe COVID-19 from a continuous sample. After screening out the ineligible cases, 54 patients were analyzed in this study. The primary outcome was in-hospital death defined as the case fatality rate. Research information and following-up data were obtained from their medical records.</p>
</sec>
<sec><title>Results</title>
<p id="Par3">The best cut-off value of NT-proBNP for predicting in-hospital death was 88.64 pg/mL with the sensitivity for 100% and the specificity for 66.67%. Patients with high NT-proBNP values (> 88.64 pg/mL) had a significantly increased risk of death during the days of following-up compared with those with low values (≤88.64 pg/mL). After adjustment for potential risk factors, NT-proBNP was independently correlated with in-hospital death.</p>
</sec>
<sec><title>Conclusion</title>
<p id="Par4">NT-proBNP might be an independent risk factor for in-hospital death in patients with severe COVID-19.</p>
</sec>
<sec><title>Trial registration</title>
<p id="Par5">ClinicalTrials, <ext-link ext-link-type="uri" xlink:href="https://clinicaltrials.gov/ct2/show/NCT04292964">NCT04292964.</ext-link>
Registered 03 March 2020,</p>
</sec>
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<author><name sortKey="Zhong, J C" uniqKey="Zhong J">J-C Zhong</name>
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<author><name sortKey="Grant, Mb" uniqKey="Grant M">MB Grant</name>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Respir Res</journal-id>
<journal-id journal-id-type="iso-abbrev">Respir. Res</journal-id>
<journal-title-group><journal-title>Respiratory Research</journal-title>
</journal-title-group>
<issn pub-type="ppub">1465-9921</issn>
<issn pub-type="epub">1465-993X</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">32293449</article-id>
<article-id pub-id-type="pmc">7156898</article-id>
<article-id pub-id-type="publisher-id">1352</article-id>
<article-id pub-id-type="doi">10.1186/s12931-020-01352-w</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research</subject>
</subj-group>
</article-categories>
<title-group><article-title>Prognostic value of NT-proBNP in patients with severe COVID-19</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" equal-contrib="yes"><name><surname>Gao</surname>
<given-names>Lei</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author" equal-contrib="yes"><name><surname>Jiang</surname>
<given-names>Dan</given-names>
</name>
<xref ref-type="aff" rid="Aff2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Wen</surname>
<given-names>Xue-song</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Cheng</surname>
<given-names>Xiao-cheng</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Sun</surname>
<given-names>Min</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>He</surname>
<given-names>Bin</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>You</surname>
<given-names>Lin-na</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Lei</surname>
<given-names>Peng</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Tan</surname>
<given-names>Xiao-wei</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Qin</surname>
<given-names>Shu</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><name><surname>Cai</surname>
<given-names>Guo-qiang</given-names>
</name>
<address><email>185161439@qq.com</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
<xref ref-type="aff" rid="Aff3">3</xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><name><surname>Zhang</surname>
<given-names>Dong-ying</given-names>
</name>
<address><email>zhangdongying@cqmu.edu.com</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<aff id="Aff1"><label>1</label>
<institution-wrap><institution-id institution-id-type="GRID">grid.452206.7</institution-id>
<institution>Department of Cardiovascular Medicine,</institution>
<institution>The First Affiliated Hospital of Chongqing Medical University,</institution>
</institution-wrap>
Chongqing, 400016 China</aff>
<aff id="Aff2"><label>2</label>
<institution-wrap><institution-id institution-id-type="GRID">grid.452206.7</institution-id>
<institution>Department of Cardiovascular Medicine,</institution>
<institution>The First Branch of the First Affiliated Hospital of Chongqing Medical University,</institution>
</institution-wrap>
Chongqing, 400016 China</aff>
<aff id="Aff3"><label>3</label>
Traditional Chinese Medicine hospital Dianjiang Chongqing, Chongqing, 408300 China</aff>
</contrib-group>
<pub-date pub-type="epub"><day>15</day>
<month>4</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>15</day>
<month>4</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="ppub"><year>2020</year>
</pub-date>
<volume>21</volume>
<elocation-id>83</elocation-id>
<history><date date-type="received"><day>11</day>
<month>3</month>
<year>2020</year>
</date>
<date date-type="accepted"><day>2</day>
<month>4</month>
<year>2020</year>
</date>
</history>
<permissions><copyright-statement>© The Author(s) 2020</copyright-statement>
<license license-type="OpenAccess"><license-p><bold>Open Access</bold>
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated in a credit line to the data.</license-p>
</license>
</permissions>
<abstract id="Abs1"><sec><title>Background</title>
<p id="Par1">The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China has been declared a public health emergency of international concern. The cardiac injury is a common condition among the hospitalized patients with COVID-19. However, whether N terminal pro B type natriuretic peptide (NT-proBNP) predicted outcome of severe COVID-19 patients was unknown.</p>
</sec>
<sec><title>Methods</title>
<p id="Par2">The study initially enrolled 102 patients with severe COVID-19 from a continuous sample. After screening out the ineligible cases, 54 patients were analyzed in this study. The primary outcome was in-hospital death defined as the case fatality rate. Research information and following-up data were obtained from their medical records.</p>
</sec>
<sec><title>Results</title>
<p id="Par3">The best cut-off value of NT-proBNP for predicting in-hospital death was 88.64 pg/mL with the sensitivity for 100% and the specificity for 66.67%. Patients with high NT-proBNP values (> 88.64 pg/mL) had a significantly increased risk of death during the days of following-up compared with those with low values (≤88.64 pg/mL). After adjustment for potential risk factors, NT-proBNP was independently correlated with in-hospital death.</p>
</sec>
<sec><title>Conclusion</title>
<p id="Par4">NT-proBNP might be an independent risk factor for in-hospital death in patients with severe COVID-19.</p>
</sec>
<sec><title>Trial registration</title>
<p id="Par5">ClinicalTrials, <ext-link ext-link-type="uri" xlink:href="https://clinicaltrials.gov/ct2/show/NCT04292964">NCT04292964.</ext-link>
Registered 03 March 2020,</p>
</sec>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd>NT-proBNT</kwd>
<kwd>COVID-19</kwd>
<kwd>SARS-CoV-2</kwd>
<kwd>Outcome</kwd>
</kwd-group>
<funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100001809</institution-id>
<institution>National Natural Science Foundation of China</institution>
</institution-wrap>
</funding-source>
<award-id>81570212</award-id>
<award-id>31800976</award-id>
</award-group>
</funding-group>
<funding-group><award-group><funding-source><institution>Chongqing Science and Health Joint Medical Research Project</institution>
</funding-source>
<award-id>2018QNXM024</award-id>
</award-group>
</funding-group>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2020</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
<affiliations><list><country><li>République populaire de Chine</li>
</country>
</list>
<tree><country name="République populaire de Chine"><noRegion><name sortKey="Gao, Lei" sort="Gao, Lei" uniqKey="Gao L" first="Lei" last="Gao">Lei Gao</name>
</noRegion>
<name sortKey="Cai, Guo Qiang" sort="Cai, Guo Qiang" uniqKey="Cai G" first="Guo-Qiang" last="Cai">Guo-Qiang Cai</name>
<name sortKey="Cai, Guo Qiang" sort="Cai, Guo Qiang" uniqKey="Cai G" first="Guo-Qiang" last="Cai">Guo-Qiang Cai</name>
<name sortKey="Cheng, Xiao Cheng" sort="Cheng, Xiao Cheng" uniqKey="Cheng X" first="Xiao-Cheng" last="Cheng">Xiao-Cheng Cheng</name>
<name sortKey="He, Bin" sort="He, Bin" uniqKey="He B" first="Bin" last="He">Bin He</name>
<name sortKey="Jiang, Dan" sort="Jiang, Dan" uniqKey="Jiang D" first="Dan" last="Jiang">Dan Jiang</name>
<name sortKey="Lei, Peng" sort="Lei, Peng" uniqKey="Lei P" first="Peng" last="Lei">Peng Lei</name>
<name sortKey="Qin, Shu" sort="Qin, Shu" uniqKey="Qin S" first="Shu" last="Qin">Shu Qin</name>
<name sortKey="Sun, Min" sort="Sun, Min" uniqKey="Sun M" first="Min" last="Sun">Min Sun</name>
<name sortKey="Tan, Xiao Wei" sort="Tan, Xiao Wei" uniqKey="Tan X" first="Xiao-Wei" last="Tan">Xiao-Wei Tan</name>
<name sortKey="Wen, Xue Song" sort="Wen, Xue Song" uniqKey="Wen X" first="Xue-Song" last="Wen">Xue-Song Wen</name>
<name sortKey="You, Lin Na" sort="You, Lin Na" uniqKey="You L" first="Lin-Na" last="You">Lin-Na You</name>
<name sortKey="Zhang, Dong Ying" sort="Zhang, Dong Ying" uniqKey="Zhang D" first="Dong-Ying" last="Zhang">Dong-Ying Zhang</name>
</country>
</tree>
</affiliations>
</record>
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