Early identification of pneumonia patients at increased risk of Middle East respiratory syndrome coronavirus infection in Saudi Arabia
Identifieur interne : 000595 ( Pmc/Checkpoint ); précédent : 000594; suivant : 000596Early identification of pneumonia patients at increased risk of Middle East respiratory syndrome coronavirus infection in Saudi Arabia
Auteurs : Anwar E. Ahmed [Arabie saoudite] ; Hamdan Al-Jahdali [Arabie saoudite] ; Abeer N. Alshukairi [Arabie saoudite] ; Mody Alaqeel [Arabie saoudite] ; Salma S. Siddiq [Arabie saoudite] ; Hanan Alsaab [Arabie saoudite] ; Ezzeldin A. Sakr [Arabie saoudite] ; Hamed A. Alyahya [Arabie saoudite] ; Munzir M. Alandonisi [Arabie saoudite] ; Alaa T. Subedar [Arabie saoudite] ; Nouf M. Aloudah [Arabie saoudite] ; Salim Baharoon [Arabie saoudite] ; Majid A. Alsalamah [Arabie saoudite] ; Sameera Al Johani [Arabie saoudite] ; Mohammed G. Alghamdi [Arabie saoudite]Source :
- International Journal of Infectious Diseases [ 1201-9712 ] ; 2018.
Abstract
The rapid and accurate identification of individuals who are at high risk of Middle East respiratory syndrome coronavirus (MERS-CoV) infection remains a major challenge for the medical and scientific communities. The aim of this study was to develop and validate a risk prediction model for the screening of suspected cases of MERS-CoV infection in patients who have developed pneumonia.
A two-center, retrospective case–control study was performed. A total of 360 patients with confirmed pneumonia who were evaluated for MERS-CoV infection by real-time reverse transcription polymerase chain reaction (rRT-PCR) between September 1, 2012 and June 1, 2016 at King Abdulaziz Medical City in Riyadh and King Fahad General Hospital in Jeddah, were included. According to the rRT-PCR results, 135 patients were positive for MERS-CoV and 225 were negative. Demographic characteristics, clinical presentations, and radiological and laboratory findings were collected for each subject.
A risk prediction model to identify pneumonia patients at increased risk of MERS-CoV was developed. The model included male sex, contact with a sick patient or camel, diabetes, severe illness, low white blood cell (WBC) count, low alanine aminotransferase (ALT), and high aspartate aminotransferase (AST). The model performed well in predicting MERS-CoV infection (area under the receiver operating characteristics curves (AUC) 0.8162), on internal validation (AUC 0.8037), and on a goodness-of-fit test (
This study provides a simple, practical, and valid algorithm to identify pneumonia patients at increased risk of MERS-CoV infection. This risk prediction model could be useful for the early identification of patients at the highest risk of MERS-CoV infection. Further validation of the prediction model on a large prospective cohort of representative patients with pneumonia is necessary.
Url:
DOI: 10.1016/j.ijid.2018.03.005
PubMed: 29550445
PubMed Central: 7110544
Affiliations:
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PMC:7110544Le document en format XML
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<author><name sortKey="Alsaab, Hanan" sort="Alsaab, Hanan" uniqKey="Alsaab H" first="Hanan" last="Alsaab">Hanan Alsaab</name>
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<author><name sortKey="Sakr, Ezzeldin A" sort="Sakr, Ezzeldin A" uniqKey="Sakr E" first="Ezzeldin A." last="Sakr">Ezzeldin A. Sakr</name>
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<author><name sortKey="Alandonisi, Munzir M" sort="Alandonisi, Munzir M" uniqKey="Alandonisi M" first="Munzir M." last="Alandonisi">Munzir M. Alandonisi</name>
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<author><name sortKey="Subedar, Alaa T" sort="Subedar, Alaa T" uniqKey="Subedar A" first="Alaa T." last="Subedar">Alaa T. Subedar</name>
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<author><name sortKey="Aloudah, Nouf M" sort="Aloudah, Nouf M" uniqKey="Aloudah N" first="Nouf M." last="Aloudah">Nouf M. Aloudah</name>
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<author><name sortKey="Baharoon, Salim" sort="Baharoon, Salim" uniqKey="Baharoon S" first="Salim" last="Baharoon">Salim Baharoon</name>
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<author><name sortKey="Alsalamah, Majid A" sort="Alsalamah, Majid A" uniqKey="Alsalamah M" first="Majid A." last="Alsalamah">Majid A. Alsalamah</name>
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<author><name sortKey="Al Johani, Sameera" sort="Al Johani, Sameera" uniqKey="Al Johani S" first="Sameera" last="Al Johani">Sameera Al Johani</name>
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<author><name sortKey="Alghamdi, Mohammed G" sort="Alghamdi, Mohammed G" uniqKey="Alghamdi M" first="Mohammed G." last="Alghamdi">Mohammed G. Alghamdi</name>
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<series><title level="j">International Journal of Infectious Diseases</title>
<idno type="ISSN">1201-9712</idno>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>The rapid and accurate identification of individuals who are at high risk of Middle East respiratory syndrome coronavirus (MERS-CoV) infection remains a major challenge for the medical and scientific communities. The aim of this study was to develop and validate a risk prediction model for the screening of suspected cases of MERS-CoV infection in patients who have developed pneumonia.</p>
</sec>
<sec><title>Methods</title>
<p>A two-center, retrospective case–control study was performed. A total of 360 patients with confirmed pneumonia who were evaluated for MERS-CoV infection by real-time reverse transcription polymerase chain reaction (rRT-PCR) between September 1, 2012 and June 1, 2016 at King Abdulaziz Medical City in Riyadh and King Fahad General Hospital in Jeddah, were included. According to the rRT-PCR results, 135 patients were positive for MERS-CoV and 225 were negative. Demographic characteristics, clinical presentations, and radiological and laboratory findings were collected for each subject.</p>
</sec>
<sec><title>Results</title>
<p>A risk prediction model to identify pneumonia patients at increased risk of MERS-CoV was developed. The model included male sex, contact with a sick patient or camel, diabetes, severe illness, low white blood cell (WBC) count, low alanine aminotransferase (ALT), and high aspartate aminotransferase (AST). The model performed well in predicting MERS-CoV infection (area under the receiver operating characteristics curves (AUC) 0.8162), on internal validation (AUC 0.8037), and on a goodness-of-fit test (<italic>p</italic>
= 0.592). The risk prediction model, which produced an optimal probability cut-off of 0.33, had a sensitivity of 0.716 and specificity of 0.783.</p>
</sec>
<sec><title>Conclusions</title>
<p>This study provides a simple, practical, and valid algorithm to identify pneumonia patients at increased risk of MERS-CoV infection. This risk prediction model could be useful for the early identification of patients at the highest risk of MERS-CoV infection. Further validation of the prediction model on a large prospective cohort of representative patients with pneumonia is necessary.</p>
</sec>
</div>
</front>
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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">Int J Infect Dis</journal-id>
<journal-id journal-id-type="iso-abbrev">Int. J. Infect. Dis</journal-id>
<journal-title-group><journal-title>International Journal of Infectious Diseases</journal-title>
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<issn pub-type="ppub">1201-9712</issn>
<issn pub-type="epub">1878-3511</issn>
<publisher><publisher-name>The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.</publisher-name>
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</journal-meta>
<article-meta><article-id pub-id-type="pmid">29550445</article-id>
<article-id pub-id-type="pmc">7110544</article-id>
<article-id pub-id-type="publisher-id">S1201-9712(18)30064-X</article-id>
<article-id pub-id-type="doi">10.1016/j.ijid.2018.03.005</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Early identification of pneumonia patients at increased risk of Middle East respiratory syndrome coronavirus infection in Saudi Arabia</article-title>
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<given-names>Sameera</given-names>
</name>
<email>Johanis@ngha.med.sa</email>
<xref rid="aff0005" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author" id="aut0075"><name><surname>Alghamdi</surname>
<given-names>Mohammed G.</given-names>
</name>
<email>dr_id2012@yahoo.com</email>
<xref rid="aff0015" ref-type="aff">c</xref>
</contrib>
</contrib-group>
<aff id="aff0005"><label>a</label>
King Abdullah International Medical Research Center (KAIMRC), King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City (KAMC), Ministry of National Guard – Health Affairs, Riyadh 11426, Saudi Arabia</aff>
<aff id="aff0010"><label>b</label>
King Faisal Specialist Hospital and Research Centre, Jeddah, Saudi Arabia</aff>
<aff id="aff0015"><label>c</label>
King Fahad General Hospital, Jeddah, Saudi Arabia</aff>
<aff id="aff0020"><label>d</label>
King Saud University, Riyadh, Saudi Arabia</aff>
<author-notes><corresp id="cor0005"><label>⁎</label>
Corresponding author. <email>ahmeda5@vcu.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="pmc-release"><day>14</day>
<month>3</month>
<year>2018</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="ppub"><month>5</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub"><day>14</day>
<month>3</month>
<year>2018</year>
</pub-date>
<volume>70</volume>
<fpage>51</fpage>
<lpage>56</lpage>
<history><date date-type="received"><day>20</day>
<month>9</month>
<year>2017</year>
</date>
<date date-type="rev-recd"><day>6</day>
<month>3</month>
<year>2018</year>
</date>
<date date-type="accepted"><day>9</day>
<month>3</month>
<year>2018</year>
</date>
</history>
<permissions><copyright-statement>© 2018 The Author(s)</copyright-statement>
<copyright-year>2018</copyright-year>
<license><license-p>Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.</license-p>
</license>
</permissions>
<abstract id="abs0005"><sec><title>Background</title>
<p>The rapid and accurate identification of individuals who are at high risk of Middle East respiratory syndrome coronavirus (MERS-CoV) infection remains a major challenge for the medical and scientific communities. The aim of this study was to develop and validate a risk prediction model for the screening of suspected cases of MERS-CoV infection in patients who have developed pneumonia.</p>
</sec>
<sec><title>Methods</title>
<p>A two-center, retrospective case–control study was performed. A total of 360 patients with confirmed pneumonia who were evaluated for MERS-CoV infection by real-time reverse transcription polymerase chain reaction (rRT-PCR) between September 1, 2012 and June 1, 2016 at King Abdulaziz Medical City in Riyadh and King Fahad General Hospital in Jeddah, were included. According to the rRT-PCR results, 135 patients were positive for MERS-CoV and 225 were negative. Demographic characteristics, clinical presentations, and radiological and laboratory findings were collected for each subject.</p>
</sec>
<sec><title>Results</title>
<p>A risk prediction model to identify pneumonia patients at increased risk of MERS-CoV was developed. The model included male sex, contact with a sick patient or camel, diabetes, severe illness, low white blood cell (WBC) count, low alanine aminotransferase (ALT), and high aspartate aminotransferase (AST). The model performed well in predicting MERS-CoV infection (area under the receiver operating characteristics curves (AUC) 0.8162), on internal validation (AUC 0.8037), and on a goodness-of-fit test (<italic>p</italic>
= 0.592). The risk prediction model, which produced an optimal probability cut-off of 0.33, had a sensitivity of 0.716 and specificity of 0.783.</p>
</sec>
<sec><title>Conclusions</title>
<p>This study provides a simple, practical, and valid algorithm to identify pneumonia patients at increased risk of MERS-CoV infection. This risk prediction model could be useful for the early identification of patients at the highest risk of MERS-CoV infection. Further validation of the prediction model on a large prospective cohort of representative patients with pneumonia is necessary.</p>
</sec>
</abstract>
<kwd-group id="kwd0005"><title>Keywords</title>
<kwd>Pneumonia</kwd>
<kwd>MERS-CoV case definitions</kwd>
<kwd>Early diagnosis</kwd>
<kwd>Saudi Arabia</kwd>
</kwd-group>
</article-meta>
<notes><p id="misc0005"><bold>Corresponding Editor:</bold>
Eskild Petersen, Aarhus, Denmark</p>
</notes>
</front>
</pmc>
<affiliations><list><country><li>Arabie saoudite</li>
</country>
</list>
<tree><country name="Arabie saoudite"><noRegion><name sortKey="Ahmed, Anwar E" sort="Ahmed, Anwar E" uniqKey="Ahmed A" first="Anwar E." last="Ahmed">Anwar E. Ahmed</name>
</noRegion>
<name sortKey="Al Jahdali, Hamdan" sort="Al Jahdali, Hamdan" uniqKey="Al Jahdali H" first="Hamdan" last="Al-Jahdali">Hamdan Al-Jahdali</name>
<name sortKey="Al Johani, Sameera" sort="Al Johani, Sameera" uniqKey="Al Johani S" first="Sameera" last="Al Johani">Sameera Al Johani</name>
<name sortKey="Alandonisi, Munzir M" sort="Alandonisi, Munzir M" uniqKey="Alandonisi M" first="Munzir M." last="Alandonisi">Munzir M. Alandonisi</name>
<name sortKey="Alaqeel, Mody" sort="Alaqeel, Mody" uniqKey="Alaqeel M" first="Mody" last="Alaqeel">Mody Alaqeel</name>
<name sortKey="Alghamdi, Mohammed G" sort="Alghamdi, Mohammed G" uniqKey="Alghamdi M" first="Mohammed G." last="Alghamdi">Mohammed G. Alghamdi</name>
<name sortKey="Aloudah, Nouf M" sort="Aloudah, Nouf M" uniqKey="Aloudah N" first="Nouf M." last="Aloudah">Nouf M. Aloudah</name>
<name sortKey="Alsaab, Hanan" sort="Alsaab, Hanan" uniqKey="Alsaab H" first="Hanan" last="Alsaab">Hanan Alsaab</name>
<name sortKey="Alsalamah, Majid A" sort="Alsalamah, Majid A" uniqKey="Alsalamah M" first="Majid A." last="Alsalamah">Majid A. Alsalamah</name>
<name sortKey="Alshukairi, Abeer N" sort="Alshukairi, Abeer N" uniqKey="Alshukairi A" first="Abeer N." last="Alshukairi">Abeer N. Alshukairi</name>
<name sortKey="Alyahya, Hamed A" sort="Alyahya, Hamed A" uniqKey="Alyahya H" first="Hamed A." last="Alyahya">Hamed A. Alyahya</name>
<name sortKey="Baharoon, Salim" sort="Baharoon, Salim" uniqKey="Baharoon S" first="Salim" last="Baharoon">Salim Baharoon</name>
<name sortKey="Sakr, Ezzeldin A" sort="Sakr, Ezzeldin A" uniqKey="Sakr E" first="Ezzeldin A." last="Sakr">Ezzeldin A. Sakr</name>
<name sortKey="Siddiq, Salma S" sort="Siddiq, Salma S" uniqKey="Siddiq S" first="Salma S." last="Siddiq">Salma S. Siddiq</name>
<name sortKey="Subedar, Alaa T" sort="Subedar, Alaa T" uniqKey="Subedar A" first="Alaa T." last="Subedar">Alaa T. Subedar</name>
</country>
</tree>
</affiliations>
</record>
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