Clinical and imaging features predict mortality in COVID-19 infection in Iran.
Identifieur interne : 000776 ( Main/Exploration ); précédent : 000775; suivant : 000777Clinical and imaging features predict mortality in COVID-19 infection in Iran.
Auteurs : Fatemeh Homayounieh [États-Unis] ; Eric W. Zhang [États-Unis] ; Rosa Babaei [Iran] ; Hadi Karimi Mobin [Iran] ; Maedeh Sharifian [Iran] ; Iman Mohseni [Iran] ; Anderson Kuo [États-Unis] ; Chiara Arru [États-Unis] ; Mannudeep K. Kalra [États-Unis] ; Subba R. Digumarthy [États-Unis]Source :
- PloS one [ 1932-6203 ] ; 2020.
Descripteurs français
- KwdFr :
- Adulte (MeSH), Adulte d'âge moyen (MeSH), Betacoronavirus (MeSH), Centres de soins tertiaires (MeSH), Comorbidité (MeSH), Facteurs de risque (MeSH), Femelle (MeSH), Humains (MeSH), Indice de gravité de la maladie (MeSH), Infections à coronavirus (imagerie diagnostique), Infections à coronavirus (mortalité), Iran (MeSH), Modèles logistiques (MeSH), Mâle (MeSH), Pandémies (MeSH), Pneumopathie virale (imagerie diagnostique), Pneumopathie virale (mortalité), Radiographie thoracique (MeSH), Sujet âgé (MeSH), Tomodensitométrie (MeSH), Traitement d'image par ordinateur (MeSH), Études rétrospectives (MeSH).
- MESH :
- imagerie diagnostique : Infections à coronavirus, Pneumopathie virale.
- mortalité : Infections à coronavirus, Pneumopathie virale.
- Adulte, Adulte d'âge moyen, Betacoronavirus, Centres de soins tertiaires, Comorbidité, Facteurs de risque, Femelle, Humains, Indice de gravité de la maladie, Iran, Modèles logistiques, Mâle, Pandémies, Radiographie thoracique, Sujet âgé, Tomodensitométrie, Traitement d'image par ordinateur, Études rétrospectives.
English descriptors
- KwdEn :
- Adult (MeSH), Aged (MeSH), Betacoronavirus (MeSH), Comorbidity (MeSH), Coronavirus Infections (diagnostic imaging), Coronavirus Infections (mortality), Female (MeSH), Humans (MeSH), Image Processing, Computer-Assisted (MeSH), Iran (MeSH), Logistic Models (MeSH), Male (MeSH), Middle Aged (MeSH), Pandemics (MeSH), Pneumonia, Viral (diagnostic imaging), Pneumonia, Viral (mortality), Radiography, Thoracic (MeSH), Retrospective Studies (MeSH), Risk Factors (MeSH), Severity of Illness Index (MeSH), Tertiary Care Centers (MeSH), Tomography, X-Ray Computed (MeSH).
- MESH :
- geographic : Iran.
- diagnostic imaging : Coronavirus Infections, Pneumonia, Viral.
- mortality : Coronavirus Infections, Pneumonia, Viral.
- Adult, Aged, Betacoronavirus, Comorbidity, Female, Humans, Image Processing, Computer-Assisted, Logistic Models, Male, Middle Aged, Pandemics, Radiography, Thoracic, Retrospective Studies, Risk Factors, Severity of Illness Index, Tertiary Care Centers, Tomography, X-Ray Computed.
Abstract
The new coronavirus disease 2019 (COVID-19) pandemic has challenged many healthcare systems around the world. While most of the current understanding of the clinical features of COVID-19 is derived from Chinese studies, there is a relative paucity of reports from the remaining global health community. In this study, we analyze the clinical and radiologic factors that correlate with mortality odds in COVID-19 positive patients from a tertiary care center in Tehran, Iran. A retrospective cohort study of 90 patients with reverse transcriptase-polymerase chain reaction (RT-PCR) positive COVID-19 infection was conducted, analyzing demographics, co-morbidities, presenting symptoms, vital signs, laboratory values, chest radiograph findings, and chest CT features based on mortality. Chest radiograph was assessed using the Radiographic Assessment of Lung Edema (RALE) scoring system. Chest CTs were assessed according to the opacification pattern, distribution, and standardized severity score. Initial and follow-up Chest CTs were compared if available. Multiple logistic regression was used to generate a prediction model for mortality. The 90 patients included 59 men and 31 women (59.4 ± 16.6 years), including 21 deceased and 69 surviving patients. Among clinical features, advanced age (p = 0.02), low oxygenation saturation (p<0.001), leukocytosis (p = 0.02), low lymphocyte fraction (p = 0.03), and low platelet count (p = 0.048) were associated with increased mortality. High RALE score on initial chest radiograph (p = 0.002), presence of pleural effusions on initial CT chest (p = 0.005), development of pleural effusions on follow-up CT chest (p = 0.04), and worsening lung severity score on follow-up CT Chest (p = 0.03) were associated with mortality. A two-factor logistic model using patient age and oxygen saturation was created, which demonstrates 89% accuracy and area under the ROC curve of 0.86 (p<0.0001). Specific demographic, clinical, and imaging features are associated with increased mortality in COVID-19 infections. Attention to these features can help optimize patient management.
DOI: 10.1371/journal.pone.0239519
PubMed: 32970733
PubMed Central: PMC7514030
Affiliations:
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Zhang</name><affiliation wicri:level="4"><nlm:affiliation>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation></author><author><name sortKey="Babaei, Rosa" sort="Babaei, Rosa" uniqKey="Babaei R" first="Rosa" last="Babaei">Rosa Babaei</name><affiliation wicri:level="1"><nlm:affiliation>Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Karimi Mobin, Hadi" sort="Karimi Mobin, Hadi" uniqKey="Karimi Mobin H" first="Hadi" last="Karimi Mobin">Hadi Karimi Mobin</name><affiliation wicri:level="1"><nlm:affiliation>Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Sharifian, Maedeh" sort="Sharifian, Maedeh" uniqKey="Sharifian M" first="Maedeh" last="Sharifian">Maedeh Sharifian</name><affiliation wicri:level="1"><nlm:affiliation>Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Mohseni, Iman" sort="Mohseni, Iman" uniqKey="Mohseni I" first="Iman" last="Mohseni">Iman Mohseni</name><affiliation wicri:level="1"><nlm:affiliation>Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran, Iran.</nlm:affiliation><country xml:lang="fr">Iran</country><wicri:regionArea>Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran</wicri:regionArea><wicri:noRegion>Tehran</wicri:noRegion></affiliation></author><author><name sortKey="Kuo, Anderson" sort="Kuo, Anderson" uniqKey="Kuo A" first="Anderson" last="Kuo">Anderson Kuo</name><affiliation wicri:level="4"><nlm:affiliation>Division of Cardiovascular Imaging, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Division of Cardiovascular Imaging, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation></author><author><name sortKey="Arru, Chiara" sort="Arru, Chiara" uniqKey="Arru C" first="Chiara" last="Arru">Chiara Arru</name><affiliation wicri:level="4"><nlm:affiliation>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation></author><author><name sortKey="Kalra, Mannudeep K" sort="Kalra, Mannudeep K" uniqKey="Kalra M" first="Mannudeep K" last="Kalra">Mannudeep K. Kalra</name><affiliation wicri:level="4"><nlm:affiliation>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation></author><author><name sortKey="Digumarthy, Subba R" sort="Digumarthy, Subba R" uniqKey="Digumarthy S" first="Subba R" last="Digumarthy">Subba R. Digumarthy</name><affiliation wicri:level="4"><nlm:affiliation>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term><term>Aged (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Comorbidity (MeSH)</term><term>Coronavirus Infections (diagnostic imaging)</term><term>Coronavirus Infections (mortality)</term><term>Female (MeSH)</term><term>Humans (MeSH)</term><term>Image Processing, Computer-Assisted (MeSH)</term><term>Iran (MeSH)</term><term>Logistic Models (MeSH)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (diagnostic imaging)</term><term>Pneumonia, Viral (mortality)</term><term>Radiography, Thoracic (MeSH)</term><term>Retrospective Studies (MeSH)</term><term>Risk Factors (MeSH)</term><term>Severity of Illness Index (MeSH)</term><term>Tertiary Care Centers (MeSH)</term><term>Tomography, X-Ray Computed (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Centres de soins tertiaires (MeSH)</term><term>Comorbidité (MeSH)</term><term>Facteurs de risque (MeSH)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Indice de gravité de la maladie (MeSH)</term><term>Infections à coronavirus (imagerie diagnostique)</term><term>Infections à coronavirus (mortalité)</term><term>Iran (MeSH)</term><term>Modèles logistiques (MeSH)</term><term>Mâle (MeSH)</term><term>Pandémies (MeSH)</term><term>Pneumopathie virale (imagerie diagnostique)</term><term>Pneumopathie virale (mortalité)</term><term>Radiographie thoracique (MeSH)</term><term>Sujet âgé (MeSH)</term><term>Tomodensitométrie (MeSH)</term><term>Traitement d'image par ordinateur (MeSH)</term><term>Études rétrospectives (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>Iran</term></keywords><keywords scheme="MESH" qualifier="diagnostic imaging" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="imagerie diagnostique" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="mortalité" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Aged</term><term>Betacoronavirus</term><term>Comorbidity</term><term>Female</term><term>Humans</term><term>Image Processing, Computer-Assisted</term><term>Logistic Models</term><term>Male</term><term>Middle Aged</term><term>Pandemics</term><term>Radiography, Thoracic</term><term>Retrospective Studies</term><term>Risk Factors</term><term>Severity of Illness Index</term><term>Tertiary Care Centers</term><term>Tomography, X-Ray Computed</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Betacoronavirus</term><term>Centres de soins tertiaires</term><term>Comorbidité</term><term>Facteurs de risque</term><term>Femelle</term><term>Humains</term><term>Indice de gravité de la maladie</term><term>Iran</term><term>Modèles logistiques</term><term>Mâle</term><term>Pandémies</term><term>Radiographie thoracique</term><term>Sujet âgé</term><term>Tomodensitométrie</term><term>Traitement d'image par ordinateur</term><term>Études rétrospectives</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The new coronavirus disease 2019 (COVID-19) pandemic has challenged many healthcare systems around the world. While most of the current understanding of the clinical features of COVID-19 is derived from Chinese studies, there is a relative paucity of reports from the remaining global health community. In this study, we analyze the clinical and radiologic factors that correlate with mortality odds in COVID-19 positive patients from a tertiary care center in Tehran, Iran. A retrospective cohort study of 90 patients with reverse transcriptase-polymerase chain reaction (RT-PCR) positive COVID-19 infection was conducted, analyzing demographics, co-morbidities, presenting symptoms, vital signs, laboratory values, chest radiograph findings, and chest CT features based on mortality. Chest radiograph was assessed using the Radiographic Assessment of Lung Edema (RALE) scoring system. Chest CTs were assessed according to the opacification pattern, distribution, and standardized severity score. Initial and follow-up Chest CTs were compared if available. Multiple logistic regression was used to generate a prediction model for mortality. The 90 patients included 59 men and 31 women (59.4 ± 16.6 years), including 21 deceased and 69 surviving patients. Among clinical features, advanced age (p = 0.02), low oxygenation saturation (p<0.001), leukocytosis (p = 0.02), low lymphocyte fraction (p = 0.03), and low platelet count (p = 0.048) were associated with increased mortality. High RALE score on initial chest radiograph (p = 0.002), presence of pleural effusions on initial CT chest (p = 0.005), development of pleural effusions on follow-up CT chest (p = 0.04), and worsening lung severity score on follow-up CT Chest (p = 0.03) were associated with mortality. A two-factor logistic model using patient age and oxygen saturation was created, which demonstrates 89% accuracy and area under the ROC curve of 0.86 (p<0.0001). Specific demographic, clinical, and imaging features are associated with increased mortality in COVID-19 infections. Attention to these features can help optimize patient management.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32970733</PMID><DateCompleted><Year>2020</Year><Month>10</Month><Day>05</Day></DateCompleted><DateRevised><Year>2020</Year><Month>10</Month><Day>05</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>15</Volume><Issue>9</Issue><PubDate><Year>2020</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS One</ISOAbbreviation></Journal><ArticleTitle>Clinical and imaging features predict mortality in COVID-19 infection in Iran.</ArticleTitle><Pagination><MedlinePgn>e0239519</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0239519</ELocationID><Abstract><AbstractText>The new coronavirus disease 2019 (COVID-19) pandemic has challenged many healthcare systems around the world. While most of the current understanding of the clinical features of COVID-19 is derived from Chinese studies, there is a relative paucity of reports from the remaining global health community. In this study, we analyze the clinical and radiologic factors that correlate with mortality odds in COVID-19 positive patients from a tertiary care center in Tehran, Iran. A retrospective cohort study of 90 patients with reverse transcriptase-polymerase chain reaction (RT-PCR) positive COVID-19 infection was conducted, analyzing demographics, co-morbidities, presenting symptoms, vital signs, laboratory values, chest radiograph findings, and chest CT features based on mortality. Chest radiograph was assessed using the Radiographic Assessment of Lung Edema (RALE) scoring system. Chest CTs were assessed according to the opacification pattern, distribution, and standardized severity score. Initial and follow-up Chest CTs were compared if available. Multiple logistic regression was used to generate a prediction model for mortality. The 90 patients included 59 men and 31 women (59.4 ± 16.6 years), including 21 deceased and 69 surviving patients. Among clinical features, advanced age (p = 0.02), low oxygenation saturation (p<0.001), leukocytosis (p = 0.02), low lymphocyte fraction (p = 0.03), and low platelet count (p = 0.048) were associated with increased mortality. High RALE score on initial chest radiograph (p = 0.002), presence of pleural effusions on initial CT chest (p = 0.005), development of pleural effusions on follow-up CT chest (p = 0.04), and worsening lung severity score on follow-up CT Chest (p = 0.03) were associated with mortality. A two-factor logistic model using patient age and oxygen saturation was created, which demonstrates 89% accuracy and area under the ROC curve of 0.86 (p<0.0001). Specific demographic, clinical, and imaging features are associated with increased mortality in COVID-19 infections. Attention to these features can help optimize patient management.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Homayounieh</LastName><ForeName>Fatemeh</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Eric W</ForeName><Initials>EW</Initials><AffiliationInfo><Affiliation>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Babaei</LastName><ForeName>Rosa</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Karimi Mobin</LastName><ForeName>Hadi</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sharifian</LastName><ForeName>Maedeh</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mohseni</LastName><ForeName>Iman</ForeName><Initials>I</Initials><AffiliationInfo><Affiliation>Department of Radiology, University of Medical Sciences, Firoozgar Hospital, Tehran, Iran.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kuo</LastName><ForeName>Anderson</ForeName><Initials>A</Initials><Identifier Source="ORCID">0000-0002-5517-7984</Identifier><AffiliationInfo><Affiliation>Division of Cardiovascular Imaging, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Arru</LastName><ForeName>Chiara</ForeName><Initials>C</Initials><Identifier Source="ORCID">0000-0002-2014-2802</Identifier><AffiliationInfo><Affiliation>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kalra</LastName><ForeName>Mannudeep K</ForeName><Initials>MK</Initials><AffiliationInfo><Affiliation>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Digumarthy</LastName><ForeName>Subba R</ForeName><Initials>SR</Initials><Identifier Source="ORCID">0000-0003-4041-6716</Identifier><AffiliationInfo><Affiliation>Division of Thoracic Imaging and Intervention, Department of Radiology, Harvard University, Massachusetts General Hospital, Boston, Massachusetts, United States of America.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>09</Month><Day>24</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName><SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName></SupplMeshList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015897" MajorTopicYN="N">Comorbidity</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000000981" MajorTopicYN="Y">diagnostic imaging</QualifierName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007091" MajorTopicYN="N">Image Processing, Computer-Assisted</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007492" MajorTopicYN="N" Type="Geographic">Iran</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016015" MajorTopicYN="N">Logistic Models</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000000981" MajorTopicYN="Y">diagnostic imaging</QualifierName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013902" MajorTopicYN="N">Radiography, Thoracic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012189" MajorTopicYN="N">Retrospective Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012307" MajorTopicYN="N">Risk Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012720" MajorTopicYN="N">Severity of Illness Index</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D062606" MajorTopicYN="N">Tertiary Care Centers</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014057" MajorTopicYN="N">Tomography, X-Ray Computed</DescriptorName></MeshHeading></MeshHeadingList><CoiStatement>The authors have declared that no competing interests exist.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>04</Month><Day>27</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>09</Month><Day>08</Day></PubMedPubDate><PubMedPubDate 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Zhang</name></country><country name="Iran"><noRegion><name sortKey="Babaei, Rosa" sort="Babaei, Rosa" uniqKey="Babaei R" first="Rosa" last="Babaei">Rosa Babaei</name></noRegion><name sortKey="Karimi Mobin, Hadi" sort="Karimi Mobin, Hadi" uniqKey="Karimi Mobin H" first="Hadi" last="Karimi Mobin">Hadi Karimi Mobin</name><name sortKey="Mohseni, Iman" sort="Mohseni, Iman" uniqKey="Mohseni I" first="Iman" last="Mohseni">Iman Mohseni</name><name sortKey="Sharifian, Maedeh" sort="Sharifian, Maedeh" uniqKey="Sharifian M" first="Maedeh" last="Sharifian">Maedeh Sharifian</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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