Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation.
Identifieur interne : 000667 ( Main/Exploration ); précédent : 000666; suivant : 000668Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation.
Auteurs : Adrian D. Haimovich [États-Unis] ; Neal G. Ravindra [États-Unis] ; Stoytcho Stoytchev [États-Unis] ; H Patrick Young [États-Unis] ; Francis P. Wilson [États-Unis] ; David Van Dijk [États-Unis] ; Wade L. Schulz [États-Unis] ; R Andrew Taylor [États-Unis]Source :
- Annals of emergency medicine [ 1097-6760 ] ; 2020.
Descripteurs français
- KwdFr :
- Adolescent (MeSH), Adulte (MeSH), Adulte d'âge moyen (MeSH), Appréciation des risques (méthodes), Betacoronavirus (MeSH), Femelle (MeSH), Humains (MeSH), Indice de gravité de la maladie (MeSH), Infections à coronavirus (complications), Infections à coronavirus (diagnostic), Infections à coronavirus (thérapie), Insuffisance respiratoire (thérapie), Insuffisance respiratoire (virologie), Jeune adulte (MeSH), Mâle (MeSH), Oxygénothérapie (MeSH), Pandémies (MeSH), Pneumopathie virale (complications), Pneumopathie virale (diagnostic), Pneumopathie virale (thérapie), Service hospitalier d'urgences (MeSH), Sujet âgé (MeSH), Techniques de laboratoire clinique (MeSH), Études rétrospectives (MeSH).
- MESH :
- diagnostic : Infections à coronavirus, Pneumopathie virale.
- méthodes : Appréciation des risques.
- thérapie : Infections à coronavirus, Insuffisance respiratoire, Pneumopathie virale.
- virologie : Insuffisance respiratoire.
- complications : Adolescent, Adulte, Adulte d'âge moyen, Betacoronavirus, Femelle, Humains, Indice de gravité de la maladie, Infections à coronavirus, Jeune adulte, Mâle, Oxygénothérapie, Pandémies, Pneumopathie virale, Service hospitalier d'urgences, Sujet âgé, Techniques de laboratoire clinique, Études rétrospectives.
English descriptors
- KwdEn :
- Adolescent (MeSH), Adult (MeSH), Aged (MeSH), Betacoronavirus (MeSH), Clinical Laboratory Techniques (MeSH), Coronavirus Infections (complications), Coronavirus Infections (diagnosis), Coronavirus Infections (therapy), Emergency Service, Hospital (MeSH), Female (MeSH), Humans (MeSH), Male (MeSH), Middle Aged (MeSH), Oxygen Inhalation Therapy (MeSH), Pandemics (MeSH), Pneumonia, Viral (complications), Pneumonia, Viral (diagnosis), Pneumonia, Viral (therapy), Respiratory Insufficiency (therapy), Respiratory Insufficiency (virology), Retrospective Studies (MeSH), Risk Assessment (methods), Severity of Illness Index (MeSH), Young Adult (MeSH).
- MESH :
- complications : Coronavirus Infections, Pneumonia, Viral.
- diagnosis : Coronavirus Infections, Pneumonia, Viral.
- methods : Risk Assessment.
- therapy : Coronavirus Infections, Pneumonia, Viral, Respiratory Insufficiency.
- virology : Respiratory Insufficiency.
- Adolescent, Adult, Aged, Betacoronavirus, Clinical Laboratory Techniques, Emergency Service, Hospital, Female, Humans, Male, Middle Aged, Oxygen Inhalation Therapy, Pandemics, Retrospective Studies, Severity of Illness Index, Young Adult.
Abstract
STUDY OBJECTIVE
The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19).
METHODS
This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score.
RESULTS
During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort.
CONCLUSION
A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system.
DOI: 10.1016/j.annemergmed.2020.07.022
PubMed: 33012378
PubMed Central: PMC7373004
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent (MeSH)</term>
<term>Adult (MeSH)</term>
<term>Aged (MeSH)</term>
<term>Betacoronavirus (MeSH)</term>
<term>Clinical Laboratory Techniques (MeSH)</term>
<term>Coronavirus Infections (complications)</term>
<term>Coronavirus Infections (diagnosis)</term>
<term>Coronavirus Infections (therapy)</term>
<term>Emergency Service, Hospital (MeSH)</term>
<term>Female (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Male (MeSH)</term>
<term>Middle Aged (MeSH)</term>
<term>Oxygen Inhalation Therapy (MeSH)</term>
<term>Pandemics (MeSH)</term>
<term>Pneumonia, Viral (complications)</term>
<term>Pneumonia, Viral (diagnosis)</term>
<term>Pneumonia, Viral (therapy)</term>
<term>Respiratory Insufficiency (therapy)</term>
<term>Respiratory Insufficiency (virology)</term>
<term>Retrospective Studies (MeSH)</term>
<term>Risk Assessment (methods)</term>
<term>Severity of Illness Index (MeSH)</term>
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<term>Adulte (MeSH)</term>
<term>Adulte d'âge moyen (MeSH)</term>
<term>Appréciation des risques (méthodes)</term>
<term>Betacoronavirus (MeSH)</term>
<term>Femelle (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Indice de gravité de la maladie (MeSH)</term>
<term>Infections à coronavirus (complications)</term>
<term>Infections à coronavirus (diagnostic)</term>
<term>Infections à coronavirus (thérapie)</term>
<term>Insuffisance respiratoire (thérapie)</term>
<term>Insuffisance respiratoire (virologie)</term>
<term>Jeune adulte (MeSH)</term>
<term>Mâle (MeSH)</term>
<term>Oxygénothérapie (MeSH)</term>
<term>Pandémies (MeSH)</term>
<term>Pneumopathie virale (complications)</term>
<term>Pneumopathie virale (diagnostic)</term>
<term>Pneumopathie virale (thérapie)</term>
<term>Service hospitalier d'urgences (MeSH)</term>
<term>Sujet âgé (MeSH)</term>
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<term>Études rétrospectives (MeSH)</term>
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<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
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<term>Pneumopathie virale</term>
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<keywords scheme="MESH" qualifier="therapy" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
<term>Respiratory Insufficiency</term>
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<keywords scheme="MESH" qualifier="thérapie" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Insuffisance respiratoire</term>
<term>Pneumopathie virale</term>
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<term>Aged</term>
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<term>Emergency Service, Hospital</term>
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<term>Humans</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Oxygen Inhalation Therapy</term>
<term>Pandemics</term>
<term>Retrospective Studies</term>
<term>Severity of Illness Index</term>
<term>Young Adult</term>
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<keywords scheme="MESH" qualifier="complications" xml:lang="fr"><term>Adolescent</term>
<term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Betacoronavirus</term>
<term>Femelle</term>
<term>Humains</term>
<term>Indice de gravité de la maladie</term>
<term>Infections à coronavirus</term>
<term>Jeune adulte</term>
<term>Mâle</term>
<term>Oxygénothérapie</term>
<term>Pandémies</term>
<term>Pneumopathie virale</term>
<term>Service hospitalier d'urgences</term>
<term>Sujet âgé</term>
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<front><div type="abstract" xml:lang="en"><p><b>STUDY OBJECTIVE</b>
</p>
<p>The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19).</p>
</div>
<div type="abstract" xml:lang="en"><p><b>METHODS</b>
</p>
<p>This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSION</b>
</p>
<p>A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system.</p>
</div>
</front>
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<ELocationID EIdType="doi" ValidYN="Y">10.1016/j.annemergmed.2020.07.022</ELocationID>
<Abstract><AbstractText Label="STUDY OBJECTIVE">The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19).</AbstractText>
<AbstractText Label="METHODS">This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score.</AbstractText>
<AbstractText Label="RESULTS">During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort.</AbstractText>
<AbstractText Label="CONCLUSION">A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system.</AbstractText>
<CopyrightInformation>Copyright © 2020 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Haimovich</LastName>
<ForeName>Adrian D</ForeName>
<Initials>AD</Initials>
<AffiliationInfo><Affiliation>Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Ravindra</LastName>
<ForeName>Neal G</ForeName>
<Initials>NG</Initials>
<AffiliationInfo><Affiliation>Department of Internal Medicine, Section of Cardiovascular Medicine, Yale University School of Medicine, New Haven, CT; Department of Computer Science, Yale University, New Haven, CT.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Stoytchev</LastName>
<ForeName>Stoytcho</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Young</LastName>
<ForeName>H Patrick</ForeName>
<Initials>HP</Initials>
<AffiliationInfo><Affiliation>Department of Internal Medicine, Yale University School of Medicine, New Haven, CT; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Wilson</LastName>
<ForeName>Francis P</ForeName>
<Initials>FP</Initials>
<AffiliationInfo><Affiliation>Department of Internal Medicine, Yale University School of Medicine, New Haven, CT; Clinical and Translational Research Accelerator, Department of Medicine, Yale University School of Medicine, New Haven, CT.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>van Dijk</LastName>
<ForeName>David</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Department of Internal Medicine, Section of Cardiovascular Medicine, Yale University School of Medicine, New Haven, CT; Department of Computer Science, Yale University, New Haven, CT.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Schulz</LastName>
<ForeName>Wade L</ForeName>
<Initials>WL</Initials>
<AffiliationInfo><Affiliation>Center for Medical Informatics, Yale University School of Medicine, New Haven, CT; Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Taylor</LastName>
<ForeName>R Andrew</ForeName>
<Initials>RA</Initials>
<AffiliationInfo><Affiliation>Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT; Center for Medical Informatics, Yale University School of Medicine, New Haven, CT. Electronic address: richard.taylor@yale.edu.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<GrantList CompleteYN="Y"><Grant><GrantID>P30 DK079310</GrantID>
<Acronym>DK</Acronym>
<Agency>NIDDK NIH HHS</Agency>
<Country>United States</Country>
</Grant>
<Grant><GrantID>R01 DK113191</GrantID>
<Acronym>DK</Acronym>
<Agency>NIDDK NIH HHS</Agency>
<Country>United States</Country>
</Grant>
</GrantList>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D064888">Observational Study</PublicationType>
<PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
<PublicationType UI="D023361">Validation Study</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2020</Year>
<Month>07</Month>
<Day>21</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>United States</Country>
<MedlineTA>Ann Emerg Med</MedlineTA>
<NlmUniqueID>8002646</NlmUniqueID>
<ISSNLinking>0196-0644</ISSNLinking>
</MedlineJournalInfo>
<SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName>
<SupplMeshName Type="Protocol" UI="C000657964">COVID-19 diagnostic testing</SupplMeshName>
<SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName>
</SupplMeshList>
<CitationSubset>AIM</CitationSubset>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName>
</MeshHeading>
<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="D019411" MajorTopicYN="N">Clinical Laboratory Techniques</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName>
<QualifierName UI="Q000150" MajorTopicYN="Y">complications</QualifierName>
<QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName>
<QualifierName UI="Q000628" MajorTopicYN="N">therapy</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D004636" MajorTopicYN="Y">Emergency Service, Hospital</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D010102" MajorTopicYN="N">Oxygen Inhalation Therapy</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName>
<QualifierName UI="Q000150" MajorTopicYN="Y">complications</QualifierName>
<QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName>
<QualifierName UI="Q000628" MajorTopicYN="N">therapy</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012131" MajorTopicYN="N">Respiratory Insufficiency</DescriptorName>
<QualifierName UI="Q000628" MajorTopicYN="N">therapy</QualifierName>
<QualifierName UI="Q000821" MajorTopicYN="Y">virology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012189" MajorTopicYN="N">Retrospective Studies</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D018570" MajorTopicYN="N">Risk Assessment</DescriptorName>
<QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012720" MajorTopicYN="Y">Severity of Illness Index</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName>
</MeshHeading>
</MeshHeadingList>
</MedlineCitation>
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<Month>05</Month>
<Day>23</Day>
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<PubMedPubDate PubStatus="revised"><Year>2020</Year>
<Month>07</Month>
<Day>02</Day>
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<PubMedPubDate PubStatus="accepted"><Year>2020</Year>
<Month>07</Month>
<Day>13</Day>
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<PubMedPubDate PubStatus="entrez"><Year>2020</Year>
<Month>10</Month>
<Day>5</Day>
<Hour>5</Hour>
<Minute>31</Minute>
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<Day>6</Day>
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<affiliations><list><country><li>États-Unis</li>
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