Value of dynamic clinical and biomarker data for mortality risk prediction in COVID-19: a multicentre retrospective cohort study.
Identifieur interne : 000113 ( Main/Corpus ); précédent : 000112; suivant : 000114Value of dynamic clinical and biomarker data for mortality risk prediction in COVID-19: a multicentre retrospective cohort study.
Auteurs : Carlo Berzuini ; Cathal Hannan ; Andrew King ; Andy Vail ; Claire O'Leary ; David Brough ; James Galea ; Kayode Ogungbenro ; Megan Wright ; Omar Pathmanaban ; Sharon Hulme ; Stuart Allan ; Luisa Bernardinelli ; Hiren C. PatelSource :
- BMJ open [ 2044-6055 ] ; 2020.
English descriptors
- KwdEn :
- Aged (MeSH), Aged, 80 and over (MeSH), Area Under Curve (MeSH), Betacoronavirus (MeSH), Bilirubin (blood), Biomarkers (blood), C-Reactive Protein (metabolism), Cohort Studies (MeSH), Coronavirus Infections (blood), Coronavirus Infections (mortality), Creatinine (blood), Female (MeSH), Hospitalization (MeSH), Humans (MeSH), Leukocyte Count (MeSH), Lymphocyte Count (MeSH), Male (MeSH), Middle Aged (MeSH), Neutrophils (MeSH), Pandemics (MeSH), Pneumonia, Viral (blood), Pneumonia, Viral (mortality), Prognosis (MeSH), ROC Curve (MeSH), Retrospective Studies (MeSH), Risk Assessment (MeSH), United Kingdom (MeSH), Urea (blood).
- MESH :
- chemical , blood : Bilirubin, Biomarkers, Creatinine, Urea.
- chemical , metabolism : C-Reactive Protein.
- geographic : United Kingdom.
- blood : Coronavirus Infections, Pneumonia, Viral.
- mortality : Coronavirus Infections, Pneumonia, Viral.
- Aged, Aged, 80 and over, Area Under Curve, Betacoronavirus, Cohort Studies, Female, Hospitalization, Humans, Leukocyte Count, Lymphocyte Count, Male, Middle Aged, Neutrophils, Pandemics, Prognosis, ROC Curve, Retrospective Studies, Risk Assessment.
Abstract
OBJECTIVES
Being able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying 'state-of-the-art' statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19.
DESIGN
The data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to 5 days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework.
SETTING
3 secondary and tertiary level centres in Greater Manchester, the UK.
PARTICIPANTS
392 hospitalised patients with a diagnosis of COVID-19.
RESULTS
392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome.
CONCLUSIONS
This study shows that clinical prediction models to predict death in hospitalised patients with COVID-19 can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.
DOI: 10.1136/bmjopen-2020-041983
PubMed: 32967887
PubMed Central: PMC7513423
Links to Exploration step
pubmed:32967887Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Value of dynamic clinical and biomarker data for mortality risk prediction in COVID-19: a multicentre retrospective cohort study.</title><author><name sortKey="Berzuini, Carlo" sort="Berzuini, Carlo" uniqKey="Berzuini C" first="Carlo" last="Berzuini">Carlo Berzuini</name><affiliation><nlm:affiliation>Centre for Biostatistics, The University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Hannan, Cathal" sort="Hannan, Cathal" uniqKey="Hannan C" first="Cathal" last="Hannan">Cathal Hannan</name><affiliation><nlm:affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK.</nlm:affiliation></affiliation></author><author><name sortKey="King, Andrew" sort="King, Andrew" uniqKey="King A" first="Andrew" last="King">Andrew King</name><affiliation><nlm:affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Vail, Andy" sort="Vail, Andy" uniqKey="Vail A" first="Andy" last="Vail">Andy Vail</name><affiliation><nlm:affiliation>Centre for Biostatistics, The University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="O Leary, Claire" sort="O Leary, Claire" uniqKey="O Leary C" first="Claire" last="O'Leary">Claire O'Leary</name><affiliation><nlm:affiliation>Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Brough, David" sort="Brough, David" uniqKey="Brough D" first="David" last="Brough">David Brough</name><affiliation><nlm:affiliation>Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Galea, James" sort="Galea, James" uniqKey="Galea J" first="James" last="Galea">James Galea</name><affiliation><nlm:affiliation>Cardiff and Vale University Health Board, Cardiff, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Ogungbenro, Kayode" sort="Ogungbenro, Kayode" uniqKey="Ogungbenro K" first="Kayode" last="Ogungbenro">Kayode Ogungbenro</name><affiliation><nlm:affiliation>Department of Pharmacy and Optometry, The University of Manchester, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Wright, Megan" sort="Wright, Megan" uniqKey="Wright M" first="Megan" last="Wright">Megan Wright</name><affiliation><nlm:affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Pathmanaban, Omar" sort="Pathmanaban, Omar" uniqKey="Pathmanaban O" first="Omar" last="Pathmanaban">Omar Pathmanaban</name><affiliation><nlm:affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Hulme, Sharon" sort="Hulme, Sharon" uniqKey="Hulme S" first="Sharon" last="Hulme">Sharon Hulme</name><affiliation><nlm:affiliation>Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Allan, Stuart" sort="Allan, Stuart" uniqKey="Allan S" first="Stuart" last="Allan">Stuart Allan</name><affiliation><nlm:affiliation>Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Bernardinelli, Luisa" sort="Bernardinelli, Luisa" uniqKey="Bernardinelli L" first="Luisa" last="Bernardinelli">Luisa Bernardinelli</name><affiliation><nlm:affiliation>Department of Brain and Behavioural Sciences, The University of Pavia, Pavia, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Patel, Hiren C" sort="Patel, Hiren C" uniqKey="Patel H" first="Hiren C" last="Patel">Hiren C. Patel</name><affiliation><nlm:affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK hiren.Patel@srft.nhs.uk.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32967887</idno><idno type="pmid">32967887</idno><idno type="doi">10.1136/bmjopen-2020-041983</idno><idno type="pmc">PMC7513423</idno><idno type="wicri:Area/Main/Corpus">000113</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000113</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Value of dynamic clinical and biomarker data for mortality risk prediction in COVID-19: a multicentre retrospective cohort study.</title><author><name sortKey="Berzuini, Carlo" sort="Berzuini, Carlo" uniqKey="Berzuini C" first="Carlo" last="Berzuini">Carlo Berzuini</name><affiliation><nlm:affiliation>Centre for Biostatistics, The University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Hannan, Cathal" sort="Hannan, Cathal" uniqKey="Hannan C" first="Cathal" last="Hannan">Cathal Hannan</name><affiliation><nlm:affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK.</nlm:affiliation></affiliation></author><author><name sortKey="King, Andrew" sort="King, Andrew" uniqKey="King A" first="Andrew" last="King">Andrew King</name><affiliation><nlm:affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Vail, Andy" sort="Vail, Andy" uniqKey="Vail A" first="Andy" last="Vail">Andy Vail</name><affiliation><nlm:affiliation>Centre for Biostatistics, The University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="O Leary, Claire" sort="O Leary, Claire" uniqKey="O Leary C" first="Claire" last="O'Leary">Claire O'Leary</name><affiliation><nlm:affiliation>Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Brough, David" sort="Brough, David" uniqKey="Brough D" first="David" last="Brough">David Brough</name><affiliation><nlm:affiliation>Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Galea, James" sort="Galea, James" uniqKey="Galea J" first="James" last="Galea">James Galea</name><affiliation><nlm:affiliation>Cardiff and Vale University Health Board, Cardiff, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Ogungbenro, Kayode" sort="Ogungbenro, Kayode" uniqKey="Ogungbenro K" first="Kayode" last="Ogungbenro">Kayode Ogungbenro</name><affiliation><nlm:affiliation>Department of Pharmacy and Optometry, The University of Manchester, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Wright, Megan" sort="Wright, Megan" uniqKey="Wright M" first="Megan" last="Wright">Megan Wright</name><affiliation><nlm:affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Pathmanaban, Omar" sort="Pathmanaban, Omar" uniqKey="Pathmanaban O" first="Omar" last="Pathmanaban">Omar Pathmanaban</name><affiliation><nlm:affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Hulme, Sharon" sort="Hulme, Sharon" uniqKey="Hulme S" first="Sharon" last="Hulme">Sharon Hulme</name><affiliation><nlm:affiliation>Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Allan, Stuart" sort="Allan, Stuart" uniqKey="Allan S" first="Stuart" last="Allan">Stuart Allan</name><affiliation><nlm:affiliation>Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Bernardinelli, Luisa" sort="Bernardinelli, Luisa" uniqKey="Bernardinelli L" first="Luisa" last="Bernardinelli">Luisa Bernardinelli</name><affiliation><nlm:affiliation>Department of Brain and Behavioural Sciences, The University of Pavia, Pavia, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Patel, Hiren C" sort="Patel, Hiren C" uniqKey="Patel H" first="Hiren C" last="Patel">Hiren C. Patel</name><affiliation><nlm:affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK hiren.Patel@srft.nhs.uk.</nlm:affiliation></affiliation></author></analytic><series><title level="j">BMJ open</title><idno type="eISSN">2044-6055</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Aged (MeSH)</term><term>Aged, 80 and over (MeSH)</term><term>Area Under Curve (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Bilirubin (blood)</term><term>Biomarkers (blood)</term><term>C-Reactive Protein (metabolism)</term><term>Cohort Studies (MeSH)</term><term>Coronavirus Infections (blood)</term><term>Coronavirus Infections (mortality)</term><term>Creatinine (blood)</term><term>Female (MeSH)</term><term>Hospitalization (MeSH)</term><term>Humans (MeSH)</term><term>Leukocyte Count (MeSH)</term><term>Lymphocyte Count (MeSH)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Neutrophils (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (blood)</term><term>Pneumonia, Viral (mortality)</term><term>Prognosis (MeSH)</term><term>ROC Curve (MeSH)</term><term>Retrospective Studies (MeSH)</term><term>Risk Assessment (MeSH)</term><term>United Kingdom (MeSH)</term><term>Urea (blood)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="blood" xml:lang="en"><term>Bilirubin</term><term>Biomarkers</term><term>Creatinine</term><term>Urea</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>C-Reactive Protein</term></keywords><keywords scheme="MESH" type="geographic" xml:lang="en"><term>United Kingdom</term></keywords><keywords scheme="MESH" qualifier="blood" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Aged</term><term>Aged, 80 and over</term><term>Area Under Curve</term><term>Betacoronavirus</term><term>Cohort Studies</term><term>Female</term><term>Hospitalization</term><term>Humans</term><term>Leukocyte Count</term><term>Lymphocyte Count</term><term>Male</term><term>Middle Aged</term><term>Neutrophils</term><term>Pandemics</term><term>Prognosis</term><term>ROC Curve</term><term>Retrospective Studies</term><term>Risk Assessment</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>OBJECTIVES</b></p><p>Being able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying 'state-of-the-art' statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19.</p></div><div type="abstract" xml:lang="en"><p><b>DESIGN</b></p><p>The data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to 5 days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework.</p></div><div type="abstract" xml:lang="en"><p><b>SETTING</b></p><p>3 secondary and tertiary level centres in Greater Manchester, the UK.</p></div><div type="abstract" xml:lang="en"><p><b>PARTICIPANTS</b></p><p>392 hospitalised patients with a diagnosis of COVID-19.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>This study shows that clinical prediction models to predict death in hospitalised patients with COVID-19 can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32967887</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"><Journal><ISSN IssnType="Electronic">2044-6055</ISSN><JournalIssue CitedMedium="Internet"><Volume>10</Volume><Issue>9</Issue><PubDate><Year>2020</Year><Month>09</Month><Day>23</Day></PubDate></JournalIssue><Title>BMJ open</Title><ISOAbbreviation>BMJ Open</ISOAbbreviation></Journal><ArticleTitle>Value of dynamic clinical and biomarker data for mortality risk prediction in COVID-19: a multicentre retrospective cohort study.</ArticleTitle><Pagination><MedlinePgn>e041983</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1136/bmjopen-2020-041983</ELocationID><Abstract><AbstractText Label="OBJECTIVES">Being able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying 'state-of-the-art' statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19.</AbstractText><AbstractText Label="DESIGN">The data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to 5 days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework.</AbstractText><AbstractText Label="SETTING">3 secondary and tertiary level centres in Greater Manchester, the UK.</AbstractText><AbstractText Label="PARTICIPANTS">392 hospitalised patients with a diagnosis of COVID-19.</AbstractText><AbstractText Label="RESULTS">392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome.</AbstractText><AbstractText Label="CONCLUSIONS">This study shows that clinical prediction models to predict death in hospitalised patients with COVID-19 can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.</AbstractText><CopyrightInformation>© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Berzuini</LastName><ForeName>Carlo</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Centre for Biostatistics, The University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hannan</LastName><ForeName>Cathal</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>King</LastName><ForeName>Andrew</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Vail</LastName><ForeName>Andy</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Centre for Biostatistics, The University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>O'Leary</LastName><ForeName>Claire</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Brough</LastName><ForeName>David</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Galea</LastName><ForeName>James</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Cardiff and Vale University Health Board, Cardiff, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ogungbenro</LastName><ForeName>Kayode</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>Department of Pharmacy and Optometry, The University of Manchester, Manchester, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wright</LastName><ForeName>Megan</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pathmanaban</LastName><ForeName>Omar</ForeName><Initials>O</Initials><AffiliationInfo><Affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hulme</LastName><ForeName>Sharon</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Allan</LastName><ForeName>Stuart</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bernardinelli</LastName><ForeName>Luisa</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of Brain and Behavioural Sciences, The University of Pavia, Pavia, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Patel</LastName><ForeName>Hiren C</ForeName><Initials>HC</Initials><Identifier Source="ORCID">0000-0002-1439-8801</Identifier><AffiliationInfo><Affiliation>Manchester Centre for Clinical Neurosciences, Salford Royal Hospitals NHS Trust, Salford, UK hiren.Patel@srft.nhs.uk.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D016448">Multicenter Study</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>09</Month><Day>23</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMJ Open</MedlineTA><NlmUniqueID>101552874</NlmUniqueID><ISSNLinking>2044-6055</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D015415">Biomarkers</NameOfSubstance></Chemical><Chemical><RegistryNumber>8W8T17847W</RegistryNumber><NameOfSubstance UI="D014508">Urea</NameOfSubstance></Chemical><Chemical><RegistryNumber>9007-41-4</RegistryNumber><NameOfSubstance UI="D002097">C-Reactive Protein</NameOfSubstance></Chemical><Chemical><RegistryNumber>AYI8EX34EU</RegistryNumber><NameOfSubstance UI="D003404">Creatinine</NameOfSubstance></Chemical><Chemical><RegistryNumber>RFM9X3LJ49</RegistryNumber><NameOfSubstance UI="D001663">Bilirubin</NameOfSubstance></Chemical></ChemicalList><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="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019540" MajorTopicYN="N">Area Under Curve</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001663" MajorTopicYN="N">Bilirubin</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="Y">blood</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015415" MajorTopicYN="N">Biomarkers</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="N">blood</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002097" MajorTopicYN="N">C-Reactive Protein</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="Y">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015331" MajorTopicYN="N">Cohort Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="N">blood</QualifierName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003404" MajorTopicYN="N">Creatinine</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="Y">blood</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006760" MajorTopicYN="N">Hospitalization</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007958" MajorTopicYN="N">Leukocyte Count</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018655" MajorTopicYN="Y">Lymphocyte Count</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009504" MajorTopicYN="Y">Neutrophils</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="N">blood</QualifierName><QualifierName UI="Q000401" MajorTopicYN="Y">mortality</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011379" MajorTopicYN="N">Prognosis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012372" MajorTopicYN="N">ROC Curve</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012189" MajorTopicYN="N">Retrospective Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018570" MajorTopicYN="N">Risk Assessment</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006113" MajorTopicYN="N" Type="Geographic">United Kingdom</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014508" MajorTopicYN="N">Urea</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="Y">blood</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">intensive & critical care</Keyword><Keyword MajorTopicYN="Y">respiratory infections</Keyword><Keyword MajorTopicYN="Y">statistics & research methods</Keyword></KeywordList><CoiStatement>Competing interests: Swedish Orphan Biovitrum have provided investigational medicinal product for public-funded, peer-reviewed trials on which AK, AV, JG, HCP and SH are coinvestigators. The other authors declare no competing interests.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>9</Month><Day>24</Day><Hour>5</Hour><Minute>27</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>9</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>10</Month><Day>6</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32967887</ArticleId><ArticleId IdType="pii">bmjopen-2020-041983</ArticleId><ArticleId IdType="doi">10.1136/bmjopen-2020-041983</ArticleId><ArticleId IdType="pmc">PMC7513423</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>Biostatistics. 2014 Oct;15(4):706-18</Citation><ArticleIdList><ArticleId IdType="pubmed">24831103</ArticleId></ArticleIdList></Reference><Reference><Citation>N Engl J Med. 2020 May 21;382(21):2049-2055</Citation><ArticleIdList><ArticleId IdType="pubmed">32202722</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 2020 Mar 28;395(10229):1054-1062</Citation><ArticleIdList><ArticleId IdType="pubmed">32171076</ArticleId></ArticleIdList></Reference><Reference><Citation>Biostatistics. 2008 Jul;9(3):432-41</Citation><ArticleIdList><ArticleId IdType="pubmed">18079126</ArticleId></ArticleIdList></Reference><Reference><Citation>Intensive Care Med. 2020 May;46(5):846-848</Citation><ArticleIdList><ArticleId IdType="pubmed">32125452</ArticleId></ArticleIdList></Reference><Reference><Citation>Kidney Int. 2020 May;97(5):829-838</Citation><ArticleIdList><ArticleId IdType="pubmed">32247631</ArticleId></ArticleIdList></Reference><Reference><Citation>BMJ. 2020 Apr 7;369:m1328</Citation><ArticleIdList><ArticleId IdType="pubmed">32265220</ArticleId></ArticleIdList></Reference><Reference><Citation>Kidney Int. 2020 Jul;98(1):219-227</Citation><ArticleIdList><ArticleId IdType="pubmed">32327202</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 2020 Feb 15;395(10223):497-506</Citation><ArticleIdList><ArticleId IdType="pubmed">31986264</ArticleId></ArticleIdList></Reference><Reference><Citation>Stat Med. 2012 Jan 30;31(2):143-54</Citation><ArticleIdList><ArticleId IdType="pubmed">22081439</ArticleId></ArticleIdList></Reference><Reference><Citation>J Allergy Clin Immunol. 2020 Jul;146(1):89-100</Citation><ArticleIdList><ArticleId IdType="pubmed">32407836</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet Rheumatol. 2020 Jul;2(7):e379-e381</Citation><ArticleIdList><ArticleId IdType="pubmed">32835233</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Methods Psychiatr Res. 2011 Mar;20(1):40-9</Citation><ArticleIdList><ArticleId IdType="pubmed">21499542</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Med Sci. 2020 May 18;17(9):1281-1292</Citation><ArticleIdList><ArticleId IdType="pubmed">32547323</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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