CovidSeniorV1, Main, Exploration, bibRecord, 000341

Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.

Identifieur interne : 000341 ( Main/Exploration ); précédent : 000340; suivant : 000342

Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.

Auteurs : Hoyt Burdick [États-Unis] ; Carson Lam [États-Unis] ; Samson Mataraso [États-Unis] ; Anna Siefkas [États-Unis] ; Gregory Braden [États-Unis] ; R Phillip Dellinger [États-Unis] ; Andrea Mccoy [États-Unis] ; Jean-Louis Vincent [Belgique] ; Abigail Green-Saxena [États-Unis] ; Gina Barnes [États-Unis] ; Jana Hoffman [États-Unis] ; Jacob Calvert [États-Unis] ; Emily Pellegrini [États-Unis] ; Ritankar Das [États-Unis]

Source :

Computers in biology and medicine [ 1879-0534 ] ; 2020.

RBID : pubmed:32798922

Descripteurs français

KwdFr :
- Adulte (MeSH), Adulte d'âge moyen (MeSH), Algorithmes (MeSH), Apprentissage machine (MeSH), Betacoronavirus (MeSH), Biologie informatique (MeSH), Femelle (MeSH), Humains (MeSH), Infections à coronavirus (diagnostic), Infections à coronavirus (physiopathologie), Infections à coronavirus (thérapie), Infections à coronavirus (traitement médicamenteux), Insuffisance respiratoire (diagnostic), Insuffisance respiratoire (physiopathologie), Insuffisance respiratoire (thérapie), Mâle (MeSH), Pandémies (MeSH), Pneumopathie virale (diagnostic), Pneumopathie virale (physiopathologie), Pneumopathie virale (thérapie), Pronostic (MeSH), Sensibilité et spécificité (MeSH), Sujet âgé (MeSH), Sujet âgé de 80 ans ou plus (MeSH), Techniques de laboratoire clinique (méthodes), Techniques de laboratoire clinique (statistiques et données numériques), Triage (méthodes), Triage (statistiques et données numériques), Ventilation artificielle (MeSH), États-Unis (épidémiologie), Études prospectives (MeSH).
MESH :
- diagnostic : Infections à coronavirus, Insuffisance respiratoire, Pneumopathie virale.
- méthodes : Techniques de laboratoire clinique, Triage.
- physiopathologie : Infections à coronavirus, Insuffisance respiratoire, Pneumopathie virale.
- statistiques et données numériques : Techniques de laboratoire clinique, Triage.
- thérapie : Infections à coronavirus, Insuffisance respiratoire, Pneumopathie virale.
- traitement médicamenteux : Infections à coronavirus.
- épidémiologie : États-Unis.
- Adulte, Adulte d'âge moyen, Algorithmes, Apprentissage machine, Betacoronavirus, Biologie informatique, Femelle, Humains, Mâle, Pandémies, Pronostic, Sensibilité et spécificité, Sujet âgé, Sujet âgé de 80 ans ou plus, Ventilation artificielle, Études prospectives.
Wicri :
- geographic : États-Unis.

English descriptors

KwdEn :
- Adult (MeSH), Aged (MeSH), Aged, 80 and over (MeSH), Algorithms (MeSH), Betacoronavirus (MeSH), Clinical Laboratory Techniques (methods), Clinical Laboratory Techniques (statistics & numerical data), Computational Biology (MeSH), Coronavirus Infections (diagnosis), Coronavirus Infections (drug therapy), Coronavirus Infections (physiopathology), Coronavirus Infections (therapy), Female (MeSH), Humans (MeSH), Machine Learning (MeSH), Male (MeSH), Middle Aged (MeSH), Pandemics (MeSH), Pneumonia, Viral (diagnosis), Pneumonia, Viral (physiopathology), Pneumonia, Viral (therapy), Prognosis (MeSH), Prospective Studies (MeSH), Respiration, Artificial (MeSH), Respiratory Insufficiency (diagnosis), Respiratory Insufficiency (physiopathology), Respiratory Insufficiency (therapy), Sensitivity and Specificity (MeSH), Triage (methods), Triage (statistics & numerical data), United States (epidemiology).
MESH :
- geographic , epidemiology : United States.
- diagnosis : Coronavirus Infections, Pneumonia, Viral, Respiratory Insufficiency.
- drug therapy : Coronavirus Infections.
- methods : Clinical Laboratory Techniques, Triage.
- physiopathology : Coronavirus Infections, Pneumonia, Viral, Respiratory Insufficiency.
- statistics & numerical data : Clinical Laboratory Techniques, Triage.
- therapy : Coronavirus Infections, Pneumonia, Viral, Respiratory Insufficiency.
- Adult, Aged, Aged, 80 and over, Algorithms, Betacoronavirus, Computational Biology, Female, Humans, Machine Learning, Male, Middle Aged, Pandemics, Prognosis, Prospective Studies, Respiration, Artificial, Sensitivity and Specificity.

Abstract

BACKGROUND

Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks.

METHODS

In a multicenter clinical trial, we evaluated the performance of a machine learning algorithm for prediction of invasive mechanical ventilation of COVID-19 patients within 24 h of an initial encounter. We enrolled patients with a COVID-19 diagnosis who were admitted to five United States health systems between March 24 and May 4, 2020.

RESULTS

197 patients were enrolled in the REspirAtory Decompensation and model for the triage of covid-19 patients: a prospective studY (READY) clinical trial. The algorithm had a higher diagnostic odds ratio (DOR, 12.58) for predicting ventilation than a comparator early warning system, the Modified Early Warning Score (MEWS). The algorithm also achieved significantly higher sensitivity (0.90) than MEWS, which achieved a sensitivity of 0.78, while maintaining a higher specificity (p < 0.05).

CONCLUSIONS

In the first clinical trial of a machine learning algorithm for ventilation needs among COVID-19 patients, the algorithm demonstrated accurate prediction of the need for mechanical ventilation within 24 h. This algorithm may help care teams effectively triage patients and allocate resources. Further, the algorithm is capable of accurately identifying 16% more patients than a widely used scoring system while minimizing false positive results.

DOI: 10.1016/j.compbiomed.2020.103949
PubMed: 32798922
PubMed Central: PMC7410013

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000629
to stream Main, to step Curation: 000629

Le document en format XML

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<author><name sortKey="Green Saxena, Abigail" sort="Green Saxena, Abigail" uniqKey="Green Saxena A" first="Abigail" last="Green-Saxena">Abigail Green-Saxena</name>
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<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Dascena, Inc., San Francisco, CA</wicri:regionArea>
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<author><name sortKey="Hoffman, Jana" sort="Hoffman, Jana" uniqKey="Hoffman J" first="Jana" last="Hoffman">Jana Hoffman</name>
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<author><name sortKey="Calvert, Jacob" sort="Calvert, Jacob" uniqKey="Calvert J" first="Jacob" last="Calvert">Jacob Calvert</name>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term>
<term>Aged (MeSH)</term>
<term>Aged, 80 and over (MeSH)</term>
<term>Algorithms (MeSH)</term>
<term>Betacoronavirus (MeSH)</term>
<term>Clinical Laboratory Techniques (methods)</term>
<term>Clinical Laboratory Techniques (statistics & numerical data)</term>
<term>Computational Biology (MeSH)</term>
<term>Coronavirus Infections (diagnosis)</term>
<term>Coronavirus Infections (drug therapy)</term>
<term>Coronavirus Infections (physiopathology)</term>
<term>Coronavirus Infections (therapy)</term>
<term>Female (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Machine Learning (MeSH)</term>
<term>Male (MeSH)</term>
<term>Middle Aged (MeSH)</term>
<term>Pandemics (MeSH)</term>
<term>Pneumonia, Viral (diagnosis)</term>
<term>Pneumonia, Viral (physiopathology)</term>
<term>Pneumonia, Viral (therapy)</term>
<term>Prognosis (MeSH)</term>
<term>Prospective Studies (MeSH)</term>
<term>Respiration, Artificial (MeSH)</term>
<term>Respiratory Insufficiency (diagnosis)</term>
<term>Respiratory Insufficiency (physiopathology)</term>
<term>Respiratory Insufficiency (therapy)</term>
<term>Sensitivity and Specificity (MeSH)</term>
<term>Triage (methods)</term>
<term>Triage (statistics & numerical data)</term>
<term>United States (epidemiology)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term>
<term>Adulte d'âge moyen (MeSH)</term>
<term>Algorithmes (MeSH)</term>
<term>Apprentissage machine (MeSH)</term>
<term>Betacoronavirus (MeSH)</term>
<term>Biologie informatique (MeSH)</term>
<term>Femelle (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Infections à coronavirus (diagnostic)</term>
<term>Infections à coronavirus (physiopathologie)</term>
<term>Infections à coronavirus (thérapie)</term>
<term>Infections à coronavirus (traitement médicamenteux)</term>
<term>Insuffisance respiratoire (diagnostic)</term>
<term>Insuffisance respiratoire (physiopathologie)</term>
<term>Insuffisance respiratoire (thérapie)</term>
<term>Mâle (MeSH)</term>
<term>Pandémies (MeSH)</term>
<term>Pneumopathie virale (diagnostic)</term>
<term>Pneumopathie virale (physiopathologie)</term>
<term>Pneumopathie virale (thérapie)</term>
<term>Pronostic (MeSH)</term>
<term>Sensibilité et spécificité (MeSH)</term>
<term>Sujet âgé (MeSH)</term>
<term>Sujet âgé de 80 ans ou plus (MeSH)</term>
<term>Techniques de laboratoire clinique (méthodes)</term>
<term>Techniques de laboratoire clinique (statistiques et données numériques)</term>
<term>Triage (méthodes)</term>
<term>Triage (statistiques et données numériques)</term>
<term>Ventilation artificielle (MeSH)</term>
<term>États-Unis (épidémiologie)</term>
<term>Études prospectives (MeSH)</term>
</keywords>
<keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>United States</term>
</keywords>
<keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
<term>Respiratory Insufficiency</term>
</keywords>
<keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Insuffisance respiratoire</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="drug therapy" xml:lang="en"><term>Coronavirus Infections</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Clinical Laboratory Techniques</term>
<term>Triage</term>
</keywords>
<keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Techniques de laboratoire clinique</term>
<term>Triage</term>
</keywords>
<keywords scheme="MESH" qualifier="physiopathologie" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Insuffisance respiratoire</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
<term>Respiratory Insufficiency</term>
</keywords>
<keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Clinical Laboratory Techniques</term>
<term>Triage</term>
</keywords>
<keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Techniques de laboratoire clinique</term>
<term>Triage</term>
</keywords>
<keywords scheme="MESH" qualifier="therapy" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
<term>Respiratory Insufficiency</term>
</keywords>
<keywords scheme="MESH" qualifier="thérapie" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Insuffisance respiratoire</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="traitement médicamenteux" xml:lang="fr"><term>Infections à coronavirus</term>
</keywords>
<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>États-Unis</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Adult</term>
<term>Aged</term>
<term>Aged, 80 and over</term>
<term>Algorithms</term>
<term>Betacoronavirus</term>
<term>Computational Biology</term>
<term>Female</term>
<term>Humans</term>
<term>Machine Learning</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Pandemics</term>
<term>Prognosis</term>
<term>Prospective Studies</term>
<term>Respiration, Artificial</term>
<term>Sensitivity and Specificity</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Algorithmes</term>
<term>Apprentissage machine</term>
<term>Betacoronavirus</term>
<term>Biologie informatique</term>
<term>Femelle</term>
<term>Humains</term>
<term>Mâle</term>
<term>Pandémies</term>
<term>Pronostic</term>
<term>Sensibilité et spécificité</term>
<term>Sujet âgé</term>
<term>Sujet âgé de 80 ans ou plus</term>
<term>Ventilation artificielle</term>
<term>Études prospectives</term>
</keywords>
<keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>États-Unis</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b>
</p>
<p>Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>METHODS</b>
</p>
<p>In a multicenter clinical trial, we evaluated the performance of a machine learning algorithm for prediction of invasive mechanical ventilation of COVID-19 patients within 24 h of an initial encounter. We enrolled patients with a COVID-19 diagnosis who were admitted to five United States health systems between March 24 and May 4, 2020.</p>
</div>
<div type="abstract" xml:lang="en"><p><b>RESULTS</b>
</p>
<p>197 patients were enrolled in the REspirAtory Decompensation and model for the triage of covid-19 patients: a prospective studY (READY) clinical trial. The algorithm had a higher diagnostic odds ratio (DOR, 12.58) for predicting ventilation than a comparator early warning system, the Modified Early Warning Score (MEWS). The algorithm also achieved significantly higher sensitivity (0.90) than MEWS, which achieved a sensitivity of 0.78, while maintaining a higher specificity (p < 0.05).</p>
</div>
<div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b>
</p>
<p>In the first clinical trial of a machine learning algorithm for ventilation needs among COVID-19 patients, the algorithm demonstrated accurate prediction of the need for mechanical ventilation within 24 h. This algorithm may help care teams effectively triage patients and allocate resources. Further, the algorithm is capable of accurately identifying 16% more patients than a widely used scoring system while minimizing false positive results.</p>
</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32798922</PMID>
<DateCompleted><Year>2020</Year>
<Month>09</Month>
<Day>24</Day>
</DateCompleted>
<DateRevised><Year>2020</Year>
<Month>09</Month>
<Day>24</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1879-0534</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>124</Volume>
<PubDate><Year>2020</Year>
<Month>09</Month>
</PubDate>
</JournalIssue>
<Title>Computers in biology and medicine</Title>
<ISOAbbreviation>Comput Biol Med</ISOAbbreviation>
</Journal>
<ArticleTitle>Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.</ArticleTitle>
<Pagination><MedlinePgn>103949</MedlinePgn>
</Pagination>
<ELocationID EIdType="pii" ValidYN="Y">S0010-4825(20)30284-5</ELocationID>
<ELocationID EIdType="doi" ValidYN="Y">10.1016/j.compbiomed.2020.103949</ELocationID>
<Abstract><AbstractText Label="BACKGROUND">Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks.</AbstractText>
<AbstractText Label="METHODS">In a multicenter clinical trial, we evaluated the performance of a machine learning algorithm for prediction of invasive mechanical ventilation of COVID-19 patients within 24 h of an initial encounter. We enrolled patients with a COVID-19 diagnosis who were admitted to five United States health systems between March 24 and May 4, 2020.</AbstractText>
<AbstractText Label="RESULTS">197 patients were enrolled in the REspirAtory Decompensation and model for the triage of covid-19 patients: a prospective studY (READY) clinical trial. The algorithm had a higher diagnostic odds ratio (DOR, 12.58) for predicting ventilation than a comparator early warning system, the Modified Early Warning Score (MEWS). The algorithm also achieved significantly higher sensitivity (0.90) than MEWS, which achieved a sensitivity of 0.78, while maintaining a higher specificity (p < 0.05).</AbstractText>
<AbstractText Label="CONCLUSIONS">In the first clinical trial of a machine learning algorithm for ventilation needs among COVID-19 patients, the algorithm demonstrated accurate prediction of the need for mechanical ventilation within 24 h. This algorithm may help care teams effectively triage patients and allocate resources. Further, the algorithm is capable of accurately identifying 16% more patients than a widely used scoring system while minimizing false positive results.</AbstractText>
<CopyrightInformation>Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Burdick</LastName>
<ForeName>Hoyt</ForeName>
<Initials>H</Initials>
<AffiliationInfo><Affiliation>Cabell Huntington Hospital, Huntington, WV, USA; Marshall University School of Medicine, Huntington, WV, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Lam</LastName>
<ForeName>Carson</ForeName>
<Initials>C</Initials>
<AffiliationInfo><Affiliation>Dascena, Inc., San Francisco, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Mataraso</LastName>
<ForeName>Samson</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Dascena, Inc., San Francisco, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Siefkas</LastName>
<ForeName>Anna</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Dascena, Inc., San Francisco, CA, USA. Electronic address: anna@dascena.com.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Braden</LastName>
<ForeName>Gregory</ForeName>
<Initials>G</Initials>
<AffiliationInfo><Affiliation>Kidney Care and Transplant Associates of New England, Springfield, MA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Dellinger</LastName>
<ForeName>R Phillip</ForeName>
<Initials>RP</Initials>
<AffiliationInfo><Affiliation>Division of Critical Care Medicine, Cooper University Hospital/Cooper Medical School of Rowan University, Camden, NJ, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>McCoy</LastName>
<ForeName>Andrea</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Cape Regional Medical Center, Cape May Court House, NJ, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Vincent</LastName>
<ForeName>Jean-Louis</ForeName>
<Initials>JL</Initials>
<AffiliationInfo><Affiliation>Department of Intensive Care, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Green-Saxena</LastName>
<ForeName>Abigail</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Dascena, Inc., San Francisco, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Barnes</LastName>
<ForeName>Gina</ForeName>
<Initials>G</Initials>
<AffiliationInfo><Affiliation>Dascena, Inc., San Francisco, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Hoffman</LastName>
<ForeName>Jana</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>Dascena, Inc., San Francisco, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Calvert</LastName>
<ForeName>Jacob</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>Dascena, Inc., San Francisco, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Pellegrini</LastName>
<ForeName>Emily</ForeName>
<Initials>E</Initials>
<AffiliationInfo><Affiliation>Dascena, Inc., San Francisco, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Das</LastName>
<ForeName>Ritankar</ForeName>
<Initials>R</Initials>
<AffiliationInfo><Affiliation>Dascena, Inc., San Francisco, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016430">Clinical Trial</PublicationType>
<PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D016448">Multicenter Study</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2020</Year>
<Month>08</Month>
<Day>06</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>United States</Country>
<MedlineTA>Comput Biol Med</MedlineTA>
<NlmUniqueID>1250250</NlmUniqueID>
<ISSNLinking>0010-4825</ISSNLinking>
</MedlineJournalInfo>
<SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName>
<SupplMeshName Type="Protocol" UI="C000657964">COVID-19 diagnostic testing</SupplMeshName>
<SupplMeshName Type="Protocol" UI="C000705127">COVID-19 drug treatment</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="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="Y">Betacoronavirus</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D019411" MajorTopicYN="N">Clinical Laboratory Techniques</DescriptorName>
<QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName>
<QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName>
<QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName>
<QualifierName UI="Q000188" MajorTopicYN="N">drug therapy</QualifierName>
<QualifierName UI="Q000503" MajorTopicYN="Y">physiopathology</QualifierName>
<QualifierName UI="Q000628" MajorTopicYN="N">therapy</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="Y">Machine Learning</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="Q000175" MajorTopicYN="Y">diagnosis</QualifierName>
<QualifierName UI="Q000503" MajorTopicYN="Y">physiopathology</QualifierName>
<QualifierName UI="Q000628" MajorTopicYN="N">therapy</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011379" MajorTopicYN="N">Prognosis</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D011446" MajorTopicYN="N">Prospective Studies</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012121" MajorTopicYN="N">Respiration, Artificial</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012131" MajorTopicYN="N">Respiratory Insufficiency</DescriptorName>
<QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName>
<QualifierName UI="Q000503" MajorTopicYN="Y">physiopathology</QualifierName>
<QualifierName UI="Q000628" MajorTopicYN="N">therapy</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012680" MajorTopicYN="N">Sensitivity and Specificity</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D014218" MajorTopicYN="N">Triage</DescriptorName>
<QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName>
<QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D014481" MajorTopicYN="N" Type="Geographic">United States</DescriptorName>
<QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName>
</MeshHeading>
</MeshHeadingList>
<KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">COVID-19</Keyword>
<Keyword MajorTopicYN="Y">Machine learning</Keyword>
<Keyword MajorTopicYN="Y">Mechanical ventilation</Keyword>
<Keyword MajorTopicYN="Y">Prediction</Keyword>
</KeywordList>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year>
<Month>06</Month>
<Day>15</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="revised"><Year>2020</Year>
<Month>07</Month>
<Day>28</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="accepted"><Year>2020</Year>
<Month>07</Month>
<Day>28</Day>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed"><Year>2020</Year>
<Month>8</Month>
<Day>18</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline"><Year>2020</Year>
<Month>9</Month>
<Day>25</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="entrez"><Year>2020</Year>
<Month>8</Month>
<Day>18</Day>
<Hour>6</Hour>
<Minute>0</Minute>
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</History>
<PublicationStatus>ppublish</PublicationStatus>
<ArticleIdList><ArticleId IdType="pubmed">32798922</ArticleId>
<ArticleId IdType="pii">S0010-4825(20)30284-5</ArticleId>
<ArticleId IdType="doi">10.1016/j.compbiomed.2020.103949</ArticleId>
<ArticleId IdType="pmc">PMC7410013</ArticleId>
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</pubmed>
<affiliations><list><country><li>Belgique</li>
<li>États-Unis</li>
</country>
<region><li>Californie</li>
<li>Massachusetts</li>
<li>New Jersey</li>
<li>Région de Bruxelles-Capitale</li>
<li>Virginie-Occidentale</li>
</region>
<settlement><li>Bruxelles</li>
</settlement>
</list>
<tree><country name="États-Unis"><region name="Virginie-Occidentale"><name sortKey="Burdick, Hoyt" sort="Burdick, Hoyt" uniqKey="Burdick H" first="Hoyt" last="Burdick">Hoyt Burdick</name>
</region>
<name sortKey="Barnes, Gina" sort="Barnes, Gina" uniqKey="Barnes G" first="Gina" last="Barnes">Gina Barnes</name>
<name sortKey="Braden, Gregory" sort="Braden, Gregory" uniqKey="Braden G" first="Gregory" last="Braden">Gregory Braden</name>
<name sortKey="Calvert, Jacob" sort="Calvert, Jacob" uniqKey="Calvert J" first="Jacob" last="Calvert">Jacob Calvert</name>
<name sortKey="Das, Ritankar" sort="Das, Ritankar" uniqKey="Das R" first="Ritankar" last="Das">Ritankar Das</name>
<name sortKey="Dellinger, R Phillip" sort="Dellinger, R Phillip" uniqKey="Dellinger R" first="R Phillip" last="Dellinger">R Phillip Dellinger</name>
<name sortKey="Green Saxena, Abigail" sort="Green Saxena, Abigail" uniqKey="Green Saxena A" first="Abigail" last="Green-Saxena">Abigail Green-Saxena</name>
<name sortKey="Hoffman, Jana" sort="Hoffman, Jana" uniqKey="Hoffman J" first="Jana" last="Hoffman">Jana Hoffman</name>
<name sortKey="Lam, Carson" sort="Lam, Carson" uniqKey="Lam C" first="Carson" last="Lam">Carson Lam</name>
<name sortKey="Mataraso, Samson" sort="Mataraso, Samson" uniqKey="Mataraso S" first="Samson" last="Mataraso">Samson Mataraso</name>
<name sortKey="Mccoy, Andrea" sort="Mccoy, Andrea" uniqKey="Mccoy A" first="Andrea" last="Mccoy">Andrea Mccoy</name>
<name sortKey="Pellegrini, Emily" sort="Pellegrini, Emily" uniqKey="Pellegrini E" first="Emily" last="Pellegrini">Emily Pellegrini</name>
<name sortKey="Siefkas, Anna" sort="Siefkas, Anna" uniqKey="Siefkas A" first="Anna" last="Siefkas">Anna Siefkas</name>
</country>
<country name="Belgique"><region name="Région de Bruxelles-Capitale"><name sortKey="Vincent, Jean Louis" sort="Vincent, Jean Louis" uniqKey="Vincent J" first="Jean-Louis" last="Vincent">Jean-Louis Vincent</name>
</region>
</country>
</tree>
</affiliations>
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Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.

Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial.

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