A SARS-CoV-2 Prediction Model from Standard Laboratory Tests.
Identifieur interne : 000851 ( Main/Exploration ); précédent : 000850; suivant : 000852A SARS-CoV-2 Prediction Model from Standard Laboratory Tests.
Auteurs : Vafa Bayat [États-Unis] ; Steven Phelps [États-Unis] ; Russell Ryono [États-Unis] ; Chong Lee [États-Unis] ; Hemal Parekh [États-Unis] ; Joel Mewton [États-Unis] ; Farshid Sedghi [États-Unis] ; Payam Etminani [États-Unis] ; Mark Holodniy [États-Unis]Source :
- Clinical infectious diseases : an official publication of the Infectious Diseases Society of America [ 1537-6591 ] ; 2020.
Abstract
BACKGROUND
With the limited availability of testing for the presence of the SARS-CoV-2 virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation between certain laboratory tests and diagnosis suggest an alternative method based on an ensemble of tests.
METHODS
We have trained a machine learning model to analyze the correlation between SARS-CoV-2 test results and 20 routine laboratory tests collected within a 2-day period around the SARS-CoV-2 test date. We used the model to compare SARS-CoV-2 positive and negative patients.
RESULTS
In a cohort of 75,991 veteran inpatients and outpatients who tested for SARS-CoV-2 in the months of March through July, 2020, 7,335 of whom were positive by RT-PCR or antigen testing, and who had at least 15 of 20 lab results within the window period, our model predicted the results of the SARS-CoV-2 test with a specificity of 86.8%, a sensitivity of 82.4%, and an overall accuracy of 86.4% (with a 95% confidence interval of [86.0%, 86.9%]).
CONCLUSIONS
While molecular-based and antibody tests remain the reference standard method for confirming a SARS-CoV-2 diagnosis, their clinical sensitivity is not well known. The model described herein may provide a complementary method of determining SARS-CoV-2 infection status, based on a fully independent set of indicators, that can help confirm results from other tests as well as identify positive cases missed by molecular testing.
DOI: 10.1093/cid/ciaa1175
PubMed: 32785701
PubMed Central: PMC7454351
Affiliations:
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Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Ryono, Russell" sort="Ryono, Russell" uniqKey="Ryono R" first="Russell" last="Ryono">Russell Ryono</name><affiliation wicri:level="2"><nlm:affiliation>Director of Clinical Applications, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Director of Clinical Applications, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Lee, Chong" sort="Lee, Chong" uniqKey="Lee C" first="Chong" last="Lee">Chong Lee</name><affiliation wicri:level="2"><nlm:affiliation>Data Science Division, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Data Science Division, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Parekh, Hemal" sort="Parekh, Hemal" uniqKey="Parekh H" first="Hemal" last="Parekh">Hemal Parekh</name><affiliation wicri:level="2"><nlm:affiliation>Data Science Division, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Data Science Division, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Mewton, Joel" sort="Mewton, Joel" uniqKey="Mewton J" first="Joel" last="Mewton">Joel Mewton</name><affiliation wicri:level="2"><nlm:affiliation>Chief Technology Officer, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Chief Technology Officer, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Sedghi, Farshid" sort="Sedghi, Farshid" uniqKey="Sedghi F" first="Farshid" last="Sedghi">Farshid Sedghi</name><affiliation wicri:level="2"><nlm:affiliation>Managing Partner and Founder, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Managing Partner and Founder, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Etminani, Payam" sort="Etminani, Payam" uniqKey="Etminani P" first="Payam" last="Etminani">Payam Etminani</name><affiliation wicri:level="2"><nlm:affiliation>Managing Partner and Founder, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Managing Partner and Founder, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Holodniy, Mark" sort="Holodniy, Mark" uniqKey="Holodniy M" first="Mark" last="Holodniy">Mark Holodniy</name><affiliation wicri:level="2"><nlm:affiliation>Public Health Surveillance and Research, Department of Veterans Affairs, Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Public Health Surveillance and Research, Department of Veterans Affairs, Palo Alto</wicri:cityArea></affiliation><affiliation wicri:level="2"><nlm:affiliation>VHA Public Health Reference Laboratory, Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>VHA Public Health Reference Laboratory, Palo Alto</wicri:cityArea></affiliation><affiliation wicri:level="2"><nlm:affiliation>Division of Infectious Disease & Geographic Medicine, Stanford University School of Medicine, Stanford, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Division of Infectious Disease & 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wicri:level="2"><nlm:affiliation>Program Head, Research and Development, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Program Head, Research and Development, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Phelps, Steven" sort="Phelps, Steven" uniqKey="Phelps S" first="Steven" last="Phelps">Steven Phelps</name><affiliation wicri:level="2"><nlm:affiliation>Director of Data Sciences, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Director of Data Sciences, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Ryono, Russell" sort="Ryono, Russell" uniqKey="Ryono R" first="Russell" last="Ryono">Russell Ryono</name><affiliation wicri:level="2"><nlm:affiliation>Director of Clinical Applications, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Director of Clinical Applications, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Lee, Chong" sort="Lee, Chong" uniqKey="Lee C" first="Chong" last="Lee">Chong Lee</name><affiliation wicri:level="2"><nlm:affiliation>Data Science Division, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Data Science Division, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Parekh, Hemal" sort="Parekh, Hemal" uniqKey="Parekh H" first="Hemal" last="Parekh">Hemal Parekh</name><affiliation wicri:level="2"><nlm:affiliation>Data Science Division, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Data Science Division, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Mewton, Joel" sort="Mewton, Joel" uniqKey="Mewton J" first="Joel" last="Mewton">Joel Mewton</name><affiliation wicri:level="2"><nlm:affiliation>Chief Technology Officer, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Chief Technology Officer, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Sedghi, Farshid" sort="Sedghi, Farshid" uniqKey="Sedghi F" first="Farshid" last="Sedghi">Farshid Sedghi</name><affiliation wicri:level="2"><nlm:affiliation>Managing Partner and Founder, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Managing Partner and Founder, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Etminani, Payam" sort="Etminani, Payam" uniqKey="Etminani P" first="Payam" last="Etminani">Payam Etminani</name><affiliation wicri:level="2"><nlm:affiliation>Managing Partner and Founder, Bitscopic Inc., Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Managing Partner and Founder, Bitscopic Inc., Palo Alto</wicri:cityArea></affiliation></author><author><name sortKey="Holodniy, Mark" sort="Holodniy, Mark" uniqKey="Holodniy M" first="Mark" last="Holodniy">Mark Holodniy</name><affiliation wicri:level="2"><nlm:affiliation>Public Health Surveillance and Research, Department of Veterans Affairs, Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Public Health Surveillance and Research, Department of Veterans Affairs, Palo Alto</wicri:cityArea></affiliation><affiliation wicri:level="2"><nlm:affiliation>VHA Public Health Reference Laboratory, Palo Alto, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>VHA Public Health Reference Laboratory, Palo Alto</wicri:cityArea></affiliation><affiliation wicri:level="2"><nlm:affiliation>Division of Infectious Disease & Geographic Medicine, Stanford University School of Medicine, Stanford, California.</nlm:affiliation><country>États-Unis</country><placeName><region type="state">Californie</region></placeName><wicri:cityArea>Division of Infectious Disease & Geographic Medicine, Stanford University School of Medicine, Stanford</wicri:cityArea></affiliation></author></analytic><series><title level="j">Clinical infectious diseases : an official publication of the Infectious Diseases Society of America</title><idno type="eISSN">1537-6591</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>With the limited availability of testing for the presence of the SARS-CoV-2 virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation between certain laboratory tests and diagnosis suggest an alternative method based on an ensemble of tests.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>We have trained a machine learning model to analyze the correlation between SARS-CoV-2 test results and 20 routine laboratory tests collected within a 2-day period around the SARS-CoV-2 test date. We used the model to compare SARS-CoV-2 positive and negative patients.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>In a cohort of 75,991 veteran inpatients and outpatients who tested for SARS-CoV-2 in the months of March through July, 2020, 7,335 of whom were positive by RT-PCR or antigen testing, and who had at least 15 of 20 lab results within the window period, our model predicted the results of the SARS-CoV-2 test with a specificity of 86.8%, a sensitivity of 82.4%, and an overall accuracy of 86.4% (with a 95% confidence interval of [86.0%, 86.9%]).</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>While molecular-based and antibody tests remain the reference standard method for confirming a SARS-CoV-2 diagnosis, their clinical sensitivity is not well known. The model described herein may provide a complementary method of determining SARS-CoV-2 infection status, based on a fully independent set of indicators, that can help confirm results from other tests as well as identify positive cases missed by molecular testing.</p></div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">32785701</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>01</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1537-6591</ISSN><JournalIssue CitedMedium="Internet"><PubDate><Year>2020</Year><Month>Aug</Month><Day>12</Day></PubDate></JournalIssue><Title>Clinical infectious diseases : an official publication of the Infectious Diseases Society of America</Title><ISOAbbreviation>Clin Infect Dis</ISOAbbreviation></Journal><ArticleTitle>A SARS-CoV-2 Prediction Model from Standard Laboratory Tests.</ArticleTitle><ELocationID EIdType="pii" ValidYN="Y">ciaa1175</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1093/cid/ciaa1175</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">With the limited availability of testing for the presence of the SARS-CoV-2 virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation between certain laboratory tests and diagnosis suggest an alternative method based on an ensemble of tests.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">We have trained a machine learning model to analyze the correlation between SARS-CoV-2 test results and 20 routine laboratory tests collected within a 2-day period around the SARS-CoV-2 test date. We used the model to compare SARS-CoV-2 positive and negative patients.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">In a cohort of 75,991 veteran inpatients and outpatients who tested for SARS-CoV-2 in the months of March through July, 2020, 7,335 of whom were positive by RT-PCR or antigen testing, and who had at least 15 of 20 lab results within the window period, our model predicted the results of the SARS-CoV-2 test with a specificity of 86.8%, a sensitivity of 82.4%, and an overall accuracy of 86.4% (with a 95% confidence interval of [86.0%, 86.9%]).</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">While molecular-based and antibody tests remain the reference standard method for confirming a SARS-CoV-2 diagnosis, their clinical sensitivity is not well known. The model described herein may provide a complementary method of determining SARS-CoV-2 infection status, based on a fully independent set of indicators, that can help confirm results from other tests as well as identify positive cases missed by molecular testing.</AbstractText><CopyrightInformation>© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Bayat</LastName><ForeName>Vafa</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Program Head, Research and Development, Bitscopic Inc., Palo Alto, California.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Phelps</LastName><ForeName>Steven</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Director of Data Sciences, Bitscopic Inc., Palo Alto, California.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ryono</LastName><ForeName>Russell</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Director of Clinical Applications, Bitscopic Inc., Palo Alto, California.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lee</LastName><ForeName>Chong</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Data Science Division, Bitscopic Inc., Palo Alto, California.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Parekh</LastName><ForeName>Hemal</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Data Science Division, Bitscopic Inc., Palo Alto, California.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mewton</LastName><ForeName>Joel</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Chief Technology Officer, Bitscopic Inc., Palo Alto, California.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sedghi</LastName><ForeName>Farshid</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Managing Partner and Founder, Bitscopic Inc., Palo Alto, California.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Etminani</LastName><ForeName>Payam</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Managing Partner and Founder, Bitscopic Inc., Palo Alto, California.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Holodniy</LastName><ForeName>Mark</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Public Health Surveillance and Research, Department of Veterans Affairs, Palo Alto, California.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>VHA Public Health Reference Laboratory, Palo Alto, California.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Division of Infectious Disease & Geographic Medicine, Stanford University School of Medicine, Stanford, California.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>08</Month><Day>12</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Clin Infect 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uniqKey="Holodniy M" first="Mark" last="Holodniy">Mark Holodniy</name><name sortKey="Holodniy, Mark" sort="Holodniy, Mark" uniqKey="Holodniy M" first="Mark" last="Holodniy">Mark Holodniy</name><name sortKey="Lee, Chong" sort="Lee, Chong" uniqKey="Lee C" first="Chong" last="Lee">Chong Lee</name><name sortKey="Mewton, Joel" sort="Mewton, Joel" uniqKey="Mewton J" first="Joel" last="Mewton">Joel Mewton</name><name sortKey="Parekh, Hemal" sort="Parekh, Hemal" uniqKey="Parekh H" first="Hemal" last="Parekh">Hemal Parekh</name><name sortKey="Phelps, Steven" sort="Phelps, Steven" uniqKey="Phelps S" first="Steven" last="Phelps">Steven Phelps</name><name sortKey="Ryono, Russell" sort="Ryono, Russell" uniqKey="Ryono R" first="Russell" last="Ryono">Russell Ryono</name><name sortKey="Sedghi, Farshid" sort="Sedghi, Farshid" uniqKey="Sedghi F" first="Farshid" last="Sedghi">Farshid Sedghi</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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