Comparison of International Classification of Diseases and Related Health Problems, Tenth Revision Codes With Electronic Medical Records Among Patients With Symptoms of Coronavirus Disease 2019.
Identifieur interne : 000725 ( Main/Exploration ); précédent : 000724; suivant : 000726Comparison of International Classification of Diseases and Related Health Problems, Tenth Revision Codes With Electronic Medical Records Among Patients With Symptoms of Coronavirus Disease 2019.
Auteurs : Brendan T. Crabb ; Ann Lyons ; Margaret Bale ; Valerie Martin ; Ben Berger ; Sara Mann ; William B. West ; Alyssa Brown ; Jordan B. Peacock ; Daniel T. Leung ; Rashmee U. ShahSource :
- JAMA network open [ 2574-3805 ] ; 2020.
Descripteurs français
- KwdFr :
- Adulte (MeSH), Adulte d'âge moyen (MeSH), Betacoronavirus (MeSH), Classification internationale des maladies (MeSH), Codage clinique (méthodes), Codage clinique (normes), Dossiers médicaux électroniques (MeSH), Dyspnée (diagnostic), Dyspnée (étiologie), Femelle (MeSH), Fièvre (diagnostic), Fièvre (étiologie), Humains (MeSH), Infections à coronavirus (complications), Infections à coronavirus (diagnostic), Infections à coronavirus (virologie), Infections à coronavirus (épidémiologie), Mâle (MeSH), Pandémies (MeSH), Pneumopathie virale (complications), Pneumopathie virale (diagnostic), Pneumopathie virale (virologie), Pneumopathie virale (épidémiologie), Reproductibilité des résultats (MeSH), Réaction de polymérisation en chaîne (MeSH), Sensibilité et spécificité (MeSH), Sujet âgé (MeSH), Toux (diagnostic), Toux (étiologie), Utah (épidémiologie), Études de cohortes (MeSH).
- MESH :
- diagnostic : Dyspnée, Fièvre, Infections à coronavirus, Pneumopathie virale, Toux.
- méthodes : Codage clinique.
- normes : Codage clinique.
- virologie : Infections à coronavirus, Pneumopathie virale.
- épidémiologie : Infections à coronavirus, Pneumopathie virale, Utah.
- étiologie : Dyspnée, Fièvre, Toux.
- Adulte, Adulte d'âge moyen, Betacoronavirus, Classification internationale des maladies, Dossiers médicaux électroniques, Femelle, Humains, Mâle, Pandémies, Reproductibilité des résultats, Réaction de polymérisation en chaîne, Sensibilité et spécificité, Sujet âgé, Études de cohortes.
English descriptors
- KwdEn :
- Adult (MeSH), Aged (MeSH), Betacoronavirus (MeSH), Clinical Coding (methods), Clinical Coding (standards), Cohort Studies (MeSH), Coronavirus Infections (complications), Coronavirus Infections (diagnosis), Coronavirus Infections (epidemiology), Coronavirus Infections (virology), Cough (diagnosis), Cough (etiology), Dyspnea (diagnosis), Dyspnea (etiology), Electronic Health Records (MeSH), Female (MeSH), Fever (diagnosis), Fever (etiology), Humans (MeSH), International Classification of Diseases (MeSH), Male (MeSH), Middle Aged (MeSH), Pandemics (MeSH), Pneumonia, Viral (complications), Pneumonia, Viral (diagnosis), Pneumonia, Viral (epidemiology), Pneumonia, Viral (virology), Polymerase Chain Reaction (MeSH), Reproducibility of Results (MeSH), Sensitivity and Specificity (MeSH), Utah (epidemiology).
- MESH :
- geographic , epidemiology : Utah.
- complications : Coronavirus Infections, Pneumonia, Viral.
- diagnosis : Coronavirus Infections, Cough, Dyspnea, Fever, Pneumonia, Viral.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- etiology : Cough, Dyspnea, Fever.
- methods : Clinical Coding.
- standards : Clinical Coding.
- virology : Coronavirus Infections, Pneumonia, Viral.
- Adult, Aged, Betacoronavirus, Cohort Studies, Electronic Health Records, Female, Humans, International Classification of Diseases, Male, Middle Aged, Pandemics, Polymerase Chain Reaction, Reproducibility of Results, Sensitivity and Specificity.
Abstract
Importance
International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes are used to characterize coronavirus disease 2019 (COVID-19)-related symptoms. Their accuracy is unknown, which could affect downstream analyses.
Objective
To compare the performance of fever-, cough-, and dyspnea-specific ICD-10 codes with medical record review among patients tested for COVID-19.
Design, Setting, and Participants
This cohort study included patients who underwent quantitative reverse transcriptase-polymerase chain reaction testing for severe acute respiratory syndrome coronavirus 2 at University of Utah Health from March 10 to April 6, 2020. Data analysis was performed in April 2020.
Main Outcomes and Measures
The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ICD-10 codes for fever (R50*), cough (R05*), and dyspnea (R06.0*) were compared with manual medical record review. Performance was calculated overall and stratified by COVID-19 test result, sex, age group (<50, 50-64, and >64 years), and inpatient status. Bootstrapping was used to generate 95% CIs, and Pearson χ2 tests were used to compare different subgroups.
Results
Among 2201 patients tested for COVD-19, the mean (SD) age was 42 (17) years; 1201 (55%) were female, 1569 (71%) were White, and 282 (13%) were Hispanic or Latino. The prevalence of fever was 66% (1444 patients), that of cough was 88% (1930 patients), and that of dyspnea was 64% (1399 patients). For fever, the sensitivity of ICD-10 codes was 0.26 (95% CI, 0.24-0.29), specificity was 0.98 (95% CI, 0.96-0.99), PPV was 0.96 (95% CI, 0.93-0.97), and NPV was 0.41 (95% CI, 0.39-0.43). For cough, the sensitivity of ICD-10 codes was 0.44 (95% CI, 0.42-0.46), specificity was 0.88 (95% CI, 0.84-0.92), PPV was 0.96 (95% CI, 0.95-0.97), and NPV was 0.18 (95% CI, 0.16-0.20). For dyspnea, the sensitivity of ICD-10 codes was 0.24 (95% CI, 0.22-0.26), specificity was 0.97 (95% CI, 0.96-0.98), PPV was 0.93 (95% CI, 0.90-0.96), and NPV was 0.42 (95% CI, 0.40-0.44). ICD-10 code performance was better for inpatients than for outpatients for fever (χ2 = 41.30; P < .001) and dyspnea (χ2 = 14.25; P = .003) but not for cough (χ2 = 5.13; P = .16).
Conclusions and Relevance
These findings suggest that ICD-10 codes lack sensitivity and have poor NPV for symptoms associated with COVID-19. This inaccuracy has implications for any downstream data model, scientific discovery, or surveillance that relies on these codes.
DOI: 10.1001/jamanetworkopen.2020.17703
PubMed: 32797176
PubMed Central: PMC7428802
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Crabb</name><affiliation><nlm:affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author><author><name sortKey="Lyons, Ann" sort="Lyons, Ann" uniqKey="Lyons A" first="Ann" last="Lyons">Ann Lyons</name><affiliation><nlm:affiliation>Data Science Services, University of Utah Health Sciences Center, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author><author><name sortKey="Bale, Margaret" sort="Bale, Margaret" uniqKey="Bale M" first="Margaret" last="Bale">Margaret Bale</name><affiliation><nlm:affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author><author><name sortKey="Martin, Valerie" sort="Martin, Valerie" uniqKey="Martin V" first="Valerie" last="Martin">Valerie Martin</name><affiliation><nlm:affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author><author><name sortKey="Berger, Ben" sort="Berger, Ben" uniqKey="Berger B" first="Ben" last="Berger">Ben Berger</name><affiliation><nlm:affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author><author><name sortKey="Mann, Sara" sort="Mann, Sara" uniqKey="Mann S" first="Sara" last="Mann">Sara Mann</name><affiliation><nlm:affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author><author><name sortKey="West, William B" sort="West, William B" uniqKey="West W" first="William B" last="West">William B. 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Crabb</name><affiliation><nlm:affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author><author><name sortKey="Lyons, Ann" sort="Lyons, Ann" uniqKey="Lyons A" first="Ann" last="Lyons">Ann Lyons</name><affiliation><nlm:affiliation>Data Science Services, University of Utah Health Sciences Center, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author><author><name sortKey="Bale, Margaret" sort="Bale, Margaret" uniqKey="Bale M" first="Margaret" last="Bale">Margaret Bale</name><affiliation><nlm:affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author><author><name sortKey="Martin, Valerie" sort="Martin, Valerie" uniqKey="Martin V" first="Valerie" last="Martin">Valerie Martin</name><affiliation><nlm:affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author><author><name sortKey="Berger, Ben" sort="Berger, Ben" uniqKey="Berger B" first="Ben" last="Berger">Ben Berger</name><affiliation><nlm:affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author><author><name sortKey="Mann, Sara" sort="Mann, Sara" uniqKey="Mann S" first="Sara" last="Mann">Sara Mann</name><affiliation><nlm:affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author><author><name sortKey="West, William B" sort="West, William B" uniqKey="West W" first="William B" last="West">William B. 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Shah</name><affiliation><nlm:affiliation>Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City.</nlm:affiliation><wicri:noCountry code="subField">Salt Lake City</wicri:noCountry></affiliation></author></analytic><series><title level="j">JAMA network open</title><idno type="eISSN">2574-3805</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term><term>Aged (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Clinical Coding (methods)</term><term>Clinical Coding (standards)</term><term>Cohort Studies (MeSH)</term><term>Coronavirus Infections (complications)</term><term>Coronavirus Infections (diagnosis)</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (virology)</term><term>Cough (diagnosis)</term><term>Cough (etiology)</term><term>Dyspnea (diagnosis)</term><term>Dyspnea (etiology)</term><term>Electronic Health Records (MeSH)</term><term>Female (MeSH)</term><term>Fever (diagnosis)</term><term>Fever (etiology)</term><term>Humans (MeSH)</term><term>International Classification of Diseases (MeSH)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (complications)</term><term>Pneumonia, Viral (diagnosis)</term><term>Pneumonia, Viral (epidemiology)</term><term>Pneumonia, Viral (virology)</term><term>Polymerase Chain Reaction (MeSH)</term><term>Reproducibility of Results (MeSH)</term><term>Sensitivity and Specificity (MeSH)</term><term>Utah (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Classification internationale des maladies (MeSH)</term><term>Codage clinique (méthodes)</term><term>Codage clinique (normes)</term><term>Dossiers médicaux électroniques (MeSH)</term><term>Dyspnée (diagnostic)</term><term>Dyspnée (étiologie)</term><term>Femelle (MeSH)</term><term>Fièvre (diagnostic)</term><term>Fièvre (étiologie)</term><term>Humains (MeSH)</term><term>Infections à coronavirus (complications)</term><term>Infections à coronavirus (diagnostic)</term><term>Infections à coronavirus (virologie)</term><term>Infections à coronavirus (épidémiologie)</term><term>Mâle (MeSH)</term><term>Pandémies (MeSH)</term><term>Pneumopathie virale (complications)</term><term>Pneumopathie virale (diagnostic)</term><term>Pneumopathie virale (virologie)</term><term>Pneumopathie virale (épidémiologie)</term><term>Reproductibilité des résultats (MeSH)</term><term>Réaction de polymérisation en chaîne (MeSH)</term><term>Sensibilité et spécificité (MeSH)</term><term>Sujet âgé (MeSH)</term><term>Toux (diagnostic)</term><term>Toux (étiologie)</term><term>Utah (épidémiologie)</term><term>Études de cohortes (MeSH)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Utah</term></keywords><keywords scheme="MESH" qualifier="complications" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term><term>Cough</term><term>Dyspnea</term><term>Fever</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Dyspnée</term><term>Fièvre</term><term>Infections à coronavirus</term><term>Pneumopathie virale</term><term>Toux</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="etiology" xml:lang="en"><term>Cough</term><term>Dyspnea</term><term>Fever</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Clinical Coding</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Codage clinique</term></keywords><keywords scheme="MESH" qualifier="normes" xml:lang="fr"><term>Codage clinique</term></keywords><keywords scheme="MESH" qualifier="standards" xml:lang="en"><term>Clinical Coding</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term><term>Utah</term></keywords><keywords scheme="MESH" qualifier="étiologie" xml:lang="fr"><term>Dyspnée</term><term>Fièvre</term><term>Toux</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Aged</term><term>Betacoronavirus</term><term>Cohort Studies</term><term>Electronic Health Records</term><term>Female</term><term>Humans</term><term>International Classification of Diseases</term><term>Male</term><term>Middle Aged</term><term>Pandemics</term><term>Polymerase Chain Reaction</term><term>Reproducibility of Results</term><term>Sensitivity and Specificity</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Betacoronavirus</term><term>Classification internationale des maladies</term><term>Dossiers médicaux électroniques</term><term>Femelle</term><term>Humains</term><term>Mâle</term><term>Pandémies</term><term>Reproductibilité des résultats</term><term>Réaction de polymérisation en chaîne</term><term>Sensibilité et spécificité</term><term>Sujet âgé</term><term>Études de cohortes</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>Importance</b></p><p>International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes are used to characterize coronavirus disease 2019 (COVID-19)-related symptoms. Their accuracy is unknown, which could affect downstream analyses.</p></div><div type="abstract" xml:lang="en"><p><b>Objective</b></p><p>To compare the performance of fever-, cough-, and dyspnea-specific ICD-10 codes with medical record review among patients tested for COVID-19.</p></div><div type="abstract" xml:lang="en"><p><b>Design, Setting, and Participants</b></p><p>This cohort study included patients who underwent quantitative reverse transcriptase-polymerase chain reaction testing for severe acute respiratory syndrome coronavirus 2 at University of Utah Health from March 10 to April 6, 2020. Data analysis was performed in April 2020.</p></div><div type="abstract" xml:lang="en"><p><b>Main Outcomes and Measures</b></p><p>The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ICD-10 codes for fever (R50*), cough (R05*), and dyspnea (R06.0*) were compared with manual medical record review. Performance was calculated overall and stratified by COVID-19 test result, sex, age group (<50, 50-64, and >64 years), and inpatient status. Bootstrapping was used to generate 95% CIs, and Pearson χ2 tests were used to compare different subgroups.</p></div><div type="abstract" xml:lang="en"><p><b>Results</b></p><p>Among 2201 patients tested for COVD-19, the mean (SD) age was 42 (17) years; 1201 (55%) were female, 1569 (71%) were White, and 282 (13%) were Hispanic or Latino. The prevalence of fever was 66% (1444 patients), that of cough was 88% (1930 patients), and that of dyspnea was 64% (1399 patients). For fever, the sensitivity of ICD-10 codes was 0.26 (95% CI, 0.24-0.29), specificity was 0.98 (95% CI, 0.96-0.99), PPV was 0.96 (95% CI, 0.93-0.97), and NPV was 0.41 (95% CI, 0.39-0.43). For cough, the sensitivity of ICD-10 codes was 0.44 (95% CI, 0.42-0.46), specificity was 0.88 (95% CI, 0.84-0.92), PPV was 0.96 (95% CI, 0.95-0.97), and NPV was 0.18 (95% CI, 0.16-0.20). For dyspnea, the sensitivity of ICD-10 codes was 0.24 (95% CI, 0.22-0.26), specificity was 0.97 (95% CI, 0.96-0.98), PPV was 0.93 (95% CI, 0.90-0.96), and NPV was 0.42 (95% CI, 0.40-0.44). ICD-10 code performance was better for inpatients than for outpatients for fever (χ2 = 41.30; P < .001) and dyspnea (χ2 = 14.25; P = .003) but not for cough (χ2 = 5.13; P = .16).</p></div><div type="abstract" xml:lang="en"><p><b>Conclusions and Relevance</b></p><p>These findings suggest that ICD-10 codes lack sensitivity and have poor NPV for symptoms associated with COVID-19. This inaccuracy has implications for any downstream data model, scientific discovery, or surveillance that relies on these codes.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32797176</PMID><DateCompleted><Year>2020</Year><Month>08</Month><Day>26</Day></DateCompleted><DateRevised><Year>2020</Year><Month>08</Month><Day>26</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2574-3805</ISSN><JournalIssue CitedMedium="Internet"><Volume>3</Volume><Issue>8</Issue><PubDate><Year>2020</Year><Month>08</Month><Day>03</Day></PubDate></JournalIssue><Title>JAMA network open</Title><ISOAbbreviation>JAMA Netw Open</ISOAbbreviation></Journal><ArticleTitle>Comparison of International Classification of Diseases and Related Health Problems, Tenth Revision Codes With Electronic Medical Records Among Patients With Symptoms of Coronavirus Disease 2019.</ArticleTitle><Pagination><MedlinePgn>e2017703</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1001/jamanetworkopen.2020.17703</ELocationID><Abstract><AbstractText Label="Importance">International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes are used to characterize coronavirus disease 2019 (COVID-19)-related symptoms. Their accuracy is unknown, which could affect downstream analyses.</AbstractText><AbstractText Label="Objective">To compare the performance of fever-, cough-, and dyspnea-specific ICD-10 codes with medical record review among patients tested for COVID-19.</AbstractText><AbstractText Label="Design, Setting, and Participants">This cohort study included patients who underwent quantitative reverse transcriptase-polymerase chain reaction testing for severe acute respiratory syndrome coronavirus 2 at University of Utah Health from March 10 to April 6, 2020. Data analysis was performed in April 2020.</AbstractText><AbstractText Label="Main Outcomes and Measures">The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ICD-10 codes for fever (R50*), cough (R05*), and dyspnea (R06.0*) were compared with manual medical record review. Performance was calculated overall and stratified by COVID-19 test result, sex, age group (<50, 50-64, and >64 years), and inpatient status. Bootstrapping was used to generate 95% CIs, and Pearson χ2 tests were used to compare different subgroups.</AbstractText><AbstractText Label="Results">Among 2201 patients tested for COVD-19, the mean (SD) age was 42 (17) years; 1201 (55%) were female, 1569 (71%) were White, and 282 (13%) were Hispanic or Latino. The prevalence of fever was 66% (1444 patients), that of cough was 88% (1930 patients), and that of dyspnea was 64% (1399 patients). For fever, the sensitivity of ICD-10 codes was 0.26 (95% CI, 0.24-0.29), specificity was 0.98 (95% CI, 0.96-0.99), PPV was 0.96 (95% CI, 0.93-0.97), and NPV was 0.41 (95% CI, 0.39-0.43). For cough, the sensitivity of ICD-10 codes was 0.44 (95% CI, 0.42-0.46), specificity was 0.88 (95% CI, 0.84-0.92), PPV was 0.96 (95% CI, 0.95-0.97), and NPV was 0.18 (95% CI, 0.16-0.20). For dyspnea, the sensitivity of ICD-10 codes was 0.24 (95% CI, 0.22-0.26), specificity was 0.97 (95% CI, 0.96-0.98), PPV was 0.93 (95% CI, 0.90-0.96), and NPV was 0.42 (95% CI, 0.40-0.44). ICD-10 code performance was better for inpatients than for outpatients for fever (χ2 = 41.30; P < .001) and dyspnea (χ2 = 14.25; P = .003) but not for cough (χ2 = 5.13; P = .16).</AbstractText><AbstractText Label="Conclusions and Relevance">These findings suggest that ICD-10 codes lack sensitivity and have poor NPV for symptoms associated with COVID-19. This inaccuracy has implications for any downstream data model, scientific discovery, or surveillance that relies on these codes.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Crabb</LastName><ForeName>Brendan T</ForeName><Initials>BT</Initials><AffiliationInfo><Affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lyons</LastName><ForeName>Ann</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Data Science Services, University of Utah Health Sciences Center, Salt Lake City.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bale</LastName><ForeName>Margaret</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Martin</LastName><ForeName>Valerie</ForeName><Initials>V</Initials><AffiliationInfo><Affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Berger</LastName><ForeName>Ben</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mann</LastName><ForeName>Sara</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>West</LastName><ForeName>William B</ForeName><Initials>WB</Initials><Suffix>Jr</Suffix><AffiliationInfo><Affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Brown</LastName><ForeName>Alyssa</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Peacock</LastName><ForeName>Jordan B</ForeName><Initials>JB</Initials><AffiliationInfo><Affiliation>Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Leung</LastName><ForeName>Daniel T</ForeName><Initials>DT</Initials><AffiliationInfo><Affiliation>Division of Infectious Diseases, University of Utah School of Medicine, Salt Lake City.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shah</LastName><ForeName>Rashmee U</ForeName><Initials>RU</Initials><AffiliationInfo><Affiliation>Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R01 AI135114</GrantID><Acronym>AI</Acronym><Agency>NIAID NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>K08 HL136850</GrantID><Acronym>HL</Acronym><Agency>NHLBI NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>UL1 TR000105</GrantID><Acronym>TR</Acronym><Agency>NCATS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>UL1 RR025764</GrantID><Acronym>RR</Acronym><Agency>NCRR NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D003160">Comparative Study</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D023361">Validation Study</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>08</Month><Day>03</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>JAMA Netw Open</MedlineTA><NlmUniqueID>101729235</NlmUniqueID><ISSNLinking>2574-3805</ISSNLinking></MedlineJournalInfo><SupplMeshList><SupplMeshName Type="Disease" UI="C000657245">COVID-19</SupplMeshName><SupplMeshName Type="Organism" UI="C000656484">severe acute respiratory syndrome coronavirus 2</SupplMeshName></SupplMeshList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D059019" MajorTopicYN="N">Clinical Coding</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName><QualifierName UI="Q000592" MajorTopicYN="Y">standards</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015331" MajorTopicYN="N">Cohort Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000150" MajorTopicYN="N">complications</QualifierName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D003371" MajorTopicYN="N">Cough</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000209" MajorTopicYN="N">etiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004417" MajorTopicYN="N">Dyspnea</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000209" MajorTopicYN="N">etiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D057286" MajorTopicYN="Y">Electronic Health Records</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005334" MajorTopicYN="N">Fever</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000209" MajorTopicYN="N">etiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D038801" MajorTopicYN="Y">International Classification of Diseases</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="Q000150" MajorTopicYN="N">complications</QualifierName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016133" MajorTopicYN="N">Polymerase Chain Reaction</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015203" MajorTopicYN="N">Reproducibility of Results</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012680" MajorTopicYN="N">Sensitivity and Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014589" MajorTopicYN="N" Type="Geographic">Utah</DescriptorName><QualifierName UI="Q000453" 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Margaret" uniqKey="Bale M" first="Margaret" last="Bale">Margaret Bale</name><name sortKey="Berger, Ben" sort="Berger, Ben" uniqKey="Berger B" first="Ben" last="Berger">Ben Berger</name><name sortKey="Brown, Alyssa" sort="Brown, Alyssa" uniqKey="Brown A" first="Alyssa" last="Brown">Alyssa Brown</name><name sortKey="Crabb, Brendan T" sort="Crabb, Brendan T" uniqKey="Crabb B" first="Brendan T" last="Crabb">Brendan T. Crabb</name><name sortKey="Leung, Daniel T" sort="Leung, Daniel T" uniqKey="Leung D" first="Daniel T" last="Leung">Daniel T. Leung</name><name sortKey="Lyons, Ann" sort="Lyons, Ann" uniqKey="Lyons A" first="Ann" last="Lyons">Ann Lyons</name><name sortKey="Mann, Sara" sort="Mann, Sara" uniqKey="Mann S" first="Sara" last="Mann">Sara Mann</name><name sortKey="Martin, Valerie" sort="Martin, Valerie" uniqKey="Martin V" first="Valerie" last="Martin">Valerie Martin</name><name sortKey="Peacock, Jordan B" sort="Peacock, Jordan B" uniqKey="Peacock J" first="Jordan B" last="Peacock">Jordan B. Peacock</name><name sortKey="Shah, Rashmee U" sort="Shah, Rashmee U" uniqKey="Shah R" first="Rashmee U" last="Shah">Rashmee U. Shah</name><name sortKey="West, William B" sort="West, William B" uniqKey="West W" first="William B" last="West">William B. West</name></noCountry></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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