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Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

dc.rights.licenseopenen_US
dc.contributor.authorDAGLIATI, Arianna
dc.contributor.authorSTRASSER, Zachary H
dc.contributor.authorHOSSEIN ABAD, Zahra Shakeri
dc.contributor.authorKLANN, Jeffrey G
dc.contributor.authorWAGHOLIKAR, Kavishwar B
dc.contributor.authorMESA, Rebecca
dc.contributor.authorVISWESWARAN, Shyam
dc.contributor.authorMORRIS, Michele
dc.contributor.authorLUO, Yuan
dc.contributor.authorHENDERSON, Darren W
dc.contributor.authorSAMAYAMUTHU, Malarkodi Jebathilagam
dc.contributor.authorTAN, Bryce W Q
dc.contributor.authorVERDY, Guillame
dc.contributor.authorOMENN, Gilbert S
dc.contributor.authorXIA, Zongqi
dc.contributor.authorBELLAZZI, Riccardo
dc.contributor.authorMURPHY, Shawn N
dc.contributor.authorHOLMES, John H
dc.contributor.authorESTIRI, Hossein
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorGRIFFIER, Romain
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorJOUHET, Vianney
dc.date.accessioned2024-05-15T14:05:44Z
dc.date.available2024-05-15T14:05:44Z
dc.date.issued2023-10-01
dc.identifier.issn2589-5370en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/199893
dc.description.abstractEnBACKGROUND: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning. METHODS: We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes. FINDINGS: Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively. INTERPRETATION: We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems. FUNDING: Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.enCOVID-19
dc.subject.enElectronic Health Records
dc.subject.enPASC
dc.subject.enPost-Acute Sequelae Of SARS-Cov-2
dc.subject.enSARS-Cov-2
dc.title.enCharacterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study
dc.title.alternativeEClinicalMedicineen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.eclinm.2023.102210en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed37745021en_US
bordeaux.journalEClinicalMedicineen_US
bordeaux.page102210en_US
bordeaux.volume64en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamAHEAD_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDNational Institutes of Healthen_US
hal.identifierhal-04576630
hal.version1
hal.date.transferred2024-05-15T14:05:47Z
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=EClinicalMedicine&rft.date=2023-10-01&rft.volume=64&rft.spage=102210&rft.epage=102210&rft.eissn=2589-5370&rft.issn=2589-5370&rft.au=DAGLIATI,%20Arianna&STRASSER,%20Zachary%20H&HOSSEIN%20ABAD,%20Zahra%20Shakeri&KLANN,%20Jeffrey%20G&WAGHOLIKAR,%20Kavishwar%20B&rft.genre=article


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