Journal article
Characterizing Long COVID: Deep Phenotype of a Complex Condition
EBioMedicine, Vol.74, pp.103722-103722
12/01/2021
DOI: 10.1016/j.ebiom.2021.103722
PMCID: 8613500
PMID: 34839263
Abstract
Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or “long COVID”), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies.
The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19.
We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies.
Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID.
U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411.
Details
- Title: Subtitle
- Characterizing Long COVID: Deep Phenotype of a Complex Condition
- Creators
- Rachel R Deer - University of Texas Medical Branch, Galveston, TX, USAMadeline A Rock - University of Texas Medical Branch, Galveston, TX, USANicole Vasilevsky - University of Colorado Anschutz Medical CampusLeigh Carmody - Monarch InitiativeHalie Rando - University of Colorado Anschutz Medical CampusAlfred J Anzalone - University of Nebraska Medical CenterMarc D Basson - University of North DakotaTellen D Bennett - University of Colorado Anschutz Medical CampusTimothy Bergquist - Sage BionetworksEilis A BoudreauCarolyn T Bramante - University of Minnesota Medical SchoolJames Brian Byrd - University of Michigan Medical SchoolTiffany J Callahan - University of Colorado Anschutz Medical CampusLauren E Chan - Monarch InitiativeHaitao Chu - University of MinnesotaChristopher G Chute - Johns Hopkins UniversityBen D Coleman - Jackson LaboratoryHannah E Davis - Patient-Led Research CollaborativeJoel Gagnier - University of MichiganCasey S Greene - University of Colorado Anschutz Medical CampusWilliam B Hillegass - University of Mississippi Medical CenterRamakanth Kavuluru - University of KentuckyWesley D Kimble - West Virginia UniversityFarrukh M Koraishy - Stony Brook UniversitySebastian Köhler - Monarch InitiativeChen Liang - University of South CarolinaFeifan Liu - University of Massachusetts Chan Medical SchoolHongfang Liu - Mayo Clinic in FloridaVithal Madhira - Palila Software LLC, Reno, NV, USACharisse R Madlock-Brown - University of Tennessee Health Science CenterNicolas Matentzoglu - Monarch InitiativeDiego R Mazzotti - University of Kansas Medical CenterJulie A McMurry - University of Colorado Anschutz Medical CampusDouglas S McNair - Gates FoundationRichard A Moffitt - Stony Brook UniversityTeshamae S Monteith - University of MiamiAnn M Parker - Johns Hopkins UniversityMallory A Perry - Children's Hospital of PhiladelphiaEmily Pfaff - University of North Carolina, Chapel HillJustin T Reese - Monarch InitiativeJoel Saltz - Stony Brook UniversityRobert A Schuff - OchinAnthony E Solomonides - NorthShore University HealthSystemJulian Solway - University of ChicagoHeidi Spratt - University of Texas Medical Branch, Galveston, TX, USAGary S Stein - University of VermontAnupam A Sule - Sisters of Mercy Health SystemUmit Topaloglu - Wake Forest School of MedicineGeorge D. Vavougios - University of ThessalyLiwei Wang - Mayo Clinic in FloridaMelissa A Haendel - University of Colorado Anschutz Medical CampusPeter N Robinson - Monarch Initiative
- Resource Type
- Journal article
- Publication Details
- EBioMedicine, Vol.74, pp.103722-103722
- DOI
- 10.1016/j.ebiom.2021.103722
- PMID
- 34839263
- PMCID
- 8613500
- NLM abbreviation
- EBioMedicine
- ISSN
- 2352-3964
- eISSN
- 2352-3964
- Publisher
- Elsevier B.V
- Grant note
- DOI: 10.13039/100000049, name: National Institute on Aging, award: P30AG024832; DOI: 10.13039/501100002674, name: Russian Academy of Sciences; DOI: 10.13039/100000051, name: National Human Genome Research Institute, award: R01 HG010067; DOI: 10.13039/100000936, name: Gordon and Betty Moore Foundation; DOI: 10.13039/100006108, name: National Center for Advancing Translational Sciences, award: U24 TR002306, UL1TR001439; DOI: 10.13039/100007531, name: AES Corporation; DOI: 10.13039/100000002, name: National Institutes of Health, award: K23HL128909, K99GM145411, UL1TR002389, UL1TR002535; DOI: 10.13039/100007865, name: University of Texas Medical Branch
- Language
- English
- Date published
- 12/01/2021
- Academic Unit
- Nursing
- Record Identifier
- 9984446984102771
Metrics
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