Characterizing Long COVID: Deep Phenotype of a Complex Condition.
COVID-19
human phenotype ontology
long COVID
of post-acute sequelae of SARS-CoV-2
phenotyping
Journal
EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039
Informations de publication
Date de publication:
Dec 2021
Dec 2021
Historique:
received:
10
09
2021
revised:
22
10
2021
accepted:
15
11
2021
pubmed:
29
11
2021
medline:
6
1
2022
entrez:
28
11
2021
Statut:
ppublish
Résumé
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.
Sections du résumé
BACKGROUND
BACKGROUND
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.
METHODS
METHODS
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.
FUNDING
BACKGROUND
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.
INTERPRETATION
CONCLUSIONS
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.
FUNDING
BACKGROUND
U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411.
Identifiants
pubmed: 34839263
pii: S2352-3964(21)00516-8
doi: 10.1016/j.ebiom.2021.103722
pmc: PMC8613500
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
103722Subventions
Organisme : NIDDK NIH HHS
ID : K23 DK124654
Pays : United States
Organisme : NCATS NIH HHS
ID : U24 TR002306
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002389
Pays : United States
Organisme : NHGRI NIH HHS
ID : R01 HG010067
Pays : United States
Organisme : NHLBI NIH HHS
ID : K23 HL128909
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002535
Pays : United States
Organisme : NIGMS NIH HHS
ID : K99 GM145411
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG024832
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001439
Pays : United States
Informations de copyright
Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest RRD, TDB, JBB, CGC, WBH, JAM, AMP, ERP, HMR, JS, RAS, AES, JS, GS, MAH, PNR report funding from NIH. MAH and JAM are co-founders of Pryzm Health.