University of Massachusetts Medical School Faculty Publications
UMMS Affiliation
Department of Population and Quantitative Health Sciences
Publication Date
2021-06-29
Document Type
Article Preprint
Disciplines
Bioinformatics | Computational Biology | Data Science | Diagnosis | Immunology and Infectious Disease | Infectious Disease | Microbiology | Virus Diseases
Abstract
Importance Since late 2019, the novel coronavirus SARS-CoV-2 has given rise to a global pandemic and introduced many health challenges with economic, social, and political consequences. In addition to a complex acute presentation that can affect multiple organ systems, there is mounting evidence of various persistent long-term sequelae. The worldwide scientific community is characterizing a diverse range of seemingly common long-term outcomes associated with SARS-CoV-2 infection, but the underlying assumptions in these studies vary widely making comparisons difficult. Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 infection (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 of long COVID.
Observations We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts of individuals three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to Human Phenotype Ontology (HPO) terms.
Conclusions and Relevance Patients and clinicians often use different terms to describe the same symptom or condition. Addressing the heterogeneous and inconsistent language used to describe the clinical manifestations of long COVID combined with the lack of standardized terminologies for long COVID will provide a necessary foundation for comparison and meta-analysis of different studies. Translating long COVID manifestations into computable HPO terms will improve the analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared or pooled more effectively. Furthermore, mapping lay terminology to HPO for long COVID manifestations will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, which may improve the stratification and thereby diagnosis and treatment of long COVID.
Keywords
Infectious Diseases, COVID-19, long COVID, post-acute sequelae of SARS-CoV-2 infection, PASC, clinical manifestations, phenotyping
Rights and Permissions
The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
DOI of Published Version
10.1101/2021.06.23.21259416
Source
medRxiv 2021.06.23.21259416; doi: https://doi.org/10.1101/2021.06.23.21259416. Link to preprint on medRxiv
Journal/Book/Conference Title
medRxiv
Repository Citation
Deer RR, Liu F, Haendel M, Robinson PN. (2021). Characterizing Long COVID: Deep Phenotype of a Complex Condition [preprint]. University of Massachusetts Medical School Faculty Publications. https://doi.org/10.1101/2021.06.23.21259416. Retrieved from https://escholarship.umassmed.edu/faculty_pubs/2071
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Included in
Bioinformatics Commons, Computational Biology Commons, Data Science Commons, Diagnosis Commons, Immunology and Infectious Disease Commons, Infectious Disease Commons, Microbiology Commons, Virus Diseases Commons
Comments
This article is a preprint. Preprints are preliminary reports of work that have not been certified by peer review.
Full author list omitted for brevity. For the full list of authors, see article.