Characterizing Long COVID: Deep Phenotype of a Complex Condition
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Deer, Rachel R, et al. Characterizing Long Covid: Deep Phenotype of a Complex Condition. Elsevier, 2021. https://doi.org/10.17615/edpt-y058APA
Deer, R., Rock, M., Vasilevsky, N., Carmody, L., Rando, H., Anzalone, A., Basson, M., Bennett, T., Bergquist, T., Boudreau, E., Bramante, C., Byrd, J., Callahan, T., Chan, L., Chu, H., Chute, C., Coleman, B., Davis, H., Gagnier, J., Greene, C., Hillegass, W., Kavuluru, R., Kimble, W., Koraishy, F., Köhler, S., Liang, C., Liu, F., Liu, H., Madhira, V., Madlock Brown, C., Matentzoglu, N., Mazzotti, D., Mc Murry, J., Mc Nair, D., Moffitt, R., Monteith, T., Parker, A., Perry, M., Pfaff, E., Reese, J., Saltz, J., Schuff, R., Solomonides, A., Solway, J., Spratt, H., Stein, G., Sule, A., Topaloglu, U., Vavougios, G., Wang, L., Haendel, M., & Robinson, P. (2021). Characterizing Long COVID: Deep Phenotype of a Complex Condition. Elsevier. https://doi.org/10.17615/edpt-y058Chicago
Deer, Rachel R, Madeline A Rock, Nicole Vasilevsky, Leigh Carmody, Halie Rando, Alfred J Anzalone, Marc D Basson et al. 2021. Characterizing Long Covid: Deep Phenotype of a Complex Condition. Elsevier. https://doi.org/10.17615/edpt-y058- Creator
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Deer, Rachel R
- ORCID: https://orcid.org/0000-0001-6307-5227
- Other Affiliation: University of Texas Medical Branch, Galveston, TX, USA
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Rock, Madeline A
- Other Affiliation: University of Texas Medical Branch, Galveston, TX, USA
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Vasilevsky, Nicole
- ORCID: https://orcid.org/0000-0001-5208-3432
- Other Affiliation: Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Carmody, Leigh
- ORCID: https://orcid.org/0000-0001-7941-2961
- Other Affiliation: Monarch Initiative
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Rando, Halie
- ORCID: https://orcid.org/0000-0001-7688-1770
- Other Affiliation: Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Anzalone, Alfred J
- ORCID: https://orcid.org/0000-0002-3212-7845
- Other Affiliation: Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
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Basson, Marc D
- Other Affiliation: Department of Surgery, University of North Dakota School of Medicine and Health Sciences
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Bennett, Tellen D
- ORCID: https://orcid.org/0000-0003-1483-4236
- Other Affiliation: Section of Informatics and Data Science, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Bergquist, Timothy
- Other Affiliation: Sage Bionetworks, Seattle, WA
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Boudreau, Eilis A
- Other Affiliation: Department of Neurology; Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR
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Bramante, Carolyn T
- ORCID: https://orcid.org/0000-0001-5858-2080
- Other Affiliation: Departments of Internal Medicine and Pediatrics, University of Minnesota Medical School, Minneapolis, MN
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Byrd, James Brian
- ORCID: https://orcid.org/0000-0002-0509-3520
- Other Affiliation: Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan Medical School, Ann Arbor, MI
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Callahan, Tiffany J
- ORCID: https://orcid.org/0000-0002-8169-9049
- Other Affiliation: Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Chan, Lauren E
- Other Affiliation: Monarch Initiative
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Chu, Haitao
- ORCID: https://orcid.org/0000-0003-0932-598X
- Other Affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN USA
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Chute, Christopher G
- ORCID: https://orcid.org/0000-0001-5437-2545
- Other Affiliation: Johns Hopkins University, Schools of Medicine, Public Health, and Nursing, Baltimore, MD, USA
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Coleman, Ben D
- ORCID: https://orcid.org/0000-0002-4422-1708
- Other Affiliation: The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
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Davis, Hannah E
- ORCID: https://orcid.org/0000-0002-1245-2034
- Other Affiliation: Patient-Led Research Collaborative
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Gagnier, Joel
- Other Affiliation: Departments of Orthopaedic Surgery & Epidemiology, University of Michigan, Ann Arbor, MI, USA
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Greene, Casey S
- ORCID: https://orcid.org/0000-0001-8713-9213
- Other Affiliation: Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Hillegass, William B
- Other Affiliation: University of Mississippi Medical Center, University of Mississippi Medical Center, Jackson, MS, USA
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Kavuluru, Ramakanth
- ORCID: https://orcid.org/0000-0003-1238-9378
- Other Affiliation: Institute for Biomedical Informatics, University of Kentucky
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Kimble, Wesley D
- ORCID: https://orcid.org/0000-0003-3325-3808
- Other Affiliation: West Virginia Clinical and Translational Science Institute, West Virginia University, Morgantown, WV, USA
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Koraishy, Farrukh M
- ORCID: https://orcid.org/0000-0001-6974-5674
- Other Affiliation: Division of Nephrology, Department of Medicine, Stony Brook University
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Köhler, Sebastian
- ORCID: https://orcid.org/0000-0002-5316-1399
- Other Affiliation: Monarch Initiative
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Liang, Chen
- ORCID: https://orcid.org/0000-0002-9803-9880
- Other Affiliation: Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
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Liu, Feifan
- ORCID: https://orcid.org/0000-0003-0881-6365
- Other Affiliation: Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA
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Liu, Hongfang
- Other Affiliation: Department of Artificial Intelligence and Informatics, Mayo Clinic, MN, USA
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Madhira, Vithal
- ORCID: https://orcid.org/0000-0001-5359-1703
- Other Affiliation: Palila Software LLC, Reno, NV, USA
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Madlock-Brown, Charisse R
- Other Affiliation: Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, Memphis TN
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Matentzoglu, Nicolas
- ORCID: https://orcid.org/0000-0002-7356-1779
- Other Affiliation: Monarch Initiative
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Mazzotti, Diego R
- ORCID: https://orcid.org/0000-0003-3924-9199
- Other Affiliation: Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center
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McMurry, Julie A
- Other Affiliation: Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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McNair, Douglas S
- ORCID: https://orcid.org/0000-0003-0965-883X
- Other Affiliation: Quantitative Sciences, Global Health Div., Gates Foundation, Seattle, WA 98109, USA
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Moffitt, Richard A
- Other Affiliation: Stony Brook University, Stony Brook, NY, USA
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Monteith, Teshamae S
- ORCID: https://orcid.org/0000-0001-8912-252X
- Other Affiliation: University of Miami, Miller School of Medicine, Miami, Fl
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Parker, Ann M
- Other Affiliation: Pulmonary and Critical Care Medicine, Johns Hopkins University, Schools of Medicine, Baltimore, MD, USA
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Perry, Mallory A
- ORCID: https://orcid.org/0000-0001-5754-7857
- Other Affiliation: Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
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Pfaff, Emily
- University of North Carolina at Chapel Hill
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Reese, Justin T
- Other Affiliation: Monarch Initiative
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Saltz, Joel
- Other Affiliation: Stony Brook University
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Schuff, Robert A
- Other Affiliation: OCHIN, Inc Portland, OR, USA
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Solomonides, Anthony E
- ORCID: https://orcid.org/0000-0003-2117-2461
- Other Affiliation: Outcomes Research Network, Research Institute, NorthShore University HealthSystem, Evanston, IL, USA
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Solway, Julian
- Other Affiliation: Institute for Translational Medicine, University of Chicago, Chicago, IL, USA
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Spratt, Heidi
- ORCID: https://orcid.org/0000-0002-9420-5028
- Other Affiliation: University of Texas Medical Branch, Galveston, TX, USA
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Stein, Gary S
- ORCID: https://orcid.org/0000-0001-8762-5422
- Other Affiliation: University of Vermont Larner College of Medicine, Departments of Biochemistry and Surgery, Burlington, Vermont
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Sule, Anupam A
- ORCID: https://orcid.org/0000-0002-5931-3381
- Other Affiliation: St Joseph Mercy Oakland, Pontiac, MI, USA
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Topaloglu, Umit
- ORCID: https://orcid.org/0000-0002-3241-8773
- Other Affiliation: Wake Forest School of Medicine
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Vavougios, George D.
- ORCID: https://orcid.org/0000-0002-0413-4028
- Other Affiliation: Department of Computer Science and Telecommunications, University of Thessaly, Papasiopoulou
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Wang, Liwei
- Other Affiliation: Department of Artificial Intelligence and Informatics, Mayo Clinic, MN, USA
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Haendel, Melissa A
- ORCID: https://orcid.org/0000-0001-9114-8737
- Other Affiliation: Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Robinson, Peter N
- ORCID: https://orcid.org/0000-0002-0736-9199
- Other Affiliation: Monarch Initiative
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Deer, Rachel R
- Abstract
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: 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: 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: 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: U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411.
- Date of publication
- November 25, 2021
- Keyword
- long COVID
- human diseases
- data
- integration
- Phenotype Ontology
- article
- features
- manuscript
- acute COVID-19
- deep phenotyping
- capture
- COVID
- post-acute sequelae of SARS-CoV-2
- analysis of COVID-19
- study
- approach
- Human Phenotype Ontology
- fatigue
- relevant manuscripts
- history of COVID-19
- manifestations
- term
- classification
- natural history
- language
- standards
- self-report questionnaires
- complex
- SARS-CoV-2
- ontological approach
- heterogeneous methods
- exchange
- medical terminology
- patient self-report questionnaires
- diagnosis
- COVID-19
- longer
- patients
- weeks
- Long COVID patients
- disparities
- questionnaire
- phenotypic abnormalities
- ontology
- clinical manifestations
- Deep
- characterization
- percentage
- long COVID manifestations
- definition
- abnormalities
- disease
- COVID patients
- clinical characterization
- research
- treatment
- clinical features
- clinicians
- lack
- complex conditions
- conditions
- laypersons
- patient-led
- phenotypic characterization
- treatment of long COVID
- symptoms
- phenotypic manifestations
- method
- publications
- authors
- cohort
- phenotype
- synonyms
- stratification
- natural history of COVID-19
- COVID manifestations
- maps
- Human Phenotype Ontology terms
- COVID studies
- analysis
- terminology
- HPO terms
- DOI
- Identifier
- Dimensions ID: pub.1143044589
- DOI: https://dx.doi.org/10.1016/j.ebiom.2021.103722
- PMID: 34839263
- PMCID: PMC8613500
- Resource type
- Article
- Rights statement
- In Copyright
- Journal title
- EBioMedicine
- Journal volume
- 74
- Page start
- 103722
- Funder
- National Center for Advancing Translational Sciences
- Gordon and Betty Moore Foundation
- National Heart Lung and Blood Institute
- National Institute on Aging
- National Institute of General Medical Sciences
- National Institute of Diabetes and Digestive and Kidney Diseases
- National Human Genome Research Institute
- ISSN
- 2352-3964
- Publisher
- Elsevier
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