Characterizing Long Covid: Deep Phenotype of a Complex Condition

Authors

• Rachel R. Deer, The University of Texas Medical Branch at Galveston
• Madeline A. Rock, The University of Texas Medical Branch at Galveston
• Nicole Vasilevsky, University of Colorado Anschutz Medical Campus
• Leigh Carmody, Monarch Initiative
• Halie Rando, University of Colorado Anschutz Medical Campus
• Alfred J. Anzalone, College of Medicine
• Marc D. Basson, School of Medicine & Health Sciences
• Tellen D. Bennett, University of Colorado Department of Pediatrics
• Timothy Bergquist, Sage Bionetworks
• Eilis A. Boudreau, OHSU School of Medicine
• Carolyn T. Bramante, University of Minnesota Medical School
• James Brian Byrd, University of Michigan Medical School
• Tiffany J. Callahan, University of Colorado Anschutz Medical Campus
• Lauren E. Chan, Monarch Initiative
• Haitao Chu, School of Public Health
• Christopher G. Chute, Johns Hopkins University School of Medicine
• Ben D. Coleman, The Jackson Laboratory
• Hannah E. Davis, Patient-Led Research Collaborative
• Joel Gagnier, University of Michigan, Ann Arbor
• Casey S. Greene, University of Colorado Anschutz Medical Campus
• William B. Hillegass, University of Mississippi Medical Center
• Ramakanth Kavuluru, University of Kentucky
• Wesley D. Kimble, West Virginia University
• Farrukh M. Koraishy, Stony Brook University
• Sebastian Köhler, Monarch Initiative
• Chen Liang, University of South Carolina
• Feifan Liu, University of Massachusetts Chan Medical School
• Hongfang Liu, Mayo Clinic
• Vithal Madhira, Palila Software LLC
• Charisse R. Madlock-Brown, University of Tennessee Health Science Center
• Nicolas Matentzoglu, Monarch Initiative
• Diego R. Mazzotti, University of Kansas Medical Center

Author ORCID Identifier

Rachel R. Deer: 0000-0001-6307-5227

Madeline A. Rock: 0000-0003-4372-9056

Nicole Vasilevsky: 0000-0001-5208-3432

Leigh Carmody: 0000-0001-7941-2961

Halie Rando: 0000-0001-7688-1770

Alfred J. Anzalone: 0000-0002-3212-7845

Marc D. Basson: 0000-0001-9696-2789

Tellen D. Bennett: 0000-0003-1483-4236

Timothy Bergquist: 0000-0001-5614-8977

Eilis A. Boudreau: 0000-0002-1386-4604

Carolyn T. Bramante: 0000-0001-5858-2080

Christopher G. Chute: 0000-0001-5437-2545

Joel Gagnier: 0000-0002-3162-3935

Casey S. Greene: 0000-0001-8713-9213

William B. Hillegass: 0000-0001-6428-0564

Ramakanth Kavuluru: 0000-0003-1238-9378

Wesley D. Kimble: 0000-0003-3325-3808

Farrukh M. Koraishy: 0000-0001-6974-5674

Vithal Madhira: 0000-0001-5359-1703

Charisse R. Madlock-Brown: 0000-0002-3647-1045

Nicolas Matentzoglu: 0000-0002-7356-1779

Document Type

Article

Publication Date

12-1-2021

Publication Title

EBioMedicine

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. Findings: 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.

Keywords

COVID-19, human phenotype ontology, long COVID, of post-acute sequelae of SARS-CoV-2, phenotyping

Volume

74

Version

Version of Record

Recommended Citation

Deer, Rachel R.; Rock, Madeline A.; Vasilevsky, Nicole; Carmody, Leigh; Rando, Halie; Anzalone, Alfred J.; Basson, Marc D.; Bennett, Tellen D.; Bergquist, Timothy; Boudreau, Eilis A.; Bramante, Carolyn T.; Byrd, James Brian; Callahan, Tiffany J.; Chan, Lauren E.; Chu, Haitao; Chute, Christopher G.; Coleman, Ben D.; Davis, Hannah E.; Gagnier, Joel; Greene, Casey S.; Hillegass, William B.; Kavuluru, Ramakanth; Kimble, Wesley D.; Koraishy, Farrukh M.; Köhler, Sebastian; Liang, Chen; Liu, Feifan; Liu, Hongfang; Madhira, Vithal; Madlock-Brown, Charisse R.; Matentzoglu, Nicolas; and Mazzotti, Diego R., "Characterizing Long Covid: Deep Phenotype of a Complex Condition" (2021). Computer Science: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/csc_facpubs/413