Characterizing Long COVID: Deep Phenotype of a Complex Condition

Rachel R Deer; Madeline A Rock; Nicole Vasilevsky; Leigh Carmody; Halie Rando; Alfred J Anzalone; Marc D Basson; Tellen D Bennett; Timothy Bergquist; Eilis A Boudreau; Carolyn T Bramante; James Brian Byrd; Tiffany J Callahan; Lauren E Chan; Haitao Chu; Christopher G Chute; Ben D Coleman; Hannah E Davis; Joel Gagnier; Casey S Greene; William B Hillegass; Ramakanth Kavuluru; Wesley D Kimble; Farrukh M Koraishy; Sebastian Köhler; Chen Liang; Feifan Liu; Hongfang Liu; Vithal Madhira; Charisse R Madlock-Brown; Nicolas Matentzoglu; Diego R Mazzotti; Julie A McMurry; Douglas S McNair; Richard A Moffitt; Teshamae S Monteith; Ann M Parker; Mallory A Perry; Emily Pfaff; Justin T Reese; Joel Saltz; Robert A Schuff; Anthony E Solomonides; Julian Solway; Heidi Spratt; Gary S Stein; Anupam A Sule; Umit Topaloglu; George D Vavougios; Liwei Wang; Melissa A Haendel; Peter N Robinson

doi:10.1016/j.ebiom.2021.103722

Characterizing Long COVID: Deep Phenotype of a Complex Condition

EBioMedicine. 2021 Dec:74:103722. doi: 10.1016/j.ebiom.2021.103722. Epub 2021 Nov 25.

Authors

Rachel R Deer¹, Madeline A Rock², Nicole Vasilevsky³, Leigh Carmody⁴, Halie Rando⁵, Alfred J Anzalone⁶, Marc D Basson⁷, Tellen D Bennett⁸, Timothy Bergquist⁹, Eilis A Boudreau¹⁰, Carolyn T Bramante¹¹, James Brian Byrd¹², Tiffany J Callahan¹³, Lauren E Chan¹⁴, Haitao Chu¹⁵, Christopher G Chute¹⁶, Ben D Coleman¹⁷, Hannah E Davis¹⁸, Joel Gagnier¹⁹, Casey S Greene⁵, William B Hillegass²⁰, Ramakanth Kavuluru²¹, Wesley D Kimble²², Farrukh M Koraishy²³, Sebastian Köhler²⁴, Chen Liang²⁵, Feifan Liu²⁶, Hongfang Liu²⁷, Vithal Madhira²⁸, Charisse R Madlock-Brown²⁹, Nicolas Matentzoglu³⁰, Diego R Mazzotti³¹, Julie A McMurry³, Douglas S McNair³², Richard A Moffitt³³, Teshamae S Monteith³⁴, Ann M Parker³⁵, Mallory A Perry³⁶, Emily Pfaff³⁷, Justin T Reese³⁸, Joel Saltz³⁹, Robert A Schuff⁴⁰, Anthony E Solomonides⁴¹, Julian Solway⁴², Heidi Spratt², Gary S Stein⁴³, Anupam A Sule⁴⁴, Umit Topaloglu⁴⁵, George D Vavougios⁴⁶, Liwei Wang²⁷, Melissa A Haendel⁴⁷, Peter N Robinson⁴⁸

Affiliations

¹ University of Texas Medical Branch, Galveston, TX, USA. Electronic address: rrdeer@utmb.edu.
² University of Texas Medical Branch, Galveston, TX, USA.
³ Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Monarch Initiative.
⁴ Monarch Initiative; The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
⁵ Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁶ Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA.
⁷ Department of Surgery, University of North Dakota School of Medicine and Health Sciences.
⁸ Section of Informatics and Data Science, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
⁹ Sage Bionetworks, Seattle, WA.
¹⁰ Department of Neurology; Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239.
¹¹ Departments of Internal Medicine and Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455.
¹² Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan Medical School, Ann Arbor, MI, 48109.
¹³ Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
¹⁴ Monarch Initiative; College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA.
¹⁵ Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN USA.
¹⁶ Johns Hopkins University, Schools of Medicine, Public Health, and Nursing, Baltimore, MD, USA.
¹⁷ The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA.
¹⁸ Patient-Led Research Collaborative.
¹⁹ Departments of Orthopaedic Surgery & Epidemiology, University of Michigan, Ann Arbor, MI, USA.
²⁰ University of Mississippi Medical Center, University of Mississippi Medical Center, Jackson, MS, USA; Departments of Data Science and Medicine.
²¹ Institute for Biomedical Informatics, University of Kentucky.
²² West Virginia Clinical and Translational Science Institute, West Virginia University, Morgantown, WV, USA.
²³ Division of Nephrology, Department of Medicine, Stony Brook University.
²⁴ Monarch Initiative; Ada Health GmbH, Berlin, Germany.
²⁵ Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
²⁶ Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA.
²⁷ Department of Artificial Intelligence and Informatics, Mayo Clinic, MN, USA.
²⁸ Palila Software LLC, Reno, NV, USA.
²⁹ Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, 920 Madison Ave. Suite 518N, Memphis TN 38613.
³⁰ Monarch Initiative; Semanticly Ltd; European Bioinformatics Institute (EMBL-EBI).
³¹ Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center.
³² Quantitative Sciences, Global Health Div., Gates Foundation, Seattle, WA 98109, USA.
³³ Stony Brook University, Stony Brook, NY 11794, USA.
³⁴ University of Miami, Miller School of Medicine, Miami, Fl 33136.
³⁵ Pulmonary and Critical Care Medicine, Johns Hopkins University, Schools of Medicine, Baltimore, MD, USA.
³⁶ Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA.
³⁷ University of North Carolina, Chapel Hill.
³⁸ Monarch Initiative; Lawrence Berkeley National Laboratory.
³⁹ Stony Brook University; Biomedical Informatics.
⁴⁰ OCHIN, Inc Portland, OR, USA.
⁴¹ Outcomes Research Network, Research Institute, NorthShore University HealthSystem, Evanston, IL 60201, USA; Institute for Translational Medicine, University of Chicago, Chicago, IL, USA.
⁴² Institute for Translational Medicine, University of Chicago, Chicago, IL, USA.
⁴³ University of Vermont Larner College of Medicine, Departments of Biochemistry and Surgery, Burlington, Vermont 05405.
⁴⁴ St Joseph Mercy Oakland, Pontiac, MI, USA.
⁴⁵ Wake Forest School of Medicine.
⁴⁶ Department of Computer Science and Telecommunications, University of Thessaly, Papasiopoulou 2 - 4, P.C.; 131 - Galaneika, Lamia, Greece; Department of Neurology, Athens Naval Hospital 70 Deinokratous Street, P.C. 115 21 Athens, Greece; Department of Respiratory Medicine, Faculty of Medicine, University of Thessaly, Biopolis, P.C. 41500 Larissa, Greece.
⁴⁷ Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Monarch Initiative. Electronic address: melissa@tislab.org.
⁴⁸ Monarch Initiative; The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA. Electronic address: peter.robinson@jax.org.

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.

Keywords: COVID-19; human phenotype ontology; long COVID; of post-acute sequelae of SARS-CoV-2; phenotyping.

Publication types

Review

MeSH terms

COVID-19 / complications*
COVID-19 / diagnosis
COVID-19 / pathology*
Humans
Post-Acute COVID-19 Syndrome
SARS-CoV-2

Abstract

Publication types

MeSH terms

Grants and funding