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Characterizing Long COVID: Deep Phenotype of a Complex Condition.
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; Mazzotti, Diego R; McMurry, Julie A; McNair, Douglas S; Moffitt, Richard A; Monteith, Teshamae S; Parker, Ann M; Perry, Mallory A; Pfaff, Emily; Reese, Justin T; Saltz, Joel; Schuff, Robert A; Solomonides, Anthony E; Solway, Julian; Spratt, Heidi; Stein, Gary S; Sule, Anupam A; Topaloglu, Umit; Vavougios, George D; Wang, Liwei.
  • Deer RR; University of Texas Medical Branch, Galveston, TX, USA. Electronic address: rrdeer@utmb.edu.
  • Rock MA; University of Texas Medical Branch, Galveston, TX, USA.
  • Vasilevsky N; Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Monarch Initiative.
  • Carmody L; Monarch Initiative; The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
  • Rando H; 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.
  • Anzalone AJ; Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA.
  • Basson MD; Department of Surgery, University of North Dakota School of Medicine and Health Sciences.
  • Bennett TD; Section of Informatics and Data Science, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
  • Bergquist T; Sage Bionetworks, Seattle, WA.
  • Boudreau EA; Department of Neurology; Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239.
  • Bramante CT; Departments of Internal Medicine and Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455.
  • Byrd JB; Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan Medical School, Ann Arbor, MI, 48109.
  • Callahan TJ; Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
  • Chan LE; Monarch Initiative; College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA.
  • Chu H; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN USA.
  • Chute CG; Johns Hopkins University, Schools of Medicine, Public Health, and Nursing, Baltimore, MD, USA.
  • Coleman BD; The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA.
  • Davis HE; Patient-Led Research Collaborative.
  • Gagnier J; Departments of Orthopaedic Surgery & Epidemiology, University of Michigan, Ann Arbor, MI, USA.
  • Greene CS; 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.
  • Hillegass WB; University of Mississippi Medical Center, University of Mississippi Medical Center, Jackson, MS, USA; Departments of Data Science and Medicine.
  • Kavuluru R; Institute for Biomedical Informatics, University of Kentucky.
  • Kimble WD; West Virginia Clinical and Translational Science Institute, West Virginia University, Morgantown, WV, USA.
  • Koraishy FM; Division of Nephrology, Department of Medicine, Stony Brook University.
  • Köhler S; Monarch Initiative; Ada Health GmbH, Berlin, Germany.
  • Liang C; Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
  • Liu F; Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA.
  • Liu H; Department of Artificial Intelligence and Informatics, Mayo Clinic, MN, USA.
  • Madhira V; Palila Software LLC, Reno, NV, USA.
  • Madlock-Brown CR; Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, 920 Madison Ave. Suite 518N, Memphis TN 38613.
  • Matentzoglu N; Monarch Initiative; Semanticly Ltd; European Bioinformatics Institute (EMBL-EBI).
  • Mazzotti DR; Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center.
  • McMurry JA; Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Monarch Initiative.
  • McNair DS; Quantitative Sciences, Global Health Div., Gates Foundation, Seattle, WA 98109, USA.
  • Moffitt RA; Stony Brook University, Stony Brook, NY 11794, USA.
  • Monteith TS; University of Miami, Miller School of Medicine, Miami, Fl 33136.
  • Parker AM; Pulmonary and Critical Care Medicine, Johns Hopkins University, Schools of Medicine, Baltimore, MD, USA.
  • Perry MA; Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA.
  • Pfaff E; University of North Carolina, Chapel Hill.
  • Reese JT; Monarch Initiative; Lawrence Berkeley National Laboratory.
  • Saltz J; Stony Brook University; Biomedical Informatics.
  • Schuff RA; OCHIN, Inc Portland, OR, USA.
  • Solomonides AE; Outcomes Research Network, Research Institute, NorthShore University HealthSystem, Evanston, IL 60201, USA; Institute for Translational Medicine, University of Chicago, Chicago, IL, USA.
  • Solway J; Institute for Translational Medicine, University of Chicago, Chicago, IL, USA.
  • Spratt H; University of Texas Medical Branch, Galveston, TX, USA.
  • Stein GS; University of Vermont Larner College of Medicine, Departments of Biochemistry and Surgery, Burlington, Vermont 05405.
  • Sule AA; St Joseph Mercy Oakland, Pontiac, MI, USA.
  • Topaloglu U; Wake Forest School of Medicine.
  • Vavougios GD; 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, Univer
  • Wang L; Department of Artificial Intelligence and Informatics, Mayo Clinic, MN, USA.
EBioMedicine ; 74: 103722, 2021 Nov 25.
Article in English | MEDLINE | ID: covidwho-1536517
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

Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Type of study: Prognostic study Language: English Journal: EBioMedicine Year: 2021

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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Type of study: Prognostic study Language: English Journal: EBioMedicine Year: 2021
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