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 .
Digital Object Identifier (DOI)
This work was supported by NCATS U24 TR002306. Rachel R. Deer supported by UTMB CTSA UL1TR001439 and NIA P30AG024832; Halie M. Rando was supported by The Gordon and Betty Moore Foundation (GBMF 4552) and the National Human Genome Research Institute (R01 HG010067); Tellen D. Bennett supported by NIH UL1TR002535 03S2 and NIH UL1TR002535; James Brian Byrd supported by NIH grant K23HL128909; Christopher G. Chute supported by U24 TR002306. Julian Solway supported by UL1TR002389. Mallory A. Perry supported by K99GM145411.
The data used to support the findings of this study are provided in the main text and supplemental files.
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.; and Kavuluru, Ramakanth, "Characterizing Long COVID: Deep Phenotype of a Complex Condition" (2021). Institute for Biomedical Informatics Faculty Publications. 16.
Supplemental file 1. Contains Figure S1 (Number of HPO terms per cohort); Figures S2-S25 (Reported frequencies of 287 HPO terms arranged according to categories). Table S1. Summary of papers reviewed for inclusion in this work. Table S2 (post-acute COVID-19 studies curated in this work, including cohort characteristics and PubMed identifiers). Table S3 (HPO terms used to annotated PICU cohorts).
1-s2.0-S2352396421005168-mmc2.pdf (327 kB)
Supplemental file 2. Excel file with detailed curations (HPO label, id, original description, PubMed identifier, first author, year, as well as the counts and percentages in the original study).
1-s2.0-S2352396421005168-mmc3.pdf (725 kB)
Supplemental file 3. Word file with tables that contain HPO ids, labels, definitions, synonyms, and plain-language labels and definitions for the 287 HPO terms used in this work.
1-s2.0-S2352396421005168-mmc4.docx (85 kB)
Supplemental file 4