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Characterizing Long COVID: Deep Phenotype of a Complex Condition.

Rachel R Deer1, Madeline A Rock1, Nicole Vasilevsky2

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Summary

Standardizing long COVID symptoms using the Human Phenotype Ontology (HPO) improves data integration and patient classification. This common language aids researchers and clinicians in diagnosis and treatment strategies for post-acute sequelae of SARS-CoV-2.

Keywords:
COVID-19human phenotype ontologylong COVIDof post-acute sequelae of SARS-CoV-2phenotyping

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Area of Science:

  • Medical Informatics
  • Clinical Research
  • Ontology Development

Background:

  • Clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC), or long COVID, are challenging to integrate due to heterogeneous methods and lack of standardized terminology.
  • Patient-led studies are crucial for understanding long COVID natural history but are hampered by varied terminology for similar symptoms.
  • Disparities in patient versus clinical characterization of long COVID motivated the development of an ontological approach.

Purpose of the Study:

  • To apply the Human Phenotype Ontology (HPO) to standardize the characterization of long COVID clinical manifestations.
  • To create a common language for researchers, clinicians, and patients to improve data capture, analysis, and classification of long COVID.
  • To facilitate the comparison and pooling of data from disparate long COVID studies.

Main Methods:

  • Identified 303 articles published before April 29, 2021, and curated 59 relevant manuscripts describing long COVID clinical manifestations in 81 cohorts.
  • Mapped 287 unique clinical findings from these studies to Human Phenotype Ontology (HPO) terms.
  • Presented layperson synonyms and definitions to bridge patient self-report questionnaires with standard medical terminology.

Main Results:

  • Long COVID clinical manifestations are inconsistently assessed across studies, with numerous synonyms reported for the same condition.
  • Across 10 cohorts, 31 unique clinical features mapped to HPO terms were reported.
  • Fatigue was the most commonly reported feature (median 45.1%), while Nausea was least common (median 3.9%), with wide percentage variations between studies.

Conclusions:

  • Translating long COVID manifestations into computable HPO terms enhances data analysis, capture, and patient classification.
  • A shared terminology (HPO) enables effective comparison and pooling of long COVID research data.
  • Mapping lay terminology to HPO empowers patients to aid in computationally accessible phenotypic characterization, improving long COVID stratification, diagnosis, and treatment.