Machine learning to understand risks for severe COVID-19 outcomes: a retrospective cohort study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities in a large US health-care system.

Journal: The Lancet. Digital health
PMID: 38670740

Abstract

BACKGROUND: In the context of immune-mediated inflammatory diseases (IMIDs), COVID-19 outcomes are incompletely understood and vary considerably depending on the patient population studied. We aimed to analyse severe COVID-19 outcomes and to investigate the effects of the pandemic time period and the risks associated with individual IMIDs, classes of immunomodulatory medications (IMMs), chronic comorbidities, and COVID-19 vaccination status.

Authors

  • Qi Wei
  • Philip J Mease
    Providence St Joseph Health-Swedish Medical Center, Seattle, WA, USA.
  • Michael Chiorean
    Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA.
  • Lulu Iles-Shih
    Gastroenterology, Swedish Health and Swedish Medical Center, Seattle, Washington, USA.
  • Wanessa F Matos
    Institute for Systems Biology, Seattle, WA, USA.
  • Andrew Baumgartner
    Institute for Systems Biology, Seattle, WA, USA.
  • Sevda Molani
    Institute for Systems Biology, Seattle, WA, USA.
  • Yeon Mi Hwang
    Institute for Systems Biology, Seattle, WA, USA.
  • Basazin Belhu
    Institute for Systems Biology, Seattle, WA, USA.
  • Alexandra Ralevski
    Institute for Systems Biology, Seattle, WA, USA.
  • Jennifer Hadlock
    Institute for Systems Biology, Seattle, WA, USA; Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA. Electronic address: jennifer.hadlock@isbscience.org.

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