Identification of biomarkers that predict severity of COVID-19 patients [Supplement added: Sex as a biological variable supplement, COVID-19 Variant Network]

  • Funded by Canadian Institutes of Health Research (CIHR)
  • Total publications:0 publications

Grant number: 170357, 171495, 175580

Grant search

Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2022
  • Known Financial Commitments (USD)

    $874,000
  • Funder

    Canadian Institutes of Health Research (CIHR)
  • Principal Investigator

    Pending
  • Research Location

    Canada, China
  • Lead Research Institution

    University of Nova Scotia
  • Research Priority Alignment

    N/A
  • Research Category

    Clinical characterisation and management

  • Research Subcategory

    Prognostic factors for disease severity

  • Special Interest Tags

    Innovation

  • Study Subject

    Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

The outbreak of the new coronavirus in Wuhan, China has infected over 75,000 people and has caused close to 2,000 deaths. One of the major problems with this outbreak is that emergency rooms, hospitals and ICU wards are overwhelmed with patients. In an effort to find a test for rapidly determining who should be admitted to the hospital and who should be placed in ICU, we have undertaken an international study to find a set of biomarkers that can be used to help Emergency Room doctors to make decisions on whether a patient will become severe. We have established an international team based in China, Vietnam, Spain, Italy, Mozambique, Sudan, Ethiopia, Egypt, Morocco, Cote D' Ivoire and Canada. This team will examine patients peripheral blood for biomarkers that predict the course of disease as mild or severe. The results of the study will be used to make a device that can be used in any situation and rapidly give results to predict the course of coronavirus infections.