CODA-19: a Collaborative Data Analysis Platform to Improve Clinical Care in Patients with COVID-19

  • Funded by Canadian Institutes of Health Research (CIHR)
  • Total publications:2 publications

Grant number: 172742

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2020
  • Known Financial Commitments (USD)

    $703,462.5
  • Funder

    Canadian Institutes of Health Research (CIHR)
  • Principal Investigator

    Jonathan Afilalo, Patrick Archambault, David Llewellyn Buckeridge, Yiorgos Alexandros Cavayas, Michael Chasse, Joelle Pineau, Alexis F Turgeon, Han Ting Wang
  • Research Location

    Canada
  • Lead Research Institution

    Centre hospitalier de l'Université de Montréal (CHUM) Medicine
  • Research Priority Alignment

    N/A
  • Research Category

    Clinical characterisation and management

  • Research Subcategory

    Disease pathogenesis

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

COVID-19 is a highly contagious acute respiratory illness that has undergone rapid global spread in the beginning of 2020. There is a pressing need to develop tools that can help physicians diagnose COVID-19 rapidly, determine if different disease presentations warrant different types of treatment, flag patients at high risk of deteriorating, and ensure healthcare resources are attributed efficiently and equitably. Through an established partnership with 9 hospitals, a collaborative analysis platform has been developed to pool data from multiple sites while minimizing the exchange of patient-level information. This collaboration is building on a large database of biological data from patients tested for COVID-19 that is being collected in these hospitals. Risk prediction models will be developed to identify patients at high risk of COVID prior to the availability of definitive testing, characterize distinct disease trajectories, intervene pre-emptively in patients at high risk of clinical deterioration, and make forecasts to plan hospital resources and staffing. The accuracy of predictions will be continuously verified using new cases, which will be identified from different hospital sites in real time. These predictive models will be used to build tools that can help physicians better treat patients with COVID-19, and provide actionable recommendations to support Canada's response to COVID-19.

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CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data.