Clinical prediction models for COVID-19: development, international validation and use

  • Funded by Netherlands Organisation for Health Research and Development (ZonMW)
  • Total publications:0 publications

Grant number: 1.043E+13

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2021
  • Known Financial Commitments (USD)

    $171,319.2
  • Funder

    Netherlands Organisation for Health Research and Development (ZonMW)
  • Principle Investigator

    Pending
  • Research Location

    Netherlands, Europe
  • Lead Research Institution

    Erasmus Medical Center
  • Research Category

    Epidemiological studies

  • Research Subcategory

    Disease susceptibility

  • Special Interest Tags

    Gender

  • Study Subject

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

Project description To be able to estimate the expected course of their disease is crucial for good care for COVID-19 patients. For example: should someone be admitted to intensive care (IC) and for how long? What is the probability of death? Research In this research, prediction models are developed that provide an answer to these questions upon admission to the hospital. Data is used for this from more than 4000 patients from seven Dutch hospitals. The models use a limited number of factors that are easily measurable, such as respiratory rate, so that the model is easy and direct to use in all Dutch hospitals. Discussions will also follow with care providers, patients and family members about how the models best meet their wishes. The model is available as decision support in a web application: https://mdmerasmusmc.shinyapps.io/COPE/ . Desired outcomes The prediction models can then be used by healthcare providers to make decisions about the optimal care for the patient in consultation with patients and their families. Results: https://doi.org/10.1101/2020.12.30.20249023