COVID-19: Investigating Strategies for Mechanical Ventilation in COVID-19 via Computational Simulation of Virtual Patients
- Funded by UK Research and Innovation (UKRI)
- Total publications:6 publications
Grant number: EP/V014455/1
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$442,319.07Funder
UK Research and Innovation (UKRI)Principal Investigator
Declan BatesResearch Location
United KingdomLead Research Institution
University of WarwickResearch Priority Alignment
N/A
Research Category
Clinical characterisation and management
Research Subcategory
Supportive care, processes of care and management
Special Interest Tags
Digital Health
Study Type
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
1]. The pathophysiological basis for this disease phenotype is currently unclear. A recent study also noted a significant time-related disease spectrum in COVID-19 patients, with at least two potential "sub-phenotypes": Type L, characterized by low elastance (i.e. high compliance), low ventilation to perfusion ratio, low lung weight and low recruitability by imaging; and a Type H, characterized by high elastance, high right-to-left shunt, high lung weight and high recruitability
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