Developing COVID-19 triaging and prognostication tools [COVID-19]
Research type
Research Study
Full title
Developing COVID-19 triaging and prognostication tools using conventional statistical analyses and Machine Learning (ML).
IRAS ID
285848
Contact name
Ingeborg D Welters
Contact email
Sponsor organisation
University of Liverpool Joint Research Office
Duration of Study in the UK
1 years, 0 months, 1 days
Research summary
The COVID-19 pandemic poses massive strains on healthcare systems. Rapid decisions are required to ensure optimal management can be maintained during a pandemic surge. Escalation planning is mandatory in hospital to provide medical care even if the number of cases exceeds routine hospital capacity, with a high need for oxygen, mechanical ventilation and other organ support.
Three major aspects have to be taken into consideration for planning healthcare provision during pandemic:
1) Which patients can safely be discharged home?
2) Which patients will need breathing support, including oxygen therapy, non-invasive respiratory support and invasive ventilation?
3) Which patients will need readmission with COVID-associated complications, including cardiovascular and cardiorespiratory disease?
Various protocols for triage tools to intensive care have been suggested for use in a pandemic. In previous work, we have investigated and refined triaging tools for influenza pandemics to specifically assess safe discharge home and need for Intensive Care Unit (ICU) admission (Morton et al BJA, 2015, and Morton et al, QJM 2016). In this project we wish to expand this work to the COVID-19 pandemic. We aim to develop triaging tools to predict the need for mechanical ventilation in COVID-19 patients and predict if patients are safe for discharge home. Furthermore we aim to predict long term sequelae of COVID-19 with a focus on cardiovascular and cardiorespiratory disease.
REC name
East of England - Cambridge South Research Ethics Committee
REC reference
20/EE/0226
Date of REC Opinion
19 Oct 2020
REC opinion
Further Information Favourable Opinion