Understanding and modelling COVID-19 mortality and severity [COVID-19]
Research type
Research Study
Full title
Understanding and modelling COVID-19 mortality and severity using Electronic Health Record data: An observational cohort study
IRAS ID
281766
Contact name
Robert J B Goudie
Contact email
Sponsor organisation
Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge
Clinicaltrials.gov Identifier
researchregistry5464, Protocol registration
Duration of Study in the UK
1 years, 9 months, 16 days
Research summary
COVID-19 has the potential to become one of the largest pandemics in human history. The number of cases in the UK is expected to rise rapidly. It may overwhelm the National Health Service.\n\nIn this study, we will use anonymous data extracted from the medical records to understand which patient characteristics are indicative of an increased chance of death (and other outcomes) in patients with COVID-19. The data in this study will come from Cambridge University Hospitals (CUH), UK. As a retrospective observational study that uses previously-collected data, this study will have no direct impact on the clinical care that any patient receives. Data will be extracted from March 2020 till December 2020. Included patients will be adults who either are diagnosed with COVID-19 at CUH or are admitted with a prior COVID-19 diagnosis.\n\nOur main aim is to develop a model that predicts the risk of death in patients diagnosed with COVID-19. We will also model the severity and resource needs of patients diagnosed with COVID-19: whether patients were admitted to ICU; how many days patients stayed on ICU; how many days patients stayed in hospital; whether patients needed higher level support (e.g. non-invasive ventilation); whether patients experienced organ failure; and whether patients were referred to specialised ICU services. We will also look at longer term outcomes.\n\nThis will help us to understand the patient characteristics that indicate an increased risk from this disease, and allow the prediction of a variety of adverse outcomes in an individual patient. Understanding the course of the disease may, in the future, inform targeting of treatments at those most likely to benefit from them, and may assist with prediction of the hospital resources required in the coming weeks. \n\nNo specific funding has been sought or obtained for this study.
REC name
West Midlands - Coventry & Warwickshire Research Ethics Committee
REC reference
20/WM/0125
Date of REC Opinion
3 Apr 2020
REC opinion
Favourable Opinion