Tool identifies COVID-19 patients at highest risk of deterioration
A new risk-stratification tool has been developed by researchers from the UK Coronavirus Clinical Characterisation Consortium (known as ISARIC4C), which includes the University of Oxford.
The tool can accurately predict the likelihood of deterioration in adults hospitalised with COVID-19. ISARIC4C was funded as part of a joint rapid research response by: UK Research and Innovation Department of Health and Social Care through the National Institute for Health Research (NIHR).
Researchers say the online tool, made freely available to NHS doctors, could support clinicians’ decision making – helping to improve patient outcomes and ultimately save lives.
The tool assesses 11 measurements routinely collected from patients including age, gender, and physical measurements (such as oxygen levels) along with some standard laboratory tests. It calculates a percentage risk of deterioration, known as the ‘4C Deterioration Score’.
This innovation, published in The Lancet Respiratory Medicine, builds on the consortium’s previous work developing the ‘4C mortality score’ to predict the percentage risk of death from COVID after admission to hospital.
The ‘4C mortality score’ is already recommended for use by NHS England (PDF, 511KB) to guide anti-viral treatments (Remdesivir). Doctors will now see both the ‘4C deterioration score’ and the ‘4C mortality score’ at the same time, using the same tool.
The tool was developed using data from 74,944 individuals with COVID-19 admitted to 260 hospitals across England, Scotland and Wales, between 6 February and 26 August 2020.
Researchers assessed how well the tool performed in nine NHS regions and found that it performed similarly well in each, suggesting that it is likely to be useful across the NHS. Importantly, the new risk score showed superior performance across the NHS, in comparison to previous risk scores.
The tool can potentially be incorporated into NHS Trusts’ Electronic Health Record System – used to manage all patient care – so that risk scores are automatically generated for patients.
