The 4C (Coronavirus Clinical Characterisation Consortium) Mortality Score uses readily available data to accurately categorise patients as being at low, intermediate, high, or very high risk of death, and should be further tested to determine its applicability in other populations, say the researchers.
As hospitals around the world are faced with an influx of patients with COVID-19, there is an urgent need for an accurate risk stratification tool to identify patients at highest risk of death and inform treatment decisions and make the best use of healthcare resources. But most existing prognostic scores have shown moderate performance at best and no benefit to clinical decision-making. So a team of UK researchers set out to develop and validate a pragmatic risk stratification score to predict mortality in patients admitted to hospital with covid-19 and then compare this with existing models.
To develop the model, they collected routine data from 35,463 adults (median age 74 years) with covid-19 who were admitted to 260 hospitals across England, Scotland and Wales between 6 February and 20 May 2020. Measures included age, sex, number of underlying conditions (comorbidities), respiratory rate (number of breaths per minute), blood oxygen concentration, level of consciousness, urea, and C-reactive protein (a chemical linked to inflammation). These were then entered into the model to give a score ranging from 0-21 points. Risk cut-off values were defined by the total point score for an individual which represented a low (less than 2% mortality rate), intermediate (2-14.9%) or high-risk (15% or more) groups, similar to commonly used pneumonia risk scores.
Patients with a score of 15 or more had a 62% mortality compared with 1% mortality for those with a score of 3 or less. This suggests that patients with a 4C Mortality Score falling within the low risk groups might be suitable for management in the community, while those within the intermediate risk group might be suitable for ward level monitoring, say the researchers. Meanwhile patients with a score of 9 or higher were at high risk of death (around 40%), which could prompt aggressive treatment, including the initiation of steroids and early escalation to critical care if appropriate. To validate the model, the researchers tested it on a further 22,361 patients admitted to the same hospitals between 21 May and 29 June 2020. They found similar score performance, even after taking account of other potentially important factors.
Finally, they compared the model with existing risk scores and found that it demonstrated high discrimination for mortality with excellent calibration. It compared favourably to 15 pre-existing models, including ‘best-in-class’ machine learning techniques and demonstrated consistency across all performance measures. This is an observational study, so can’t establish cause, and the researchers point to some limitations that may have affected the performance and generalisability of the score, for example among younger patients and in settings outside the UK.
However, the researchers conclude that the 4C Mortality Score is an easy-to-use and valid prediction tool for in-hospital mortality, accurately categorising patients as being at low, intermediate, high, or very high risk of death. The score outperformed other risk stratification tools, showed clinical decision making utility, and had similar performance to more complex models, they add, and should be further validated to determine its applicability in other populations.
Source: The BMJ today