This group of researchers from the UK sought to develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with COVID.
This prospective cohort study was performed at 260 hospitals in the UK in early 2020.
35,463 patients were included in the derivation phase and 22,361 in the internal validation. The mortality was about 30% of these admitted patients… yikes!
They used a complicated three stage model building process and used some regression analysis, machine learning and lots of other things probably best understood by those with a PhD in biostatistics.
In the end, they came up with 8 variables to predict mortality. They included age, sex, number of comorbidities, respiratory rate, oxygen saturations, GCS, urea, and CRP.
Although they intended for this score to be quite simple, nobody is going to memorize the components and how to add things up. Fortunately, mdcalc.com can do it for you.
The researchers also compared their score to 15 others in existence and thought theirs to be the best.
So, use this score if you like.
Some would argue that this score is already outdated. In the last year we have learned a lot more about COVID. Treatments have changed. And biggest limitation of all, most of the data was derived from an unvaccinated cohort.
Either way, this score has some utility. And it’s just like all risk stratification scores out there.
Yes… (wait for it) … sicker patients do worse.
Or more specifically; sicker patients with worse manifestation of disease, who are older with more comorbid illness, have worse x-rays & blood tests, and lack of response to initial treatment do worse.
I use that score for everything. But for some reason, I can't find it on Mdcalc.
Knight SR, Ho A, Pius R, et al. Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score. BMJ 2020;370:m3339. [Link to full text article]
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