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


Stephen R Knight, Antonia Ho, Riinu Pius, Iain Buchan, Gail Carson, Thomas M Drake, Jake Dunning, Cameron J Fairfield, Carrol Gamble, Christopher A Green, Rishi K Gupta, Sophie Halpin, Hayley Hardwick, Karl Holden, Peter W Horby, Clare Jackson, Kenneth A, Ewen M Harrison Knight, Stephen R Ho, Antonia Pius, Riinu Buchan, Iain Carson, Gail Drake, Thomas M Dunning, Jake Fairfield, Cameron J Gamble, Carrol Green, Christopher A Gupta, Rishi K Halpin, Sophie Hardwick, Hayley Holden, Karl Horby, Peter W Jackson, Clare McLean, Kenneth A Merson, Laura Nguyen-Van-Tam, Jonathan S Norman, Lisa Noursadeghi, Mahdad Olliaro, Piero L Pritchard, Mark G Russell, Clark D Shaw, Catherine A Sheikh, Aziz Solomon, Tom Sudlow, Cathie Swann, Olivia V Turtle, Lance Openshaw, Peter J M Baillie, J Kenneth Semple, Malcolm Gracie Docherty, Annemarie B Harrison, Ewen M


Objectives To develop and validate a pragmatic risk score to predict mortality for patients admitted to hospital with covid-19. Design Prospective observational cohort study: ISARIC WHO CCP-UK study (ISARIC Coronavirus Clinical Characterisation Consortium [4C]). Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited between 21 May and 29 June 2020. Setting 260 hospitals across England, Scotland, and Wales. Participants Adult patients (≥18 years) admitted to hospital with covid-19 admitted at least four weeks before final data extraction. Main outcome measures In-hospital mortality. Results There were 34 692 patients included in the derivation dataset (mortality rate 31.7%) and 22 454 in the validation dataset (mortality 31.5%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea, and C-reactive protein (score range 0-21 points). The 4C risk stratification score demonstrated high discrimination for mortality (derivation cohort: AUROC 0.79; 95% CI 0.78 - 0.79; validation cohort 0.78, 0.77-0.79) with excellent calibration (slope = 1.0). Patients with a score ≥15 (n = 2310, 17.4%) had a 67% mortality (i.e., positive predictive value 67%) compared with 1.0% mortality for those with a score ≤3 (n = 918, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (AUROC range 0.60-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). Conclusions We have developed and validated an easy-to-use risk stratification score based on commonly available parameters at hospital presentation. This outperformed existing scores, demonstrated utility to directly inform clinical decision making, and can be used to stratify inpatients with covid-19 into different management groups. The 4C Mortality Score may help clinicians identify patients with covid-19 at high risk of dying during current and subsequent waves of the pandemic.