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

Abstract

ObjectiveTo develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19).DesignProspective observational cohort study.SettingInternational Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study (performed by the ISARIC Coronavirus Clinical Characterisation Consortium—ISARIC-4C) in 260 hospitals across England, Scotland, and Wales. 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 after model development between 21 May and 29 June 2020.ParticipantsAdults (age ≥18 years) admitted to hospital with covid-19 at least four weeks before final data extraction.Main outcome measureIn-hospital mortality.Results35 463 patients were included in the derivation dataset (mortality rate 32.2%) and 22 361 in the validation dataset (mortality rate 30.1%). 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 level, and C reactive protein (score range 0-21 points). The 4C Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.78 to 0.79; validation cohort: 0.77, 0.76 to 0.77) with excellent calibration (validation: calibration-in-the-large=0, slope=1.0). Patients with a score of at least 15 (n=4158, 19%) had a 62% mortality (positive predictive value 62%) compared with 1% mortality for those with a score of 3 or less (n=1650, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (area under the receiver operating characteristic curve range 0.61-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73).ConclusionsAn easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups. The score should be further validated to determine its applicability in other populations.Study registrationISRCTN66726260

Development and validation of the 4C Deterioration model for adults hospitalised with COVID-19

“…Our 4C Deterioration model can be implemented programmatically alongside our previously reported 4C Mortality Score 9 . The comparison of 4C Deterioration vs. 4C Mortality predictions among the London validation cohort showed higher predictions for deterioration overall; these differences were amplified among younger age groups.…”

“…We specified candidate predictor variables a priori based on previous prognostic scores, and emerging literature describing routinely measured biomarkers associated with COVID-19 prognosis, as described previously 9 . We only considered predictors available in at least 60% of the study population for further analysis.…”

“…Decision curve analysis was also done in the internal-external cross-validation validation sets to quantify the net benefit of implementing the model in clinical practice 28 , compared to: (a) a 'treat all' approach; (b) a 'treat none' approach; and (c) using other candidate generic and COVID-specific clinical prognostic models to stratify treatment, identified by recent systematic reviews 6,8,9 . Only candidate models where constituent variables were available among >60% of the cohort were considered.…”

“…In summary, we present a prognostic model for clinical deterioration among hospitalised adults with community or hospital acquired COVID-19, validated in nine NHS regions in England, Scotland and Wales. The model uses readily available clinical predictors and will be made freely available online alongside our previously reported mortality risk score (https://isaric4c.net/outputs/4c_score/) 9…”

The Simple Science of Sanity, Certainty, & Peace-of-Mind—Empowering ‘Intent’ Detoxifies Psychosis

“…The harsh reality that this graph points to, is that more people die when what the government says isn't relied on, isn't TRUSTED. The overall death rate from Covid is around 1%, rising, for instance in the UK, to 32% in those hospitalized (Knight, 2020). But viral diseases aren't mysterious-for the virus to make you ill, you have to have direct personal, physical contact with it.…”