%0 Journal Article %1 knight2020stratification %A Knight, Stephen R %A Ho, Antonia %A Pius, Riinu %A Buchan, Iain %A Carson, Gail %A Drake, Thomas M %A Dunning, Jake %A Fairfield, Cameron J %A Gamble, Carrol %A Green, Christopher A %A Gupta, Rishi %A Halpin, Sophie %A Hardwick, Hayley E %A Holden, Karl A %A Horby, Peter W %A Jackson, Clare %A Mclean, Kenneth A %A Merson, Laura %A Nguyen-Van-Tam, Jonathan S %A Norman, Lisa %A Noursadeghi, Mahdad %A Olliaro, Piero L %A Pritchard, Mark G %A Russell, Clark D %A Shaw, Catherine A %A Sheikh, Aziz %A Solomon, Tom %A Sudlow, Cathie %A Swann, Olivia V %A Turtle, Lance CW %A Openshaw, Peter JM %A Baillie, J Kenneth %A Semple, Malcolm G %A Docherty, Annemarie B %A Harrison, Ewen M %D 2020 %I BMJ Publishing Group Ltd %J BMJ %K covid-19 predictors %R 10.1136/bmj.m3339 %T 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 %U https://www.bmj.com/content/370/bmj.m3339 %V 370 %X Objective To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19).Design Prospective observational cohort study.Setting International 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.Participants Adults (age >=18 years) admitted to hospital with covid-19 at least four weeks before final data extraction.Main outcome measure In-hospital mortality.Results 35 463 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).Conclusions An 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 registration ISRCTN66726260

@article{knight2020stratification, abstract = {Objective To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19).Design Prospective observational cohort study.Setting International 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{\textemdash}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.Participants Adults (age >=18 years) admitted to hospital with covid-19 at least four weeks before final data extraction.Main outcome measure In-hospital mortality.Results 35 463 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).Conclusions An 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 registration ISRCTN66726260}, added-at = {2021-11-26T14:14:29.000+0100}, author = {Knight, Stephen R and Ho, Antonia and Pius, Riinu and Buchan, Iain and Carson, Gail and Drake, Thomas M and Dunning, Jake and Fairfield, Cameron J and Gamble, Carrol and Green, Christopher A and Gupta, Rishi and Halpin, Sophie and Hardwick, Hayley E and Holden, Karl A and Horby, Peter W and Jackson, Clare and Mclean, Kenneth A and Merson, Laura and Nguyen-Van-Tam, Jonathan S and Norman, Lisa and Noursadeghi, Mahdad and Olliaro, Piero L and Pritchard, Mark G and Russell, Clark D and Shaw, Catherine A and Sheikh, Aziz and Solomon, Tom and Sudlow, Cathie and Swann, Olivia V and Turtle, Lance CW and Openshaw, Peter JM and Baillie, J Kenneth and Semple, Malcolm G and Docherty, Annemarie B and Harrison, Ewen M}, biburl = {https://www.bibsonomy.org/bibtex/29fd11d3e01aba63bbc67a4fa65ca7649/fordham1}, description = {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 | The BMJ}, doi = {10.1136/bmj.m3339}, elocation-id = {m3339}, eprint = {https://www.bmj.com/content/370/bmj.m3339.full.pdf}, interhash = {cc94f3bd2958ae5e46b49ae24f3cbf67}, intrahash = {9fd11d3e01aba63bbc67a4fa65ca7649}, journal = {BMJ}, keywords = {covid-19 predictors}, publisher = {BMJ Publishing Group Ltd}, timestamp = {2021-11-26T14:14:29.000+0100}, title = {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}, url = {https://www.bmj.com/content/370/bmj.m3339}, volume = 370, year = 2020 }