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Publication Detail
Development and validation of the 4C Deterioration model for adults hospitalised with COVID-19
  • Publication Type:
    Working discussion paper
  • Authors:
    Gupta R, Harrison E, Ho A, Docherty A, Knight S, van Smeden M, Abubakar I, Lipman M, Quartagno M, Pius R, Buchan I, Carson G, Drake T, Dunning J, Fairfield C, Gamble C, Green C, Halpin S, Hardwick H, Holden K, Horby P, Jackson C, Mclean K, Merson L, Nguyen-Van-Tam J, Norman L, Olliaro P, Pritchard M, Russell C, Scott-Brown J, Shaw C, Sheikh A, Solomon T, Sudlow C, Swann O, Turtle L, Openshaw PJM, Baillie K, Semple M, Noursadeghi M
  • Publication date:
    13/10/2020
  • Status:
    Published
Abstract
Prognostic models to predict the risk of clinical deterioration in acute COVID-19 are required to inform clinical management decisions. Among 75,016 consecutive adults across England, Scotland and Wales prospectively recruited to the ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C) study, we developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) using 11 routinely measured variables. We used internal-external cross-validation to show consistent measures of discrimination, calibration and clinical utility across eight geographical regions. We further validated the final model in held-out data from 8,252 individuals in London, with similarly consistent performance (C-statistic 0.77 (95% CI 0.75 to 0.78); calibration-in-the-large 0.01 (−0.04 to 0.06); calibration slope 0.96 (0.90 to 1.02)). Importantly, this model demonstrated higher net benefit than using other candidate scores to inform decision-making. Our 4C Deterioration model thus demonstrates unprecedented clinical utility and generalisability to predict clinical deterioration among adults hospitalised with COVID-19.
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Faculty of Pop Health Sciences
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Clinical Epidemiology
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Respiratory Medicine
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Div of Infection & Immunity
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MRC Clinical Trials Unit at UCL
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Population Science & Experimental Medicine
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