Rishi K Gupta1, Ewen M Harrison2, Antonia Ho3, Annemarie B Docherty4, Stephen R Knight5, Maarten van Smeden6, Ibrahim Abubakar1, Marc Lipman7, Matteo Quartagno8, Riinu Pius5, Iain Buchan9, Gail Carson10, Thomas M Drake5, Jake Dunning11, Cameron J Fairfield5, Carrol Gamble12, Christopher A Green13, Sophie Halpin12, Hayley E Hardwick14, Karl A Holden14, Peter W Horby10, Clare Jackson12, Kenneth A Mclean5, Laura Merson10, Jonathan S Nguyen-Van-Tam15, Lisa Norman5, Piero L Olliaro10, Mark G Pritchard16, Clark D Russell17, James Scott-Brown18, Catherine A Shaw5, Aziz Sheikh5, Tom Solomon19, Cathie Sudlow20, Olivia V Swann21, Lance Turtle22, Peter J M Openshaw23, J Kenneth Baillie24, Malcolm G Semple25, Mahdad Noursadeghi26. 1. Institute for Global Health, University College London, London, UK. 2. Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK; Department of Clinical Surgery, University of Edinburgh, Edinburgh, UK. 3. Medical Research Council, University of Glasgow Centre for Virus Research, Glasgow, UK; Department of Infectious Diseases, Queen Elizabeth University Hospital, Glasgow, UK. 4. Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK; Intensive Care Unit, Royal Infirmary Edinburgh, Edinburgh, UK. 5. Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK. 6. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands. 7. UCL Respiratory, Division of Medicine, University College London, London, UK; Royal Free Hospitals NHS Trust, London, UK. 8. MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK. 9. Institute of Population Health Sciences, University of Liverpool, Liverpool, UK. 10. ISARIC Global Support Centre, Nuffield Department of Medicine, University of Oxford, Oxford, UK. 11. National Infection Service, Public Health England, London, UK; National Heart and Lung Institute, Imperial College London, London, UK. 12. Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK. 13. Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK. 14. NIHR Health Protection Research Unit, Institute of Infection, Veterinary, and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK. 15. Division of Epidemiology and Public Health, University of Nottingham School of Medicine, Nottingham, UK. 16. Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK. 17. Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK. 18. School of Informatics, University of Edinburgh, Edinburgh, UK. 19. NIHR Health Protection Research Unit, Institute of Infection, Veterinary, and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK; Walton Centre NHS Foundation Trust, Liverpool, UK. 20. Health Data Research UK, London, UK. 21. Department of Child Life and Health, University of Edinburgh, Edinburgh, UK. 22. NIHR Health Protection Research Unit, Institute of Infection, Veterinary, and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK; Tropical and Infectious Disease Unit, Royal Liverpool University Hospital, Liverpool, UK. 23. National Heart and Lung Institute, Imperial College London, London, UK. 24. Roslin Institute, University of Edinburgh, Edinburgh, UK; Intensive Care Unit, Royal Infirmary Edinburgh, Edinburgh, UK. 25. NIHR Health Protection Research Unit, Institute of Infection, Veterinary, and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK; Respiratory Medicine, Alder Hey Children's Hospital, Institute in The Park, University of Liverpool, Liverpool, UK. Electronic address: m.g.semple@liverpool.ac.uk. 26. Division of Infection and Immunity, University College London, London, UK. Electronic address: m.noursadeghi@ucl.ac.uk.
Abstract
BACKGROUND: Prognostic models to predict the risk of clinical deterioration in acute COVID-19 cases are urgently required to inform clinical management decisions. METHODS: 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) among consecutively hospitalised adults with highly suspected or confirmed COVID-19 who were prospectively recruited to the International Severe Acute Respiratory and Emerging Infections Consortium Coronavirus Clinical Characterisation Consortium (ISARIC4C) study across 260 hospitals in England, Scotland, and Wales. Candidate predictors that were specified a priori were considered for inclusion in the model on the basis of previous prognostic scores and emerging literature describing routinely measured biomarkers associated with COVID-19 prognosis. We used internal-external cross-validation to evaluate discrimination, calibration, and clinical utility across eight National Health Service (NHS) regions in the development cohort. We further validated the final model in held-out data from an additional NHS region (London). FINDINGS: 74 944 participants (recruited between Feb 6 and Aug 26, 2020) were included, of whom 31 924 (43·2%) of 73 948 with available outcomes met the composite clinical deterioration outcome. In internal-external cross-validation in the development cohort of 66 705 participants, the selected model (comprising 11 predictors routinely measured at the point of hospital admission) showed consistent discrimination, calibration, and clinical utility across all eight NHS regions. In held-out data from London (n=8239), the model showed a similarly consistent performance (C-statistic 0·77 [95% CI 0·76 to 0·78]; calibration-in-the-large 0·00 [-0·05 to 0·05]); calibration slope 0·96 [0·91 to 1·01]), and greater net benefit than any other reproducible prognostic model. INTERPRETATION: The 4C Deterioration model has strong potential for clinical utility and generalisability to predict clinical deterioration and inform decision making among adults hospitalised with COVID-19. FUNDING: National Institute for Health Research (NIHR), UK Medical Research Council, Wellcome Trust, Department for International Development, Bill & Melinda Gates Foundation, EU Platform for European Preparedness Against (Re-)emerging Epidemics, NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool, NIHR HPRU in Respiratory Infections at Imperial College London.
BACKGROUND: Prognostic models to predict the risk of clinical deterioration in acute COVID-19 cases are urgently required to inform clinical management decisions. METHODS: 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) among consecutively hospitalised adults with highly suspected or confirmed COVID-19 who were prospectively recruited to the International Severe Acute Respiratory and Emerging Infections Consortium Coronavirus Clinical Characterisation Consortium (ISARIC4C) study across 260 hospitals in England, Scotland, and Wales. Candidate predictors that were specified a priori were considered for inclusion in the model on the basis of previous prognostic scores and emerging literature describing routinely measured biomarkers associated with COVID-19 prognosis. We used internal-external cross-validation to evaluate discrimination, calibration, and clinical utility across eight National Health Service (NHS) regions in the development cohort. We further validated the final model in held-out data from an additional NHS region (London). FINDINGS: 74 944 participants (recruited between Feb 6 and Aug 26, 2020) were included, of whom 31 924 (43·2%) of 73 948 with available outcomes met the composite clinical deterioration outcome. In internal-external cross-validation in the development cohort of 66 705 participants, the selected model (comprising 11 predictors routinely measured at the point of hospital admission) showed consistent discrimination, calibration, and clinical utility across all eight NHS regions. In held-out data from London (n=8239), the model showed a similarly consistent performance (C-statistic 0·77 [95% CI 0·76 to 0·78]; calibration-in-the-large 0·00 [-0·05 to 0·05]); calibration slope 0·96 [0·91 to 1·01]), and greater net benefit than any other reproducible prognostic model. INTERPRETATION: The 4C Deterioration model has strong potential for clinical utility and generalisability to predict clinical deterioration and inform decision making among adults hospitalised with COVID-19. FUNDING: National Institute for Health Research (NIHR), UK Medical Research Council, Wellcome Trust, Department for International Development, Bill & Melinda Gates Foundation, EU Platform for European Preparedness Against (Re-)emerging Epidemics, NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool, NIHR HPRU in Respiratory Infections at Imperial College London.
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