TY - JOUR
T1 - Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19
T2 - a prospective cohort study
AU - Gupta, Rishi K.
AU - Harrison, Ewen M.
AU - Ho, Antonia
AU - Docherty, Annemarie B.
AU - Knight, Stephen R.
AU - van Smeden, Maarten
AU - Abubakar, Ibrahim
AU - Lipman, Marc
AU - Quartagno, Matteo
AU - Pius, Riinu
AU - Buchan, Iain
AU - Carson, Gail
AU - Drake, Thomas M.
AU - Dunning, Jake
AU - Fairfield, Cameron J.
AU - Gamble, Carrol
AU - Green, Christopher A.
AU - Halpin, Sophie
AU - Hardwick, Hayley E.
AU - Holden, Karl A.
AU - Horby, Peter W.
AU - Jackson, Clare
AU - Mclean, Kenneth A.
AU - Merson, Laura
AU - Nguyen-Van-Tam, Jonathan S.
AU - Norman, Lisa
AU - Olliaro, Piero L.
AU - Pritchard, Mark G.
AU - Russell, Clark D.
AU - Scott-Brown, James
AU - Shaw, Catherine A.
AU - Sheikh, Aziz
AU - Solomon, Tom
AU - Sudlow, Cathie
AU - Swann, Olivia V.
AU - Turtle, Lance
AU - Openshaw, Peter J.M.
AU - Baillie, J. Kenneth
AU - Semple, Malcolm G.
AU - Noursadeghi, Mahdad
AU - Openshaw, Peter JM
AU - Alex, Beatrice
AU - Bach, Benjamin
AU - Barclay, Wendy S.
AU - Bogaert, Debby
AU - Chand, Meera
AU - Cooke, Graham S.
AU - Filipe, Ana da Silva
AU - Fletcher, Tom
AU - Hiscox, Julian A.
N1 - Funding Information:
This work uses data provided by patients and collected by the NHS as part of their care and support. We are extremely grateful to the 2648 front-line NHS clinical and research staff and volunteer medical students who collected this data in challenging circumstances; and the generosity of the participants and their families for their individual contributions in these difficult times. We also acknowledge the support of Jeremy J Farrar (Wellcome Trust) and Nahoko Shindo (WHO). This work is supported by grants from the NIHR (award CO-CIN-01); the MRC (grant MC_PC_19059); the NIHR HPRU in Emerging and Zoonotic Infections at the University of Liverpool, in partnership with Public Health England (PHE), in collaboration with the Liverpool School of Tropical Medicine and the University of Oxford (award 200907); the NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927); the Wellcome Trust and Department for International Development (215091/Z/18/Z); the Bill & Melinda Gates Foundation (OPP1209135); the Liverpool Experimental Cancer Medicine Centre (grant reference C18616/A25153); the NIHR BRC at Imperial College London (IS-BRC-1215–20013); the EU Platform for European Preparedness Against (Re-)emerging Epidemics (FP7 project 602525); and the NIHR Clinical Research Network, who provided infrastructure support for this research. RKG is funded by the NIHR (DRF-2018-11-ST2-004). IA acknowledges NIHR Senior Investigator Award (NF-SI-0616-10037) funding. ML acknowledges NIHR funding (HTA 16/88/06). PJMO is supported by an NIHR senior investigator award (201385). LT is supported by the Wellcome Trust (award 205228/Z/16/Z). MN is funded by a Wellcome Trust investigator award (207511/Z/17/Z) and the NIHR University College London Hospitals BRC. The views expressed in this Article are those of the authors and not necessarily those of the DHSC, Department for International Development, NIHR, MRC, Wellcome Trust, or PHE.
Funding Information:
ABD reports grants from the UK Department of Health and Social Care (DHSC) during the conduct of the study and grants from Wellcome Trust outside of the submitted work. CAG reports grants from the DHSC NIHR during the conduct of the study. PWH reports grants from the Wellcome Trust, Department for International Development, Bill & Melinda Gates Foundation, and NIHR during the conduct of the study. JSN-V-T reports grants from DHSC, during the conduct of the study, and is seconded to DHSC. MN is supported by a Wellcome Trust investigator award and the NIHR University College London Hospitals Biomedical Research Centre (BRC). PJMO reports personal fees from consultancies and from the European Respiratory Society, grants from the UK Medical Research Council (MRC), MRC Global Challenge Research Fund, EU, NIHR BRC, MRC/GSK, Wellcome Trust, NIHR (Health Protection Research Unit [HPRU] in Respiratory Infection), and is an NIHR senior investigator outside of the submitted work; his role as President of the British Society for Immunology was unpaid but travel and accommodation at some meetings was provided by the Society. JKB reports grants from the MRC. MGS reports grants from the DHSC NIHR, MRC, and the HPRU in Emerging and Zoonotic Infections, University of Liverpool, during the conduct of the study; and is chair of the scientific advisory board and a minority share holder at Integrum Scientific (Greensboro, NC, USA) outside of the submitted work. LT reports grants from the HPRU in Emerging and Zoonotic Infections, University of Liverpool, during the conduct of the study, and grants from Wellcome Trust outside of the submitted work. All other authors declare no competing interests.
Funding Information:
This work uses data provided by patients and collected by the NHS as part of their care and support. We are extremely grateful to the 2648 front-line NHS clinical and research staff and volunteer medical students who collected this data in challenging circumstances; and the generosity of the participants and their families for their individual contributions in these difficult times. We also acknowledge the support of Jeremy J Farrar (Wellcome Trust) and Nahoko Shindo (WHO). This work is supported by grants from the NIHR (award CO-CIN-01); the MRC (grant MC_PC_19059); the NIHR HPRU in Emerging and Zoonotic Infections at the University of Liverpool, in partnership with Public Health England (PHE), in collaboration with the Liverpool School of Tropical Medicine and the University of Oxford (award 200907); the NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927); the Wellcome Trust and Department for International Development (215091/Z/18/Z); the Bill & Melinda Gates Foundation (OPP1209135); the Liverpool Experimental Cancer Medicine Centre (grant reference C18616/A25153); the NIHR BRC at Imperial College London (IS-BRC-1215?20013); the EU Platform for European Preparedness Against (Re-)emerging Epidemics (FP7 project 602525); and the NIHR Clinical Research Network, who provided infrastructure support for this research. RKG is funded by the NIHR (DRF-2018-11-ST2-004). IA acknowledges NIHR Senior Investigator Award (NF-SI-0616-10037) funding. ML acknowledges NIHR funding (HTA 16/88/06). PJMO is supported by an NIHR senior investigator award (201385). LT is supported by the Wellcome Trust (award 205228/Z/16/Z). MN is funded by a Wellcome Trust investigator award (207511/Z/17/Z) and the NIHR University College London Hospitals BRC. The views expressed in this Article are those of the authors and not necessarily those of the DHSC, Department for International Development, NIHR, MRC, Wellcome Trust, or PHE.
Publisher Copyright:
© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
KW - Aged
KW - Aged, 80 and over
KW - COVID-19/diagnosis
KW - Clinical Decision Rules
KW - Clinical Decision-Making/methods
KW - Clinical Deterioration
KW - Critical Care/statistics & numerical data
KW - Female
KW - Hospital Mortality
KW - Humans
KW - Intensive Care Units/statistics & numerical data
KW - Logistic Models
KW - Male
KW - Middle Aged
KW - Patient Admission/statistics & numerical data
KW - Prognosis
KW - Prospective Studies
KW - Reproducibility of Results
KW - Respiration, Artificial/statistics & numerical data
KW - SARS-CoV-2/isolation & purification
KW - Severity of Illness Index
KW - United Kingdom/epidemiology
UR - http://www.scopus.com/inward/record.url?scp=85099982800&partnerID=8YFLogxK
U2 - 10.1016/S2213-2600(20)30559-2
DO - 10.1016/S2213-2600(20)30559-2
M3 - Article
C2 - 33444539
SN - 2213-2600
VL - 9
SP - 349
EP - 359
JO - The Lancet Respiratory Medicine
JF - The Lancet Respiratory Medicine
IS - 4
ER -