Abstract
SummaryBackgroundPrognostic models to predict the risk of clinical deterioration in acute COVID-19 cases are urgently required to inform clinical management decisions.MethodsWe 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).Findings74 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.InterpretationThe 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.FundingNational 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.
Highlights
The COVID-19 pandemic has continued to overwhelm health-care systems worldwide.[1]
Added value of this study We developed and validated the 4C Deterioration model, including 11 routinely measured demographic, clinical, and laboratory predictors, for prediction of in-hospital clinical deterioration among 74 944 consecutive adults recruited to the ISARIC4C study across 260 hospitals in England, Scotland, and Wales, in accordance with TRIPOD standards
In this Article, we extend this work through a larger study cohort to develop and validate a prognostic model for in-hospital clinical deterioration
Summary
The COVID-19 pandemic has continued to overwhelm health-care systems worldwide.[1] Effective triage of patients presenting to hospital for risk of progressive deterioration is crucial to inform clinical decision making and facilitate effective resource allocation, including hospital beds, critical care resources, and targeted drug therapies. Early identification of subgroups at higher risk of death or deterioration requiring ventilatory or critical care support enables targeted recruitment for randomised controlled trials of therapies with equipoise,[2] and more precise delivery of treatments for which effectiveness is known to vary according to disease severity (including corticosteroids and remdesivir).[3,4,5]. Most have been classified as being at a high risk of bias, and might not be generalisable, often because of inadequate sample sizes, reliance on single-centre data, and non-adherence to best practice methods or reporting standards during model development.[6,7] None of the Lancet Respir Med 2021; 9: 349–59
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