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Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study.

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Peer-reviewed

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Abstract

OBJECTIVES: To develop a disease stratification model for COVID-19 that updates according to changes in a patient's condition while in hospital to facilitate patient management and resource allocation. DESIGN: In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression. SETTING: All data used in this study were obtained from a single UK teaching hospital. PARTICIPANTS: We developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation. RESULTS: Our final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88). CONCLUSIONS: Our model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient's clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool. TRIAL REGISTRATION: The study is registered as 'researchregistry5464' on the Research Registry (www.researchregistry.com).

Description

Journal Title

BMJ Open

Conference Name

Journal ISSN

2044-6055
2044-6055

Volume Title

12

Publisher

BMJ

Rights and licensing

Except where otherwised noted, this item's license is described as Attribution-NonCommercial 4.0 International
Sponsorship
MRC (unknown)
National Institute for Health Research (IS-BRC-1215-20014)
Cambridge University Hospitals NHS Foundation Trust (CUH) (BRC)
Medical Research Council (G0701652)
MRC (MR/T023902/1)
Martin Wiegand was funded by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). Victoria L. Keevil was funded by the MRC/NIHR Clinical Academic Research Partnership Grant (CARP) [grant code MR/T023902/1]. Vince Taylor was funded by the Cancer Research UK Cambridge Centre. Effrossyni Gkrania-Klotsas was supported by the NIHR Clinical Research Network (CRN) Greenshoots Award. Brian D. M. Tom and Robert J. B. Goudie were funded by the UKRI Medical Research Council (MRC) [programme code MC_UU_00002/2] and supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014).