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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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Wiegand, Martin 
Cowan, Sarah L 
Waddington, Claire S 
Halsall, David J 
Keevil, Victoria L 


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 was 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 March 1 and September 12, 2020; and temporally validated using data on 1119 patients presenting between September 13, 2020 and March 17, 2021.

Primary and secondary Outcomes: 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, SpO2/FiO2 ratio, white cell count, presence of acidosis (pH < 7.35) and Interleukin-6. Internal validation achieved an AUROC of 0.90 (95% CI 0.87–0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83-0.88).

Conclusion: Our model incorporates both static risk factors (e.g. 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. Upon 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 (



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BMJ Open

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BMJ Journals
MRC (unknown)
National Institute for Health Research (IS-BRC-1215-20014)
Cambridge University Hospitals NHS Foundation Trust (CUH) (BRC)
Medical Research Council (G0701652)
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).