Wiegand M, Cowan SL, Waddington CS, Halsall DJ, Keevil VL, Tom BDM, Taylor V, Gkrania-Klotsas E, Preller J, Goudie RJB. 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.
BMJ Open 2022;
12:e060026. [PMID:
36691139 PMCID:
PMC9445230 DOI:
10.1136/bmjopen-2021-060026]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/13/2022] [Indexed: 02/02/2023] Open
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).
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