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. Issue 9 (5th September 2022)

• Record Type: Journal Article
• Authors: Wiegand, Martin; Cowan, Sarah L; Waddington, Claire S; Halsall, David J; Keevil, Victoria L; Tom, Brian D M; Taylor, Vince; Gkrania-Klotsas, Effrossyni; Preller, Jacobus; Goudie, Robert J B
• Is Part Of: BMJ open. Volume 12:Issue 9(2022)
• Publication Date: 2022-09-05
• Subjects: COVID-19 -- epidemiology -- risk management -- statistics & research methods; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2021-060026
• Languages: English
• ISSNs: 2044-6055

Abstract

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 ).