BMJ Open (Sep 2022)

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

  • Claire S Waddington,
  • Effrossyni Gkrania-Klotsas,
  • Jacobus Preller,
  • Martin Wiegand,
  • Sarah L Cowan,
  • David J Halsall,
  • Victoria L Keevil,
  • Brian D M Tom,
  • Vince Taylor,
  • Robert J B Goudie

DOI
https://doi.org/10.1136/bmjopen-2021-060026
Journal volume & issue
Vol. 12, no. 9

Abstract

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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).