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: a retrospective cohort study

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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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).
Original languageEnglish
Article numbere060026
JournalBMJ Open
Volume12
Issue number9
DOIs
Publication statusPublished - 5 Sept 2022
Externally publishedYes

Keywords

  • COVID-19
  • epidemiology
  • risk management
  • statistics & research methods

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