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Evaluation of Early Warning Scores on In-Hospital Mortality in COVID-19 Patients: A Tertiary Hospital Study from Taiwan.

Weide TsaiChun ChenSzu-Yang JoChien-Han HsiaoDing-Kuo ChienWen-Han ChangTse-Hao Chen
Published in: Medicina (Kaunas, Lithuania) (2023)
Coronavirus disease 2019 (COVID-19) remains a global pandemic. Early warning scores (EWS) are used to identify potential clinical deterioration, and this study evaluated the ability of the Rapid Emergency Medicine score (REMS), National Early Warning Score (NEWS), and Modified EWS (MEWS) to predict in-hospital mortality in COVID-19 patients. This study retrospectively analyzed data from COVID-19 patients who presented to the emergency department and were hospitalized between 1 May and 31 July 2021. The area under curve (AUC) was calculated to compare predictive performance of the three EWS. Data from 306 COVID-19 patients (61 ± 15 years, 53% male) were included for analysis. REMS had the highest AUC for in-hospital mortality (AUC: 0.773, 95% CI: 0.69-0.85), followed by NEWS (AUC: 0.730, 95% CI: 0.64-0.82) and MEWS (AUC: 0.695, 95% CI: 0.60-0.79). The optimal cut-off value for REMS was 6.5 (sensitivity: 71.4%; specificity: 76.3%), with positive and negative predictive values of 27.9% and 95.4%, respectively. Computing REMS for COVID-19 patients who present to the emergency department can help identify those at risk of in-hospital mortality and facilitate early intervention, which can lead to better patient outcomes.
Keyphrases
  • coronavirus disease
  • sars cov
  • emergency department
  • randomized controlled trial
  • electronic health record
  • risk assessment
  • machine learning
  • big data
  • quality improvement