Prognostic accuracy of early warning scores for predicting serious illness and in-hospital mortality in patients with COVID-19.

A simple bedside triage tool is essential to stratify COVID-19 patients in the emergency department (ED). This study aimed to identify an early warning score (EWS) that could best predict the clinical outcomes in COVID-19 patients. Data were obtained from medical records of 219 laboratory-confirmed COVID-19 positive patients. We calculated 13 EWSs based on the admission characteristics of the patients. Receiver operating characteristic (ROC) curve analysis was used to assess the performance of the scores in predicting serious illness and in-hospital mortality. The median patient age was 51 (38, 60) years, and 25 (11.4%) patients died. Among patients admitted with mild to moderate illness (n = 175), 61 (34.9%) developed serious illness. Modified National Early Warning Score (m-NEWS) (AUROC 0.766; 95% CI: 0.693, 0.839) and Rapid Emergency Medicine Score (REMS) (AUROC 0.890; 95% CI: 0.818, 0.962) demonstrated the highest AUROC point estimates in predicting serious illness and in-hospital mortality, respectively. Both m-NEWS and REMS demonstrated good accuracy in predicting both the outcomes. However, no significant difference was found between m-NEWS (p = 0.983) and REMS (p = 0.428) as well as some other EWSs regarding the AUROCs in predicting serious illness and in-hospital mortality. We propose m-NEWS could be used as a triage score to identify COVID-19 patients at risk of disease progression and death especially in resource-poor settings because it has been explicitly developed for risk stratification of COVID-19 patients in some countries like China and Italy. However, this tool needs to be validated by further large-scale prospective studies.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Kamal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)