“Underneath the visible” - COVID-19 Risk prediction tools in a high-volume, low-resource Emergency Department

Keywords: COVID-19, Mortality, Brescia COVID Respiratory Severity Scale

Abstract

Objectives: Patient risk stratification is the cornerstone of COVID-19 disease management; that has impacted health systems globally. We evaluated the performance of the Brescia-COVID Respiratory Severity Scale (BCRSS), CALL (Co-morbid, age, Lymphocyte and Lactate dehydrogenase) Score, and World Health Organization (WHO) guidelines in Emergency department (ED) on arrival, as predictors of outcomes; Intensive care unit (ICU) admission and in-hospital mortality.

Methods: A two-month retrospective chart review of 88 adult patients with confirmed COVID-19 pneumonia; requiring emergency management was conducted at ED, Indus Hospital and Health Network (IHHN), Karachi, Pakistan, (April 1 to May 31, 2020). The sensitivity, specificity, receiver operator characteristic curve (ROC) and area under the curve (AUC) for the scores were obtained to assess their predictive capability for outcomes.

Results: The in-hospital mortality rate was 48.9 % with 59.1 % ICU admissions and with a mean age at presentation of 56 ± 13 years. Receiver operator curve for BCRSS depicted good predicting capability for in hospital mortality [AUC 0.81(95% CI 0.71-0.91)] and ICU admission [AUC 0.73(95%CI 0.62-0.83)] amongst all models of risk assessment.

Conclusion: BCRSS depicted better prediction of in-hospital mortality and ICU admission. Prospective studies using this tool are needed to assess its utility in predicting high-risk patients and guide treatment escalation in LMIC’s.

doi: https://doi.org/10.12669/pjms.39.1.6043

How to cite this: Mukhtar S, Khatri SA, Khatri A, Ghouri N, Rybarczyk M. “Underneath the visible” - COVID-19 Risk prediction tools in a high-volume, low-resource Emergency Department. Pak J Med Sci. 2023;39(1):---------. doi: https://doi.org/10.12669/pjms.39.1.6043

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Published
2022-11-16
How to Cite
Mukhtar, S., Khatri, S. A., Khatri, A., Ghouri, N., & Rybarczyk, M. (2022). “Underneath the visible” - COVID-19 Risk prediction tools in a high-volume, low-resource Emergency Department. Pakistan Journal of Medical Sciences, 39(1). https://doi.org/10.12669/pjms.39.1.6043