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

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 Hea...

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Autores principales: Mukhtar, Sama, Khatri, Sarfaraz Ahmed, Khatri, Adeel, Ghouri, Nida, Rybarczyk, Megan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Professional Medical Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9842993/
https://www.ncbi.nlm.nih.gov/pubmed/36694781
http://dx.doi.org/10.12669/pjms.39.1.6043
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author Mukhtar, Sama
Khatri, Sarfaraz Ahmed
Khatri, Adeel
Ghouri, Nida
Rybarczyk, Megan
author_facet Mukhtar, Sama
Khatri, Sarfaraz Ahmed
Khatri, Adeel
Ghouri, Nida
Rybarczyk, Megan
author_sort Mukhtar, Sama
collection PubMed
description 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.
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spelling pubmed-98429932023-01-23 “Underneath the visible” - COVID-19 Risk prediction tools in a high-volume, low-resource Emergency Department Mukhtar, Sama Khatri, Sarfaraz Ahmed Khatri, Adeel Ghouri, Nida Rybarczyk, Megan Pak J Med Sci Original Article 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. Professional Medical Publications 2023 /pmc/articles/PMC9842993/ /pubmed/36694781 http://dx.doi.org/10.12669/pjms.39.1.6043 Text en Copyright: © Pakistan Journal of Medical Sciences https://creativecommons.org/licenses/by/3.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0 (https://creativecommons.org/licenses/by/3.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Mukhtar, Sama
Khatri, Sarfaraz Ahmed
Khatri, Adeel
Ghouri, Nida
Rybarczyk, Megan
“Underneath the visible” - COVID-19 Risk prediction tools in a high-volume, low-resource Emergency Department
title “Underneath the visible” - COVID-19 Risk prediction tools in a high-volume, low-resource Emergency Department
title_full “Underneath the visible” - COVID-19 Risk prediction tools in a high-volume, low-resource Emergency Department
title_fullStr “Underneath the visible” - COVID-19 Risk prediction tools in a high-volume, low-resource Emergency Department
title_full_unstemmed “Underneath the visible” - COVID-19 Risk prediction tools in a high-volume, low-resource Emergency Department
title_short “Underneath the visible” - COVID-19 Risk prediction tools in a high-volume, low-resource Emergency Department
title_sort “underneath the visible” - covid-19 risk prediction tools in a high-volume, low-resource emergency department
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9842993/
https://www.ncbi.nlm.nih.gov/pubmed/36694781
http://dx.doi.org/10.12669/pjms.39.1.6043
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