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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Professional Medical Publications
2023
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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. |
format | Online Article Text |
id | pubmed-9842993 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Professional Medical Publications |
record_format | MEDLINE/PubMed |
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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