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COVID-19: An evaluation of predictive scoring systems in South Africa

BACKGROUND: | The Coronavirus Disease 2019 (COVID-19) pandemic, caused by SARS-CoV-2, has resulted in more than 700 million cases worldwide. Sepsis and pneumonia severity scores assist in risk assessment of critical outcomes in patients with COVID-19. This allows healthcare workers to triage patient...

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Autores principales: Prim, Brent Tyler Aloysius, Kalla, Ismail Sikander, Zamparini, Jarrod, Mohamed, Farzahna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665741/
https://www.ncbi.nlm.nih.gov/pubmed/38027857
http://dx.doi.org/10.1016/j.heliyon.2023.e21733
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author Prim, Brent Tyler Aloysius
Kalla, Ismail Sikander
Zamparini, Jarrod
Mohamed, Farzahna
author_facet Prim, Brent Tyler Aloysius
Kalla, Ismail Sikander
Zamparini, Jarrod
Mohamed, Farzahna
author_sort Prim, Brent Tyler Aloysius
collection PubMed
description BACKGROUND: | The Coronavirus Disease 2019 (COVID-19) pandemic, caused by SARS-CoV-2, has resulted in more than 700 million cases worldwide. Sepsis and pneumonia severity scores assist in risk assessment of critical outcomes in patients with COVID-19. This allows healthcare workers to triage patients, by using clinical parameters and limited special investigations, thus offering the most appropriate level of care. METHODS: | A retrospective cohort study of 605 adult patients hospitalised with moderate to severe COVID-19, at a tertiary state hospital in South Africa. Evaluating the utility of the CURB65, NEWS2 and ISARIC-4C Mortality Score, in predicting critical outcomes, using clinical characteristics on admission. Outcomes included in-hospital mortality, invasive mechanical ventilation, and intensive care unit admission (ICU). Performance of severity scores and risk factors was assessed by area under the receiver operator characteristics (AUROC) analysis and logistic regression. FINDINGS |: A total of 605 records were used, 129 (21 %) non-survivors, 101 (17 %) ICU admissions and 77 (13 %) requiring invasive ventilation. Greater odds of mortality was associated with moderate and severe risk groups of the CURB65, ISARIC-4C and NEWS2 score. Mortality AUROC curve analysis for the CURB65 score was 0·76 (95 % CI: 0·71–0·8), 0·77 (95 % CI: 0·73–0·81) for the ISARIC-4C and 0·77 (95 % CI: 0·73–0·82) for the NEWS2 score. The CURB65 score had a sensitivity of 86 % with 12·8 % mortality, ISARIC-4C score a sensitivity of 87·6 % with 8 % mortality and NEWS2 score a sensitivity of 92·2 % with 8·6 % mortality. INTERPRETATION |: In 605 hospitalised patients with moderate to severe COVID-19, predominantly infected by the ancestral strain, good performance of the NEWS2 and ISARIC-4C score in predicting in-hospital mortality was noted. The CURB65 score had a high mortality rate in its low-risk group suggesting unexplained risk factors, not accounted for in the score, thus limiting its utility in the South African setting.
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spelling pubmed-106657412023-11-04 COVID-19: An evaluation of predictive scoring systems in South Africa Prim, Brent Tyler Aloysius Kalla, Ismail Sikander Zamparini, Jarrod Mohamed, Farzahna Heliyon Research Article BACKGROUND: | The Coronavirus Disease 2019 (COVID-19) pandemic, caused by SARS-CoV-2, has resulted in more than 700 million cases worldwide. Sepsis and pneumonia severity scores assist in risk assessment of critical outcomes in patients with COVID-19. This allows healthcare workers to triage patients, by using clinical parameters and limited special investigations, thus offering the most appropriate level of care. METHODS: | A retrospective cohort study of 605 adult patients hospitalised with moderate to severe COVID-19, at a tertiary state hospital in South Africa. Evaluating the utility of the CURB65, NEWS2 and ISARIC-4C Mortality Score, in predicting critical outcomes, using clinical characteristics on admission. Outcomes included in-hospital mortality, invasive mechanical ventilation, and intensive care unit admission (ICU). Performance of severity scores and risk factors was assessed by area under the receiver operator characteristics (AUROC) analysis and logistic regression. FINDINGS |: A total of 605 records were used, 129 (21 %) non-survivors, 101 (17 %) ICU admissions and 77 (13 %) requiring invasive ventilation. Greater odds of mortality was associated with moderate and severe risk groups of the CURB65, ISARIC-4C and NEWS2 score. Mortality AUROC curve analysis for the CURB65 score was 0·76 (95 % CI: 0·71–0·8), 0·77 (95 % CI: 0·73–0·81) for the ISARIC-4C and 0·77 (95 % CI: 0·73–0·82) for the NEWS2 score. The CURB65 score had a sensitivity of 86 % with 12·8 % mortality, ISARIC-4C score a sensitivity of 87·6 % with 8 % mortality and NEWS2 score a sensitivity of 92·2 % with 8·6 % mortality. INTERPRETATION |: In 605 hospitalised patients with moderate to severe COVID-19, predominantly infected by the ancestral strain, good performance of the NEWS2 and ISARIC-4C score in predicting in-hospital mortality was noted. The CURB65 score had a high mortality rate in its low-risk group suggesting unexplained risk factors, not accounted for in the score, thus limiting its utility in the South African setting. Elsevier 2023-11-04 /pmc/articles/PMC10665741/ /pubmed/38027857 http://dx.doi.org/10.1016/j.heliyon.2023.e21733 Text en © 2023 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Prim, Brent Tyler Aloysius
Kalla, Ismail Sikander
Zamparini, Jarrod
Mohamed, Farzahna
COVID-19: An evaluation of predictive scoring systems in South Africa
title COVID-19: An evaluation of predictive scoring systems in South Africa
title_full COVID-19: An evaluation of predictive scoring systems in South Africa
title_fullStr COVID-19: An evaluation of predictive scoring systems in South Africa
title_full_unstemmed COVID-19: An evaluation of predictive scoring systems in South Africa
title_short COVID-19: An evaluation of predictive scoring systems in South Africa
title_sort covid-19: an evaluation of predictive scoring systems in south africa
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665741/
https://www.ncbi.nlm.nih.gov/pubmed/38027857
http://dx.doi.org/10.1016/j.heliyon.2023.e21733
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