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SARS due to COVID-19: Predictors of death and profile of adult patients in the state of Rio de Janeiro, 2020

INTRODUCTION: We aimed to describe the profile of adult patients and analyze the predictors of death from severe acute respiratory syndrome (SARS) due to coronavirus disease 2019 (COVID-19) in the state of Rio de Janeiro. Knowledge of the predictors of death by COVID-19 in Rio de Janeiro, a state wi...

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Autores principales: Eleuterio, Tatiana de Araujo, Oliveira, Marcella Cini, Velasco, Mariana dos Santos, Menezes, Rachel de Almeida, Gomes, Regina Bontorim, Martins, Marlos Melo, Raymundo, Carlos Eduardo, Medronho, Roberto de Andrade
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648756/
https://www.ncbi.nlm.nih.gov/pubmed/36355856
http://dx.doi.org/10.1371/journal.pone.0277338
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author Eleuterio, Tatiana de Araujo
Oliveira, Marcella Cini
Velasco, Mariana dos Santos
Menezes, Rachel de Almeida
Gomes, Regina Bontorim
Martins, Marlos Melo
Raymundo, Carlos Eduardo
Medronho, Roberto de Andrade
author_facet Eleuterio, Tatiana de Araujo
Oliveira, Marcella Cini
Velasco, Mariana dos Santos
Menezes, Rachel de Almeida
Gomes, Regina Bontorim
Martins, Marlos Melo
Raymundo, Carlos Eduardo
Medronho, Roberto de Andrade
author_sort Eleuterio, Tatiana de Araujo
collection PubMed
description INTRODUCTION: We aimed to describe the profile of adult patients and analyze the predictors of death from severe acute respiratory syndrome (SARS) due to coronavirus disease 2019 (COVID-19) in the state of Rio de Janeiro. Knowledge of the predictors of death by COVID-19 in Rio de Janeiro, a state with one of the highest mortality rates in Brazil, is essential to improve health care for these patients. METHODS: Data from the Information System for Epidemiological Surveillance of Influenza and the Mortality Information System were used. A binary logistic regression model evaluated the outcome of death, sociodemographic data, and clinical-epidemiological and health care covariates. Univariate, bivariate, and multivariate statistics were performed with the R program, version 4.0.0. RESULTS: Overall, 51,383 cases of SARS due to COVID-19 among adults were reported in the state between March 5 and December 2, 2020. Mortality was high (40.5%). The adjusted final model presented the following predictors of death in SARS patients due to COVID-19: male sex (odds ratio [OR] = 1.10, 95% confidence interval [CI], 1.04–1.17); age (OR = 5.35, 95%CI, 4.88–5.88; ≥75 years); oxygen saturation <95% (OR = 1.48, 95%CI, 1.37–1.59), respiratory distress (OR = 1.31, 95%CI, 1.21–1.41) and dyspnoea (OR = 1.25, 95%CI, 1.15–1.36), the presence of at least one risk factor/comorbidity (OR = 1.32, 95%CI, 1.23–1.42), chronic kidney disease (OR = 1.94, 95%CI, 1.69–2.23), immunosuppression (OR = 1.51, 95%CI, 1.26–1.81) or chronic neurological disease (OR = 1.36, 95%CI, 1.18–1.58), and ventilatory support, invasive (OR = 8.89, 95%CI, 8.08–9.79) or non-invasive (OR = 1.25, 95%CI, 1.15–1.35). CONCLUSIONS: Factors associated with death were male sex, old age, oxygen saturation <95%, respiratory distress, dyspnoea, chronic kidney and neurological diseases, immunosuppression, and use of invasive or noninvasive ventilatory support. Identifying factors associated with disease progression can help the clinical management of patients with COVID-19 and improve outcomes.
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spelling pubmed-96487562022-11-15 SARS due to COVID-19: Predictors of death and profile of adult patients in the state of Rio de Janeiro, 2020 Eleuterio, Tatiana de Araujo Oliveira, Marcella Cini Velasco, Mariana dos Santos Menezes, Rachel de Almeida Gomes, Regina Bontorim Martins, Marlos Melo Raymundo, Carlos Eduardo Medronho, Roberto de Andrade PLoS One Research Article INTRODUCTION: We aimed to describe the profile of adult patients and analyze the predictors of death from severe acute respiratory syndrome (SARS) due to coronavirus disease 2019 (COVID-19) in the state of Rio de Janeiro. Knowledge of the predictors of death by COVID-19 in Rio de Janeiro, a state with one of the highest mortality rates in Brazil, is essential to improve health care for these patients. METHODS: Data from the Information System for Epidemiological Surveillance of Influenza and the Mortality Information System were used. A binary logistic regression model evaluated the outcome of death, sociodemographic data, and clinical-epidemiological and health care covariates. Univariate, bivariate, and multivariate statistics were performed with the R program, version 4.0.0. RESULTS: Overall, 51,383 cases of SARS due to COVID-19 among adults were reported in the state between March 5 and December 2, 2020. Mortality was high (40.5%). The adjusted final model presented the following predictors of death in SARS patients due to COVID-19: male sex (odds ratio [OR] = 1.10, 95% confidence interval [CI], 1.04–1.17); age (OR = 5.35, 95%CI, 4.88–5.88; ≥75 years); oxygen saturation <95% (OR = 1.48, 95%CI, 1.37–1.59), respiratory distress (OR = 1.31, 95%CI, 1.21–1.41) and dyspnoea (OR = 1.25, 95%CI, 1.15–1.36), the presence of at least one risk factor/comorbidity (OR = 1.32, 95%CI, 1.23–1.42), chronic kidney disease (OR = 1.94, 95%CI, 1.69–2.23), immunosuppression (OR = 1.51, 95%CI, 1.26–1.81) or chronic neurological disease (OR = 1.36, 95%CI, 1.18–1.58), and ventilatory support, invasive (OR = 8.89, 95%CI, 8.08–9.79) or non-invasive (OR = 1.25, 95%CI, 1.15–1.35). CONCLUSIONS: Factors associated with death were male sex, old age, oxygen saturation <95%, respiratory distress, dyspnoea, chronic kidney and neurological diseases, immunosuppression, and use of invasive or noninvasive ventilatory support. Identifying factors associated with disease progression can help the clinical management of patients with COVID-19 and improve outcomes. Public Library of Science 2022-11-10 /pmc/articles/PMC9648756/ /pubmed/36355856 http://dx.doi.org/10.1371/journal.pone.0277338 Text en © 2022 Eleuterio et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Eleuterio, Tatiana de Araujo
Oliveira, Marcella Cini
Velasco, Mariana dos Santos
Menezes, Rachel de Almeida
Gomes, Regina Bontorim
Martins, Marlos Melo
Raymundo, Carlos Eduardo
Medronho, Roberto de Andrade
SARS due to COVID-19: Predictors of death and profile of adult patients in the state of Rio de Janeiro, 2020
title SARS due to COVID-19: Predictors of death and profile of adult patients in the state of Rio de Janeiro, 2020
title_full SARS due to COVID-19: Predictors of death and profile of adult patients in the state of Rio de Janeiro, 2020
title_fullStr SARS due to COVID-19: Predictors of death and profile of adult patients in the state of Rio de Janeiro, 2020
title_full_unstemmed SARS due to COVID-19: Predictors of death and profile of adult patients in the state of Rio de Janeiro, 2020
title_short SARS due to COVID-19: Predictors of death and profile of adult patients in the state of Rio de Janeiro, 2020
title_sort sars due to covid-19: predictors of death and profile of adult patients in the state of rio de janeiro, 2020
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648756/
https://www.ncbi.nlm.nih.gov/pubmed/36355856
http://dx.doi.org/10.1371/journal.pone.0277338
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