Keywords
Coronavirus infections
Risk factors
Artificial respiration
Pandemic
How to Cite
Copyright © | All rights reserved.
The journal holds the exclusive copyright for the publication of this article under the terms of Brazilian law 9610/98.
Partial reproduction
The use of parts of the texts, figures and questionnaire of the article is free, being mandatory the citation of the authors and journal.
Total reproduction
It is expressly prohibited and must be authorized by the journal.
Abstract
Objective: To describe the predictors for the use of mechanical ventilation in patients hospitalized for COVID-19 in the state of Paraná. Method: analytical, cross-sectional and retrospective epidemiological study, based on severe acute respiratory syndrome records from the Ministry of Health. Sociodemographic, clinical, epidemiological data and outcomes of adult and elderly patients hospitalized between March and December 2020 in the state of Paraná. Statistical analyzes were performed to identify associations between these variables and the need for mechanical ventilation. Results: Of the 27,045 patients analyzed, 72.6% required mechanical ventilation. Advanced age, respiratory discomfort, low oxygen saturation and the presence of morbidities increased the likelihood of using mechanical ventilation. Flu vaccination reduced this likelihood. The eastern region of the state recorded the highest number of hospitalizations and use of mechanical ventilation, while the 15th region had the highest rate of mechanical ventilation per 10 thousand inhabitants. Conclusion: The importance of age, educational level, clinical signs and vaccination in the need for mechanical ventilation in patients with COVID-19 is highlighted. This provides valuable insights for resource management and disease prevention and treatment strategies. Future studies should consider the impact of vaccination against COVID-19.
References
BACKER JA, et al. Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020.
BASTOS GAN, et al. Características clínicas e preditores de ventilação mecânica em pacientes com COVID-19 hospitalizados no sul do país. Revista Brasileira de Terapia Intensiva, 2020; 32(4): 487-492.
BRASIL. Ministério da Saúde. Disponível em: https://covid.saude.gov.br/. Acessado em: 05 de abr. 2023.
BUSSAB WO e MORETTIN PA. Estatística básica. São Paulo: Editora Saraiva, 8 ed., 2017.
CHEN J, et al. Clinical progression of patients with COVID-19 in Shanghai, China. J Infect., 2020; 80(5): e1-e6.
CHOMISTEK AK, et al. Predictors of critical care, mechanical ventilation, and mortality among hospitalized patients with COVID-19 in an electronic health record database. BMC Infectious Diseases, 2022; 22(1): 413.
CONLON A. et al. Impact of the influenza vaccine on COVID-19 infection rates and severity. American Journal of Infection Control, 2021; 49(6): 694-700.
DA COSTA SOUSA V, et al. Factors associated with mortality, length of hospital stay and diagnosis of COVID-19: Data from a field hospital. Journal of Infection and Public Health, 2022; 15(7): 800–805.
DATASUS - Departamento de Informática do SUS. Disponível em: http://www2.datasus.gov.br/datasus/index.php?area=02. Acessado em: 05 de abr. 2023.
FAN E, et al. An official American Thoracic Society/European Society of Intensive Care Medicine/Society of Critical Care Medicine clinical practice guideline: mechanical ventilation in adult patients with acute respiratory distress syndrome. Am J Respir Crit Care Med, 2017; 195:1253-63.
HOLANDA MA e PINHEIRO BV. COVID-19 pandemic and mechanical ventilation: facing the present, designing the future. Jornal Brasileiro de Pneumologia, 2020; 46(04): e2020028.
HOSMER JR, et al. Applied logistic regression. Hoboken, New Jersey: John Wiley & Sons, Inc., 3.ed., 2013
IBGE - Instituto Brasileiro de Geografia e Estatística. Disponível em: https://www.ibge.gov.br/. Acessado em: 06 de abr. 2023
KHANIJAHANI A, et al. A systematic review of racial/ethnic and socioeconomic disparities in COVID-19. International Journal for Equity in Health, 2021; 20(1): 1-30.
LAGUARDIA J. O uso da variável “raça” na pesquisa em saúde. Physis: Revista de Saúde Coletiva, 2004; 14(2): 197–234.
MAGALHÃES MN. Probabilidade e variáveis aleatórias. São Paulo, SP: Editora da Universidade de São Paulo (Edusp), 3 ed., 2011; 424p.
NORONHA KVMC, et al. Pandemia por COVID-19 no Brasil: análise da demanda e da oferta de leitos hospitalares e equipamentos de ventilação assistida segundo diferentes cenários. Cadernos de Saúde Pública, 2020; 36(6): e00115320.
PAIVA CI et al. Perfil epidemiológico da Covid-19 no Estado do Paraná. Revista de Saúde Pública do Paraná, 2020; 3(Sup.1): 39-61.
PEARSON K. Mathematical contributions to the theory of evolution. VII. On the correlation of characters not quantitatively measurable. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 1900; 195:1-47.
QUINTÃO VC, et al. The anesthesiologist and COVID-19. Revista Brasileira de Anestesiologia, 2020; 70(2): 77-81.
R Core Team. R: A language and environment for statistical computing. Version 4.0.4. R Foundation for Statistical Computing, Vienna, Austria, 2021. Disponível em: https://www.R-project.org/. Acessado em: 20 de jun. 2023.
Secretaria de Estado da Saúde do Paraná. Coronavírus - COVID-19. Disponível em: https://www.saude.pr.gov.br/Pagina/Coronavirus-COVID-19. Acessado em: 06 de abr. 2023.
TEICH VD, et al. Epidemiologic and clinical features of patients with COVID-19 in Brazil. Einstein, 2020; 18: eAO6022.