| Autor: |
Passarelli-Araujo H; Department of Demography, Federal University of Minas Gerais, Belo Horizonte, Brazil., Passarelli-Araujo H; Department of Biochemistry and Immunology, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil., Pescim RR; Department of Statistics, State University of Londrina, Londrina, Brazil., Olak AS; Department of Architecture and Urbanism, State University of Londrina, Londrina, Brazil., Susuki AM; Department of Architecture and Urbanism, State University of Londrina, Londrina, Brazil., Tomimatsu MFAI; Health Department of Londrina, Prefeitura de Londrina, Londrina, Brazil., Volce CJ; Health Department of Londrina, Prefeitura de Londrina, Londrina, Brazil., Neves MAZ; Health Department of Londrina, Prefeitura de Londrina, Londrina, Brazil., Silva FF; Health Department of Londrina, Prefeitura de Londrina, Londrina, Brazil., Narciso SG; Health Department of Londrina, Prefeitura de Londrina, Londrina, Brazil., Paoliello MMB; Department of Molecular Pharmacology, Albert Einstein College of Medicine, New York, USA., Pott-Junior H; Department of Medicine, Federal University of São Carlos, São Carlos, São Paulo, Brazil., Urbano MR; Department of Statistics, State University of Londrina, Londrina, Brazil. |
| Abstrakt: |
Probabilistic survival methods have been used in health research to analyze risk factors and adverse health outcomes associated with COVID-19. The aim of this study was to employ a probabilistic model selected among three distributions (exponential, Weibull, and lognormal) to investigate the time from hospitalization to death and determine the mortality risks among hospitalized patients with COVID-19. A retrospective cohort study was conducted for patients hospitalized due to COVID-19 within 30 days in Londrina, Brazil, between January 2021 and February 2022, registered in the database for severe acute respiratory infections (SIVEP-Gripe). Graphical and Akaike Information Criterion (AIC) methods were used to compare the efficiency of the three probabilistic models. The results from the final model were presented as hazard and event time ratios. Our study comprised of 7,684 individuals, with an overall case fatality rate of 32.78%. Data suggested that older age, male sex, severe comorbidity score, intensive care unit admission, and invasive ventilation significantly increased risks for in-hospital mortality. Our study highlights the conditions that confer higher risks for adverse clinical outcomes attributed to COVID-19. The step-by-step process for selecting appropriate probabilistic models may be extended to other investigations in health research to provide more reliable evidence on this topic. |
