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

Abstract

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.

Fig 1. Age distribution of SARS due to COVID-19 cases according to outcome (death/non-death), state of Rio de Janeiro, March–December 2020.

Fig 2. Odds ratio and 95% confidence intervals of the covariates included in the final model.