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In BMC infectious diseases ; h5-index 58.0

BACKGROUND : COVID-19 can occur asymptomatically, as influenza-like illness, or as more severe forms, which characterize severe acute respiratory syndrome (SARS). Its mortality rate is higher in individuals over 80 years of age and in people with comorbidities, so these constitute the risk group for severe forms of the disease. We analyzed the factors associated with death in confirmed cases of COVID-19 in the state of Rio de Janeiro. This cross-sectional study evaluated the association between individual demographic, clinical, and epidemiological variables and the outcome (death) using data from the Unified Health System information systems.

METHODS : We used the extreme boosting gradient (XGBoost) model to analyze the data, which uses decision trees weighted by the estimation difficulty. To evaluate the relevance of each independent variable, we used the SHapley Additive exPlanations (SHAP) metric. From the probabilities generated by the XGBoost model, we transformed the data to the logarithm of odds to estimate the odds ratio for each independent variable.

RESULTS : This study showed that older individuals of black race/skin color with heart disease or diabetes who had dyspnea or fever were more likely to die.

CONCLUSIONS : The early identification of patients who may progress to a more severe form of the disease can help improve the clinical management of patients with COVID-19 and is thus essential to reduce the lethality of the disease.

Cini Oliveira Marcella, de Araujo Eleuterio Tatiana, de Andrade CorrĂȘa Allan Bruno, da Silva Lucas Dalsenter Romano, Rodrigues Renata Coelho, de Oliveira Bruna Andrade, Martins Marlos Melo, Raymundo Carlos Eduardo, de Andrade Medronho Roberto


COVID-19, Coronavirus death, Coronavirus infection, Machine learning, Pandemic, SARS-CoV-2, XGBoost