NDLI: Factors associated with death in confirmed cases of COVID-19 in the state of Rio de Janeiro

Content Provider	Springer Nature : BioMed Central
Author	Cini Oliveira, Marcella de Araujo Eleuterio, Tatiana de Andrade Corrêa, Allan Bruno da Silva, Lucas Dalsenter Romano Rodrigues, Renata Coelho de Oliveira, Bruna Andrade Martins, Marlos Melo Raymundo, Carlos Eduardo de Andrade Medronho, Roberto
Abstract	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.
Related Links	https://bmcinfectdis.biomedcentral.com/counter/pdf/10.1186/s12879-021-06384-1.pdf
Ending Page	16
Page Count	16
Starting Page	1
File Format	HTM / HTML
ISSN	14712334
DOI	10.1186/s12879-021-06384-1
Journal	BMC Infectious Diseases
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2021-07-16
Access Restriction	Open
Subject Keyword	Infectious Diseases Parasitology Medical Microbiology Tropical Medicine Internal Medicine COVID-19 SARS-CoV-2 XGBoost Machine learning Pandemic Coronavirus infection Coronavirus death
Content Type	Text
Resource Type	Article
Subject	Infectious Diseases
Journal Impact Factor	3.4/2023
5-Year Journal Impact Factor	3.3/2023

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