Marcella Cini Oliveira1, Tatiana de Araujo Eleuterio2,3, Allan Bruno de Andrade Corrêa4, Lucas Dalsenter Romano da Silva5, Renata Coelho Rodrigues6, Bruna Andrade de Oliveira6, Marlos Melo Martins7, Carlos Eduardo Raymundo2, Roberto de Andrade Medronho2. 1. Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil. cini.marcella@gmail.com. 2. Instituto de Estudos em Saúde Pública / Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil. 3. Faculdade de Enfermagem, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brasil. 4. Instituto de Física, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil. 5. Departamento de Medicina Preventiva, Instituto de Estudos em Saúde Pública / Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil. 6. Faculdade de Medicina, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil. 7. Department of Child Neurology, Martagão Gesteira Institute of Childcare and Pediatrics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
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.
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.
Authors: Takahisa Mikami; Hirotaka Miyashita; Takayuki Yamada; Matthew Harrington; Daniel Steinberg; Andrew Dunn; Evan Siau Journal: J Gen Intern Med Date: 2020-06-30 Impact factor: 5.128
Authors: Qun Li; Xuhua Guan; Peng Wu; Xiaoye Wang; Lei Zhou; Yeqing Tong; Ruiqi Ren; Kathy S M Leung; Eric H Y Lau; Jessica Y Wong; Xuesen Xing; Nijuan Xiang; Yang Wu; Chao Li; Qi Chen; Dan Li; Tian Liu; Jing Zhao; Man Liu; Wenxiao Tu; Chuding Chen; Lianmei Jin; Rui Yang; Qi Wang; Suhua Zhou; Rui Wang; Hui Liu; Yinbo Luo; Yuan Liu; Ge Shao; Huan Li; Zhongfa Tao; Yang Yang; Zhiqiang Deng; Boxi Liu; Zhitao Ma; Yanping Zhang; Guoqing Shi; Tommy T Y Lam; Joseph T Wu; George F Gao; Benjamin J Cowling; Bo Yang; Gabriel M Leung; Zijian Feng Journal: N Engl J Med Date: 2020-01-29 Impact factor: 176.079
Authors: Marcella Cini Oliveira; Tatiana de Araujo Eleuterio; Allan Bruno de Andrade Corrêa; Lucas Dalsenter Romano da Silva; Renata Coelho Rodrigues; Bruna Andrade de Oliveira; Marlos Melo Martins; Carlos Eduardo Raymundo; Roberto de Andrade Medronho Journal: BMC Infect Dis Date: 2021-08-02 Impact factor: 3.090