This study aimed to analyse the survival of patients admitted to Brazilian hospitals due to the COVID-19 and estimate prognostic factors. This is a retrospective, multicentre cohort study, based on data from 46 285 hospitalisations for COVID-19 in Brazil. Survival functions were calculated using the Kaplan–Meier’s method. The log-rank test compared the survival functions for each variable and from that, hazard ratios (HRs) were calculated, and the proportional hazard model was used in Cox multiple regression. The smallest survival curves were the ones for patients at the age of 68 years or more, black/mixed race, illiterate, living in the countryside, dyspnoea, respiratory distress, influenza-like outbreak, O 2 saturation <95%, X-ray change, length of stay in the intensive care unit (ICU), invasive ventilatory support, previous heart disease, pneumopathy, diabetes, Down's syndrome, neurological disease and kidney disease. Better survival was observed in the influenza-like outbreak and in an asthmatic patient. The multiple model for increased risk of death when they were admitted to the ICU HR 1.28, diabetes HR 1.17, neurological disease HR 1.34, kidney disease HR 1.11, heart disease HR 1.14, black or mixed race of HR 1.50, asthma HR 0.71 and pneumopathy HR 1.12. This reinforces the importance of socio-demographic and clinical factors as a prognosis for death.
【저자키워드】 Risk factors, SARS virus, Coronavirus infections, survival analysis, hospitalisations, 【초록키워드】 COVID-19, Brazil, Asthma, intensive care, Prognosis, hospital, diabetes, ICU, cohort study, X-ray, survival, outbreak, Patient, death, Kidney disease, Prognostic factors, age, multicentre, hospitalisation, disease, function, Dyspnoea, retrospective, distress, ventilatory support, Neurological disease, survival curve, increased risk, Multiple regression, hazard ratio, log-rank test, syndrome, asthmatic, survival function, Kaplan–Meier, socio-demographic, invasive, was used, calculated, Better, diabete, clinical factor, HRs, pneumopathy, 【제목키워드】 COVID-19, Brazil, cohort study, Patient, death, multicentre, predictor, retrospective, hospitalised,