Abstract
Introduction: Since the start of COVID-19 pandemic in December 2019, until mid-April 2020 the total number of cases worldwide exceeded two millions and the death toll exceeded 130000 cases. Objective: The current study conducted to explore the clinical and epidemiological characteristics of COVID-19 patients, fatality of this disease and its mortality risk factors in major hospitals affiliated with Tehran University of Medical Sciences (TUMS). Methods: The data were collected in four major teaching hospitals affiliated with TUMS for all the patients that were admitted between Feb 19th and Apr 15th 2020 and were diagnosed as COVID-19 using reverse transcription polymerase chain reaction (RT-PCR), clinical diagnosis and/or lung computed tomography (CT) scan. The case fatality rate of the disease was estimated by age, sex, symptoms, comorbidities, and type of diagnosis. Logistic regression model was used to examine the associations between different factors and in-hospital deaths. Results: By Apr 15th 2020, a total of 4377 patients were admitted with COVID-19 diagnosis in four selected hospitals and 496 (11.3%) of these patients died in hospital. The case fatality rate of this disease was 28.8% in the ≥80-year age group, which was the highest compared to the other age groups. The case fatality rates were 12.5% and 9.8% among men and women, respectively. The results of multiple logistic regression on the outcome of death indicated that age, sex, cough, myalgia, reduced consciousness at arrival and past history of cancer were significantly associated with in-hospital death. Adjusting the effect of other variables, for each 10-year increase in age, the odds of death due to COVID-19 was 1.61 times greater (adjusted OR 1.61, 95% CI: 1.51 to 1.72, p<0.001). Conclusions: Older age, the male gender, past history of comorbidities (particularly cancer) and reduced consciousness at arrival are among the factors that can significantly increase the odds of in-hospital death in COVID 19 patients. These factors might be helpful in detecting and managing patients with poorer prognosis.
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