Hospital Mortality in COVID-19 Patients with Chronic Diseases: A Retrospective Cohort Study in Sirjan, Iran, in 2021

Background: The news of several people being affected by unusual pneumonia at the beginning of the year 2020 in China led to the introduction of a new type of coronavirus as the cause of a new respiratory disease. With the rapid spread of the disease in China and then to other parts of the world, the new coronavirus became scientifically known as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) and the resulting disease as coronavirus disease 2019 (COVID-19). This disease has a variety of symptoms and can cause SARS. Objectives: This study aimed to investigate the chronic diseases affecting the mortality rate of this disease. Methods: This retrospective cohort study was performed on 7222 eligible patients admitted to hospitals in Sirjan, Iran, from March 2020 to June 2021 with a positive polymerase chain reaction for COVID-19. The researcher extracted information from the patients' files using the questionnaire of demographic characteristics, clinical signs and symptoms, and the history of underlying diseases. The data were analyzed by the SPSS software using descriptive and analytical statistical tests, namely chi-squared, independent samples t-test, and univariate and multivariate logistic regression tests. Results: Out of 7,222 patients with COVID-19, 294 died, of whom 152 were male and 142 were female (OR = 1.23, CI: 0.97 - 1.55). Moreover, 220 people out of 294 dead cases were over 60 years old (OR = 8.92, CI: 6.82 - 11.67). The most common diseases were hypertension (OR = 3.63, CI: 2.81 - 4.69) and diabetes (OR = 3.16, CI: 2.38 - 4.20). The most common symptoms among those who died were respiratory distress (51.4%) and cough (34.7%). Conclusions: The mortality rate is directly related to the underlying diseases in COVID-19, and the most common underlying disease in the statistical population of this study was hypertension. In addition, respiratory distress is a common symptom in dead patients, which can be used as a predictor of death.