Background: Decision-making on allocating scarce medical resources is crucial in the context of a strong health system reaction to the coronavirus disease 2019 (COVID-19) pandemic. Therefore, understanding the risk factors related to a high mortality rate can enable the physicians for a better decision-making process.
Methods: Information was collected regarding clinical, demographic, and epidemiological features of the definite COVID-19 cases. Through Cox regression and statistical analysis, the risk factors related to mortality were determined. The Kaplan-Meier curve was used to estimate survival function and measure the mean length of living time in the patients.
Results: Among about 3000 patients admitted in the Taleghani hospital as outpatients with suspicious signs and symptoms of COVID-19 in 2 months, 214 people were confirmed positive for this virus using the polymerase chain reaction (PCR) technique. Median time to death was 30 days. In this population, 24.29% of the patients died and 24.76% of them were admitted to the ICU (intensive care unit) during hospitalization. The results of Multivariate Cox regression Analysis showed that factors including age (HR, 1.031; 95% CI, 1.001–1.062; P value=0.04), and C-reactive protein (CRP) (HR, 1.007; 95% CI, 1.000–1.015; P value=0.04) could independently predict mortality. Furthermore, the results showed that age above 59 years directly increased mortality rate and decreased survival among our study population.
Conclusion: Predictor factors play an important role in decisions on public health policy-making. Our findings suggested that advanced age and CRP were independent mortality rate predictors in the admitted patients.