Prognostic Factors Associated with Survival in Patients Infected with COVID-19: A Retrospective Study on 214 Patients from Iran

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.

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.

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.

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.