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Prognostic Factors Associated with Survival in Patients Infected with COVID-19: A Retrospective Study on 214 Patients
Amir Sadeghi1, Pegah Eslami1, Arash Dooghaie Moghadam1
1Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Insights
Advanced age and C-reactive protein (CRP) levels are key predictors of mortality in COVID-19 patients. Identifying these risk factors aids physicians in critical resource allocation decisions during the pandemic.
Area of Science:
- Infectious Diseases
- Epidemiology
- Critical Care Medicine
Background:
- The COVID-19 pandemic necessitated difficult decisions regarding scarce medical resource allocation.
- Understanding mortality risk factors is vital for effective healthcare system responses.
Purpose of the Study:
- To identify independent predictors of mortality in patients with COVID-19.
- To inform clinical decision-making and public health strategies.
Main Methods:
- Retrospective analysis of clinical, demographic, and epidemiological data from confirmed COVID-19 cases.
- Application of Multivariate Cox regression and Kaplan-Meier survival analysis.
- Utilized polymerase chain reaction (PCR) for case confirmation.
Main Results:
- Out of 214 confirmed COVID-19 cases, 24.29% died, with a median time to death of 30 days.
- Advanced age (HR, 1.031) and elevated C-reactive protein (CRP) (HR, 1.007) were identified as independent predictors of mortality.
- Age over 59 significantly increased mortality risk and reduced survival.
Conclusions:
- Advanced age and elevated CRP levels are significant independent predictors of mortality in hospitalized COVID-19 patients.
- These findings can guide clinical management and resource allocation during pandemics.
- Public health policies can be informed by these identified risk factors.
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.
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