Factors influencing the mortality of COVID-19 patients
Keywords:
Covid-19, Comorbidity, ROC-curve, Logistic regressionAbstract
COVID-19 has spread quickly throughout the world. The public health system has been significantly impacted by this pandamic. For people infected with COVID-19 who have comorbidity, this increases the severity of symptoms and increases the risk of mortality. The objective of this research was to characterize the COVID-19 patient and find the influence factors for the mortality of COVID-19 patients. The study population included COVID-19 cases collected between 14 February and 31 April 2020. Real-time data were collected from open-source COVID-19 repositories which collected data on 481,289 COVID-19 cases from 141 countries with a sample size of 1,143 people with complete data. The data were analyzed using descriptive statistics and inferential statistics. The Chi-square test or Fisher's exact test and multivariable logistic regression were used for identifying the factors associated with mortality in patients with COVID-19 and constructed ROC curves to determine the appropriate cut-off point to predict the chance of mortality in patients with COVID-19. The results found that five factors: Gender (OR=2.262 ; 95%CI= 1.519-3.367), Age (OR= 1.118 ; 95%CI= 1.102 - 1.134), Malignancy (OR= 0.193 ; 95%CI=0.039 - 0.949), Pneumonia (OR= 7.173 ; 95%CI= 2.818 - 18.254), and ARDS (OR=11.488 ; 95%CI=4.105 - 32.148) influenced the mortality of COVID-19 patients with percentage of correct predictions of 86%. Moreover, the sensitivity by ROC curve also showed very high accuracy.
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