Clinical course and risk factors for mortality and fatal adverse outcomes in COVID-19 infected patients in Korea: a nationwide retrospective cohort study

Introduction Since an outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China, COVID-19 has become a global catastrophic health problem. We investigated the association between epidemiological and clinical characteristics of COVID-19 infected patients and clinical outcomes in Korea population. Material & Method In this nationwide retrospective cohort study, we used clinical and epidemiological data collected by Korea Disease Control and Prevention Agency (KDCA). We included all patients with laboratory confirmed COVID-19 from Korea who had been discharged (unlockdown) or had died by April 30, 2020. Clinical, demographic, and laboratory data were extracted from KDCA database and compared between survivors and non-survivors. Kaplan-Meier curves and log-rank test for age and gender were used for survival analyses. We used logistic regression analysis and Cox proportional hazards model to explore the risk factors associated with death and fatal adverse outcomes. Result A total of 5621 patients were included in this study, of whom 5387 were discharged and 234 died in hospital. Hypertension (21.3%) was the most common comorbidity, followed by diabetes (12.3%) and dementia (4.0%). Multivariable logistic regression showed increasing odds of mortality associated with age ≥60 (OR 11.685, 95% CI 4.655-34.150, p <0.001), isolation period, dyspnea, altered mentality, diabetes, malignancy, dementia, and ICU admission. Multivariable Cox proportional hazards model showed increasing hazards of mortality associated with dementia (HR 6.376, 95% CI 3.736-10.802, p <0.001), ICU admission (HR 4.233, 95% CI 2.661-6.734, p <0.001), age ≥ 60 (HR 3.530, 95% CI 1.664-7.485, p = 0.001), malignancy (HR 3.054, 95% CI 1.494-6.245, p = 0.002), and dyspnea (HR 1.823, 95% CI 1.125-2.954, p = 0.015). The multiple regression equation using all the potential variables could effectively predict mortality in COVID-19 infected patients (AUC = 0.979, 95% CI 0.964-0.993). Conclusion The potential risk factors of dementia, ICU admission, age ≥60, malignancy, and dyspnea could help clinicians to identify COVID-19 infected patients with poor prognosis. The multiple regression equation using clinical and epidemiological variables can predict mortality.