Analysis of Risk Factors on Readmission Cases of COVID-19 in the Republic of Korea: Using Nationwide Health Claims Data.
Woo-Hwi JeonJeong Yeon SeonSo-Youn ParkIn-Hwan OhPublished in: International journal of environmental research and public health (2020)
In South Korea, 4.5% patients of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were readmitted to hospitals after discharge. However, there is insufficient research on risk factors for readmission and management of patients after discharge is poor. In this study, 7590 confirmed coronavirus disease (COVID-19) patients were defined as a target for analysis using nationwide medical claims data. The demographic characteristics, underlying diseases, and the use of medical resources were used to examine the association with readmission through the chi-square test and then logistic regression analysis was performed to analyze factors affecting readmission. Of the 7590 subjects analyzed, 328 patients were readmitted. The readmission rates of men, older age and patients with medical benefits showed a high risk of readmission. The Charlson Comorbidity Index score was also related to COVID-19 readmission. Concerning requiring medical attention, there was a higher risk of readmission for the patients with chest radiographs, computed tomography scans taken and lopinavir/ritonavir at the time of their first admission. Considering the risk factors presented in this study, classifying patients with a high risk of readmission and managing patients before and after discharge based on priority can make patient management and medical resource utilization more efficient. This study also indicates the importance of lifestyle management after discharge.
Keyphrases
- magnetic resonance imaging
- computed tomography
- sars cov
- coronavirus disease
- healthcare
- respiratory syndrome coronavirus
- risk factors
- end stage renal disease
- ejection fraction
- newly diagnosed
- physical activity
- prognostic factors
- cardiovascular disease
- emergency department
- type diabetes
- cross sectional
- mental health
- electronic health record
- metabolic syndrome
- social media
- case report
- big data
- weight loss
- health information
- drug induced