How closely is COVID-19 related to HCoV, SARS, and MERS? : Clinical comparison of coronavirus infections and identification of risk factors influencing the COVID-19 severity using common data model (CDM)

Abstract

South Korea was one of the epicenters for both the 2015 Middle East Respiratory Syndrome (MERS) and 2019 COVID-19 outbreaks. However, there has been a lack of published literature, especially using the EMR records, that provides a comparative summary of the prognostic factors present in the coronavirus-derived diseases. Therefore, in this study, we aimed to compare and evaluate the distinct clinical traits between the infected patients of different coronaviruses, including the lesser pathogenic HCoV strains, SARS-CoV, MERS-CoV, and SARS-CoV-2. We aimed to observe the extent of resemblance within the clinical features between the different coronavirus disease groups and to identify unique factors by disease severity that may influence the prognosis of COVID-19 patients. Here, we utilize the common data model (CDM), which is the database that houses EMR records transformed into the common format to be used by multiple institutions. For the comparative analyses between the different disease groups as well as the mild and non-mild COVID-19 patients, we used independent t-test, Scheffe post-hoc test, and Games-howell post-hoc test for continuous variables, and chi-square test and Fisher’s exact test for categorical variables. From the analyses, we selected variables that showed p-values less than 0.05 to predict COVID-19 severity by a nominal logistic regression with adjustments to age and gender. The results showed diabetes, cardiovascular and cerebrovascular diseases, cancer, pulmonary disease, gastrointestinal disease, and renal disease in all patient groups. The proportions of cancer patients were the highest compared to other comorbidities in every comparative analysis, with no statistical significance. Additionally, we observed high degree of clinical similarity between COVID-19 and SARS patients within more than 50% of the selected clinical variables in the analyses, with no statistical significance between the two groups. Our research effectively utilized the integrated CDM to reflect real-world health challenges in the context of coronavirus. We expect the results from our study to provide clinical insights that can serve as predictor of risk factors for the future coronavirus-derived outbreak as well as the prospective guidelines for the clinical treatments.