A Multivariate Logistic Regression Model for Severity Classification at Admission among 1098 COVID-19 Patients in Gansu Province

Abstract

Abstract Background By analyzing clinical characteristics and laboratory results among patients infected with severe acute respiratory disease coronavirus 2 (SARS-CoV-2) Omicron strains, this study aimed to investigate influencing factors of severity classification at admission in order to provide references for the prevention and treatment of SARS-CoV-2. Methods A total of 1,098 patients with SARS-CoV-2 Omicron strains from July 2022 to August 2022 in Lanzhou city, Gansu Province were retrospectively analyzed. Results All patients received traditional Chinese medicine (TCM) intervention, including 510 males and 588 females. 918 (83.683%) had no fever symptoms. Age, underlying diseases and vaccination were the most significant factors of coronavirus disease 2019 (COVID-19) severity. Specifically, age was positively correlated with moderate and severe COVID-19 while number of vaccinations had negative impact on classification at admission. For each additional unit of COVID-19 vaccination, the risk of mild, moderate and severe classification decreased by 0.532, 0.530 and 0.183 times, respectively. Besides, compared with unvaccinated patients, patients with underlying diseases were more likely to develop into critical COVID-19. Early use of TCM in the exposed population might be one of the reasons for the mild symptoms in this study. Conclusions Age, underlying diseases, number of COVID-19 vaccinations were three main risk factors of severity classification among COVID-19 patients at admission. We highly recommended to focus on and strengthen control for elderly patients with chronic underlying diseases, as well as the unvaccinated patients, followed by actively antiviral and control treatment. Vaccination and TCM intervention in advance might play an important role in the prevention of COVID-19.