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Background: Prior studies reported that 5 ~ 32% COVID-19 patients were critically ill, a situation that poses great challenge for the management of the patients and ICU resources. We aim to identify independent risk factors to serve as prediction markers for critical illness of SARS-CoV-2 infection., Methods: Fifty-two critical and 200 non-critical SARS-CoV-2 nucleic acid positive patients hospitalized in 15 hospitals outside Wuhan from January 19 to March 6, 2020 were enrolled in this study. Multivariable logistic regression and LASSO logistic regression were performed to identify independent risk factors for critical illness., Results: Age older than 60 years, dyspnea, respiratory rate > 24 breaths per min, leukocytosis > 9.5 × 10 9 /L, neutrophilia > 6.3 × 10 9 /L, lymphopenia < 1.1 × 10 9 /L, neutrophil-to-lymphocyte ratio > 3.53, fibrinogen > 4 g/L, d-dimer > 0.55 μg/mL, blood urea nitrogen > 7.1 mM, elevated aspartate transaminase, elevated alanine aminotransferase, total bilirubin > 21 μM, and Sequential Organ Failure Assessment (SOFA) score ≥ 2 were identified as risk factors for critical illness. LASSO logistic regression identified the best combination of risk factors as SOFA score, age, dyspnea, and leukocytosis. The Area Under the Receiver-Operator Curve values for the risk factors in predicting critical illness were 0.921 for SOFA score, 0.776 for age, 0.764 for dyspnea, 0.658 for leukocytosis, and 0.960 for the combination of the four risk factors., Conclusions: Our findings advocate the use of risk factors SOFA score ≥ 2, age > 60, dyspnea and leukocytosis > 9.5 × 10 9 /L on admission, alone or in combination, to determine the optimal management of the patients and health care resources.

Background: The application of factor analysis in the study of the clinical symptoms of coronavirus disease 2019 (COVID-19) was investigated, to provide a reference for basic research on COVID-19 and its prevention and control., Methods: The data of 60 patients with COVID-19 in Jingzhou Hospital of Traditional Chinese Medicine and the Second People's Hospital of Longgang District in Shenzhen were extracted using principal component analysis. Factor analysis was used to investigate the factors related to symptoms of COVID-19. Based on the combination of factors, the clinical types of the factors were defined according to our professional knowledge. Factor loadings were calculated, and pairwise correlation analysis of symptoms was performed., Results: Factor analysis showed that the clinical symptoms of COVID-19 cases could be divided into respiratory-digestive, neurological, cough-wheezing, upper respiratory, and digestive symptoms. Pairwise correlation analysis showed that there were a total of eight pairs of symptoms: fever-palpitation, coughexpectoration, expectoration-wheezing, dry mouth-bitter taste in the mouth, poor appetite-fatigue, fatiguedizziness, diarrhea-palpitation, and dizziness-headache., Conclusions: The symptoms and syndromes of COVID-19 are complex. Respiratory symptoms dominate, and digestive symptoms are also present. Factor analysis is suitable for studying the characteristics of the clinical symptoms of COVID-19, providing a new idea for the comprehensive analysis of clinical symptoms.