Prediction of COVID-19 Patients at High Risk of Progression to Severe Disease.

In order to develop a novel scoring model for the prediction of coronavirus disease-19 (COVID-19) patients at high risk of severe disease, we retrospectively studied 419 patients from five hospitals in Shanghai, Hubei, and Jiangsu Provinces from January 22 to March 30, 2020. Multivariate Cox regression and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were both used to identify high-risk factors for disease severity in COVID-19 patients. The prediction model was developed based on four high-risk factors. Multivariate analysis showed that comorbidity [hazard ratio (HR) 3.17, 95% confidence interval (CI) 1.96-5.11], albumin (ALB) level (HR 3.67, 95% CI 1.91-7.02), C-reactive protein (CRP) level (HR 3.16, 95% CI 1.68-5.96), and age ≥60 years (HR 2.31, 95% CI 1.43-3.73) were independent risk factors for disease severity in COVID-19 patients. OPLS-DA identified that the top five influencing parameters for COVID-19 severity were CRP, ALB, age ≥60 years, comorbidity, and lactate dehydrogenase (LDH) level. When incorporating the above four factors, the nomogram had a good concordance index of 0.86 (95% CI 0.83-0.89) and had an optimal agreement between the predictive nomogram and the actual observation with a slope of 0.95 (R2 = 0.89) in the 7-day prediction and 0.96 (R2 = 0.92) in the 14-day prediction after 1,000 bootstrap sampling. The area under the receiver operating characteristic curve of the COVID-19-American Association for Clinical Chemistry (AACC) model was 0.85 (95% CI 0.81-0.90). According to the probability of severity, the model divided the patients into three groups: low risk, intermediate risk, and high risk. The COVID-19-AACC model is an effective method for clinicians to screen patients at high risk of severe disease.

Epidemiological and clinical characteristics of 70 cases of coronavirus disease and concomitant hepatitis B virus infection: A multicentre descriptive study.

The interaction between existing chronic liver diseases caused by hepatitis B virus (HBV) infection and COVID-19 has not been studied. We analysed 70 COVID-19 cases combined with HBV infection (CHI) to determine the epidemiological, clinical characteristics, treatment and outcome. We investigated clinical presentation, imaging and laboratory parameters of COVID-19 patients of seven hospitals from Jan 20 to March 20, 2020. Multivariate analysis was used to analyse risk factors for progression of patients with COVID-19 combined with HBV infection. Compared with COVID-19 without HBV infection (WHI) group, patients with dual infection had a higher proportion of severe/critically ill disease (32.86% vs. 15.27%, P = .000), higher levels of alanine aminotransferase (ALT), aspartate transaminase (AST) and activated partial thromboplastin (APTT) [50(28-69)vs 21(14-30), P = .000; 40(25-54) vs 23(18-30), P = .000; 34.0(27.2-38.7) vs 37.2(31.1-41.4), P = .031]. The utilization rates of Arbidol and immunoglobulin were significantly higher than those in the co-infected group [48.57% vs. 35.64%, P < .05; 21.43% vs. 8.18%, P < .001], while the utilization rate of chloroquine phosphate was lower (1.43% vs 14.00%, P < .05) in the co-infected patients group. Age and c-reactive protein (CRP) level were independent risk factors for recovery of patients with COVID-19 combined with HBV infection. The original characteristics of COVID-19 cases combined with HBV infection were higher rate of liver injury, coagulation disorders, severe/critical tendency and increased susceptibility. The elderly and patients with higher level of CRP were more likely to experience a severe outcome of COVID-19.