Background: Patients with critical illness due to infection with the 2019 coronavirus disease (COVID-19) show rapid disease progression to acute respiratory failure. The study aimed to screen the most useful predictive factor for critical illness caused by COVID-19. Methods: The study prospectively involved 61 patients with COVID-19 infection as a derivation cohort, and 54 patients as a validation cohort. The predictive factor for critical illness was selected using LASSO regression analysis. A nomogram based on non-specific laboratory indicators was built to predict the probability of critical illness. Results: The neutrophil-to-lymphocyte ratio (NLR) was identified as an independent risk factor for critical illness in patients with COVID-19 infection. The NLR had an area under receiver operating characteristic of 0.849 (95% confidence interval [CI], 0.707 to 0.991) in the derivation cohort and 0.867 (95% CI 0.747 to 0.944) in the validation cohort, the calibration curves fitted well, and the decision and clinical impact curves showed that the NLR had high standardized net benefit. In addition, the incidence of critical illness was 9.1% (1/11) for patients aged ≥ 50 and having an NLR < 3.13, and 50% (7/14) patients with age ≥ 50 and NLR ≥ 3.13 were predicted to develop critical illness. Based on the risk stratification of NLR according to age, this study has developed a COVID-19 pneumonia management process. Conclusions: We found that NLR is a predictive factor for early-stage prediction of patients infected with COVID-19 who are likely to develop critical illness. Patients aged ≥ 50 and having an NLR ≥ 3.13 are predicted to develop critical illness, and they should thus have rapid access to an intensive care unit if necessary.

Background: Severe ill patients with 2019 novel coronavirus (2019-nCoV) infection progressed rapidly to acute respiratory failure. We aimed to select the most useful prognostic factor for severe illness incidence. Methods: The study prospectively included 61 patients with 2019-nCoV infection treated at Beijing Ditan Hospital from January 13, 2020 to January 31, 2020. Prognostic factor of severe illness was selected by the LASSO COX regression analyses, to predict the severe illness probability of 2019-CoV pneumonia. The predictive accuracy was evaluated by concordance index, calibration curve, decision curve and clinical impact curve. Results: The neutrophil-to-lymphocyte ratio (NLR) was identified as the independent risk factor for severe illness in patients with 2019-nCoV infection. The NLR had a c-index of 0.807 (95% confidence interval, 0.676–0.38), the calibration curves fitted well, and the decision curve and clinical impact curve showed that the NLR had superior standardized net benefit. In addition, the incidence of severe illness was 9.1% in age ≥ 50 and NLR < 3.13 patients, and half of patients with age ≥ 50 and NLR ≥ 3.13 would develop severe illness. Based on the risk stratification of NLR with age, the study developed a 2019-nCoV pneumonia management process. Conclusions: The NLR was the early identification of risk factors for 2019-nCoV severe illness. Patients with age ≥ 50 and NLR ≥ 3.13 facilitated severe illness, and they should rapidly access to intensive care unit if necessary.

Severe fever with thrombocytopenia syndrome (SFTS) caused by the SFTS virus is an emerging infectious disease that was first identified in the rural areas of China in 2011. Severe cases often result in death due to multiple organ failure. To date, there are still numerous problems remain unresolved in SFTS, including unclear pathogenesis, lack of specific treatment, and no effective vaccines available. To analyze the clinical information of patients with early-stage SFTS and to establish a nomogram for the mortality risk. Between April 2011 and December 2018, data on consecutive patients who were diagnosed with SFTS were prospectively collected from five medical centers distributed in central and northeastern China. Multivariable Cox analyses were used to identify the factors independently associated with mortality. A nomogram for mortality was established using those factors. During the study period, 429 consecutive patients were diagnosed with SFTS at the early stage of the disease (within 7 days of fever), among whom 69 (16.1%) died within 28 days. The multivariable Cox proportional hazard regression analysis showed that low lymphocyte percentage, early-stage encephalopathy, and elevated concentration of serum LDH and BUN were independent risk factors for fatal outcomes. Received-operating characteristic curves for 7-, 14-, and 28-days survival had AUCs of 0.944 (95% CI: 0.920–0.968), 0.924 (95% CI: 0.896–0.953), and 0.924 (95% CI: 0.895–0.952), respectively. Among low-risk patients, 6 patients died (2.2%). Among moderate-risk patients, 25 patients died (24.0%, hazard ratio (HR) = 11.957). Among high-risk patients, the mortality rate was 69.1% (HR = 57.768). We established a simple and practical clinical scoring system, through which we can identify critically ill patients and provide intensive medical intervention for patients as soon as possible to reduce mortality. We established a SFTS nomogram scoring system, which is the first nomogram for this disease. According to this nomogram, patients were divided into three levels of mortality risk: low, moderate, and high. This scoring system is helpful to identify critically ill patients, allowing for early intervention and intensive care, which may contribute to reducing the mortality of SFTS.

Emerging mutations in SARS-CoV-2 cause several waves of COVID-19 pandemic. Here we investigate the infectivity and antigenicity of ten emerging SARS-CoV-2 variants—B.1.1.298, B.1.1.7(Alpha), B.1.351(Beta), P.1(Gamma), P.2(Zeta), B.1.429(Epsilon), B.1.525(Eta), B.1.526-1(Iota), B.1.526-2(Iota), B.1.1.318—and seven corresponding single amino acid mutations in the receptor-binding domain using SARS-CoV-2 pseudovirus. The results indicate that the pseudovirus of most of the SARS-CoV-2 variants (except B.1.1.298) display slightly increased infectivity in human and monkey cell lines, especially B.1.351, B.1.525 and B.1.526 in Calu-3 cells. The K417N/T, N501Y, or E484K-carrying variants exhibit significantly increased abilities to infect mouse ACE2-overexpressing cells. The activities of furin, TMPRSS2, and cathepsin L are increased against most of the variants. RBD amino acid mutations comprising K417T/N, L452R, Y453F, S477N, E484K, and N501Y cause significant immune escape from 11 of 13 monoclonal antibodies. However, the resistance to neutralization by convalescent serum or vaccines elicited serum is mainly caused by the E484K mutation. The convalescent serum from B.1.1.7- and B.1.351-infected patients neutralized the variants themselves better than other SARS-CoV-2 variants. Our study provides insights regarding therapeutic antibodies and vaccines, and highlights the importance of E484K mutation.