Background Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19. Methods In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance. Results The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846–.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790–.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit. Conclusions Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.

Background: Severe cytokine storm syndrome (CSS) is considered as the cause of death among critically ill COVID-19 cases. Early identi cation of the high-risk severe cases is crucial to lower the fatality and healthcare costs. Methods: In this study, we retrospectively analyzed the rst and second-week serum levels of IL-6, IL-8, and IL-10 of 50 COVID-19 cases. We calculated the ratios of IL-6/IL-10 and IL-8/IL-10 at 3 rd , 6 th , 9 th , and 12 th days of hospitalization. Results: We collected 50 COVID-19 cases (male 54%, mean age 51.2, range 18-86), including 39 mild cases (78%), 7 severe/recovered cases (14%), and 4 died cases (8%).The ratios of IL 6/IL-10 and IL-8/IL-10 among mild cases were below 27 (the highest, 26.9) along the 4 testing points of two week hospitalization, while we found that the IL-6/IL-10 and IL-8/IL-10 ratios were as high as 187.51 and 225.3 respectively in the death group on 3 rd day with the highest IL-6/IL-10 ratio of 297.28 on the 6 th day of hospitalization. Conclusions: Our preliminary results suggest that the ratios of IL-6/IL-10 and IL-8/IL-10 at the early stage (the rst two weeks) of COVID-19 could be a predictive marker for the disease prognosis, of which the cutoff lines were suggested below 50 for a mild and recoverable severe cases.

The SARS-CoV-2 Delta variant has spread rapidly worldwide. To provide data on its virological profile, we here report the first local transmission of Delta in mainland China. All 167 infections could be traced back to the first index case. Daily sequential PCR testing of quarantined individuals indicated that the viral loads of Delta infections, when they first become PCR-positive, were on average ~1000 times greater compared to lineage A/B infections during the first epidemic wave in China in early 2020, suggesting potentially faster viral replication and greater infectiousness of Delta during early infection. The estimated transmission bottleneck size of the Delta variant was generally narrow, with 1-3 virions in 29 donor-recipient transmission pairs. However, the transmission of minor iSNVs resulted in at least 3 of the 34 substitutions that were identified in the outbreak, highlighting the contribution of intra-host variants to population-level viral diversity during rapid spread.