Carvedilol to prevent hepatic decompensation of cirrhosis in patients with clinically significant portal hypertension stratified by new non-invasive model (CHESS2306).

Background & Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvedilol-treating cohort. Results: In the meta-analysis with six studies (n = 819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new "CSPH risk" model. In the HVPG cohort (n = 151), the new model accurately predicted CSPH with cutoff values of 0 and -0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n = 1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <-0.68 (low-risk), -0.68 to 0 (medium-risk), and >0 (high-risk). In the carvedilol-treated cohort, patients with high-risk CSPH treated with carvedilol (n = 81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n = 613 before propensity score matching [PSM], n = 162 after PSM). Conclusions: Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Initial whole-genome sequencing and analysis of the host genetic contribution to COVID-19 severity and susceptibility.

The COVID-19 pandemic has accounted for millions of infections and hundreds of thousand deaths worldwide in a short-time period. The patients demonstrate a great diversity in clinical and laboratory manifestations and disease severity. Nonetheless, little is known about the host genetic contribution to the observed interindividual phenotypic variability. Here, we report the first host genetic study in the Chinese population by deeply sequencing and analyzing 332 COVID-19 patients categorized by varying levels of severity from the Shenzhen Third People's Hospital. Upon a total of 22.2 million genetic variants, we conducted both single-variant and gene-based association tests among five severity groups including asymptomatic, mild, moderate, severe, and critical ill patients after the correction of potential confounding factors. Pedigree analysis suggested a potential monogenic effect of loss of function variants in GOLGA3 and DPP7 for critically ill and asymptomatic disease demonstration. Genome-wide association study suggests the most significant gene locus associated with severity were located in TMEM189-UBE2V1 that involved in the IL-1 signaling pathway. The p.Val197Met missense variant that affects the stability of the TMPRSS2 protein displays a decreasing allele frequency among the severe patients compared to the mild and the general population. We identified that the HLA-A*11:01, B*51:01, and C*14:02 alleles significantly predispose the worst outcome of the patients. This initial genomic study of Chinese patients provides genetic insights into the phenotypic difference among the COVID-19 patient groups and highlighted genes and variants that may help guide targeted efforts in containing the outbreak. Limitations and advantages of the study were also reviewed to guide future international efforts on elucidating the genetic architecture of host-pathogen interaction for COVID-19 and other infectious and complex diseases.

Shorter incubation period is associated with severe disease progression in patients with COVID-19.

The diagnosed COVID-19 cases revealed that the incubation periods (IP) varied a lot among patients. However, few studies had emphasized on the different clinical features and prognosis of patients with different IP. A total of 330 patients with laboratory-confirmed COVID-19 were enrolled and classified into immediate onset group(IP<3 days, I group, 57 cases) and late onset group(IP>10 days, L group, 75 cases) based on IP. The difference of clinical characteristics and prognosis of the two groups were compared. There were more patients with fever in I group than in L group(P = 0.003), and counts of all the total lymphocytes, total T lymphocytes, CD4 + and CD8 + T lymphocytes were significantly different between the two groups(all P < 0.01). Besides, patients in L group had more GGOs in CT scan than I group and there were more patients in I group receiving antibiotic treatment than in L group(P < 0.001). For disease aggravation, the median CT scores were comparable between the two groups, but individually, there were more patients with increased CT score during hospitalization in I group than in L group. The aggravation incidence of CT presentation was 21.1% in I group, significantly higher than L group(8.0%, P = 0.042). Multivariable COX models suggested that IP was the only independent factors for CT aggravation. Conclusively, patients with different IP were different in clinical symptoms, laboratory tests, and CT presentations. Shorter IP was associated with the aggravation of lung involvement in CT scan.

The timeline and risk factors of clinical progression of COVID-19 in Shenzhen, China.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) broke out globally. Early prediction of the clinical progression was essential but still unclear. We aimed to evaluate the timeline of COVID-19 development and analyze risk factors of disease progression. METHODS: In this retrospective study, we included 333 patients with laboratory-confirmed COVID-19 infection hospitalized in the Third People's Hospital of Shenzhen from 10 January to 10 February 2020. Epidemiological feature, clinical records, laboratory and radiology manifestations were collected and analyzed. 323 patients with mild-moderate symptoms on admission were observed to determine whether they exacerbated to severe-critically ill conditions (progressive group) or not (stable group). We used logistic regression to identify the risk factors associated with clinical progression. RESULTS: Of all the 333 patients, 70 (21.0%) patients progressed into severe-critically ill conditions during hospitalization and assigned to the progressive group, 253 (76.0%) patients belonged to the stable group, another 10 patients were severe before admission. we found that the clinical features of aged over 40 (3.80 [1.72, 8.52]), males (2.21 [1.20, 4.07]), with comorbidities (1.78 [1.13, 2.81]) certain exposure history (0.38 [0.20, 0.71]), abnormal radiology manifestations (3.56 [1.13, 11.40]), low level of T lymphocytes (0.99 [0.997, 0.999]), high level of NLR (0.99 [0.97, 1.01]), IL-6 (1.05 [1.03, 1.07]) and CRP (1.67 [1.12, 2.47]) were the risk factors of disease progression by logistic regression. CONCLUSIONS: The potential risk factors of males, older age, with comorbidities, low T lymphocyte level and high level of NLR, CRP, IL-6 can help to predict clinical progression of COVID-19 at an early stage.