Study on mechanism of qufeidu no.1 prescription in treatment of corona virus disease 2019 based on network pharmacology

Objective: To explore the material basis and potential mechanism of Qufeidu No.1 Prescription in the treatment of corona virus disease 2019 (COVID-19) by network pharmacology and molecular docking technology.

A Novel Prediction Model of COVID-19 Progression: A Retrospective Cohort Study.

INTRODUCTION: Estimating the risk of disease progression is of utmost importance for planning appropriate setting of care and treatment for patients with coronavirus disease 2019 (COVID-19). This study aimed to develop and validate a novel prediction model of COVID-19 progression. METHODS: In total, 814 patients in the training set were included to develop a novel scoring system; and 420 patients in the validation set were included to validate the model. RESULTS: A prediction score, called ACCCDL, was developed on the basis of six risk factors associated with COVID-19 progression: age, comorbidity, CD4+ T cell count, C-reactive protein (CRP), D-dimer, and lactate dehydrogenase (LDH). For predicting COVID-19 progression, the ACCCDL score yielded a significantly higher area under the receiver operating characteristic curve (AUROC) compared with the CALL score, CoLACD score, PH-COVID-19 score, neutrophil-lymphocyte ratio, and lymphocyte-monocyte ratio both in the training set (0.92, 0.84, 0.83, 0.83, 0.76, and 0.65, respectively) and in the validation set (0.97, 0.83, 0.83, 0.78, 0.74, and 0.60, respectively). Over 99% of patients with the ACCCDL score < 12 points will not progress to severe cases, and over 30% of patients with the ACCCDL score > 20 points will progress to severe cases. CONCLUSION: The ACCCDL score could stratify patients with at risk of COVID-19 progression, and was useful in regulating the large flow of patients with COVID-19 between primary health care and tertiary centers.

Efficacy Evaluation of Thymosin Alpha 1 in Non-severe Patients With COVID-19: A Retrospective Cohort Study Based on Propensity Score Matching.

Objective: Thymosin alpha 1 (Thymosin-α1) is a potential treatment for patients with COVID-19. We aimed to determine the effect of Thymosin-α1 in non-severe patients with COVID-19. Methods: We retrospectively enrolled 1,388 non-severe patients with COVID-19. The primary and secondary clinical outcomes were evaluated with comparisons between patients treated with or without Thymosin-α1 therapy. Results: Among 1,388 enrolled patients, 232 patients (16.7%) received both Thymosin-α1 therapy and standard therapy (Thymosin-α1 group), and 1,156 patients (83.3%) received standard therapy (control group). After propensity score matching (1:1 ratio), baseline characteristics were well-balanced between the Thymosin-α1 group and control group. The proportion of patients that progressed to severe COVID-19 is 2.17% for the Thymosin-α1 group and 2.71% for the control group (p = 0.736). The COVID-19-related mortality is 0.54% for the Thymosin-α1 group and 0 for the control group (p = 0.317). Compared with the control group, the Thymosin-α1 group had significantly shorter SARS-CoV-2 RNA shedding duration (13 vs. 16 days, p = 0.025) and hospital stay (14 vs. 18 days, p < 0.001). No statistically significant difference was found between the Thymosin-α1 group and control group in duration of symptoms (median, 4 vs. 3 days, p = 0.843) and antibiotic utilization rate (14.1% vs. 15.2%, p = 0.768). Conclusion: For non-severe patients with COVID-19, Thymosin-α1 can shorten viral RNA shedding duration and hospital stay but did not prevent COVID-19 progression and reduce COVID-19-related mortality rate.

A Novel Prediction Model for Long-Term SARS-CoV-2 RNA Shedding in Non-Severe Adult Hospitalized Patients with COVID-19: A Retrospective Cohort Study.

INTRODUCTION: Due to the lack of clear direction (evidence) on the duration of viral shedding and thus potential for transmission, this retrospective study aimed to come up with a prediction model of prolonged coronavirus disease-19 (COVID-19) transmission or infection-spreading potential. METHODS: A total of 1211 non-severe patients with COVID-19 were retrospectively enrolled. Multivariate Cox regression was performed to identify the risk factors associated with long-term SARS-CoV-2 RNA shedding, and a prediction model was established. RESULTS: In the training set, 796 patients were divided into the long-term (> 21 days) group (n = 116, 14.6%) and the short-term (≤ 21 days) group (n = 680, 85.4%) based on their viral shedding duration. Multivariate analysis identified that age > 50 years, comorbidity, CD4-positive T-lymphocytes count (CD4 + T cell) ≤ 410 cells/ul, C-reactive protein (CRP) > 10 mg/L, and the corticosteroid use were independent risk factors for long-term SARS-CoV-2 RNA shedding. Incorporating the five risk factors, a prediction model, named as the CCCCA score, was established, and its area under the receiver operator characteristic curve (AUROC) was 0.87 in the training set and 0.83 in the validation set, respectively. In the validation set, using a cut-off of 8 points, we found sensitivity, specificity, positive predictive value, and negative predictive value of 51.7%, 92.2%, 33.3%, and 96.2%, respectively. Long-term SARS-CoV-2 RNA shedding increased from 14/370 (3.8%) in patients with CCCCA < 8 points to 15/45 (33.3%) in patients with CCCCA ≥ 8 points. CONCLUSION: Using the CCCCA score, clinicians can identify patients with long-term SARS-CoV-2 RNA shedding.

Efficacy evaluation of intravenous immunoglobulin in non-severe patients with COVID-19: A retrospective cohort study based on propensity score matching.

OBJECTIVES: At the present time, there is an absence of any proven effective antiviral therapy for patients with coronavirus disease 2019 (COVID-19). The aim of this study was to assess the efficacy of intravenous immunoglobulin (IVIG) in non-severe patients with COVID-19. METHODS: A retrospective study based on propensity score matching (PSM) was designed. Primary outcomes included the severity and mortality rates. Secondary outcomes included the duration of fever, virus clearance time, length of hospital stay, and use of antibiotics. RESULTS: A total of 639 non-severe patients with COVID-19 were enrolled. Forty-five patients received IVIG therapy and 594 received non-IVIG therapy. After PSM (1:2 ratio), the baseline characteristics were well balanced between the IVIG group (n = 45) and control group (n = 90). No statistically significant difference was found between the IVIG group and control group in the duration of fever (median 3 vs 3 days, p = 0.667), virus clearance time (median 11 vs 10 days, p = 0.288), length of hospital stay (median 14 vs 13 days, p = 0.469), or use of antibiotics (40% vs 38.9%, p = 0.901). Meanwhile, compared to the IVIG group, no more patients in the control group progressed to severe disease (3.3% vs 6.6%, p = 0.376) or died (0 vs 2.2%, p = 0.156). CONCLUSIONS: In non-severe patients with COVID-19, no benefit was observed with IVIG therapy beyond standard therapy.

Efficacy Evaluation of Early, Low-Dose, Short-Term Corticosteroids in Adults Hospitalized with Non-Severe COVID-19 Pneumonia: A Retrospective Cohort Study.

OBJECTIVES: This study aimed to observe the efficacy of corticosteroids in non-severe COVID-19 pneumonia. METHODS: A retrospective study based on propensity score matching was designed to explore the effects of corticosteroids. Primary outcomes included the rate of patients who developed severe disease and mortality. Secondary outcomes included duration of fever, virus clearance time, length of hospital stay, and the use of antibiotics. RESULTS: A total of 475 patients with non-severe COVID-19 pneumonia were enrolled, 55 patients received early, low-dose, and short-term corticosteroids therapy, 420 patients received non-corticosteroids therapy. Compared to the non-corticosteroids group, there was a prolonged duration of fever (median 5 vs 3 days, p < 0.001), virus clearance time (median 18 vs 11 days, p < 0.001), and length of hospital stay (median 23 vs 15 days, p < 0.001) in the corticosteroids group. The percentages of antibiotics therapy (89.1% vs 23.6%, p < 0.001), use of at least two antibiotics (38.2% vs 12.7%, p = 0.002), and antifungal therapy (7.3% vs 0, p = 0.042) were higher in the corticosteroids group than those in the non-corticosteroids group. Compared to the non-corticosteroids group, more patients developed severe disease (12.7% vs 1.8%, p = 0.028) in the corticosteroids group. There was no significant difference between the two groups in mortality (1.8% vs 0, p = 0.315). CONCLUSION: In adult patients with non-severe COVID-19 pneumonia, early, low-dose, and short-term corticosteroids therapy was associated with worse clinical outcomes.

A simple algorithm helps early identification of SARS-CoV-2 infection patients with severe progression tendency.

OBJECTIVES: We aimed to develop a simple algorithm to help early identification of SARS-CoV-2 infection patients with severe progression tendency. METHODS: The univariable and multivariable analysis were computed to identify the independent predictors of COVID-19 progression. The prediction model was established in a retrospective training set of 322 COVID-19 patients and was re-evaluated in a prospective validation set of 317 COVID-19 patients. RESULTS: The multivariable analysis identified age (OR = 1.061, p = 0.028), lactate dehydrogenase (LDH) (OR = 1.006, p = 0.037), and CD4 count (OR = 0.993, p = 0.006) as the independent predictors of COVID-19 progression. Consequently, the age-LDH-CD4 algorithm was derived as (age × LDH)/CD4 count. In the training set, the area under the ROC curve (AUROC) of age-LDH-CD4 model was significantly higher than that of single CD4 count, LDH, or age (0.92, 0.85, 0.80, and 0.75, respectively). In the prospective validation set, the AUROC of age-LDH-CD4 model was also significantly higher than that of single CD4 count, LDH, or age (0.92, 0.75, 0.81, and 0.82, respectively). The age-LDH-CD4 ≥ 82 has high sensitive (81%) and specific (93%) for the early identification of COVID-19 patients with severe progression tendency. CONCLUSIONS: The age-LDH-CD4 model is a simple algorithm for early identifying patients with severe progression tendency following SARS-CoV-2 infection, and warrants further validation.

Anesthesia Management and Perioperative Infection Control in Patients With the Novel Coronavirus.

Anesthesiologists have a high risk of infection with COVID-19 during perioperative care and as first responders to airway emergencies. The potential of becoming infected can be reduced by a systematic and integrated approach that assesses infection risk. The latter leads to an acceptable choice of materials and techniques for personal protection and prevention of cross-contamination to other patients and staff. The authors have presented a protocolized approach that uses diagnostic criteria to clearly define benchmarks from the medical history along with clinical symptoms and laboratory tests. Patients can then be rapidly assigned into 1 of 3 risk categories that direct the choice of protective materials and/or techniques. Each hospital can adapt this approach to develop a system that fits its individual resources. Educating medical staff about the proper use of high-risk areas for containment serves to protect staff and patients.