ABSTRACT
Background: Accurate prognosis is important either after acute infection or during long-term follow-up of patients infected by severe acute respiratory syndrome coronavirus 2. This study aims to predict coronavirus disease 2019 (COVID-19) severity based on clinical and biological indicators, and to identify biomarkers for prognostic assessment. Methods: We included 261 Vietnamese COVID-19 patients, who were classified into moderate and severe groups. Disease severity prediction based on biomarkers and clinical parameters was performed by applying machine learning and statistical methods using the combination of clinical and biological data. Results: The random forest model could predict with 97% accuracy the likelihood of COVID-19 patients who subsequently worsened to the severe condition. The most important indicators were interleukin (IL)-6, ferritin and D-dimer. The model could still predict with 92% accuracy after removing IL-6 from the analysis to generalise the applicability of the model to hospitals with limited capacity for IL-6 testing. The five most effective indicators were C-reactive protein (CRP), D-dimer, IL-6, ferritin and dyspnoea. Two different sets of biomarkers (D-dimer, IL-6 and ferritin, and CRP, D-dimer and IL-6) are applicable for the assessment of disease severity and prognosis. The two biomarker sets were further tested through machine learning algorithms and relatively validated on two Danish COVID-19 patient groups (n=32 and n=100). The results indicated that various biomarker sets combined with clinical data can be used for detection of the potential to develop the severe condition. Conclusion: This study provided a simple and reliable model using two different sets of biomarkers to assess disease severity and predict clinical outcomes in COVID-19 patients in Vietnam.
ABSTRACT
The current coronavirus disease 2019 (COVID-19) outbreak is alarmingly escalating and raises challenges in finding efficient compounds for treatment. Repurposing phytochemicals in herbs is an ideal and economical approach for screening potential herbal components against COVID-19. Andrographis paniculata, also known as Chuan Xin Lian, has traditionally been used as an anti-inflammatory and antibacterial herb for centuries and has recently been classified as a promising herbal remedy for adjuvant therapy in treating respiratory diseases. This study aimed to screen Chuan Xin Lian's bioactive components and elicit the potential pharmacological mechanisms and plausible pathways for treating COVID-19 using network pharmacology combined with molecular docking. The results found terpenoid (andrographolide) and flavonoid (luteolin, quercetin, kaempferol, and wogonin) derivatives had remarkable potential against COVID-19 and sequelae owing to their high degrees in the component-target-pathway network and strong binding capacities in docking scores. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the PI3K-AKT signaling pathway might be the most vital molecular pathway in the pathophysiology of COVID-19 and long-term sequelae whereby therapeutic strategies can intervene.
ABSTRACT
Introduction Accurate prognosis is important either after acute infection or during long-term follow-up of patients infected by SARS-CoV-2. This study aims to predict COVID-19 severity based on clinical and biological indicators, and to identify biomarkers for prognostic assessment. Methods We included 261 Vietnamese COVID-19 patients, who were classified into moderate and severe groups. Disease severity prediction based on biomarkers and clinical parameters was performed by applying machine learning and statistical methods using the combination of clinical and biological data. Results The random forest model could predict with 97% accuracy the likelihood of COVID-19 patients who subsequently worsened to severe condition. The most important indicators were IL-6, Ferritin, and D-dimer. The model could still predict with 92% accuracy after removing IL-6 from analysis to generalize applicability of the model to hospitals with limited capacity for IL-6 testing. The five most effective indicators were CRP, D-dimer, IL-6, Ferritin, and dyspnoea. Two different sets of biomarkers (D-dimer, IL-6, and Ferritin;and CRP, D-dimer, and IL-6) are applicable for the assessment of disease severity and prognosis. The two biomarker sets were further tested through the machine learning algorithms random forest;and relatively validated on two Danish COVID-19 patient groups (n=32;and n=100). The results indicated various biomarker sets combined with clinical data can be used for detection of potential develop severe conditions. Conclusion This study provided a simple and reliable model using two different sets of biomarkers to assess disease severity and predict clinical outcomes in COVID-19 patients in Vietnam.
ABSTRACT
Starting from April 1st, 2020, the nationwide partial lockdown in Vietnam has shown the effectiveness in stopping the community transmission of COVID-19, however, it also produced adverse impacts on the economy and inhabitants' life. A cross-sectional study using a web-based approach was conducted in the second week of April 2020 to examine the influence of the national social distancing on the quality of life and economic well-being of Vietnamese citizens under COVID-19 pandemic. The data included socio-economic characteristics, impact of COVID-19 on household income, health status, and health-related quality of life (HRQOL). Ordered logistic regression and multivariable Tobit regression model were employed to examine factors correlated to income change and HRQOL. Results showed that among 341 participants, 66.9% reported household income loss due to the impact of COVID-19. People holding undergraduate degrees, working in other sectors rather than healthcare, and having definite-term contract had a higher likelihood of income reduction. The mean score of EQ-5D-5L and EQ-VAS was 0.95 (± 0.07) and 88.2 (± 11.0), respectively. The domain of Anxiety/Depression had the highest proportion of reporting any problems among 5 dimensions of EQ-5D-5L (38.7%). Being female, having chronic conditions and living in the family with 3-5 members were associated with lower HRQOL scores. A comprehensive assessment of the influence of COVID-19 along with public health interventions, especially mental health programs, should be implemented to mitigate the negative effects of this pandemic on the economic status and quality of life of citizens.