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
Infections with HEV in low- and middle-income countries (LMICs) are associated with increased rates of preterm birth, miscarriage, and stillbirth. The aim of the present study was to investigate HEV infections in pregnant women and the possibility of mother-to-child transmission, and associated outcomes. A total of 183 pregnant women in their third trimester were recruited and followed until delivery. Anti-HEV IgG and IgM were determined via enzyme-linked immunosorbent assay (ELISA), and HEV nucleic acids were detected in stool and cord blood samples. HEV genotypes were identified by Sanger sequencing, and phylogenetic analyses were performed. Mother-to-child transmission and associated adverse outcomes were not observed. Only 2% of patients (n = 4/183) tested positive for anti-HEV IgM, and 8% (n = 14/183) tested positive for anti-HEV IgG antibodies. Cord blood (n = 150) analysis showed that there was no IgM detected, while 4% (n = 6/150) tested positive for anti-HEV IgG, which was consistent with mothers testing positive for anti-HEV IgG. Nucleic acid tests for HEV RNA yielded 2% (n = 4/183) from the serum and stool of pregnant women, and none from cord blood. The HEV isolates belonged to the genotype HEV-3a, with 99% homology with humans and 96% with pigs. No association was found between the risk of HEV infection and pregnancy outcomes or HEV transmission from mother to child. HEV-3 infections of zoonotic origin in pregnancy might have eventually resolved without complications.