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IntroductionThe novel coronavirus disease 2019 (COVID-19) has rapidly spread across the globe, overwhelming healthcare systems and depleting resources. The infection has a wide spectrum of presentations, and pre-existing comorbidities have been found to have a dramatic effect on the disease course and prognosis. We sought to analyze the effect of asthma on the disease progression and outcomes of COVID-19 patients. MethodsWe conducted a multi-center retrospective study of positively confirmed COVID-19 patients from multiple hospitals in Louisiana. Demographics, medical history, comorbidities, clinical presentation, daily laboratory values, complications, and outcomes data were collected and analyzed. The primary outcome of interest was in-hospital mortality. Secondary outcomes were Intensive Care Unit (ICU) admission, risk of intubation, duration of mechanical ventilation, and length of hospital stay. ResultsA total of 502 COVID-19 patients (72 asthma and 430 non-asthma cohorts) were included in the study. The frequency of asthma in hospitalized cohorts was 14.3%, higher than the national prevalence of asthma (7.7%). Univariate analysis revealed that asthma patients were more likely to be obese (75% vs 54.2%, p=0.001), with higher frequency of intubation (40.3% vs 27.8%, p = 0.036), and required longer duration of hospitalization (15.1{+/-}12.5 vs 11.5{+/-}10.6, p=0.015). After adjustment, multivariable analysis showed that asthmatic patients were not associated with higher risk of ICU admission (OR=1.81, 95%CI=0.98-3.09, p=0.06), endotracheal intubation (OR=1.77, 95%CI=0.99-3.04, p=0.06) or complications (OR=1.37, 95%CI=0.82-2.31, p=0.23). Asthmatic patients were not associated with higher odds of prolonged hospital length of stay (OR=1.48, 95%CI=0.82-2.66, p=0.20) or with the duration of ICU stay (OR=0.76, 95%CI=0.28-2.02, p=0.58). Kaplan-Meier curve showed no significant difference in overall survival of the two groups (p=0.65). ConclusionDespite the increased prevalence of hospitalization in asthmatic COVID-19 patients compared to the general population, after adjustment for other variables, it was neither associated with increased severity nor worse outcomes.
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ObjectiveEvidence-based characterization of the diagnostic and prognostic value of the hematological and immunological markers related to the epidemic of Coronavirus Disease 2019 (COVID-19) is critical to understand the clinical course of the infection and to assess in development and validation of biomarkers. MethodsBased on systematic search in Web of Science, PubMed, Scopus, and Science Direct up to April 22, 2020, a total of 52 eligible articles with 6,320 laboratory-confirmed COVID-19 cohorts were included. Pairwise comparison between severe versus mild disease, Intensive Care Unit (ICU) versus general ward admission, and expired versus survivors were performed for 36 laboratory parameters. The pooled standardized mean difference (SMD) and 95% confidence intervals (CI) were calculated using the DerSimonian Laird method/random effects model and converted to Odds ratio (OR). The decision tree algorithm was employed to identify the key risk factor(s) attributed to severe COVID-19 disease. ResultsCohorts with elevated levels of white blood cells (WBCs) (OR=1.75), neutrophil count (OR=2.62), D-dimer (OR=3.97), prolonged prothrombin time (PT) (OR=1.82), fibrinogen (OR=3.14), erythrocyte sedimentation rate (OR=1.60), procalcitonin (OR=4.76), IL-6 (OR=2.10), and IL-10 (OR=4.93) had higher odds of progression to severe phenotype. Decision tree model (sensitivity=100%, specificity=81%) showed the high performance of neutrophil count at a cut-off value of more than 3.74{square}x109/L for identifying patients at high risk of severe COVID{square}19. Likewise, ICU admission was associated with higher levels of WBCs (OR=5.21), neutrophils (OR=6.25), D-dimer (OR=4.19), and prolonged PT (OR=2.18). Patients with high IL-6 (OR=13.87), CRP (OR=7.09), D-dimer (OR=6.36), and neutrophils (OR=6.25) had the highest likelihood of mortality. ConclusionsSeveral hematological and immunological markers, in particular neutrophilic count, could be helpful to be included within the routine panel for COVID-19 infection evaluation to ensure risk stratification and effective management.
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To investigate the relationship between BCG vaccination and SARS-CoV-2 by bioinformatic approach. Two datasets for Sars-CoV-2 infection group and BCG-vaccinated group were downloaded. Differentially Expressed Genes were identified. Gene ontology and pathways were functionally enriched, and networking was constructed in NetworkAnalyst. Lastly, correlation between post-BCG vaccination and COVID-19 transcriptome signatures were established. A total of 161 DEGs (113 upregulated DEGs and 48 downregulated genes) were identified in the Sars-CoV-2 group. In the pathway enrichment analysis, cross-reference of upregulated KEGG pathways in Sars-CoV-2 with downregulated counterparts in the BCG-vaccinated group, resulted in the intersection of 45 common pathways, accounting for 86.5% of SARS-CoV-2 upregulated pathways. Of these intersecting pathways, a vast majority were immune and inflammatory pathways with top significance in IL-17, TNF, NOD-like receptors, and NF-{kappa}B signaling pathways. Our data suggests BCG-vaccination may incur a protective role in COVID-19 patients until a targeted vaccine is developed. Supplementary Materials(https://drive.google.com/open?id=15Na738L282XNaQAJUh0cZf1WoG9jJfzJ)
