Longitudinal peripheral blood transcriptional analysis of COVID-19 patients captures disease progression and reveals potential biomarkers (preprint)

COVID-19, caused by SARS-CoV-2, is an acute self-resolving disease in most of the patients, but some patients can develop a severe illness or even death. To characterize the host responses and identify potential biomarkers during disease progression, we performed a longitudinal transcriptome analysis for peripheral blood mononuclear cells (PBMCs) collected from 4 COVID-19 patients at 4 different time points from symptom onset to recovery. We found that PBMCs at different COVID-19 disease stages exhibited unique transcriptome characteristics. SARS-CoV-2 infection dysregulated innate immunity especially type I interferon response as well as the disturbed release of inflammatory cytokines and lipid mediators, and an aberrant increase of low-density neutrophils may cause tissue damage. Activation of cell death, exhaustion and migratory pathways may lead to the reduction of lymphocytes and dysfunction of adaptive immunity. COVID-19 induced hypoxia may exacerbate disorders in blood coagulation. Based on our analysis, we proposed a set of potential biomarkers for monitoring disease progression and predicting the risk of severity.

Prognostic factors for COVID-19 pneumonia progression to severe symptom based on the earlier clinical features: a retrospective analysis (preprint)

Background Approximately 15-20% of COVID-19 patients will develop severe pneumonia, about 10 % of which will die if not properly managed. Methods 125 COVID-19 patients enrolled in this study were classified into mild (93 cases) and severe (32 cases) groups, basing on their 3 to 7-days clinical outcomes. Patients' gender, age, comorbid with underlying diseases, epidemiological history, clinical manifestations, and laboratory tests on admission were collected and subsequently analyzed with single-factor and multivariate logistic regression methods. Finally, we evaluate their prognostic values with the receiver operating characteristic curve (ROC) analysis. Results Seventeen factors on admission differed significantly between mild and severe groups. Next, only four factors, including the comorbid with underlying diseases, increased respiratory rate (>24/min), elevated C-reactive protein (CRP >10mg/liter), and lactate dehydrogenase (LDH >250U/liter), were found to be independently associated with the later disease development. Prognostic value analysis by ROC indicated that individual factors could not confidently predict the occurrence of severe pneumonia, but that the combination of fast respiratory rate and elevated LDH significantly increase the predictive confidence (AUC= 0.944, sensitivity= 0.941, and specificity= 0.902). Three- or four-factors combinations, including elevated LDH and fast respiratory rate, further increased the prognostic value. Additionally, measurable serum viral RNA post-admission could independently predict the severe illness occurrence. Conclusions General clinical characteristics and laboratory tests, such as combinations consisting of elevated LDH and fast respiratory rate, and detectable viral RNA in serum post-admission could provide high confident prognostic value for identifying potential severe COVID-19 pneumonia patients.