Evidence-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. Based 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 the Odds ratio (OR). The decision tree algorithm was employed to identify the key risk factor(s) attributed to severe COVID-19 disease. Cohorts 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 3.74x10⁹/L for identifying patients at high risk of severe COVID‐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. Several 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.

Background: Coronavirus disease‐2019 (COVID‐19) has a deleterious effect on several systems, including the cardiovascular system. We aim to systematically explore the association of COVID‐19 severity and mortality rate with the history of cardiovascular diseases and/or other comorbidities and cardiac injury laboratory markers.Methods: The standardized mean difference (SMD) or odds ratio (OR) and 95% confidence intervals (CIs) were applied to estimate pooled results from the 56 studies. The prognostic performance of cardiac markers for predicting adverse outcomes and to select the best cutoff threshold was estimated by receiver operating characteristic curve analysis. Decision tree analysis by combining cardiac markers with demographic and clinical features was applied to predict mortality and severity in patients with COVID‐19.Results: A meta‐analysis of 17 794 patients showed patients with high cardiac troponin I (OR = 5.22, 95% CI = 3.73‐7.31, P < .001) and aspartate aminotransferase (AST) levels (OR = 3.64, 95% CI = 2.84‐4.66, P < .001) were more likely to develop adverse outcomes. High troponin I more than 13.75 ng/L combined with either advanced age more than 60 years or elevated AST level more than 27.72 U/L was the best model to predict poor outcomes.Conclusions: COVID‐19 severity and mortality are complicated by myocardial injury. Assessment of cardiac injury biomarkers may improve the identification of those patients at the highest risk and potentially lead to improved therapeutic approaches.

A meta‐analysis was performed to identify patients with coronavirus disease 2019 (COVID‐19) presenting with gastrointestinal (GI) symptoms during the first and second pandemic waves and investigate their association with the disease outcomes. A systematic search in PubMed, Scopus, Web of Science, ScienceDirect, and EMBASE was performed up to July 25, 2020. The pooled prevalence of the GI presentations was estimated using the random‐effects model. Pairwise comparison for the outcomes was performed according to the GI manifestations' presentation and the pandemic wave of infection. Data were reported as relative risk (RR), or odds ratio and 95% confidence interval. Of 125 articles with 25,252 patients, 20.3% presented with GI manifestations. Anorexia (19.9%), dysgeusia/ageusia (15.4%), diarrhea (13.2%), nausea (10.3%), and hematemesis (9.1%) were the most common. About 26.7% had confirmed positive fecal RNA, with persistent viral shedding for an average time of 19.2 days before being negative. Patients presenting with GI symptoms on admission showed a higher risk of complications, including acute respiratory distress syndrome (RR = 8.16), acute cardiac injury (RR = 5.36), and acute kidney injury (RR = 5.52), intensive care unit (ICU) admission (RR = 2.56), and mortality (RR = 2.01). Although not reach significant levels, subgroup‐analysis revealed that affected cohorts in the first wave had a higher risk of being hospitalized, ventilated, ICU admitted, and expired. This meta‐analysis suggests an association between GI symptoms in COVID‐19 patients and unfavorable outcomes. The analysis also showed improved overall outcomes for COVID‐19 patients during the second wave compared to the first wave of the outbreak.

The gut–liver-axis is a bidirectional coordination between the gut, including microbial residents, the gut microbiota, from one side and the liver on the other side. Any disturbance in this crosstalk may lead to a disease status that impacts the functionality of both the gut and the liver. A major cause of liver disorders is hepatitis C virus (HCV) infection that has been illustrated to be associated with gut microbiota dysbiosis at different stages of the disease progression. This dysbiosis may start a cycle of inflammation and metabolic disturbance that impacts the gut and liver health and contributes to the disease progression. This review discusses the latest literature addressing this interplay between the gut microbiota and the liver in HCV infection from both directions. Additionally, we highlight the contribution of gut microbiota to the metabolism of antivirals used in HCV treatment regimens and the impact of these medications on the microbiota composition. This review sheds light on the potential of the gut microbiota manipulation as an alternative therapeutic approach to control the liver complications post HCV infection.