Gastrointestinal symptoms in COVID-19 could be associated with severe lung involvement and increased readmission rates

COVID-19 severity is associated with population-level gut microbiome variations

The human gut microbiome interacts with many diseases, with recent small studies suggesting a link with COVID-19 severity. Exploring this association at the population-level may provide novel insights and help to explain differences in COVID-19 severity between countries. We explore whether there is an association between the gut microbiome of people within different countries and the severity of COVID-19, measured as hospitalisation rate. We use data from the large (n = 3,055) open-access gut microbiome repository curatedMetagenomicData, as well as demographic and country-level metadata. Twelve countries were placed into two groups (high/low) according to COVID-19 hospitalisation rate before December 2020 (ourworldindata.com). We use an unsupervised machine learning method, Topological Data Analysis (TDA). This method analyses both the local geometry and global topology of a high-dimensional dataset, making it particularly suitable for population-level microbiome data. We report an association of distinct baseline population-level gut microbiome signatures with COVID-19 severity. This was found both with a PERMANOVA, as well as with TDA. Specifically, it suggests an association of anti-inflammatory bacteria, including Bifidobacteria species and Eubacterium rectale, with lower severity, and pro-inflammatory bacteria such as Prevotella copri with higher severity. This study also reports a significant impact of country-level confounders, specifically of the proportion of over 70-year-olds in the population, GDP, and human development index. Further interventional studies should examine whether these relationships are causal, as well as considering the contribution of other variables such as genetics, lifestyle, policy, and healthcare system. The results of this study support the value of a population-level association design in microbiome research in general and for the microbiome-COVID-19 relationship, in particular. Finally, this research underscores the potential of TDA for microbiome studies, and in identifying novel associations.

Role of H2 receptor blocker famotidine over the clinical recovery of COVID-19 patients: A randomized controlled trial

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a global pandemic putting the population at a high risk of infection-related health hazards, mortality and a potential failure of proper medical therapies. Therefore, it is necessary to evaluate the potential use of the existing drugs that could be used as options for the medical management of COVID-19 patients.

AIM

To evaluate the role of the H₂ receptor blocker “famotidine” in COVID-19 illness.

METHODS

This study was done on seriously ill COVID-19 patients admitted to the intensive care unit (ICU) from different institutes in Bangladesh. Patients were divided into famotidine treatment group “A” (famotidine 40 mg to 60 mg oral formulation every 8 h with other treatment as given), and control group “B” (treatment as given). National early warning score (NEWS)-2, and sequential organ failure assessment day-1 score was calculated to evaluate the outcome. Outcomes were evaluated by the time required for clinical improvement, characterized as duration required from enrollment to the achievement of NEWS-2 of ≤ 2 maintained for 24 h; time to symptomatic recovery, defined as the duration in days (from randomization) required for the recovery of the COVID-19 symptoms; mortality rate; duration of ICU and hospital stay; total period of hospitalization; the rate of supplementary oxygen requirement; the computed tomography (CT) chest recovery (%), the time required for the viral clearance and “NEWS-2” on discharge.

RESULTS

A total of 208 patients were enrolled in this study with 104 patients in each group. The famotidine treatment group had comparatively better recovery of 75% and a low mortality of 25% than the control with a recovery of 70% and a mortality of 30%. Duration of clinical improvement (group A 9.53 d, group B 14.21 d); hospitalization period among the recovered patients (group A 13.04 d, group B 16.31 d), pulmonary improvement in chest CT (group A 21.7%, group B 13.2%), and the time for viral clearance (group A 20.7 d, group B 23.8 d) were found to be statistically significant P ≤ 0.05. However, the Kaplan Meier survival test was not significant among the two study groups, P = 0.989.

CONCLUSION

According to our study, treatment with famotidine achieved a better clinical outcome compared to the control group in severe COVID-19 illness, although no significant survival benefit was found.

Systematic Review on COVID-19 Readmission and Risk Factors: Future of Machine Learning in COVID-19 Readmission Studies

In this review, current studies on hospital readmission due to infection of COVID-19 were discussed, compared, and further evaluated in order to understand the current trends and progress in mitigation of hospital readmissions due to COVID-19. Boolean expression of (“COVID-19” OR “covid19” OR “covid” OR “coronavirus” OR “Sars-CoV-2”) AND (“readmission” OR “re-admission” OR “rehospitalization” OR “rehospitalization”) were used in five databases, namely Web of Science, Medline, Science Direct, Google Scholar and Scopus. From the search, a total of 253 articles were screened down to 26 articles. In overall, most of the research focus on readmission rates than mortality rate. On the readmission rate, the lowest is 4.2% by Ramos-Martínez et al. from Spain, and the highest is 19.9% by Donnelly et al. from the United States. Most of the research (n = 13) uses an inferential statistical approach in their studies, while only one uses a machine learning approach. The data size ranges from 79 to 126,137. However, there is no specific guide to set the most suitable data size for one research, and all results cannot be compared in terms of accuracy, as all research is regional studies and do not involve data from the multi region. The logistic regression is prevalent in the research on risk factors of readmission post-COVID-19 admission, despite each of the research coming out with different outcomes. From the word cloud, age is the most dominant risk factor of readmission, followed by diabetes, high length of stay, COPD, CKD, liver disease, metastatic disease, and CAD. A few future research directions has been proposed, including the utilization of machine learning in statistical analysis, investigation on dominant risk factors, experimental design on interventions to curb dominant risk factors and increase the scale of data collection from single centered to multi centered.