Figure - available via license: Creative Commons Attribution-NonCommercial 4.0 International
Content may be subject to copyright.
Source publication
Introduction
SARS-CoV-2 virus manifests itself with primary lung damage but also has intestinal involvement. In this study, we aimed to investigate the frequency of gastrointestinal symptoms (GIS) and the relationship of GIS with readmission to the hospital within 30 days in SARS-CoV-2 infected patients who were hospitalized in a specified pandemic...
Context in source publication
Citations
... Of note, it did not resolve when the infection cleared . Additionally, these patients show an increase in gastrointestinal symptoms (Bozkurt and Bilen, 2021a). While such reports show that the microbiome differs in more severely ill patients, they cannot explain the causality of this association. ...
... The depleted levels of the Bifidobacteria genus are consistent with other studies (Bozkurt and Bilen, 2021a). Additionally, hospitalised patients that received a B.animalis probiotic were reported to have reduced mortality and improved symptomatology, as well as a reduction in IL-6 (Bozkurt and Bilen, 2021b). ...
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.
... The interferon-stimulated gene 15 from host cell proteins cleaves the C-terminal end of the consensus sequence LXGG, a process termed deISGylation [11][12][13]. Freedberg et al [14,15] reported that results from a retrospective study tested associations between the use of famotidine and the outcome of patients with COVID-19. They classified the use of famotidine based on COVID-19 exposure within 24 h following hospital admission and maintained a follow-up for up to 30 d. ...
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.
... The same goes for diabetes, COPD, and other risk factors, more research could be done specifically on the certain risk factor, or interventional studies could be designed to tackle and curb the COVID-19 readmission. Apart from the conventional risk factors which increase the risk of readmission, interestingly, the rate of readmission increases significantly in patients with dysbiotic gastrointestinal symptoms, according to a gastrointestinal study in Istanbul (32). Also, the study found that intestinal microbiota affects disease morbidity and mortality (33). ...
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.
