Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), which has been characterized by fever, respiratory, and gastrointestinal ...symptoms as well as shedding of virus RNA into feces. We performed a systematic review and meta-analysis of published gastrointestinal symptoms and detection of virus in stool and also summarized data from a cohort of patients with COVID-19 in Hong Kong.
We collected data from the cohort of patients with COVID-19 in Hong Kong (N = 59; diagnosis from February 2 through February 29, 2020),and searched PubMed, Embase, Cochrane, and 3 Chinese databases through March 11, 2020, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We analyzed pooled data on the prevalence of overall and individual gastrointestinal symptoms (loss of appetite, nausea, vomiting, diarrhea, and abdominal pain or discomfort) using a random effects model.
Among the 59 patients with COVID-19 in Hong Kong, 15 patients (25.4%) had gastrointestinal symptoms, and 9 patients (15.3%) had stool that tested positive for virus RNA. Stool viral RNA was detected in 38.5% and 8.7% among those with and without diarrhea, respectively (P = .02). The median fecal viral load was 5.1 log10 copies per milliliter in patients with diarrhea vs 3.9 log10 copies per milliliter in patients without diarrhea (P = .06). In a meta-analysis of 60 studies comprising 4243 patients, the pooled prevalence of all gastrointestinal symptoms was 17.6% (95% confidence interval CI, 12.3–24.5); 11.8% of patients with nonsevere COVID-19 had gastrointestinal symptoms (95% CI, 4.1–29.1), and 17.1% of patients with severe COVID-19 had gastrointestinal symptoms (95% CI, 6.9–36.7). In the meta-analysis, the pooled prevalence of stool samples that were positive for virus RNA was 48.1% (95% CI, 38.3–57.9); of these samples, 70.3% of those collected after loss of virus from respiratory specimens tested positive for the virus (95% CI, 49.6–85.1).
In an analysis of data from the Hong Kong cohort of patients with COVID-19 and a meta-analysis of findings from publications, we found that 17.6% of patients with COVID-19 had gastrointestinal symptoms. Virus RNA was detected in stool samples from 48.1% patients, even in stool collected after respiratory samples had negative test results. Health care workers should therefore exercise caution in collecting fecal samples or performing endoscopic procedures in patients with COVID-19, even during patient recovery.
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Novel oral anticoagulants(NOACs), which include direct thrombin inhibitor(dabigatran) and direct factor Xa inhibitors(rivaroxaban, apixaban and edoxaban), are gaining popularity in the prevention of ...embolic stroke in non-valvular atrial fibrillation as well as in the prevention and treatment of venous thromboembolism. However, similar to traditional anticoagulants, NOACs have the side effects of bleeding, including gastrointestinal bleeding(GIB). Results from both randomized clinical trials and observations studies suggest that high-dose dabigatran(150 mg b.i.d), rivaroxaban and high-dose edoxaban(60 mg daily) are associated with a higher risk of GIB compared with warfarin. Other risk factors of NOAC-related GIB include concomitant use of ulcerogenic agents, older age, renal impairment, Helicobacter pylori infection and a past history of GIB. Prevention of NOAC-related GIB includes proper patient selection, using a lower dose of certain NOACs and in patients with renal impairment, correction of modifiable risk factors, and prescription of gastroprotective agents. Overt GIB can be managed by withholding NOACs followed by delayed endoscopic treatment. In severe bleeding, additional measures include administration of activated charcoal, use of specific reversal agents such as idarucizumab for dabigatran and andexanent alfa for factor Xa inhibitors, and urgent endoscopic management.
Gastric cancer remains one of the leading cancers in the world with a high mortality, particularly in East Asia. Helicobacter pylori infection accounts for the majority of the noncardia gastric ...cancers by triggering gastric inflammation and subsequent neoplastic progression. Eradication of H. pylori can reduce, but not totally eliminate, subsequent risk of developing gastric cancer. Proton-pump inhibitors (PPIs) are one of the most widely prescribed medications worldwide. With their profound gastric-acid suppression, there are concerns about a possible carcinogenic role in gastric cancer, due to induced hypergastrinemia, gastric atrophy and bacterial overgrowth in the stomach. While randomized clinical trials to establish causality between long-term PPI use and gastric cancer are lacking, current evidence based on observational studies suggests PPIs are associated with an increased gastric cancer risk. However, opinions on causality remain divergent due to unmeasured and possible residual confounding in various studies. Our recent study has showed that even after H. pylori eradication, long-term PPI use is still associated with an increased risk of gastric cancer by more than twofold. Hence, long-term PPIs should be used judiciously after considering individual’s risk–benefit profile, particularly among those with history of H. pylori infection. Further well-designed prospective studies are warranted to confirm the potential role of PPIs in gastric cancer according to baseline gastric histology and its interaction with other chemopreventive agents like aspirin, statins and metformin.
Abstract
Background
Although prior studies showed metformin could reduce gastric cancer (GC) risk in patients with diabetes mellitus, they failed to adjust for Helicobacter pylori infection and ...glycemic control. We aimed to investigate whether metformin reduced GC risk in H. pylori-eradicated diabetic patients and its association with glycemic control.
Methods
This was a territory-wide cohort study using hospital registry database, recruiting all diabetic patients who were prescribed clarithromycin-based triple therapy for H. pylori infection from 2003 to 2012. Subjects were observed from H. pylori therapy prescription until GC diagnosis, death, or end of study (December 2015). Exclusion criteria included GC diagnosed within first year of H. pylori therapy, prior history of GC or gastrectomy, and failure of H. pylori eradication. The hazard ratio (HR) of GC with metformin (defined as at least 180-day use) was estimated by Cox model with propensity score adjustment for covariates (age, sex, comorbidities, medications including insulin, and time-weighted average hemoglobin A1c HbA1c). All statistical tests were two-sided.
Results
During a median follow-up of 7.1 years (IQR = 4.7–9.8), 37 (0.51%) of 7266 diabetic patients developed GC at a median age of 76.4 years (IQR = 64.8–81.5 years). Metformin use was associated with a reduced GC risk (adjusted HR = 0.49, 95% CI = 0.24 to 0.98). There was a trend towards a lower GC risk with increasing duration (Ptrend = .01) and dose of metformin (Ptrend = .02). HbA1c level was not an independent risk factor for GC.
Conclusions
Metformin use was associated with a lower GC risk among H. pylori-eradicated diabetic patients in a duration- and dose-response manner, which was independent of HbA1c level.
The role of bacteria other than Helicobacter pylori (HP) in the stomach remains elusive. We characterized the gastric microbiota in individuals with different histological stages of gastric ...carcinogenesis and after receiving HP eradication therapy. Endoscopic gastric biopsies were obtained from subjects with HP gastritis, gastric intestinal metaplasia (IM), gastric cancer (GC) and HP negative controls. Gastric microbiota was characterized by Illumina MiSeq platform targeting the 16 S rDNA. Apart from dominant H. pylori, we observed other Proteobacteria including Haemophilus, Serratia, Neisseria and Stenotrophomonas as the major components of the human gastric microbiota. Although samples were largely converged according to the relative abundance of HP, a clear separation of GC and other samples was recovered. Whilst there was a strong inverse association between HP relative abundance and bacterial diversity, this association was weak in GC samples which tended to have lower bacterial diversity compared with other samples with similar HP levels. Eradication of HP resulted in an increase in bacterial diversity and restoration of the relative abundance of other bacteria to levels similar to individuals without HP. In conclusion, HP colonization results in alterations of gastric microbiota and reduction in bacterial diversity, which could be restored by antibiotic treatment.
Artificial intelligence (AI)-assisted detection is increasingly used in upper endoscopy. We performed a meta-analysis to determine the diagnostic accuracy of AI on detection of gastric and esophageal ...neoplastic lesions and Helicobacter pylori (HP) status.
We searched Embase, PubMed, Medline, Web of Science, and Cochrane databases for studies on AI detection of gastric or esophageal neoplastic lesions and HP status. After assessing study quality using the Quality Assessment of Diagnostic Accuracy Studies tool, a bivariate meta-analysis following a random-effects model was used to summarize the data and plot hierarchical summary receiver-operating characteristic curves. The diagnostic accuracy was determined by the area under the hierarchical summary receiver-operating characteristic curve (AUC).
Twenty-three studies including 969,318 images were included. The AUC of AI detection of neoplastic lesions in the stomach, Barrett’s esophagus, and squamous esophagus and HP status were .96 (95% confidence interval CI, .94-.99), .96 (95% CI, .93-.99), .88 (95% CI, .82-.96), and .92 (95% CI, .88-.97), respectively. AI using narrow-band imaging was superior to white-light imaging on detection of neoplastic lesions in squamous esophagus (.92 vs .83, P < .001). The performance of AI was superior to endoscopists in the detection of neoplastic lesions in the stomach (AUC, .98 vs .87; P < .001), Barrett’s esophagus (AUC, .96 vs .82; P < .001), and HP status (AUC, .90 vs .82; P < .001).
AI is accurate in the detection of upper GI neoplastic lesions and HP infection status. However, most studies were based on retrospective reviews of selected images, which requires further validation in prospective trials.
We performed a meta-analysis of all published studies to determine the diagnostic accuracy of artificial intelligence (AI) on histology prediction and detection of colorectal polyps.
We searched ...Embase, PubMed, Medline, Web of Science, and Cochrane library databases to identify studies using AI for colorectal polyp histology prediction and detection. The quality of included studies was measured by the Quality Assessment of Diagnostic Accuracy Studies tool. We used a bivariate meta-analysis following a random-effects model to summarize the data and plotted hierarchical summary receiver operating characteristic curves. The area under the hierarchical summary receiver operating characteristic curve (AUC) served as an indicator of the diagnostic accuracy and during head-to-head comparisons.
A total of 7680 images of colorectal polyps from 18 studies were included in the analysis of histology prediction. The accuracy of the AI (AUC) was .96 (95% confidence interval CI, .95-.98), with a corresponding pooled sensitivity of 92.3% (95% CI, 88.8%-94.9%) and specificity of 89.8% (95% CI, 85.3%-93.0%). The AUC of AI using narrow-band imaging (NBI) was significantly higher than the AUC using non-NBI (.98 vs .84, P < .01). The performance of AI was superior to nonexpert endoscopists (.97 vs .90, P < .01). For characterization of diminutive polyps using a deep learning model with nonmagnifying NBI, the pooled negative predictive value was 95.1% (95% CI, 87.7%-98.1%). For polyp detection, the pooled AUC was .90 (95% CI, .67-1.00) with a sensitivity of 95.0% (95% CI, 91.0%-97.0%) and a specificity of 88.0% (95% CI, 58.0%-99.0%).
AI was accurate in histology prediction and detection of colorectal polyps, including diminutive polyps. The performance of AI was better under NBI and was superior to nonexpert endoscopists. Despite the difference in AI models and study designs, AI performances are rather consistent, which could serve as a reference for future AI studies.
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