RESUMEN
BACKGROUND: Helicobacter pylori (H. pylori) eradication rates have fallen globally, likely in large part due to increasing antibiotic resistance to traditional therapy. In areas of high clarithromycin and metronidazole resistance such as ours, Maastricht VI guidelines suggest high dose amoxicillin dual therapy (HDADT) can be considered, subject to evidence for local efficacy. In this study we assess efficacy of HDADT therapy for H. pylori eradication in an Irish cohort. AIM: To assess the efficacy of HDADT therapy for H. pylori eradication in an Irish cohort as both first line, and subsequent therapy for patients diagnosed with H. pylori. METHODS: All patients testing positive for H. pylori in a tertiary centre were treated prospectively with HDADT (amoxicillin 1 g tid and esomeprazole 40 mg bid × 14 d) over a period of 8 months. Eradication was confirmed with Urea Breath Test at least 4 wk after cessation of therapy. A delta-over-baseline > 4% was considered positive. Patient demographics and treatment outcomes were recorded, analysed and controlled for basic demographics and prior H. pylori treatment. RESULTS: One hundred and ninety-eight patients were identified with H. pylori infection, 10 patients were excluded due to penicillin allergy and 38 patients refused follow up testing. In all 139 were included in the analysis, 55% (n = 76) were female, mean age was 46.6 years. Overall, 93 (67%) of patients were treatment-naïve and 46 (33%) had received at least one previous course of treatment. The groups were statistically similar. Self-reported compliance with HDADT was 97%, mild side-effects occurred in 7%. There were no serious adverse drug reactions. Overall the eradication rate for our cohort was 56% (78/139). Eradication rates were worse for those with previous treatment [43% (20/46) vs 62% (58/93), P = 0.0458, odds ratio = 2.15]. Age and Gender had no effect on eradication status. CONCLUSION: Overall eradication rates with HDADT were disappointing. Despite being a simple and possibly better tolerated regime, these results do not support its routine use in a high dual resistance country. Further investigation of other regimens to achieve the > 90% eradication target is needed.
RESUMEN
Background and study aims Capsule endoscopy is a time-consuming procedure with a significance error rate. Artificial intelligence (AI) can potentially reduce reading time significantly by reducing the number of images that need human review. An OMOM Artificial Intelligence-enabled small bowel capsule has been recently trained and validated for small bowel capsule endoscopy video review. This study aimed to assess its performance in a real-world setting in comparison with standard reading methods. Patients and methods In this single-center retrospective study, 40 patient studies performed using the OMOM capsule were analyzed first with standard reading methods and later using AI-assisted reading. Reading time, pathology identified, intestinal landmark identification and bowel preparation assessment (Brotz Score) were compared. Results Overall diagnosis correlated 100% between the two reading methods. In a per-lesion analysis, 1293 images of significant lesions were identified combining standard and AI-assisted reading methods. AI-assisted reading captured 1268 (98.1%, 95% CI 97.15-98.7) of these findings while standard reading mode captured 1114 (86.2%, 95% confidence interval 84.2-87.9), P < 0.001. Mean reading time went from 29.7 minutes with standard reading to 2.3 minutes with AI-assisted reading ( P < 0.001), for an average time saving of 27.4 minutes per study. Time of first cecal image showed a wide discrepancy between AI and standard reading of 99.2 minutes (r = 0.085, P = 0.68). Bowel cleansing evaluation agreed in 97.4% (r = 0.805 P < 0.001). Conclusions AI-assisted reading has shown significant time savings without reducing sensitivity in this study. Limitations remain in the evaluation of other indicators.