Evaluation of a single-use gastroscope in patients presenting with suspected upper gastrointestinal hemorrhage: a pilot feasibility study (One-Scope I).

BACKGROUND : Outbreaks of multidrug-resistant bacteria due to contaminated duodenoscopes and infection risks during the COVID-19 pandemic have driven the development of single-use endoscopes. The first single-use gastroscope is now available in Europe. Besides waste disposal and cost issues, the infection risk and performance remain unclear. We aimed to evaluate a single-use gastroscope in patients with signs of upper gastrointestinal bleeding. METHODS : 20 consecutive patients presenting with clinical signs of upper gastrointestinal bleeding between October and November 2022 were included in this case series. The primary aim was technical success, defined as access to the descending duodenum and adequate assessment of the upper gastrointestinal tract for the presence of a bleeding site. RESULTS : The primary aim was achieved in 19/20 patients (95 %). The bleeding site was identified in 18 patients. A therapeutic intervention was performed in six patients (two cap-mounted clips, one standard hemostatic clip, two variceal band ligations, one hemostatic powder, two adrenaline injections); technical and clinical success were achieved in all six patients. Two crossovers to a standard gastroscope occurred. CONCLUSIONS : Use of single-use gastroscopes may be feasible for patients presenting for urgent endoscopic evaluation and treatment of upper gastrointestinal bleeding.

Impact of Small Intestinal Bacterial Overgrowth in Patients with Inflammatory Bowel Disease and Other Gastrointestinal Disorders-A Retrospective Analysis in a Tertiary Single Center and Review of the Literature.

BACKGROUND: Small intestinal bacterial overgrowth (SIBO) is often found in patients with gut dysbiosis such as irritable bowel syndrome. Recently, the association of SIBO and inflammatory bowel disease (IBD) has been described in some cases. While clinical symptoms might be similar in IBD and SIBO, treatment is quite different for both diseases. Therefore, the differentiation between SIBO or a flare in IBD patients is key to optimizing treatment for these patients. METHODS: We retrospectively investigated our patients with IBD receiving a glucose breath test for SIBO and correlated the results with the clinical symptoms (clinical remission or active disease). RESULTS: 128 patients with the diagnosis "colitis" were analyzed in our cohort. Fifty-three (41.4%) patients had Crohn's disease and 22 (17.2%) patients were suffering from ulcerative colitis. Seventy-four (57.8%) were female and 54 (42.2%) were male patients. A total of 18 (14.1%) patients had a positive testing for SIBO. Eleven (61.1%) cases were associated with CD patients and two (11.1%) with UC. IBD patients in clinical remission had a positive SIBO in six (19.4%) cases, while IBD patients with active disease were positive in nine (15.3%) cases. The proportion of positive SIBO in active IBD patients was higher; however, it did not reach significance. Older age was a risk factor for SIBO in patients with CD (p < 0.003). CONCLUSIONS: In our study, we could show that an increased amount of SIBO was found in IBD patients and was especially more frequent in patients with CD than in those with UC. In UC patients, SIBO rates were not different to patients with other gastrointestinal diseases investigated (e.g., infectious colitis, collagenous colitis, or irritable bowel syndrome). In active IBD, positive SIBO was detected more often numerically compared to quiescent disease; however, due to the low number of patients included, it was not significant. However, older age was a significant risk factor for SIBO in patients with CD. SIBO is of clinical relevance in the vulnerable patient cohort with IBD, and its real prevalence and impact needs to be investigated in further and larger clinical trials.

Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm.

BACKGROUND AND AIMS: Celiac disease with its endoscopic manifestation of villous atrophy (VA) is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of VA at routine EGD may improve diagnostic performance. METHODS: A dataset of 858 endoscopic images of 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet18 deep learning model to detect VA. An external dataset was used to test the algorithm, in addition to 6 fellows and 4 board-certified gastroenterologists. Fellows could consult the AI algorithm's result during the test. From their consultation distribution, a stratification of test images into "easy" and "difficult" was performed and used for classified performance measurement. RESULTS: External validation of the AI algorithm yielded values of 90%, 76%, and 84% for sensitivity, specificity, and accuracy, respectively. Fellows scored corresponding values of 63%, 72%, and 67% and experts scored 72%, 69%, and 71%, respectively. AI consultation significantly improved all trainee performance statistics. Although fellows and experts showed significantly lower performance for difficult images, the performance of the AI algorithm was stable. CONCLUSIONS: In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of VA on endoscopic still images. AI decision support significantly improved the performance of nonexpert endoscopists. The stable performance on difficult images suggests a further positive add-on effect in challenging cases.

Efficacy and Safety of Vedolizumab in Patients with Inflammatory Bowel Disease in Association with Vedolizumab Drug Levels.

BACKGROUND: Vedolizumab (VDZ) is a well-established and important therapeutic option in the treatment of patients with inflammatory bowel disease (IBD). However, the significance of therapeutic drug monitoring (TDM) with VDZ remains a contradictory field in daily clinical practice. Our study aims to clarify the predictive impact of VDZ drug levels in long-term clinical outcomes in a real-world cohort. METHODS: Patients with moderate to severe ulcerative colitis (UC) and Crohn's disease (CD) from a tertiary IBD referral center at the University Hospital Augsburg, Germany, were enrolled in this single-center retrospective data analysis. Clinical and endoscopic data were collected at month 6, month 12, and at the last time of follow-up, and outcomes were correlated with VDZ levels at week 6. RESULTS: This study included 95 patients, 68.4% (n = 65) with UC, 24.2% (n = 23) with CD, and 7.4% (n = 7) with indeterminate colitis (CI). Patients with a mean VDZ treatment time of 17.83 months ± 14.56 showed clinical response in 29.5% (n = 28) and clinical remission in 45.3% (n = 43) at the end of the study. Endoscopic response occurred in 20.0% (n = 19) and endoscopic remission in 29.5% (n = 28) at the end of the study. The sustained beneficial effect of VDZ was also reflected in a significant change in biomarker levels. VDZ trough level at week 6 was determined in 48.4% (n = 46) with a mean of 41.79 µg/mL ± 24.58. A significant association between VDZ level at week 6 and both short and long-term outcomes could not be demonstrated. However, numerically higher VDZ levels were seen in patients with endoscopic and clinical improvement at month 6 and at the time of last follow-up. CONCLUSIONS: This study demonstrated efficacy and safety for VDZ in a real-world cohort. Although, for some parameters, a clear trend for higher VDZ levels at week 6 was seen, the efficacy of VDZ was not significantly correlated to VDZ level at week 6, which questions the predictive value of VDZ levels in the real world.

An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis.

The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoE-EREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.