Evaluating no fixation, endoscopic suture fixation, and an over-the-scope clip for anchoring fully covered self-expanding metal stents in benign upper gastrointestinal conditions: a comparative multicenter international study (with video).

BACKGROUND AND AIMS: Fully covered self-expandable metal stents (FCSEMSs) are widely used in benign upper gastrointestinal (GI) conditions, but stent migration remains a limitation. An over-the-scope clip (OTSC) device (Ovesco Endoscopy) for stent anchoring has been recently developed. The aim of this study was to evaluate the effect of OTSC fixation on SEMS migration rate. METHODS: A retrospective review of consecutive patients who underwent FCSEMS placement for benign upper GI conditions between 1/2011 and 10/2022 at 16 centers. The primary outcome was rate of stent migration. The secondary outcomes were clinical success and adverse events. RESULTS: A total of 311 (no fixation 122, OTSC 94, endoscopic suturing 95) patients underwent 316 stenting procedures. Compared to the no fixation (NF) group (n=49, 39%), the rate of stent migration was significantly lower in the OTSC (SF) (n=16, 17%, p=0.001) and endoscopic suturing (ES) group (n=23, 24%, p=0.01). The rate of stent migration was not different between the SF and ES groups (p=0.2). On multivariate analysis, SF (OR 0.34, CI 0.17-0.70, p<0.01) and ES (OR 0.46, CI 0.23-0.91, p=0.02) were independently associated with decreased risk of stent migration. Compared to the NF group (n=64, 52%), there was a higher rate of clinical success in the SF (n=64, 68%; p=0.03) and ES group (n=66, 69%; p = 0.02). There was no significant difference in the rate of adverse events between the three groups. CONCLUSION: Stent fixation using OTSC is safe and effective at preventing stent migration and may also result in improved clinical response.

The safety and efficacy of endoscopic approaches for the management of Zenker's diverticulum: a multicentre retrospective study.

INTRODUCTION: Minimally invasive endoscopic options are safe and effective alternatives to surgery for the treatment of symptomatic Zenker's diverticulum (ZD). However, there is no consensus on the gold-standard approach. We compared the safety and efficacy of Zenker's peroral endoscopic myotomy (Z-POEM), flexible diverticulotomy (FD), and rigid diverticulotomy (RD) for the management of ZD. METHODS: Patients undergoing treatment for ZD at three UK tertiary referral centres were identified and analysed between 2013 and 2023. Patient demographics, procedural details, clinical success, and 30-day adverse events (AE) were recorded. The primary outcomes were technical and clinical success defined as a fall in Dakkak and Bennett dysphagia score to ≤ 1 without re-intervention. RESULTS: There was no difference in baseline characteristics amongst 126 patients undergoing intervention (50 RD, 31 FD, 45 Z-POEM). Technical success for RD, FD, and Z-POEM was 80%, 100%, and 100%, respectively (p < 0.001). Over a mean follow-up of 11.0 months (95% CI 8.2-13.9), clinical success amongst those treated was 85.3% (RD), 74.1% (FD), and 83.7% (Z-POEM; p = 0.48) with recurrence in 17.2% (RD), 20.0% (FD), and 8.3% (Z-POEM; p = 0.50). AEs were equivalent between groups (p = 0.98). During this time, 11 patients underwent surgical myotomy with low clinical success (36.4%) and high morbidity. CONCLUSION: Endoscopic options for the treatment of ZD show equivalent rates of success, but failed RD often led to open myotomy with worse outcomes. Flexible endoscopic modalities are both safe and highly effective treatments that may be considered first-line in experienced centres and should be offered before surgery.

Using saliva epigenetic data to develop and validate a multivariable predictor of esophageal cancer status.

Background: Salivary epigenetic biomarkers may detect esophageal cancer. Methods: A total of 256 saliva samples from esophageal adenocarcinoma patients and matched volunteers were analyzed with Illumina EPIC methylation arrays. Three datasets were created, using 64% for discovery, 16% for testing and 20% for validation. Modules of gene-based methylation probes were created using weighted gene coexpression network analysis. Module significance to disease and gene importance to module were determined and a random forest classifier generated using best-scoring gene-related epigenetic probes. A cost-sensitive wrapper algorithm maximized cancer diagnosis. Results: Using age, sex and seven probes, esophageal adenocarcinoma was detected with area under the curve of 0.72 in discovery, 0.73 in testing and 0.75 in validation datasets. Cancer sensitivity was 88% with specificity of 31%. Conclusion: We have demonstrated a potentially clinically viable classifier of esophageal cancer based on saliva methylation.

Development and validation of a multivariable risk factor questionnaire to detect oesophageal cancer in 2-week wait patients.

INTRODUCTION: Oesophageal cancer is associated with poor health outcomes. Upper GI (UGI) endoscopy is the gold standard for diagnosis but is associated with patient discomfort and low yield for cancer. We used a machine learning approach to create a model which predicted oesophageal cancer based on questionnaire responses. METHODS: We used data from 2 separate prospective cross-sectional studies: the Saliva to Predict rIsk of disease using Transcriptomics and epigenetics (SPIT) study and predicting RIsk of diSease using detailed Questionnaires (RISQ) study. We recruited patients from National Health Service (NHS) suspected cancer pathways as well as patients with known cancer. We identified patient characteristics and questionnaire responses which were most associated with the development of oesophageal cancer. Using the SPIT dataset, we trained seven different machine learning models, selecting the best area under the receiver operator curve (AUC) to create our final model. We further applied a cost function to maximise cancer detection. We then independently validated the model using the RISQ dataset. RESULTS: 807 patients were included in model training and testing, split in a 70:30 ratio. 294 patients were included in model validation. The best model during training was regularised logistic regression using 17 features (median AUC: 0.81, interquartile range (IQR): 0.69-0.85). For testing and validation datasets, the model achieved an AUC of 0.71 (95% CI: 0.61-0.81) and 0.92 (95% CI: 0.88-0.96) respectively. At a set cut off, our model achieved a sensitivity of 97.6% and specificity of 59.1%. We additionally piloted the model in 12 patients with gastric cancer; 9/12 (75%) of patients were correctly classified. CONCLUSIONS: We have developed and validated a risk stratification tool using a questionnaire approach. This could aid prioritising patients at high risk of having oesophageal cancer for endoscopy. Our tool could help address endoscopic backlogs caused by the COVID-19 pandemic.