A simple clinical score to stratify the risk of procedure-related adverse events in ERCP procedures with trainee involvement.

BACKGROUND : Optimal training strategies in endoscopic retrograde cholangiopancreatography (ERCP) remain controversial despite the shift toward competence-based training models, with limited data available on patient safety during training. We aimed to assess whether pre-procedural clinical predictors could identify patients at low risk of developing procedure-related adverse-events (AEs) in a training environment. METHODS : We performed a prospective, multicenter, cohort study in five training centers. A data collection system documenting indication, clinical data, trainee performance (assessed using a validated competence assessment tool), technical outcomes, and AEs over a 30-day follow-up was utilized. We developed a clinical risk score (Trainee Involvement in ERCP Risk Score [TIERS]) for patients undergoing ERCP and compared the rate of AEs in a training environment between low-risk and high-risk groups. The association between trainee performance and AE rate was also evaluated. RESULTS : 1283 ERCPs (409 [31.9 %, 95 %CI 29.3 %-34.4 %] with trainee involvement) performed by 11 trainers and 10 trainees were analyzed. AEs were more frequent in the high-risk compared with the low-risk group: 26.7 % (95 %CI 20.5 %-34.7 %) vs. 17.1 % (95 %CI 12.8 %-22.2 %). TIERS demonstrated a high negative predictive value for AEs (82.9 %, 95 %CI 79.4 %-85.8 %) and was the only predictor of AEs on multivariable analysis (odds ratio 1.38, 95 %CI 1.09-1.75). Suboptimal trainee performance was associated with an increase in AE rates. CONCLUSION : Simple, clinical-based predictive tools could improve ERCP training by selecting the most appropriate cases for hands-on training, with the aim of increasing patient safety.

The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now.

Crohn's disease and ulcerative colitis remain debilitating disorders, characterized by progressive bowel damage and possible lethal complications. The growing number of applications for artificial intelligence in gastrointestinal endoscopy has already shown great potential, especially in the field of neoplastic and pre-neoplastic lesion detection and characterization, and is currently under evaluation in the field of inflammatory bowel disease management. The application of artificial intelligence in inflammatory bowel diseases can range from genomic dataset analysis and risk prediction model construction to the disease grading severity and assessment of the response to treatment using machine learning. We aimed to assess the current and future role of artificial intelligence in assessing the key outcomes in inflammatory bowel disease patients: endoscopic activity, mucosal healing, response to treatment, and neoplasia surveillance.

EUS-guided fine needle biopsy is able to provide diagnosis in rare osteoclast-like giant cells undifferentiated carcinoma of the pancreas: report of two cases.

Undifferentiated carcinoma of the pancreas with osteoclast-like giant cells (UC-OGC) is a rare subtype of pancreatic cancer, accounting for less than 1% of all pancreatic tumors. Preoperative diagnosis is cumbersome as cross-sectional imaging is often not capable to distinguish between UC-OGC and other pancreatic tumors such as pancreatic adenocarcinoma, mucinous carcinoma or neuroendocrine tumors and specific tumor markers seem to be lacking. Endoscopic ultrasound r `m(EUS) with tissue acquisition via fine-needle aspiration (FNA) or biopsy (FNB) with microscopic HE staining and immunohistochemistry allows for an accurate diagnosis, thus influencing further treatment. We present herein the cases of two patients with osteoclast-like giant cells tumors of the pancreas diagnosed by EUS-guided fine needle biopsy and perform a literature review on the role of EUS-guided biopsy for diagnosis.