STATE-OF-THE-ART LECTURE: ARE WE TEACHING WELL? TRAINING MODALITIES THAT OPTIMIZE ENDOSCOPY SKILLS AMONG FELLOWS AND CLINICIANS

Background: There is a lack of data on training benchmarks to define competence in colorectal EMR (C-EMR) among advanced endoscopy trainees (AETs). Previous pilot data from our group demonstrated a relatively low proportion of AETs achieve competence on key cognitive and technical aspects of C-EMR. We aimed to perform an interim analysis on C-EMR training among AETs and assess their performance using the EMR-STAT during the first trimester of their advanced endoscopy fellowship (AEF).
Methods: Prospective multicenter study evaluating AETs C-EMR training using the EMR-STAT. The tool was previously validated in the pilot study for standardized evaluation of key cognitive and technical C-EMR skills (Figure 1). A 4-point scoring system was used to grade these endpoints. Global rating was provided using a 10-point scoring system. For interim analysis, competence was defined as a score of 3 or 4 for each endpoint and ≥7 for overall assessment. Cumulative sum analysis was used to establish competence for cognitive and technical components of C-EMR and overall performance. Prior to the study, participating AETs completed questionnaire about their GI fellowship training in endoscopic resection.
Results: Twenty-five AETs from 18 institutions are enrolled in this ongoing study. On survey questionnaire, the AETs reported having performed a mean of 41.4 C-EMRs (interquartile range [IQR]: 10-50) before the onset of their AEF and most received cognitive training in C-EMR during their general GI fellowship (n=20; 80%). In the first trimester of their AEF, out of the 25 AETs, 15 have performed a mean of 9.1 C-EMRs (range 1-30). Mean lesion size was 26.7±11.6 and mean EMR time of 26.1±18.1 minutes. En-bloc resection rate for polyp sizes 11-20 mm was 41.3% (19/46). Competence in cognitive skills, such as assessment of polyp morphology and pit/vascular pattern, was achieved by AETs in 90.4% and 83.1%, respectively. AETs were graded as competent in submucosal lift injection and snare resection in 69.9% and 63.2%, respectively. Overall competence based on the global score was attained in 53.7% of the cases. On cumulative sum analysis, only 2 AETs crossed the competence threshold for cognitive skills and 1 AET for technical skills. The minimum threshold to achieve competence was 18 C-EMRs (Figure 2).
Conclusions: Standardized evaluation of competence in C-EMR training is critical for quality assurance in patient care. There was high variability in the number of C-EMRs performed by AETs and low overall en-bloc resection rates for polyps 11-20 mm in size. In aggregate, AETs were graded as competent in only half of the C-EMR cases and only 2 AETs have crossed the minimum threshold of competence. Ongoing data acquisition from this study will provide insight into the current state of C-EMR training during AEF and establish competence thresholds for quality metrics.

Background: The recently developed CAD EYE system (Fujifilm, Tokyo, Japan), which provides artificial intelligence (AI) -aided endoscopic diagnosis, has the potential to improve the detection for colorectal polyps. It is essential that gastroenterology trainees improve the quality of total colonoscopy (CS) operations and accelerate their technical progress. The aim of this study was to determine the utility of CAD EYE for CS by comparing endoscopic observation using CAD EYE with conventional endoscopic observation (i.e., white light imaging) in outpatients undergoing CS performed by gastroenterology trainees (i.e., beginner endoscopists).
Methods: This was a multi-center, randomized controlled trial at Ureshino Medical Center, Karatsu Red Cross Hospital and Saga University Hospital (UMIN000044031). The study received an academic research grant from the Japanese Society of Gastrointestinal Endoscopy in 2021. Patients were divided into group A (observed using CAD EYE) and group B (observed using white light imaging). Six gastroenterologists with limited experience in CS (i.e., trainees in their third or fourth year after graduation) performed CS using a back-to-back method in pairs with a gastroenterology specialist. The primary endpoint was the adenoma detection rate. The secondary endpoints were the adenoma miss rate (AMR) and 14 assessment of competency in endoscopy tool scores. The learning curve of each trainee was evaluated using the cumulative sum control chart.
Results: We analyzed 231 cases (113 in group A, 118 in group B) enrolled from May 2021 to March 2022. There was no difference in the adenoma detection rate of trainees between group A and group B (58.4% versus 61.0%, respectively; p=0.690). There was a significantly lower AMR (26.6% versus 39.7%, respectively; p=0.036) and number of missed adenomas per patient (0.5 versus 0.9, respectively; p=0.004) in group A compared with group B. Group A also scored significantly higher than group B on two items of the assessment of competency in endoscopy tool score—i.e., pathology identification (2.26 versus 2.07, respectively; p=0.030) and interpretation and identifying location of pathology (2.18 versus 2.00, respectively; p=0.038). For the cumulative sum learning curve of trainees, the number of cases in which multiple adenomas were missed by the six trainees who performed CS was lower in group A. Even after accumulating cases, the number of missed adenomas remained consistently lower in group A.
Conclusions: The use of CAD EYE can decrease the AMR and improve the ability to accurately locate and identify colorectal adenomas. Thus, CAD EYE is particularly useful for CS in beginning endoscopists.