Society: ASGE

INTRODUCTION:
Training in interventional endoscopy is offered by interventional endoscopy fellowship programs (IEFPs) not accredited by the ACGME. The number of these programs has increased exponentially with a concurrent increase in the breadth and complexity of these procedures. ACGME-accredited fellowships are governed by competency-based education, yet what constitutes a “high-quality” non-accredited IEFP has not been defined. Using an evidence-based consensus process, we aimed to establish minimum standards for IEFPs.

METHODS:
The RAND UCLA Appropriateness Method, a modified Delphi process to develop quality indicators (QIs), was utilized. A task force drafted potential QIs (structure, process and outcome) in 6 categories: a) activity preceding training, b) structure of IEFPs, c) training in ERCP, d) EUS, e) endoscopic mucosal resection (EMR) and f) luminal stenting. Three rounds of iterative feedback from 20 experts were conducted. Round 0 involved discussion of project details. In Round 1 experts independently ranked proposed QIs on a 9-point scale ranging from highly inappropriate (1) to highly appropriate (9). Next, proposed QIs were discussed and re-worded in a group meeting followed by Round 2 in which experts independently re-ranked proposed QIs and provided benchmarks (when applicable). The median score for each QI was calculated. Mean absolute deviation from the median was calculated and appropriateness of potential QIs was assessed using: (i) BIOMED Concerted Action on Appropriateness definition, (ii) P-value method and (iii) inter-percentile range adjusted for symmetry definition. A QI was deemed appropriate if median score was ≥7 and met criteria for appropriateness using all 3 defined statistical methods.

RESULTS:
Of 89 proposed QIs, 37 met criteria as appropriate for a QI (activity preceding training, 2; structure of IEFPs, 10; ERCP, 7; EUS, 8; EMR, 7; luminal stenting, 3) (Tables 1 & 2). Minimum thresholds were defined for 19 relevant QIs for number of trainers, procedures during fellowship and procedures prior to assessment of competence. Among the final appropriate QIs were that all trainees should undergo qualitative and quantitative competence assessments using validated tools at least quarterly with documented feedback throughout the training period and that trainees should track outcomes and relevant quality metrics for specific procedures.

CONCLUSION:
This ASGE-led initiative established minimum standards for training in interventional endoscopy. These may be assessed by all stakeholders and would ensure adequate training in interventional endoscopic procedures (ERCP, EUS, EMR, luminal stenting) during fellowship. This would also facilitate compliance with the ACGME/Next Accreditation System requirements of ensuring that trainees reach specific milestones in their progression to achieving cognitive and technical competency.

<b>Table 1: Appropriate measures for minimum standards for interventional endoscopy fellowship programs (IEFPs) – activity preceding training, structure of training programs and ERCP training (all measures rated as appropriate using BIOMED, p-value and IPRAS)</b>

Table 1: Appropriate measures for minimum standards for interventional endoscopy fellowship programs (IEFPs) – activity preceding training, structure of training programs and ERCP training (all measures rated as appropriate using BIOMED, p-value and IPRAS)

<b>Table 2: Appropriate measures for minimum standards for interventional endoscopy training programs – EUS, EMR and luminal stenting training (all measures rated as appropriate using BIOMED, p-value and IPRAS)</b>

Table 2: Appropriate measures for minimum standards for interventional endoscopy training programs – EUS, EMR and luminal stenting training (all measures rated as appropriate using BIOMED, p-value and IPRAS)

Background: Assessment of competence in endoscopic retrograde cholangiopancreatography (ERCP) is essential to ensure trainees possess the skills needed for independent practice. Traditionally, ERCP training has used the apprenticeship model, whereby novices learn skills under the supervision of an expert. A growing focus on procedural quality, however, has supported the implementation of competency-based medical education models which require documentation of a trainee’s competence for independent practice. Observational assessment tools with strong evidence of validity are critical to this process. Validity evidence supporting ERCP observational assessment tools has not been systematically evaluated.

Purpose: To conduct a systematic review of ERCP assessment tools and identify tools with strong evidence of validity using a unified validity evidence framework

Methods: We conducted a systematic search using electronic databases and hand-searching from inception until August 2021 for studies evaluating observational assessment tools of ERCP performance. We used a unified validity framework to characterize validity evidence from five sources: content, response process, internal structure, relations to other variables, and consequences. Each domain was assigned a score of 0-3 (maximum score 15). We assessed educational utility and methodological quality using the Accreditation Council for Graduate Medical Education framework and the Medical Education Research Quality Instrument, respectively.

Results: From 2769 records, we included 17 studies evaluating 7 assessment tools. Five tools were studied for clinical ERCP, one on simulated ERCP, and one on simulated and clinical ERCP. Validity evidence scores ranged from 2-12. The Bethesda ERCP Skills Assessment Tool (BESAT), ERCP Direct Observation of Procedural Skills Tool (ERCP DOPS), and The Endoscopic Ultrasound (EUS) and ERCP Skills Assessment Tool (TEESAT) had the strongest validity evidence with scores of 10, 12, and 11, respectively. Regarding educational utility, most tools were easy to use and interpret, and required minimal additional resources. Overall methodological quality was strong, with scores ranging from 10-12.5 (maximum 13.5).

Conclusions: The BESAT, ERCP DOPS, and TEESAT have strong validity evidence compared to other assessments. Integrating tools into training may help drive learners’ development and support competency decision-making.

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.

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.

Figure 1. EMR Standardized Assessment Tool (EMR-STAT)

Figure 1. EMR Standardized Assessment Tool (EMR-STAT)

Figure 2. Representation of the learning curves among AETs by using cumulative sum (CUSUM) analysis for core cognitive and technical skills during C-EMR training.

Figure 2. Representation of the learning curves among AETs by using cumulative sum (CUSUM) analysis for core cognitive and technical skills during C-EMR training.

Sanmeet Singh

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