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| Name | Class |
|---|---|
| Duke University | OTHER |
| University of South Florida | OTHER |
| Stony Brook University | OTHER |
| Atrium Medical Center |
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Quality improvement project with the aim to use a standardized assessment tool (EMR-STAT) to establish learning curves and competence thresholds for key cognitive and technical colorectal EMR core skills among advanced endoscopy trainees (AETs).
Baseline and Post-Training Questionnaires: The EMR-STAT Baseline Questionnaire is to better understand the participant's level of comfort with the C-EMR procedure. At the end of their training, the participant will complete the EMR-STAT Post-Training Questionnaire to evaluate their training experience.
Standardized Assessment tool (EMR-STAT) and Grading Protocol (Phase 1): The EMR-STAT is a tool designed for competence assessment. The tool will be used in a continuous fashion during the AETs training in colorectal EMR. The aim of the tool is to evaluate key concepts and core skills necessary for high-quality colorectal EMR as recently outlined by the US Multi-Society Task Force on Colorectal Cancer10. We have previously demonstrated the feasibility of this tool in a prior study9. The instrument evaluates for key cognitive and technical steps, including scope positioning, lesion assessment, submucosal lifting, endoscopic resection, adjunct resection/ablative techniques, management of adverse events, and elective defect closure. A 4-point scoring system was developed to grade each endpoint: 4 (superior), achieves task without instruction; 3 (advanced), achieves with minimal verbal cues; 2 (intermediate), achieves with multiple verbal cues or hands-on assistance; 1 (novice), unable to complete and requires trainer to take over. Setting these pre-defined anchors for specific individual cognitive and technical skills during grading ensures that the data collected are reproducible from one evaluator to the next. In addition, a 10-point overall assessment score (1-3, below average; 4-6, intermediate; 7-9, advanced; 10, superior) will be provided for each case. This grading format for endoscopic performance has been previously validated6,11,12
The trainees will be evaluated during live cases as part of the training experience. There will patient data that will be collected on the EMR_STAT but no patient identifiers will be documented. This data collection is integral to the study because these factors may play a role in the complexity of the procedure and impact trainees' EMR performance (ex. a cancerous lesion is more difficult to dissect than a non-cancerous lesion). This data will be analyzed as part of the learning curve for the trainees
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| EMR STAT - Survey | Behavioral | Surveys are completed before and after Advanced Fellow Training. |
| Measure | Description | Time Frame |
|---|---|---|
| Primary Endpoint | The primary endpoint will be the establishment of minimum thresholds in colorectal EMR training, including benchmarks for pre-defined cognitive and technical core skills based on the survey responses from study participants. | 12 months |
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Inclusion Criteria:
Exclusion Criteria:
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Advanced Endoscopy Trainees (AETs) who intend to train in colorectal Endoscopic Mucosal Reseaction (EMR) during their fellowship training.
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| Name | Affiliation | Role |
|---|---|---|
| Dennis Yang, MD | AdventHealth | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| AdventHealth Orlando | Orlando | Florida | 32804 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30267866 | Background | Yang D, Othman M, Draganov PV. Endoscopic Mucosal Resection vs Endoscopic Submucosal Dissection For Barrett's Esophagus and Colorectal Neoplasia. Clin Gastroenterol Hepatol. 2019 May;17(6):1019-1028. doi: 10.1016/j.cgh.2018.09.030. Epub 2018 Sep 26. | |
| 27109458 | Background | Bhurwal A, Bartel MJ, Heckman MG, Diehl NN, Raimondo M, Wallace MB, Woodward TA. Endoscopic mucosal resection: learning curve for large nonpolypoid colorectal neoplasia. Gastrointest Endosc. 2016 Dec;84(6):959-968.e7. doi: 10.1016/j.gie.2016.04.020. Epub 2016 Apr 22. |
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| OTHER |
| Hofstra University | OTHER |
| Long Island Jewish Medical Center | OTHER |
| Vanderbilt University | OTHER |
| Yale University | OTHER |
| Columbia University | OTHER |
| Geisinger Clinic | OTHER |
| University of Virginia | OTHER |
| Baylor University | OTHER |
| University of Kentucky | OTHER |
| Cedars-Sinai Medical Center | OTHER |
| University of Chicago | OTHER |
| University of California, Irvine | OTHER |
| University of Alabama at Birmingham | OTHER |
| Beth Israel Deaconess Medical Center | OTHER |
| Unity Health Toronto | OTHER |
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| 31925890 | Background | Rajendran A, Pannick S, Thomas-Gibson S, Oke S, Anele C, Sevdalis N, Haycock A. Systematic literature review of learning curves for colorectal polyp resection techniques in lower gastrointestinal endoscopy. Colorectal Dis. 2020 Sep;22(9):1085-1100. doi: 10.1111/codi.14960. Epub 2020 Jan 30. |
| 32504699 | Background | Yang D, Wagh MS, Draganov PV. The status of training in new technologies in advanced endoscopy: from defining competence to credentialing and privileging. Gastrointest Endosc. 2020 Nov;92(5):1016-1025. doi: 10.1016/j.gie.2020.05.047. Epub 2020 Jun 3. |
| 29709305 | Background | Wani S, Keswani RN, Petersen B, Edmundowicz SA, Walsh CM, Huang C, Cohen J, Cote G. Training in EUS and ERCP: standardizing methods to assess competence. Gastrointest Endosc. 2018 Jun;87(6):1371-1382. doi: 10.1016/j.gie.2018.02.009. Epub 2018 Apr 27. No abstract available. |
| 28625816 | Background | Wani S, Keswani R, Hall M, Han S, Ali MA, Brauer B, Carlin L, Chak A, Collins D, Cote GA, Diehl DL, DiMaio CJ, Dries A, El-Hajj I, Ellert S, Fairley K, Faulx A, Fujii-Lau L, Gaddam S, Gan SI, Gaspar JP, Gautamy C, Gordon S, Harris C, Hyder S, Jones R, Kim S, Komanduri S, Law R, Lee L, Mounzer R, Mullady D, Muthusamy VR, Olyaee M, Pfau P, Saligram S, Piraka C, Rastogi A, Rosenkranz L, Rzouq F, Saxena A, Shah RJ, Simon VC, Small A, Sreenarasimhaiah J, Walker A, Wang AY, Watson RR, Wilson RH, Yachimski P, Yang D, Edmundowicz S, Early DS. A Prospective Multicenter Study Evaluating Learning Curves and Competence in Endoscopic Ultrasound and Endoscopic Retrograde Cholangiopancreatography Among Advanced Endoscopy Trainees: The Rapid Assessment of Trainee Endoscopy Skills Study. Clin Gastroenterol Hepatol. 2017 Nov;15(11):1758-1767.e11. doi: 10.1016/j.cgh.2017.06.012. Epub 2017 Jun 16. |
| 28061339 | Background | Klein A, Bourke MJ. How to Perform High-Quality Endoscopic Mucosal Resection During Colonoscopy. Gastroenterology. 2017 Feb;152(3):466-471. doi: 10.1053/j.gastro.2016.12.029. Epub 2017 Jan 3. No abstract available. |
| 28089029 | Background | ASGE Standards of Practice Committee; Faulx AL, Lightdale JR, Acosta RD, Agrawal D, Bruining DH, Chandrasekhara V, Eloubeidi MA, Gurudu SR, Kelsey L, Khashab MA, Kothari S, Muthusamy VR, Qumseya BJ, Shaukat A, Wang A, Wani SB, Yang J, DeWitt JM. Guidelines for privileging, credentialing, and proctoring to perform GI endoscopy. Gastrointest Endosc. 2017 Feb;85(2):273-281. doi: 10.1016/j.gie.2016.10.036. No abstract available. |
| 32961243 | Background | Yang D, Perbtani YB, Wang Y, Rumman A, Wang AY, Kumta NA, DiMaio CJ, Antony A, Trindade AJ, Rolston VS, D'Souza LS, Corral Hurtado JE, Gomez V, Pohl H, Draganov PV, Beyth RJ, Lee JH, Cheesman A, Uppal DS, Sejpal DV, Bucobo JC, Wallace MB, Ngamruengphong S, Ajayeoba O, Khara HS, Diehl DL, Jawaid S, Forsmark CE; C-EMR STAT Study Group. Evaluating learning curves and competence in colorectal EMR among advanced endoscopy fellows: a pilot multicenter prospective trial using cumulative sum analysis. Gastrointest Endosc. 2021 Mar;93(3):682-690.e4. doi: 10.1016/j.gie.2020.09.023. Epub 2020 Sep 19. |