Browsing by Author "Saligram, Shreyas"

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    Impact of COVID-19 on gastroenterology fellowship training: a multicenter analysis of endoscopy volumes
    (Thieme, 2021-09-16) Paleti, Swathi; Sobani, Zain A.; McCarty, Thomas R.; Gutta, Aditya; Gremida, Anas; Shah, Raj; Nutalapati, Venkat; Bazerbachi, Fateh; Jesudoss, Randhir; Amin, Shreya; Okwara, Chinemerem; Kathi, Pradeep Reddy; Ahmed, Ali; Gessel, Luke; Hung, Kenneth; Masoud, Amir; Yu, Jessica; Mony, Shruti; Akshintala, Venkata; Jamil, Laith; Nasereddin, Thayer; Kochhar, Gursimran; Vyas, Neil; Saligram, Shreyas; Garg, Rajat; Sandhu, Dalbir; Benrajab, Karim; Konjeti, Rajesh; Agnihotri, Abhishek; Trivedi, Hirsh; Grunwald, Matthew; Mayer, Ira; Mohanty, Arpan; Rustagi, Tarun; Medicine, School of Medicine
    Abstract Background and study aims The COVID-19 pandemic has had a profound impact on gastroenterology training programs. We aimed to objectively evaluate procedural training volume and impact of COVID-19 on gastroenterology fellowship programs in the United States. Methods This was a retrospective, multicenter study. Procedure volume data on upper and lower endoscopies performed by gastroenterology fellows was abstracted directly from the electronic medical record. The study period was stratified into 2 time periods: Study Period 1, SP1 (03/15/2020 to 06/30/2020) and Study Period 2, SP2 (07/01/2020 to 12/15/2020). Procedure volumes during SP1 and SP2 were compared to Historic Period 1 (HP1) (03/15/2019 to 06/30/2019) and Historic Period 2 (HP2) (07/01/2019 to 12/15/2019) as historical reference. Results Data from 23 gastroenterology fellowship programs (total procedures = 127,958) with a median of 284 fellows (range 273–289; representing 17.8 % of all trainees in the United States) were collected. Compared to HP1, fellows performed 53.6 % less procedures in SP1 (total volume: 28,808 vs 13,378; mean 105.52 ± 71.94 vs 47.61 ± 41.43 per fellow; P < 0.0001). This reduction was significant across all three training years and for both lower and upper endoscopies (P < 0.0001). However, the reduction in volume was more pronounced for lower endoscopy compared to upper endoscopy [59.03 % (95 % CI: 58.2–59.86) vs 48.75 % (95 % CI: 47.96–49.54); P < 0.0001]. The procedure volume in SP2 returned to near baseline of HP2 (total volume: 42,497 vs 43,275; mean 147.05 ± 96.36 vs 150.78 ± 99.67; P = 0.65). Conclusions Although there was a significant reduction in fellows’ endoscopy volume in the initial stages of the pandemic, adaptive mechanisms have resulted in a return of procedure volume to near baseline without ongoing impact on endoscopy training.
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    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 (RATES) Study
    (Elsevier, 2017) Wani, Sachin; Keswani, Rajesh; Hall, Matt; Han, Samuel; Ali, Meer Akbar; Brauer, Brian; Carlin, Linda; Chak, Amitabh; Collins, Dan; Cote, Gregory A.; Diehl, David L.; DiMaio, Christopher J.; Dries, Andrew; El-Hajj, Ihab; Ellert, Swan; Fairley, Kimberley; Faulx, Ashley; Fujii-Lau, Larissa; Gaddam, Srinivas; Gan, Seng-Ian; Gaspar, Jonathan P.; Gautamy, Chitiki; Gordon, Stuart; Harris, Cynthia; Hyder, Sarah; Jones, Ross; Kim, Stephen; Komanduri, Srinadh; Law, Ryan; Lee, Linda; Mounzer, Rawad; Mullady, Daniel; Muthusamy, V. Raman; Olyaee, Mojtaba; Pfau, Patrick; Saligram, Shreyas; Piraka, Cyrus; Rastogi, Amit; Rosenkranz, Laura; Rzouq, Fadi; Saxena, Aditi; Shah, Raj J.; Simon, Violette C.; Small, Aaron; Sreenarasimhaiah, Jayaprakash; Walker, Andrew; Wang, Andrew Y.; Watson, Rabindra R.; Wilson, Robert H.; Yachimski, Patrick; Yang, Dennis; Edmundowicz, Steven; Early, Dayna S.; Department of Medicine, IU School of Medicine
    Background and aims Based on the Next Accreditation System, trainee assessment should occur on a continuous basis with individualized feedback. We aimed to validate endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP) learning curves among advanced endoscopy trainees (AETs) using a large national sample of training programs and to develop a centralized database that allows assessment of performance in relation to peers. Methods ASGE recognized training programs were invited to participate and AETs were graded on ERCP and EUS exams using a validated competency assessment tool that assesses technical and cognitive competence in a continuous fashion. Grading for each skill was done using a 4-point scoring system and a comprehensive data collection and reporting system was built to create learning curves using cumulative sum analysis. Individual results and benchmarking to peers were shared with AETs and trainers quarterly. Results Of the 62 programs invited, 20 programs and 22 AETs participated in this study. At the end of training, median number of EUS and ERCP performed/AET was 300 (range 155-650) and 350 (125-500). Overall, 3786 exams were graded (EUS:1137; ERCP–biliary 2280, pancreatic 369). Learning curves for individual endpoints, and overall technical/cognitive aspects in EUS and ERCP demonstrated substantial variability and were successfully shared with all programs. The majority of trainees achieved overall technical (EUS: 82%; ERCP: 60%) and cognitive (EUS: 76%; ERCP: 100%) competence at conclusion of training. Conclusions These results demonstrate the feasibility of establishing a centralized database to report individualized learning curves and confirm the substantial variability in time to achieve competence among AETs in EUS and ERCP.
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    Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer
    (MDPI, 2020-10-15) Goyal, Hemant; Mann, Rupinder; Gandhi, Zainab; Perisetti, Abhilash; Ali, Aman; Aman Ali, Khizar; Sharma, Neil; Saligram, Shreyas; Tharian, Benjamin; Inamdar, Sumant; Medicine, School of Medicine
    Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a "second look" for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.