Andrew Williams1, Morgan McWilliam2, James Ahlin3, Jacob Davidson4, Mackenzie A Quantz5, Andreana Bütter6. 1. Department of Medical Science, Schulich School of Medicine and Dentistry, London, ON, Canada. 2. Division of General Surgery, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada. 3. Division of General Surgery, Queen's University, Kingston, ON, Canada. 4. Division of Pediatric Surgery, Children's Hospital, London Health Sciences Centre, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada. 5. Simply Simulators, London, Ontario. 6. Division of Pediatric Surgery, Children's Hospital, London Health Sciences Centre, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada. Electronic address: Andreana.Butter@lhsc.on.ca.
Abstract
BACKGROUND: Hypertrophic pyloric stenosis (HPS) is a common neonatal condition treated with open or laparoscopic pyloromyotomy. 3D-printed organs offer realistic simulations to practice surgical techniques. The purpose of this study was to validate a 3D HPS stomach model and assess model reliability and surgical realism. METHODS: Medical students, general surgery residents, and adult and pediatric general surgeons were recruited from a single center. Participants were videotaped three times performing a laparoscopic pyloromyotomy using box trainers and 3D-printed stomachs. Attempts were graded independently by three reviewers using GOALS and Task Specific Assessments (TSA). Participants were surveyed using the Index of Agreement of Assertions on Model Accuracy (IAAMA). RESULTS: Participants reported their experience levels as novice (22%), inexperienced (26%), intermediate (19%), and experienced (33%). Interrater reliability was similar for overall average GOALS and TSA scores. There was a significant improvement in GOALS (p<0.0001) and TSA scores (p=0.03) between attempts and overall. Participants felt the model accurately simulated a laparoscopic pyloromyotomy (82%) and would be a useful tool for beginners (100%). CONCLUSION: A 3D-printed stomach model for simulated laparoscopic pyloromyotomy is a useful training tool for learners to improve laparoscopic skills. The GOALS and TSA provide reliable technical skills assessments. LEVEL OF EVIDENCE: II.
BACKGROUND:Hypertrophic pyloric stenosis (HPS) is a common neonatal condition treated with open or laparoscopic pyloromyotomy. 3D-printed organs offer realistic simulations to practice surgical techniques. The purpose of this study was to validate a 3D HPS stomach model and assess model reliability and surgical realism. METHODS: Medical students, general surgery residents, and adult and pediatric general surgeons were recruited from a single center. Participants were videotaped three times performing a laparoscopic pyloromyotomy using box trainers and 3D-printed stomachs. Attempts were graded independently by three reviewers using GOALS and Task Specific Assessments (TSA). Participants were surveyed using the Index of Agreement of Assertions on Model Accuracy (IAAMA). RESULTS:Participants reported their experience levels as novice (22%), inexperienced (26%), intermediate (19%), and experienced (33%). Interrater reliability was similar for overall average GOALS and TSA scores. There was a significant improvement in GOALS (p<0.0001) and TSA scores (p=0.03) between attempts and overall. Participants felt the model accurately simulated a laparoscopic pyloromyotomy (82%) and would be a useful tool for beginners (100%). CONCLUSION: A 3D-printed stomach model for simulated laparoscopic pyloromyotomy is a useful training tool for learners to improve laparoscopic skills. The GOALS and TSA provide reliable technical skills assessments. LEVEL OF EVIDENCE: II.
Authors: José Cornejo; Jorge A Cornejo-Aguilar; Mariela Vargas; Carlos G Helguero; Rafhael Milanezi de Andrade; Sebastian Torres-Montoya; Javier Asensio-Salazar; Alvaro Rivero Calle; Jaime Martínez Santos; Aaron Damon; Alfredo Quiñones-Hinojosa; Miguel D Quintero-Consuegra; Juan Pablo Umaña; Sebastian Gallo-Bernal; Manolo Briceño; Paolo Tripodi; Raul Sebastian; Paul Perales-Villarroel; Gabriel De la Cruz-Ku; Travis Mckenzie; Victor Sebastian Arruarana; Jiakai Ji; Laura Zuluaga; Daniela A Haehn; Albit Paoli; Jordan C Villa; Roxana Martinez; Cristians Gonzalez; Rafael J Grossmann; Gabriel Escalona; Ilaria Cinelli; Thais Russomano Journal: Biomed Res Int Date: 2022-03-24 Impact factor: 3.411