Training and learning curves in robotic pancreatic surgery

• Published in: Clinical Surgical Oncology Vol. 4; no. 2; p. 100081
• Main Authors: Preukschas, Anas A., Cizmic, Amila, Müller, Philip C., Kümmerli, Christoph, Uzunoglu, Faik Güntac, Hackert, Thilo, Nickel, Felix
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2025
• Subjects: Learning curves; Pancreatic surgery; Robotic surgery; Surgical training; Robotic surgery; Learning curves; Pancreatic surgery; Surgical training
• ISSN: 2773-160X; 2773-160X
• DOI: 10.1016/j.cson.2025.100081

Robotic pancreatic surgery is complex, and its establishment in an institution require a structured approach to secure optimal short- and long-term outcomes. This article provides a structured training proposition for robotic pancreatic surgery and gives an overview of the learning curves and examines the key takeaways. The preclinical training in robotic pancreatic surgery can be divided into a basic and advanced phase. The basic phase includes virtual reality training, biotissue drills, and specialized training courses. The advanced phase consists of reaching benchmarks for the biotissue drills and completing video-based training. After establishing a dedicated interprofessional surgical team index procedures and first robotic pancreatic cases can be performed under the supervision of a proctor. Three phases of clinical training are proposed: competency, proficiency, and mastery. Competency referring to be able to perform the procedure without supervision in patients without risk factors and with average technical difficulty. Proficiency signifying consistently reaching benchmark- and textbook outcome in patients with risk factors and extended indications. Mastery is achieving benchmark values for morbidity rates even in complex cases requiring vessel or multi-visceral resections and with patients having multiple risk factors. The number of cases to overcome the initial phase of the learning curve vary between 7 and 46 for robotic distal pancreatectomy and 8–100 for robotic partial pancreaticoduodenectomy. Significantly longer learning phases of 60–200 cases are reported to complete all three learning phases. In conclusion the hallmarks for safe and efficient implementation of robotic pancreatic surgery are a dedicated team, structured training program and stepwise patient selection.