Comparative learning curves and outcomes of robotic versus laparoscopic transabdominal preperitoneal inguinal hernia repair

Background: Transabdominal preperitoneal inguinal hernia repair is a common surgical procedure that can be performed using either robotic or laparoscopic approaches. Understanding the learning curve for each method is crucial for optimizing surgical outcomes and training. This study aims to compare the learning curves of each approach and to evaluate the outcomes of the proficiency phases. Methods: All consecutive transabdominal preperitoneal procedures performed in 2 centers were included. The learning curves, based on operative time, of 5 surgeons (2 performing robotic transabdominal preperitoneal and 3 performing laparoscopic transabdominal preperitoneal) were analyzed using cumulative sum analysis and compared after pooling the data according to the surgical approach. In addition, a comparison of the outcomes of the postlearning phase was conducted. Results: A total of 463 transabdominal preperitoneal procedures were included (248 robotic approach). The laparoscopic transabdominal preperitoneal learning curve indicated a learning phase up to the 39th procedure, and the robotic up to the 29th. Comparing the postlearning phase data revealed similar postoperative complication rates (14/187 vs 3/103, P = .126) with comparable baseline characteristics. In addition, the median operative time was significantly lower in the robotic group than in the laparoscopic group (59 vs 65 minutes, P < .0001). Conclusion: This study demonstrates that robotic and laparoscopic transabdominal preperitoneal have distinct learning curves, with the robotic approach reaching proficiency slightly faster. Postlearning phase safety outcomes were similar between the 2 techniques. The robotic approach also had a significantly shorter operative time than the laparoscopic approach. These findings suggest that both approaches are safe, and the choice of method may depend on surgeon preference and experience. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.