1019 Quantified Learning Curves for Robotic Gastrointestinal Surgery

Abstract

Abstract Aim Robotic techniques are increasingly being adopted by gastrointestinal surgeons. It is important to understand the learning curves (LCs) for robotic surgery, to protect patients from harm caused by surgeon inexperience. The aim of this study was to summarise reports of the LC for three robotic gastrointestinal procedures: cholecystectomy, oesophagectomy and Roux-en-Y gastric bypass (RYGB). Method Three systematic reviews were conducted by the trainee led RoboSurg Collaborative. Systematic searches identified reports of primary clinical research involving robotic cholecystectomy, oesophagectomy and RYGB. Articles were screened in duplicate by title, abstract and then full text. References to the LC were extracted and coded. Quantifications of the learning curve were summarised using descriptive statistics. Results 259 articles were identified: 56 measured the LC, with 23 (9%) of these calculating the number of cases required to complete the LC. The mean reported number of cases at which the LC plateaued was: 16 for oesophagectomy (N = 6, SD = 3.7), 18 for cholecystectomy (N = 5, SD = 15.1), 34 for RYGB (N = 12, SD = 24.6). The reported LCs often incorporated equipment setup times, and so represents learning of the team as well as the surgeon. These values represent points on the LC that authors deemed their surgeons to have ‘completed’ learning. Definitions for when these points occurred varied greatly but largely fell in to two categories: ‘plateau of operative time’ or ‘matching operating time of laparoscopic control procedure’. Conclusions The heterogeneity in how LCs are defined, measured, and reported highlights the need for a more standardised approach when evaluating novel techniques such as robotics.