Learning curve in robotic colorectal surgery

With the rapid adoption of robotics in colorectal surgery, there has been growing interest in the pace at which surgeons gain competency, as it may aid in self-assessment or credentialing. Therefore, we sought to evaluate the learning curve of an expert laparoscopic colorectal surgeon who performed a variety of colorectal procedures robotically. This is a retrospective review of a prospective database of 111 subsequent colorectal procedures performed by a single colorectal surgeon. The cumulative summation technique (CUSUM) was used to construct a learning curve for robotic proficiency by analyzing total operative and console times. Postoperative outcomes including length of stay, 30-day complications, and 30-day readmission rates were evaluated. Chi-square and one-way ANOVA (including Kruskal–Wallis) tests were used to evaluate categorical and continuous variables. Our patient cohort had a mean age of 62.4, mean BMI of 26.9, and mean ASA score of 2.41. There were two conversions to open surgery. The CUSUM graph for console time indicated an initial decrease at case 13 and another decrease at case 83, generating 3 distinct performance phases: learning (n = 13), competence (n = 70), and mastery (n = 28). An interphase comparison revealed no significant differences in age, gender, BMI, ASA score, types of procedures, or indications for surgery between the three phases. Over the course of the study, both mean surgeon console time and median length of stay decreased significantly (p = 0.00017 and p = 0.016, respectively). Although statistically insignificant, there was a downward trend in total operative time and postoperative complication rates. Learning curves for robotic colorectal surgery are commonly divided into three performance phases. Our findings contribute to the construction of a reliable learning curve for the transition of colorectal surgeons to robotics. Furthermore, they may help guide the stepwise training and credentialing of new robotic surgeons.