Cumulative sum analysis of the robotic learning curve in the surgical management of malignant pelvic neoplasms

Abstract

Background: Minimally invasive surgery of the pelvis is technically demanding, limiting its application. Previous studies have reported the potential advantages of robotic-assisted surgery (RAS) for pelvic malignancies. These advantages might facilitate the surgeons to advance effortlessly along the learning phase. However, there are limited studies evaluating the learning curve (LC) and none have compared different surgical specialties. The objective of this study is to evaluate and compare the robotic LC of different oncological pelvic specialties. Methods: This retrospective study evaluates consecutive patients operated on by a robotic platform between January 2012 and June 2016 by urological, gynecological and rectal surgeons. Pre-operative and intraoperative parameters including docking time (DT), surgeon console time (SCT) and total operative time (TOT) were analyzed by linear regression and cumulative sum (CUSUM) methods. Body mass index (BMI), conversion rate (CR) to open surgery and estimated blood loss (EBL) were also studied in order to determine if there is a correlation with the LC. Results: Three hundred and forty-three RAS and seven surgeons were included in the analysis, 103 RAS for rectal cancer were performed by 3 rectal surgeons, 55 RAS for endometrial cancer and 58 RAS for cervical cancer were performed by 2 surgeons and 127 RAS prostatectomies were performed by 2 urologists. For most surgeons, the CUSUM graphs exhibited a 3 phases LC with turning points reflecting competency and proficiency. Urological surgeons had the most well-defined LC followed by the gynecologists. All surgeons were able to master docking with few cases. Rectal surgeons were not able to show a 3 phase LC for SCT and TOT. There was a clear inverse correlation between BMI and DT, patients with higher BMI had a shorter DT and patients with lower BMI showed increased DT. EBL had no statistical correlation with the LC and the CR was low (2%). Conclusions: Analysis of our data suggests that the LC for each respective robotic operative step, surgeon and specialty is unique. Urological and gynecological RAS might have a less steep LC compared to RAS for rectal cancer. Therefore, robotic proctoring and training for rectal cancer should be more diligent. Prospective multicenter study with different methods of LC analysis is necessary to validate our results.