Femke van Zanten1, Steven E Schraffordt Koops2, Pieternel C M Pasker-De Jong3, Egbert Lenters2, Henk W R Schreuder4. 1. Department of Gynecology, Amersfoort, The Netherlands; Twente University, Faculty of Electrical Engineering, Mathematics and Computer Science, Institute of Technical Medicine, Enschede, The Netherlands. Electronic address: f.van.zanten@meandermc.nl. 2. Department of Gynecology, Amersfoort, The Netherlands. 3. Department of Science, Amersfoort, The Netherlands. 4. Department of Gynecologic Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.
Abstract
BACKGROUND: Determination of the learning curve of new techniques is essential to improve safety and efficiency. Limited information is available regarding learning curves in robot-assisted laparoscopic pelvic floor surgery. OBJECTIVE: The purpose of this study was to assess the learning curve in robot-assisted laparoscopic pelvic floor surgery. STUDY DESIGN: We conducted a prospective cohort study. Consecutive patients who underwent robot-assisted laparoscopic sacrocolpopexy or sacrocolporectopexy were included (n=372). Patients were treated in a teaching hospital with a tertiary referral function for gynecologic/multicompartment prolapse. Procedures were performed by 2 experienced conventional laparoscopic surgeons (surgeons A and B). Baseline demographics were scored per groups of 25 consecutive patients. The primary outcome was the determination of proficiency, which was based on intraoperative complications. Cumulative sum control chart analysis allowed us to detect small shifts in a surgeon's performance. Proficiency was obtained when the first acceptable boundary line of cumulative sum control chart analysis was crossed. Secondary outcomes that were examined were shortening and/or stabilization of surgery time (measured with the use of cumulative sum control chart analysis and the moving average method). RESULTS: Surgeon A performed 242 surgeries; surgeon B performed 137 surgeries (n=7 surgeries were performed by both surgeons). Intraoperative complications occurred in 1.9% of the procedures. The learning curve never fell below the unacceptable failure limits and stabilized after 23 of 41 cases. Proficiency was obtained after 78 cases for both surgeons. Surgery time decreased after 24-29 cases in robot-assisted sacrocolpopexy (no distinct pattern for robot-assisted sacrocolporectopexy). Limitations were the inclusion of 2 interventions and concomitant procedures, which limited homogeneity. Furthermore, analyses treated all complications in cumulative sum as equal weight, although there are differences in the clinical relevance of complications. CONCLUSION: After 78 cases, proficiency was obtained. After 24-29 cases, surgery time stabilized for robot-assisted sacrocolpopexy. In this age of rapidly changing surgical techniques, it can be difficult to determine the learning curve of each procedure. Cumulative sum control chart analysis can assist with this determination and prove to be a valuable tool. Training programs could be individualized to improve both surgical performance and patient benefits.
BACKGROUND: Determination of the learning curve of new techniques is essential to improve safety and efficiency. Limited information is available regarding learning curves in robot-assisted laparoscopic pelvic floor surgery. OBJECTIVE: The purpose of this study was to assess the learning curve in robot-assisted laparoscopic pelvic floor surgery. STUDY DESIGN: We conducted a prospective cohort study. Consecutive patients who underwent robot-assisted laparoscopic sacrocolpopexy or sacrocolporectopexy were included (n=372). Patients were treated in a teaching hospital with a tertiary referral function for gynecologic/multicompartment prolapse. Procedures were performed by 2 experienced conventional laparoscopic surgeons (surgeons A and B). Baseline demographics were scored per groups of 25 consecutive patients. The primary outcome was the determination of proficiency, which was based on intraoperative complications. Cumulative sum control chart analysis allowed us to detect small shifts in a surgeon's performance. Proficiency was obtained when the first acceptable boundary line of cumulative sum control chart analysis was crossed. Secondary outcomes that were examined were shortening and/or stabilization of surgery time (measured with the use of cumulative sum control chart analysis and the moving average method). RESULTS: Surgeon A performed 242 surgeries; surgeon B performed 137 surgeries (n=7 surgeries were performed by both surgeons). Intraoperative complications occurred in 1.9% of the procedures. The learning curve never fell below the unacceptable failure limits and stabilized after 23 of 41 cases. Proficiency was obtained after 78 cases for both surgeons. Surgery time decreased after 24-29 cases in robot-assisted sacrocolpopexy (no distinct pattern for robot-assisted sacrocolporectopexy). Limitations were the inclusion of 2 interventions and concomitant procedures, which limited homogeneity. Furthermore, analyses treated all complications in cumulative sum as equal weight, although there are differences in the clinical relevance of complications. CONCLUSION: After 78 cases, proficiency was obtained. After 24-29 cases, surgery time stabilized for robot-assisted sacrocolpopexy. In this age of rapidly changing surgical techniques, it can be difficult to determine the learning curve of each procedure. Cumulative sum control chart analysis can assist with this determination and prove to be a valuable tool. Training programs could be individualized to improve both surgical performance and patient benefits.
Authors: Igt Baeten; J P Hoogendam; Hwr Schreuder; I M Jürgenliemk-Schulz; Rhm Verheijen; R P Zweemer; C G Gerestein Journal: BJOG Date: 2020-07-28 Impact factor: 6.531