BACKGROUND: Little data are available about the learning curve for robotic rectal resection. OBJECTIVE: The purpose of this work was to provide a multidimensional analysis of the learning process in patients undergoing robotic total mesorectal excision for rectal cancer. DESIGN: This was a retrospective review of a prospectively collected database designed to evaluate the results of robotic rectal resection. SETTINGS: The study was conducted at a tertiary-care hospital. PATIENTS: From December 2007 to August 2012, 167 patients who underwent robotic total mesorectal excision for rectal cancer were included. MAIN OUTCOME MEASURES: A single hybrid variable including operative time, conversion, perioperative morbidity, and circumferential margin was generated to measure the success of the procedure. A moving average method for operative time and a risk-adjusted cumulative sum analysis were used to derive the learning curve. RESULTS: Overall conversion was noted in 2 cases (1.2%). The cumulative sum plot of a single hybrid variable representing the success of each operation demonstrated that the composite event was more frequent at the beginning of the series and began to decrease after 32 cases. The moving average for robotic console time decreased steadily and showed 2 plateaus; the first plateau was noted after 33 cases, and the second plateau was noted after 72 cases. The learning process was divided into 3 phases based on 2 cutoff points. The robotic console time decreased significantly with each phase (p < 0.001). Complicated rectal cancer was more frequent in the later phases; however, the incidence of postoperative complications remained constant throughout the series (p = 0.82). LIMITATIONS: This study is limited by a single surgeon's experience. CONCLUSIONS: The learning process for robotic total mesorectal excision has a greater effect on the first 32 cases. These results help form a basis for performance monitoring of robotic total mesorectal excision.
BACKGROUND: Little data are available about the learning curve for robotic rectal resection. OBJECTIVE: The purpose of this work was to provide a multidimensional analysis of the learning process in patients undergoing robotic total mesorectal excision for rectal cancer. DESIGN: This was a retrospective review of a prospectively collected database designed to evaluate the results of robotic rectal resection. SETTINGS: The study was conducted at a tertiary-care hospital. PATIENTS: From December 2007 to August 2012, 167 patients who underwent robotic total mesorectal excision for rectal cancer were included. MAIN OUTCOME MEASURES: A single hybrid variable including operative time, conversion, perioperative morbidity, and circumferential margin was generated to measure the success of the procedure. A moving average method for operative time and a risk-adjusted cumulative sum analysis were used to derive the learning curve. RESULTS: Overall conversion was noted in 2 cases (1.2%). The cumulative sum plot of a single hybrid variable representing the success of each operation demonstrated that the composite event was more frequent at the beginning of the series and began to decrease after 32 cases. The moving average for robotic console time decreased steadily and showed 2 plateaus; the first plateau was noted after 33 cases, and the second plateau was noted after 72 cases. The learning process was divided into 3 phases based on 2 cutoff points. The robotic console time decreased significantly with each phase (p < 0.001). Complicated rectal cancer was more frequent in the later phases; however, the incidence of postoperative complications remained constant throughout the series (p = 0.82). LIMITATIONS: This study is limited by a single surgeon's experience. CONCLUSIONS: The learning process for robotic total mesorectal excision has a greater effect on the first 32 cases. These results help form a basis for performance monitoring of robotic total mesorectal excision.
Authors: Luise I M Pernar; Faith C Robertson; Ali Tavakkoli; Eric G Sheu; David C Brooks; Douglas S Smink Journal: Surg Endosc Date: 2017-04-14 Impact factor: 4.584
Authors: Rosa M Jiménez-Rodríguez; Mercedes Rubio-Dorado-Manzanares; José Manuel Díaz-Pavón; M Luisa Reyes-Díaz; Jorge Manuel Vazquez-Monchul; Ana M Garcia-Cabrera; Javier Padillo; Fernando De la Portilla Journal: Int J Colorectal Dis Date: 2016-10-06 Impact factor: 2.571
Authors: E Duchalais; N Machairas; S R Kelley; R G Landmann; A Merchea; D T Colibaseanu; K L Mathis; E J Dozois; D W Larson Journal: Surg Endosc Date: 2018-03-15 Impact factor: 4.584