BACKGROUND: Robot-assisted McKeown esophagectomy is a promising but technically demanding procedure; thus, a learning curve should be defined to guide training and allow implementation of this technique. METHODS: This study retrospectively reviewed the prospectively collected data of 72 consecutive patients undergoing robot-assisted McKeown esophagectomy by a single surgical team experienced in open and thoracolaparoscopic esophagectomy. The cumulative sum method was used to analyze the learning curve. Patients were divided into two groups in chronological order, defining the surgeon's early (group 1: the first 26 patients) and late experience (group 2: the next 46 patients). Demographic data, intraoperative characteristics, and short-term surgical outcomes were compared between the two groups. RESULTS: Cumulative sum plots revealed decreasing thoracic and abdominal docking time, thoracic and abdominal console time, and total surgical time after patient 9, 16, 26, 14, and 26, respectively. The mean number of lymph nodes resected was greater in group 2 than in group 1 (22.6 ± 8.2 vs 17.4 ± 6.7, p = 0.008). No other clinic or pathologic characteristics were observed as significantly different. CONCLUSIONS: For a surgeon experienced in open and thoracolaparoscopic esophagectomy, experience of 26 cases is required to gain early proficiency of robot-assisted McKeown esophagectomy. A learning curve for robot-assisted esophagus dissection would require operations on 26 patients and stomach mobilization would require operations on 14 patients. For the tableside assistant, experience of at least nine cases is needed to achieve an optimal technical level for thoracic docking and 16 cases for abdominal docking.
BACKGROUND: Robot-assisted McKeown esophagectomy is a promising but technically demanding procedure; thus, a learning curve should be defined to guide training and allow implementation of this technique. METHODS: This study retrospectively reviewed the prospectively collected data of 72 consecutive patients undergoing robot-assisted McKeown esophagectomy by a single surgical team experienced in open and thoracolaparoscopic esophagectomy. The cumulative sum method was used to analyze the learning curve. Patients were divided into two groups in chronological order, defining the surgeon's early (group 1: the first 26 patients) and late experience (group 2: the next 46 patients). Demographic data, intraoperative characteristics, and short-term surgical outcomes were compared between the two groups. RESULTS: Cumulative sum plots revealed decreasing thoracic and abdominal docking time, thoracic and abdominal console time, and total surgical time after patient 9, 16, 26, 14, and 26, respectively. The mean number of lymph nodes resected was greater in group 2 than in group 1 (22.6 ± 8.2 vs 17.4 ± 6.7, p = 0.008). No other clinic or pathologic characteristics were observed as significantly different. CONCLUSIONS: For a surgeon experienced in open and thoracolaparoscopic esophagectomy, experience of 26 cases is required to gain early proficiency of robot-assisted McKeown esophagectomy. A learning curve for robot-assisted esophagus dissection would require operations on 26 patients and stomach mobilization would require operations on 14 patients. For the tableside assistant, experience of at least nine cases is needed to achieve an optimal technical level for thoracic docking and 16 cases for abdominal docking.