ArticleSurgical endoscopy2023
Analysis of training pathway to reach expert performance levels based on proficiency-based progression in robotic-assisted minimally invasive esophagectomy (RAMIE).
Article in Surgical endoscopy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- The learning curve of robotic esophagectomy: a systematic review with a proposed training and competency assessment framework.Frontiers in oncology · 2026Pooled it
- Learning curve and perioperative outcomes: a retrospective cohort study between synchronous and combined inflatable mediastinoscope-laparoscopic radical esophagectomy.Surgical endoscopy · 2026Article
- Automatic recognition of anatomical structures and surgical phases in robot-assisted minimally invasive esophagectomy (RAMIE) using deep learning: a retrospective cohort study.Surgical endoscopy · 2026Article
- Textbook outcome can be reached in the learning curve for hybrid robot-assisted esophagectomy-experience from a German high-volume center.Journal of robotic surgery · 2026Article
- Learning curve of Robotic Assisted Minimally Invasive Ivor Lewis Esophagectomy (RAMIE): initial experience.Surgical endoscopy · 2025Article
- Artificial intelligence for intraoperative video analysis in robotic-assisted esophagectomy.Surgical endoscopy · 2025Review
- Novel robot-assisted minimally invasive surgical technique for the treatment of esophageal cancer.Wideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques · 2024Article
- Caseload per Year in Robotic-Assisted Minimally Invasive Esophagectomy: A Narrative Review.Cancers · 2024Review
- Skill progress during a dedicated societal robotic surgery training curriculum including several robotic surgery platforms.Surgical endoscopy · 2024Observational
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Authors and funding
12 authors at 2 institutions in 1 country.
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Abstract
backgroundRobotic-assisted minimally invasive esophagectomy (RAMIE) was first introduced in 2003 and has since then shown to significantly improve the postoperative course. Previous studies have shown that a structured training pathway based on proficiency-based progression using individual skill levels as measures of reach of competence can enhance surgical performance. The aim of this study was to evaluate and help understand our pathway to reach surgical expert levels using a proficiency-based approach introducing RAMIE at our German high-volume center.
methodsAll patients undergoing RAMIE performed by two experienced surgeons for esophageal cancer since the introduction of the robotic technique in 2017 was included in this analysis. Intraoperative outcomes and postoperative outcomes were included in the analysis. The cumulative sum method was used to analyze how many cases are needed to reach expert levels for different performance characteristics and skill sets during robotic-assisted minimally invasive esophagectomy.
resultsFrom 06/2017 to 03/2022, a total of 154 patients underwent RAMIE at our facility and were included in the analysis. An advancement in performance level was observed for total operating time after 70 cases and for thoracic operative time after 79 cases. Lymph node yield showed an increase up until case 60 in the CUSUM analysis. Length of hospital stay stabilized after case 55. The CCI score inflection point was at case 55 in both CUSUM and regression analyses. Anastomotic leak rate stabilized at case 38 and showed another inflection point after case 83.
conclusionOur data and analysis showed the progression from proficient to expert performance levels during the implementation of RAMIE at a European high-volume center. Further analysis of surgeons, especially with a different training status has yet to reveal if the caseloads found in this study are universally applicable. However, skill acquisition and respective measures of such are diverse and as a great range of number of cases was observed, we believe that the learning curve and ascent in performance levels cannot be defined by one parameter alone.
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