Surgical endoscopy · 2025
Learning curve analysis of 100 consecutive robotic liver resections.
СтатьяНазвание и аннотация: Перевод в очереди
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Rahimli Mirhasan, Gumbs Andrew A, Perrakis Aristotelis, Al-Madhi Sara, Dölling Maximilian, Stelter Frederike, Lorenz Eric, Andric Mihailo, Franz Mareike, Arend Jörg, Croner Roland S
Аннотация
BACKGROUND: Robotic liver surgery, as part of minimally invasive liver surgery (MILS), offers advantages like enhanced dexterity and stable camera and instrument control. However, the learning curve - particularly the number of cases required for proficiency - remains underexplored. This study analyzes 100 consecutive robotic liver resections performed by a single surgeon to assess the learning curve and outcomes.
PATIENTS AND METHODS: This retrospective analysis reviewed data from the Magdeburg registry of minimally invasive liver surgery (MD-MILS) from June 2013 to July 2024, to identify surgeons who performed at least 100 robotic liver resections. Operation time and Iwate difficulty score were used as indicators of surgical proficiency and learning progression.
RESULTS: One surgeon fulfilled the inclusion criteria of performing 100 robotic liver resections. Of these, 41 (41%) were major and 59 (59%) were minor liver resections. A significant reduction in average operation time was observed across the series, decreasing from an initial average of 450 min to approximately 300 min by the 100th operation. Similarly, the Iwate difficulty score showed a significant upward trend, reflecting the increasing technical challenges of the procedures. The learning curve was divided into four distinct phases: Competency (Cases 1-20), Proficiency (Cases 21-30), Early Mastery (Cases 31-65), and Full Mastery (Cases 66-100), highlighting the progression in skill acquisition and case complexity. Major complications (21%), such as bile leak, bilioma, and pulmonary embolism, were observed during the study period.
CONCLUSION: Over 100 robotic liver resections, significant learning curve progression was evident, with marked improvements in operation time and handling of complex cases. The results suggest that approximately 65 cases are needed for a surgeon experienced in open and laparoscopic liver surgery to achieve consistent proficiency in robotic liver resections. These findings underscore the importance of standardized training protocols to support the learning curve in robotic liver surgery.
PATIENTS AND METHODS: This retrospective analysis reviewed data from the Magdeburg registry of minimally invasive liver surgery (MD-MILS) from June 2013 to July 2024, to identify surgeons who performed at least 100 robotic liver resections. Operation time and Iwate difficulty score were used as indicators of surgical proficiency and learning progression.
RESULTS: One surgeon fulfilled the inclusion criteria of performing 100 robotic liver resections. Of these, 41 (41%) were major and 59 (59%) were minor liver resections. A significant reduction in average operation time was observed across the series, decreasing from an initial average of 450 min to approximately 300 min by the 100th operation. Similarly, the Iwate difficulty score showed a significant upward trend, reflecting the increasing technical challenges of the procedures. The learning curve was divided into four distinct phases: Competency (Cases 1-20), Proficiency (Cases 21-30), Early Mastery (Cases 31-65), and Full Mastery (Cases 66-100), highlighting the progression in skill acquisition and case complexity. Major complications (21%), such as bile leak, bilioma, and pulmonary embolism, were observed during the study period.
CONCLUSION: Over 100 robotic liver resections, significant learning curve progression was evident, with marked improvements in operation time and handling of complex cases. The results suggest that approximately 65 cases are needed for a surgeon experienced in open and laparoscopic liver surgery to achieve consistent proficiency in robotic liver resections. These findings underscore the importance of standardized training protocols to support the learning curve in robotic liver surgery.