Learning curve analysis of 100 consecutive robotic liver resections.

Area of Science:

• Minimally Invasive Surgery
• Surgical Robotics
• Hepatobiliary Surgery

Background:

• Robotic liver surgery, a component of minimally invasive liver surgery (MILS), offers enhanced surgical precision.
• The learning curve for robotic liver resections is not well-defined, particularly regarding the number of cases needed for proficiency.
• This study investigates the learning curve and outcomes of robotic liver resections performed by a single surgeon.

Purpose of the Study:

• To analyze the learning curve of a single surgeon performing robotic liver resections.
• To assess surgical proficiency and progression over 100 consecutive cases.
• To identify the approximate number of cases required to achieve consistent proficiency in robotic liver surgery.

Main Methods:

• Retrospective analysis of 100 consecutive robotic liver resections from the Magdeburg registry of minimally invasive liver surgery (MD-MILS).
• Data collected from June 2013 to July 2024.
• Operation time and Iwate difficulty score were used as metrics for learning progression and proficiency.

Main Results:

• Operation time significantly decreased from an average of 450 minutes to 300 minutes by the 100th case.
• The Iwate difficulty score showed an upward trend, indicating increasing complexity of handled procedures.
• Four learning phases were identified: Competency (1-20), Proficiency (21-30), Early Mastery (31-65), and Full Mastery (66-100). Major complications occurred in 21% of cases.

Conclusions:

• Significant learning curve progression was observed, with improved operation times and ability to manage complex cases.
• Approximately 65 cases are estimated to be necessary for experienced surgeons to achieve consistent proficiency in robotic liver resections.
• Standardized training protocols are essential to facilitate the learning curve in robotic liver surgery.