Case Sequence Analysis of the Robotic Colorectal Resection Learning Curve

Area of Science:

• Minimally Invasive Surgery
• Surgical Oncology
• Robotic Surgery

Background:

• Robotic surgery for colorectal cancer presents potential benefits but involves a learning curve for surgeons.
• The adoption of robotic resection for colorectal cancer has been rapid.

Purpose of the Study:

• To identify trends in the adoption of robotic colorectal resection.
• To analyze the association between surgeon experience and complication rates.

Main Methods:

• A case sequence analysis was performed using the New York Statewide Planning and Research Cooperation System database (2008-2016).
• Adults undergoing colorectal resection for cancer were analyzed.
• Surgeons' cases were grouped into quartiles based on chronological order to assess learning curve progression.
• Outcomes included major in-hospital events and iatrogenic complications; generalized linear mixed models were used.

Main Results:

• A total of 2,763 robotic procedures were analyzed, with a significant increase in volume from 2010 to 2015.
• Surgeons earliest in their learning curve (first quartile) performed 18.2% of cases in 2015 and had higher rates of iatrogenic complications.
• Increasing cumulative surgeon case sequence was associated with reduced iatrogenic complications (OR = 0.29) and shorter hospital stays (OR = 0.50).

Conclusions:

• Robotic colorectal resection adoption is accelerating.
• Surgeons early in their experience are associated with increased iatrogenic complications.
• Further experience significantly improves outcomes, reducing iatrogenic complications and length of stay.