Learning curves in Versius-assisted colorectal surgery: a risk-adjusted CUSUM analysis from a single center

Area of Science:

• Robotic Surgery
• Surgical Oncology
• Medical Education

Background:

• Robotic-assisted surgery is increasingly adopted for colorectal cancer.
• Standardized methods are needed to assess surgical learning curves.
• The Versius system is a novel robotic platform for minimally invasive procedures.

Purpose of the Study:

• To evaluate procedure- and operator-specific learning curves in Versius-assisted colorectal cancer surgery.
• To assess the utility of risk-adjusted cumulative sum (RA-CUSUM) analysis in this context.
• To analyze learning patterns in a real-world, single-center setting.

Main Methods:

• Retrospective analysis of 156 consecutive colorectal cancer surgeries using the Versius system.
• Procedures included anterior rectal resection (AR), right hemicolectomy (RHC), and sigmoid resection (SR).
• RA-CUSUM analysis of skin-to-skin operative time, adjusted for patient and procedure variables, was performed for each operator and procedure.

Main Results:

• RA-CUSUM demonstrated distinct procedure- and operator-specific learning curves.
• Right hemicolectomy and sigmoid resection showed earlier stabilization compared to anterior rectal resection.
• Anterior rectal resection required higher case volume for consistent operative time reduction, with prolonged learning phase variability.

Conclusions:

• Learning curves for Versius-assisted colorectal surgery are influenced by both the specific procedure and the individual surgeon.
• RA-CUSUM analysis offers a structured approach to monitor surgical performance trends.
• This method can support structured training and quality assurance in robotic surgery implementation.