Navigating the learning curve of robotic pancreatoduodenectomy: Competency, proficiency, and mastery in a first-generation robotic surgeon with established open pancreatic expertise
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Updated: Jul 31, 2026
Technical Detail for Robot Assisted Pancreaticoduodenectomy
Published on: September 28, 2019
Niccolò Napoli1, Michael Ginesini1, Emanuele Federico Kauffmann1
1Division of General and Transplant Surgery, University of Pisa, Pisa, Italy.
Objective:
This study delineates the learning curve of robotic pancreatoduodenectomy for first-generation surgeons, using the Comprehensive Complication Index to assess patient outcomes.
Background:
Robotic pancreatoduodenectomy is a promising alternative to open pancreatoduodenectomy, but patient safety and quality of outcomes during the learning phase remain critical.
Methods:
We retrospectively analyzed 313 consecutive robotic pancreatoduodenectomies performed by a single surgeon. The cumulative-sum method defined the learning curve, and Comprehensive Complication Index, adjusted for robotic pancreatoduodenectomy difficulty by PD-ROBOSCORE, was the dependent variable.
Results:
The median PD-ROBOSCORE was 8 (4.8-10.9), and the median Comprehensive Complication Index was 29.6 (20.9-39.5). At 90 days, severe morbidity and mortality rates were 24% and 5.4%, respectively. Three learning phases were identified: competency (63 procedures), proficiency (176), and mastery (263). Early phases involved simpler cases, whereas later phases showed greater complexity and a higher proportion of patients with ASA scores >2. Pancreatic cancer cases tripled in phases 2 and 3. Each phase showed progressive reductions in operative time and Comprehensive Complication Index. The mastery phase demonstrated further improvements in Comprehensive Complication Index, lymph node harvest, and margin status. Compared with proficiency, mastery saw improved outcomes in delayed gastric emptying, harvested lymph nodes, and R1 rates in pancreatic cancer. Operative time was longer, but morbidity and mortality remained stable.
Conclusion:
The robotic pancreatoduodenectomy learning process involves competency, proficiency, and mastery phases. Structured training programs may accelerate this learning curve, but high procedural volumes are essential to improve outcomes. Future studies should account for surgeon experience and case complexity when evaluating robotic pancreatoduodenectomy outcomes.
