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Learning curve analysis after 500 robotic hepatectomies.

Michelle M Dugan1, Maria Christodoulou1, Sharona B Ross1

  • 1Department of Hepatopancreatobiliary and Gastrointestinal Surgery, Digestive Health Institute AdventHealth Tampa, Tampa, FL, United States.

Journal of Gastrointestinal Surgery : Official Journal of the Society for Surgery of the Alimentary Tract
|April 18, 2024
PubMed
Summary

This study analyzed over 500 robotic hepatectomies to define the learning curve for surgical mastery. Achieving proficiency in robotic hepatectomy requires a significant number of cases for minor, major, and complex resections.

Keywords:
Robotic hepatectomyRobotic hepatectomy learning curveRobotic liver resection

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Area of Science:

  • Hepatobiliary surgery
  • Minimally invasive surgery
  • Surgical robotics

Background:

  • Robotic platform use is increasing for hepatobiliary resections.
  • Previous studies reported learning curves for basic competency.
  • This study is the first to analyze the learning curve for long-term mastery in robotic hepatectomies.

Purpose of the Study:

  • To determine the learning curve for achieving mastery in robotic hepatectomies.
  • To establish the number of cases required for surgeons to become proficient.
  • To guide surgeons in optimizing patient safety and outcomes during robotic hepatectomy.

Main Methods:

  • Analysis of 500 consecutive robotic hepatectomies performed between 2013 and 2023.
  • Utilized cumulative sum (CUSUM) analysis based on operative duration to define learning curves.
  • Included patient demographics, operative details, and postoperative outcomes.

Main Results:

  • Learning curve for minor resections: 75 cases; major resections: 100 cases; technically challenging minor resections: 57 cases.
  • Increased experience correlated with shorter operative times, reduced blood loss, and improved R0 resection rates.
  • Postoperative outcomes included a 4.0% major complication rate, 17.0% 30-day readmission, and 3.0% 90-day mortality.

Conclusions:

  • Significant case numbers are necessary to overcome learning curves for robotic hepatectomy mastery.
  • The findings provide benchmarks for surgeons to optimize patient safety and outcomes.
  • This study contributes to understanding the proficiency required for complex robotic liver surgery.