Proctored adoption of robotic hiatus hernia surgery: outcomes and learning curves in a high-volume UK centre

Surg Endosc. 2023 Oct;37(10):7608-7615. doi: 10.1007/s00464-023-10210-x. Epub 2023 Jul 20.

Abstract

Background: The adoption of new surgical technologies is inevitably accompanied by a learning curve. With the increasing adoption of robotic techniques in benign foregut surgery, it is imperative to define optimal learning pathways, to ensure a clinically safe introduction of such a technique. The aim of this study was to assess the learning curve for robotic hiatal hernia repair with a pre-defined adoption process and proctoring.

Methods: The learning curve was assessed in four surgeons in a high-volume tertiary referral centre, performing over a 100 hiatal hernia repairs annually. The robotic adoption process included simulation-based training and a multi-day wet lab-based course, followed by robotic operations proctored by robotic upper GI experts. CUSUM analysis was performed to assess changes in operating time in sequential cases.

Results: Each surgeon (A, B, C and D) performed between 22 and 32 cases, including a total of 109 patients. Overall, 40 cases were identified as 'complex' (36.7%), including 16 revisional cases (16/109, 14.7%). With CUSUM analysis inflection points for operating time were seen after 7 (surgeon B) to 15 cases (surgeon B).

Conclusion: The learning curve for robotic laparoscopic fundoplication may be as little as 7-15 cases in the setting of a clearly organized learning pathway with proctoring. By integrating these organized learning pathways learning curves may be shortened, ensuring patient safety, preventing detrimental outcomes due to longer learning curves, and accelerating adoption and integration of novel surgical techniques.

Keywords: Hiatus hernia; Learning curve; Proctoring; Robotic surgery.

MeSH terms

  • Hernia, Hiatal* / surgery
  • Humans
  • Laparoscopy* / methods
  • Learning Curve
  • Operative Time
  • Retrospective Studies
  • Robotic Surgical Procedures* / methods
  • United Kingdom