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The learning curve for robotic thyroidectomy: a multicenter study.

Jandee Lee1, Jong Ho Yun, Kee Hyun Nam

  • 1Department of Surgery, Ajou University School of Medicine, Suwon, Korea.

Annals of Surgical Oncology
|August 4, 2010
PubMed
Summary
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The learning curve for robotic thyroidectomy with central compartment node dissection (CCND) was established. Experienced endoscopic surgeons reached proficiency after 50 total or 40 subtotal thyroidectomy cases.

Area of Science:

  • Endocrine Surgery
  • Minimally Invasive Surgery
  • Surgical Education

Background:

  • Robotic thyroidectomy with central compartment node dissection (CCND) is a complex procedure.
  • The learning curve for this technique has not been previously defined.
  • Understanding the learning curve is crucial for surgeon training and patient safety.

Purpose of the Study:

  • To establish the learning curve for robotic thyroidectomy with CCND.
  • To compare perioperative outcomes between novice and experienced surgeons.
  • To identify the number of cases required for proficiency.

Main Methods:

  • A prospective, controlled, multicenter study was conducted.
  • Four endocrine surgeons (one experienced, three novice robotic surgeons) participated.

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  • 644 robotic thyroidectomies with CCND were analyzed for operative time, blood loss, hospital stay, and complications.

    • Main Results:

    • Novice surgeons initially had longer operative times and higher complication rates compared to the experienced surgeon.
    • Proficiency was achieved by novice surgeons after approximately 50 cases for total thyroidectomy.
    • Proficiency was achieved by novice surgeons after approximately 40 cases for subtotal thyroidectomy.

    Conclusions:

    • The learning curve for robotic thyroidectomy with CCND is approximately 50 cases for total thyroidectomy and 40 cases for subtotal thyroidectomy for surgeons experienced in endoscopic techniques.
    • This data provides valuable benchmarks for training new robotic thyroid surgeons.
    • Standardized training protocols can optimize the adoption of robotic thyroidectomy.