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Learning curve of transoral robotic thyroidectomy

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Abstract

Background

Transoral thyroidectomy has superior cosmesis and better postoperative voice outcomes than conventional thyroidectomy. However, it usually requires a steep learning curve and longer operative time. The transoral robotic thyroidectomy (TORT) learning curve has not been well investigated. This study aimed to evaluate the TORT learning curve and factors affecting operative time.

Methods

We retrospectively studied 173 consecutive patients who underwent TORT with or without central neck dissection from July 2017 to August 2021. We assessed the TORT learning curve using operative time, complication rate, and surgical success (procedure conversion) rate. The operative time and surgical success rate learning curves were calculated using the cumulative summation (CUSUM) method. Additionally, we analyzed factors affecting operative time in TORT.

Results

Total thyroidectomy operative time was significantly longer than those of lobectomy and isthmusectomy (p < 0.001). In correlation analysis, a significantly positive correlation was observed between body mass index (BMI) and operative time (R2 = 0.04, p = 0.025). The TORT learning curve was 52 cases in the CUSUM operative time analysis. In the CUSUM surgical success rate chart, the turning point was the 55th case. Complication and procedure conversion rates were significantly decreased after the learning curve.

Conclusions

The CUSUM learning curve of TORT was about 52–55 cases, and the operative time, total complication rate, and procedure conversion decreased significantly after the learning curve. The operative time was associated with the extent of thyroidectomy and BMI.

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Authors and Affiliations

  1. Department of Otolaryngology-Head and Neck Surgery, College of Medicine, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea

    Keon-ho Kim, Yong Bae Ji, Chang Myeon Song & Kyung Tae

  2. Department of Anesthesiology and Pain Medicine, College of Medicine, Hanyang University, Seoul, Republic of Korea

    Eugene Kim & Kyu Nam Kim

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  1. Keon-ho Kim
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  2. Yong Bae Ji
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  3. Chang Myeon Song
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  4. Eugene Kim
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  5. Kyu Nam Kim
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Correspondence to Kyung Tae.

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Keon-ho Kim, Yong Bae Ji, Chang Myeon Song, Eugene Kim, Kyu Nam Kim, and Kyung Tae have no conflicts of interest or financial ties to disclose.

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Kim, Kh., Ji, Y.B., Song, C.M. et al. Learning curve of transoral robotic thyroidectomy. Surg Endosc 37, 535–543 (2023). https://doi.org/10.1007/s00464-022-09549-4

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