Quantitative assessment of the learning curve for robotic thyroid surgery

Hyungoo Kim, Hyungju Kwon, Woosung Lim, Byung In Moon, Nam Sun Paik

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

With the increased utilization of robot thyroidectomy in recent years, surgical proficiency is the paramount consideration. However, there is no single perfect or ideal method for measuring surgical proficiency. In this study, we evaluated the learning curve of robotic thyroidectomy using various parameters. A total of 172 robotic total thyroidectomies were performed by a single surgeon between March 2014 and February 2018. Cumulative summation analysis revealed that it took 50 cases for the surgeon to significantly improve the operation time. Mean operation time was significantly shorter in the group that included the 51st to the 172nd case, than in the group that included only the first 50 cases (132.8 ± 27.7 min vs. 166.9 ± 29.5 min; p < 0.001). On the other hand, the surgeon was competent after the 75th case when postoperative transient hypoparathyroidism was used as the outcome measure. The incidence of hypoparathyroidism gradually decreased from 52.0%, for the first 75 cases, to 40.2% after the 76th case. These results indicated that the criteria used to assess proficiency greatly influenced the interpretation of the learning curve. Incorporation of the operation time, complications, and oncologic outcomes should be considered in learning curve assessment.

Original languageEnglish
Article number402
JournalJournal of Clinical Medicine
Volume8
Issue number3
DOIs
StatePublished - Mar 2019

Keywords

  • CUSUM
  • Learning curve
  • Robotic
  • Thyroid

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