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Learning curve of robotic portal lobectomy for pulmonary neoplasms: A prospective observational study.
Yang, Mu-Zi; Lai, Ren-Chun; Abbas, Abbas E; Park, Bernard J; Li, Ji-Bin; Yang, Jie; Wu, Jin-Chun; Wang, Gang; Yang, Hao-Xian.
Affiliation
  • Yang MZ; Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Lai RC; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Abbas AE; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Park BJ; Department of Anesthesiology, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Li JB; Division of Thoracic Surgery, Department of Thoracic Medicine and Surgery, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania, USA.
  • Yang J; Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • Wu JC; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Wang G; Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Yang HX; Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.
Thorac Cancer ; 12(9): 1431-1440, 2021 05.
Article in En | MEDLINE | ID: mdl-33709571
ABSTRACT

BACKGROUND:

We aim to assess the learning curve of robotic portal lobectomy with four arms (RPL-4) in patients with pulmonary neoplasms using prospectively collected data.

METHODS:

Data from 100 consecutive cases with lung neoplasms undergoing RPL-4 were prospectively accumulated into a database between June 2018 and August 2019. The Da Vinci Si system was used to perform RPL-4. Regression curves of cumulative sum analysis (CUSUM) and risk-adjusted CUSUM (RA-CUSUM) were fit to identify different phases of the learning curve. Clinical indicators and patient characteristics were compared between different phases.

RESULTS:

The mean operative time, console time, and docking time for the entire cohort were 130.6 ± 53.8, 95.5 ± 52.3, and 6.4 ± 3.0 min, respectively. Based on CUSUM analysis of console time, the surgical experience can be divided into three different phases 1-10 cases (learning phase), 11-51 cases (plateau phase), and >51 cases (mastery phase). RA-CUSUM analysis revealed that experience based on 56 cases was required to truly master this technique. Total operative time (p < 0.001), console time (p < 0.001), and docking time (p = 0.026) were reduced as experience increased. However, other indicators were not significantly different among these three phases.

CONCLUSIONS:

The RPL-4 learning curve can be divided into three phases. Ten cases were required to pass the learning curve, but the mastery of RPL-4 for satisfactory surgical outcomes requires experience with at least 56 cases.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Anterior Temporal Lobectomy / Robotic Surgical Procedures / Lung Neoplasms Type of study: Observational_studies / Prognostic_studies Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: En Journal: Thorac Cancer Year: 2021 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Anterior Temporal Lobectomy / Robotic Surgical Procedures / Lung Neoplasms Type of study: Observational_studies / Prognostic_studies Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: En Journal: Thorac Cancer Year: 2021 Document type: Article Affiliation country: China