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Learning curve of robotic portal lobectomy for pulmonary neoplasms: A prospective observational study

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 bet...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons Australia, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8088972/
https://www.ncbi.nlm.nih.gov/pubmed/33709571
http://dx.doi.org/10.1111/1759-7714.13927
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author 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
author_facet 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
author_sort Yang, Mu‐Zi
collection PubMed
description 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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spelling pubmed-80889722021-05-10 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 Thorac Cancer Original Articles 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. John Wiley & Sons Australia, Ltd 2021-03-11 2021-05 /pmc/articles/PMC8088972/ /pubmed/33709571 http://dx.doi.org/10.1111/1759-7714.13927 Text en © 2021 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Original Articles
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
Learning curve of robotic portal lobectomy for pulmonary neoplasms: A prospective observational study
title Learning curve of robotic portal lobectomy for pulmonary neoplasms: A prospective observational study
title_full Learning curve of robotic portal lobectomy for pulmonary neoplasms: A prospective observational study
title_fullStr Learning curve of robotic portal lobectomy for pulmonary neoplasms: A prospective observational study
title_full_unstemmed Learning curve of robotic portal lobectomy for pulmonary neoplasms: A prospective observational study
title_short Learning curve of robotic portal lobectomy for pulmonary neoplasms: A prospective observational study
title_sort learning curve of robotic portal lobectomy for pulmonary neoplasms: a prospective observational study
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8088972/
https://www.ncbi.nlm.nih.gov/pubmed/33709571
http://dx.doi.org/10.1111/1759-7714.13927
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