BACKGROUND: Robotic surgery has been developed as a sophisticated tool to expand possibilities in minimal invasive surgery. The learning curve for this method is short in various surgical fields; however, limited data exist on the learning curve in robotic thoracic surgery. METHODS: This study analyzes a single center experience of robotic lobectomies using a prospectively kept database. Perioperative data and outcome of patients during the learning curve were compared with patients operated with increased institutional experience. The learning curve was defined as the initial 20 lobectomies. RESULTS: Sixty-four robotic lobectomies were performed between January 2014 and February 2017. Indications, preoperative lung functions, comorbidities, patient age, and tumor stage were comparable between patients operated during the learning curve and thereafter. The mean operative time could be significantly reduced after the learning curve (286 ± 86 vs. 211 ± 62 minutes; p = 0.0003). The conversion rate dropped from 4 of 20 (20%) during the learning curve to 2 of 44 (4.5%, p = 0.07) thereafter. Chest tube duration (4.3 ± 2.9 vs. 3.8 ± 2.1 days) and hospital stay (8.3 ± 3.4 vs. 7.9 ± 4.5 days) were not different in the two phases. The number of resected lymph nodes increased from 11.2 ± 6.8 to 13.9 ± 6.5 (p = 0.0797). Lymph node upstaging was achieved in 8 (12.9%) cases. Ninety-day mortality was 0%, and 2-year overall survival was 83%. CONCLUSIONS: Robotic thoracic surgery can be safely performed and trained with low complication rates and contributes to the extension of minimal invasive thoracic surgery. The initial learning curve in our experience is overcome after 20 cases. However, to become proficient in more advanced procedures and to further reduce operative time, additional training is required. Prospective studies are required to clearly determine the role of robotic surgery in comparison to the video-assisted thoracoscopic surgery (VATS) procedures. Georg Thieme Verlag KG Stuttgart · New York.
BACKGROUND: Robotic surgery has been developed as a sophisticated tool to expand possibilities in minimal invasive surgery. The learning curve for this method is short in various surgical fields; however, limited data exist on the learning curve in robotic thoracic surgery. METHODS: This study analyzes a single center experience of robotic lobectomies using a prospectively kept database. Perioperative data and outcome of patients during the learning curve were compared with patients operated with increased institutional experience. The learning curve was defined as the initial 20 lobectomies. RESULTS: Sixty-four robotic lobectomies were performed between January 2014 and February 2017. Indications, preoperative lung functions, comorbidities, patient age, and tumor stage were comparable between patients operated during the learning curve and thereafter. The mean operative time could be significantly reduced after the learning curve (286 ± 86 vs. 211 ± 62 minutes; p = 0.0003). The conversion rate dropped from 4 of 20 (20%) during the learning curve to 2 of 44 (4.5%, p = 0.07) thereafter. Chest tube duration (4.3 ± 2.9 vs. 3.8 ± 2.1 days) and hospital stay (8.3 ± 3.4 vs. 7.9 ± 4.5 days) were not different in the two phases. The number of resected lymph nodes increased from 11.2 ± 6.8 to 13.9 ± 6.5 (p = 0.0797). Lymph node upstaging was achieved in 8 (12.9%) cases. Ninety-day mortality was 0%, and 2-year overall survival was 83%. CONCLUSIONS: Robotic thoracic surgery can be safely performed and trained with low complication rates and contributes to the extension of minimal invasive thoracic surgery. The initial learning curve in our experience is overcome after 20 cases. However, to become proficient in more advanced procedures and to further reduce operative time, additional training is required. Prospective studies are required to clearly determine the role of robotic surgery in comparison to the video-assisted thoracoscopic surgery (VATS) procedures. Georg Thieme Verlag KG Stuttgart · New York.
Authors: Mu-Zi Yang; Ren-Chun Lai; Abbas E Abbas; Bernard J Park; Ji-Bin Li; Jie Yang; Jin-Chun Wu; Gang Wang; Hao-Xian Yang Journal: Thorac Cancer Date: 2021-03-11 Impact factor: 3.500