Yajie Zhang1, Shengjun Liu2, Yu Han1, Jie Xiang1, Robert J Cerfolio3, Hecheng Li4. 1. Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 2. School of Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 3. Division of Cardiothoracic Surgery, New York University Langone Health, New York, New York. 4. Department of Thoracic Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: lihecheng2000@hotmail.com.
Abstract
BACKGROUND: Robotic segmentectomy has been suggested as a safe and effective management for early lung cancer and benign lung diseases. However, no large case series have documented the learning curve for this technically demanding procedure. METHODS: We conducted a retrospective study for robotic segmentectomy performed by the same surgeon between June 2015 and November 2017. The learning curve was initially analyzed using the cumulative sum (CUSUM) method to assess changes in the total operative times across the case sequence. Subsequently, an in-depth learning curve was generated using the risk-adjusted CUMSUM method, which considered perioperative risk factors and surgical failure. RESULTS: This study included 104 cases, and 87 were malignant. The median operative time was 145 minutes (interquartile range [IQR], 120 to 180) and the median blood loss was 100 mL (IQR, 50 to 100). The median length of stay was 4 days (IQR, 3 to 5). Based on the CUSUM and risk-adjusted CUSUM analyses, the learning curve could be divided into 3 different phases: phase I, the initial learning period (first to 21st operation); phase II, the consolidation period (22nd to 46th operation); and phase III, the experienced period (47th to 104th operation). The operative time and intraoperative blood loss tended to decrease after the initial learning phase. Other perioperative outcomes were not significantly different among the 3 phases. CONCLUSIONS: The learning curve of robotic segmentectomy consisted of 3 phases. The technical competency for assuring feasible perioperative outcomes was achieved in phase II at the 40th operation.
BACKGROUND: Robotic segmentectomy has been suggested as a safe and effective management for early lung cancer and benign lung diseases. However, no large case series have documented the learning curve for this technically demanding procedure. METHODS: We conducted a retrospective study for robotic segmentectomy performed by the same surgeon between June 2015 and November 2017. The learning curve was initially analyzed using the cumulative sum (CUSUM) method to assess changes in the total operative times across the case sequence. Subsequently, an in-depth learning curve was generated using the risk-adjusted CUMSUM method, which considered perioperative risk factors and surgical failure. RESULTS: This study included 104 cases, and 87 were malignant. The median operative time was 145 minutes (interquartile range [IQR], 120 to 180) and the median blood loss was 100 mL (IQR, 50 to 100). The median length of stay was 4 days (IQR, 3 to 5). Based on the CUSUM and risk-adjusted CUSUM analyses, the learning curve could be divided into 3 different phases: phase I, the initial learning period (first to 21st operation); phase II, the consolidation period (22nd to 46th operation); and phase III, the experienced period (47th to 104th operation). The operative time and intraoperative blood loss tended to decrease after the initial learning phase. Other perioperative outcomes were not significantly different among the 3 phases. CONCLUSIONS: The learning curve of robotic segmentectomy consisted of 3 phases. The technical competency for assuring feasible perioperative outcomes was achieved in phase II at the 40th operation.
Authors: Alexandra D Power; Desmond M D'Souza; Susan D Moffatt-Bruce; Robert E Merritt; Peter J Kneuertz Journal: Surg Endosc Date: 2019-08-02 Impact factor: 4.584
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