Proficiency of Robotic Lobectomy Based on Prior Surgical Technique in The Society of Thoracic Surgeons General Thoracic Database

Feczko, Andrew F.; Wang, Hongwei; Nishimura, K.; Farivar, Alexander S.; Bograd, Adam J.; Vallières, Eric; Aye, Ralph W.; Louie, Brian E.

doi:10.1016/j.athoracsur.2019.04.046

Cited by 22 publications

(16 citation statements)

References 19 publications

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“…First, the prospectively collected data of this study makes the outcomes more reliable. Second, the attending surgeon in this study was an experienced surgeon in VATS procedure, and the rich experience in VATS may help the surgeon to cross the learning curve of RPL‐4 earlier 23 . Third, the techniques we used, including the use of all the four arms of the robot, the use of CO 2 insufflation, and the operation ports' setting, were suitable for learning RATS.…”

Section: Discussionmentioning

confidence: 99%

“…Second, the attending surgeon in this study was an experienced surgeon in VATS procedure, and the rich experience in VATS may help the surgeon to cross the learning curve of RPL-4 earlier. 23 Third, the techniques we used, including the use of all the four arms of the robot, the use of CO 2 insufflation, and the operation ports' setting, were suitable for learning RATS. The ports setting in this study originated from Cerfolio RJ's procedure 6 but we modified it to adapt to Chinese patients.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

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“…New knowledge and skills are required for surgeons transitioning to robotics, such as instrument exchange and control, comfort with distance from the patient bedside, and maturation of visual cues to guide tissue handling. In review of the Society for Thoracic Surgery (STS) General Thoracic Database, Feczko and colleagues compared the transitioning process to robotic lobectomy among "de novo", open-experienced, and VATS-experienced surgeons (5). Initial and sustained proficiency related to major morbidity was lower in the de novo group, but similar among open and VATS surgeons.…”

Section: The Robotic Learning Curvementioning

confidence: 99%

Beyond the learning curve: a review of complex cases in robotic thoracic surgery

Geraci¹,

Scheinerman²,

Chen³

et al. 2021

J Thorac Dis

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The number of thoracic surgery cases performed on the robotic platform has increased steadily over the last two decades. An increasing number of surgeons are training on the robotic system, which like any new technique or technology, has a progressive learning curve. Central to establishing a successful robotic program is the development of a dedicated thoracic robotic team that involves anesthesiologists, nurses, and bed-side assistants. With an additional surgeon console, the robot is an excellent platform for teaching. Compared to current methods of video-assisted thoracoscopic surgery (VATS), the robot offers improved wristed motion, a magnified, high definition three-dimensional vision, and greater surgeon control of the operation. These advantages are paired with integrated adjunctive technology such as infrared imaging. For pulmonary resection, these advantages of the robotic platform have translated into several clinical benefits, such as fewer overall complications, reduced pain, shorter length of stay, better postoperative pulmonary function, lower operative blood loss, and a lower 30-day mortality rate compared to open thoracotomy. With increased experience, cases of greater complexity are being performed. This review article details the process of becoming an experienced robotic thoracic surgeon and discusses a series of challenging cases in robotic thoracic surgery that a surgeon may encounter "beyond the learning curve". Nearly all thoracic surgery can now be approached robotically, including sleeve lobectomy, pneumonectomy, resection of large pulmonary and mediastinal masses, decortication, thoracic duct ligation, rib resection, and pulmonary resection after prior chest surgery and/or chemoradiation.

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“…Because of its robust, high-quality data specifically focused on thoracic surgery, the STS-GTSD has been a rich data source for thoracic surgery health services research. [41][42][43][44][45] The STS-GTSD includes comprehensive data on patient demographic characteristics (ie, age, race, sex, insurance status, and comorbidities), preoperative evaluation (ie, diagnostic testing and clinical staging), operative details (ie, length of surgery, approach, and need for conversion), pathologic data (including completeness of resection and number of lymph nodes evaluated), perioperative outcomes, and vital status at 30 days. In addition to the granularity of data, there is a high degree of completeness, quality, and accuracy of the collected data.…”

Section: Sts-gtsdmentioning

confidence: 99%

United States Administrative Databases and Cancer Registries for Thoracic Surgery Health Services Research

Groth

Habermann

Massarweh

2020

The Annals of Thoracic Surgery

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Proficiency of Robotic Lobectomy Based on Prior Surgical Technique in The Society of Thoracic Surgeons General Thoracic Database

Cited by 22 publications

References 19 publications

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

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

Beyond the learning curve: a review of complex cases in robotic thoracic surgery

United States Administrative Databases and Cancer Registries for Thoracic Surgery Health Services Research

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