Crossing the bridge to VATS lobectomy.

ANNALS OF THE ROYAL COLLEGE OF SURGEONS OF ENGLAND(2017)

引用 1|浏览16
暂无评分
摘要
INTRODUCTION The impact of the introduction of video assisted thoracoscopic surgery (VATS) on the management of lung cancer 20 years ago has been well documented. However, the uptake of VATS lobectomy in surgical practice worldwide has been slower than expected. We believe that this is partly due to a lack of consensus on how this procedure should be integrated into training programmes. We present our initial experience with a newly developed training model, which could help bridge the divide between open and VATS lobectomy. METHODS Two surgical registrars were initiated into this model, supervised by a single consultant. All cases were performed using a standardised three-port anterior approach with systematic lymph node dissection. Both registrars were scrubbed for each case, alternating as first surgeon and assistant, with the supervising consultant operating the camera. RESULTS Over a 6-month period, 22 lung resections for non-small cell lung carcinoma were performed as VATS lobectomies. Thirteen of them were upper lobectomies. There were no emergency conversions to open surgery. The mean operative time for the registrars was 155 minutes compared with 140 minutes for consultant-led operations (p=0.22). There was no perioperative mortality. The most common postoperative complications were atrial fibrillation (4 cases) and prolonged air leak (3 cases). CONCLUSIONS VATS lobectomy involves a team approach. Especially in upper lobectomies, the assistant surgeon plays a significant role in the operation, often helping with the dissection as well as stapling of the bronchial and vascular structures. With a team consisting of two trainees and a supervising surgeon, the teaching process becomes more intuitive and is accelerated. This should reduce the learning curve considerably and improve safety during training.
更多
查看译文
关键词
Video assisted thoracoscopic surgery,Lobectomy,Lung cancer,Training
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要