Learning curves for minimally invasive major lung resections: facts and action points!

Over the past 2 decades, minimally invasive approaches have improved post-operative outcomes after anatomic lung resections. There is an increasing demand to include exposure to these novel approaches into training curricula, but also to train confirmed consultants who graduated prior to the advent of these techniques. The objective of this article was to review recent articles on the learning curve (LC) of minimally invasive techniques applied to anatomic lung resections and to discuss its impact onto teaching and quality of care. While we cannot generalize on LCs of trainees learning video-assisted lobectomy, defined by individual abilities, there is evidence that consultants progress along a bimodal LC. Some level of competence is reached after 30 cases, where quality parameters of the operation become more reproducible, mostly by decreasing operating time. After 90 cases appear features of proficiency where other indicators such as complications, duration of air leak or blood loss decrease. The switch towards robot-assisted lobectomy or novel video-assisted thoracic surgery (VATS) techniques such as uniportal VATS are bound to similar additional LCs. There is an ethical question about introducing minimally invasive techniques for more complex procedures such as sleeve lobectomy or segmentectomy into low-volume centers because, there again, at least 30 additional cases are required to reach competence with minimally invasive approach. Cumulative sum analysis utilized to interpret individual LC may also be applied to team evaluation. The LC can be facilitated by simulation training to develop technical skills before moving to real life surgery.