A novel robotic-assisted lung lobectomy simulation model.

Objective: Although most thoracic surgery programs seek robotic-competent partners, more than one half of graduating residents report needing more training. We aimed to develop a reproducible, high-fidelity model that serves as an effective training tool for surgeons at all levels. Methods: Porcine heart-lung blocks were prepped for a left upper lobectomy and cannulated to distend the vasculature using an artificial blood substitute capable of simulating bleeding. A linear actuator was positioned beneath a platform to simulate a heartbeat, and a da Vinci Xi robotic system (Intuitive Surgical) was docked above it. Participants performed 3 key steps of a left upper lobectomy, then evaluated fidelity of model features and training value using the Likert scale. Pre- and postsimulation confidence were reported (institutional review board approval no. 76506). Results: Among 20 participants, 15 were trainees (75%) and 5 were faculty (25%). Trainees reported a median of 26 bedside (interquartile range, 15-48) and 5 console cases (interquartile range, 3-30). Faculty experience ranged from <5 to >20 years. The model was rated highly for fidelity, with 100% (n = 9) of features receiving a Likert score ≥4 from faculty, with stapling rated highest (5.0 ± 0.0). Trainees rated 89% of features ≥4, with stapling (4.7 ± 0.4) and lung tissue handling (4.7 ± 0.5) rated highest. Both groups rated the simulation as highly valuable, with trainee confidence significantly improving postsimulation (2.5-3.9, P = .0014). Conclusions: The model was rated highly for fidelity and value by both trainees and faculty, and significantly improved trainee confidence. This model offers an effective and reproducible training tool for individual program implementation.