Examining the learning curves in robotic cardiac surgery wet lab simulation training

Simulation-based training has gained distinction in cardiothoracic surgery as robotic-assisted cardiac procedures evolve. Despite the increasing use of wet lab simulators, the effectiveness of these training methods and skill acquisition rates remain poorly understood.

This study aimed to compare learning curves and assess the robotic cardiac surgical skill acquisition rate for cardiac and noncardiac surgeons who had no robotic experience in a wet lab simulation setting.

In this prospective cohort study, participants practiced 3 robotic tasks in a porcine model: left atriotomy closure, internal thoracic artery harvesting and mitral annular suturing. Participants were novice robotic cardiac and noncardiac surgeons alongside experienced robotic cardiac surgeons who established performance benchmarks. Performance was evaluated using the time-based score and modified global evaluative assessment of robotic skills (mGEARS).

The participants were 15 novice surgeons (7 cardiac; 8 noncardiac) and 4 experienced robotic surgeons. Most novices reached mastery in 52 (±22) min for atrial closure, 32 (±18) for internal thoracic artery harvesting and 34 (±12) for mitral stitches, with no significant differences between the cardiac and noncardiac surgeons. However, for mGEARS, noncardiac novices faced more challenges in internal thoracic artery harvesting. The Thurstone learning curve model indicated no significant difference in the learning rates between the groups.

Wet lab simulation facilitates the rapid acquisition of robotic cardiac surgical skills to expert levels, irrespective of surgeons' experience in open cardiac surgery. These findings support the use of wet lab simulators for standardized, competency-based training in robotic cardiac surgery.