ArticleJournal of robotic surgery2025
Reshaping the robotic learning curve in anatomic lung resection: the role of prior video-assisted thoracoscopic surgery experience in a multi-surgeon single-center series.
Article in Journal of robotic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
The adoption of robotic-assisted thoracoscopic surgery for lung cancer has steadily increased; however, the learning curve and influence of prior video-assisted thoracoscopic surgery (VATS) experience remain underexplored. This study investigates the impact of prior VATS experience on the learning trajectory of robotic anatomic lung resection. We conducted a retrospective, single-center study analyzing 341 consecutive robotic anatomic lung resections performed by three surgeons between January 2018 and December 2024 at a tertiary referral hospital. Three thoracic surgeons with varying VATS experience-A (1,500 cases), B (350 cases), and C (50 cases)-were included. Learning curves were assessed using cumulative sum analysis for operative time, complication rates, lymph node yield, and postoperative hospital stay. Change points were identified, and early versus late-phase outcomes were compared. All surgeons demonstrated significant reductions in operative time after reaching their respective cumulative sum thresholds (cases 54, 35, and 34 for Surgeons A, B, and C, respectively). While Surgeon A exhibited early procedural stability, Surgeons B and C showed more rapid improvement with experience. Lymph node yield increased significantly for Surgeon B (p = 0.002) and marginally for Surgeon C (p = 0.074). Complication rates and hospital stay showed modest increases in the later phase, which were not consistently statistically significant and appeared to be associated with a shift toward more complex cases. Although prior VATS experience supports initial operative consistency, it does not necessarily shorten the robotic learning curve. Instead, case volume and intensity of robotic exposure appear more critical. These findings underscore the need for structured training programs emphasizing high case density and progressive complexity to optimize robotic surgical proficiency.
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