"Idealized" vs. "True" learning curves: the case of laparoscopic liver resection. Issue 6 (June 2016) - Villani, Vincenzo; Bohnen, Jordan D.; Torabi, Radbeh; Sabbatino, Francesco; Chang, David C.; Ferrone, Cristina R.
Abstract: Background: Learning curves are believed to resemble an "idealized" model, in which continuous improvement occurs until a plateau is reached. We hypothesized that this "idealized" model would not adequately describe the learning process for a complex surgical technique, specifically laparoscopic liver resection (LLR). Methods: We analyzed the first 150 LLRs performed by a surgeon with expertise in hepatobiliary/laparoscopic surgery but with no previous LLR experience. We divided the procedures performed in 5 consecutive groups of 30 procedures, then compared groups in terms of complications, operative time, length of stay, and estimated blood loss. Results: We observed an increase in operative complexity (3.3% major operations in Group 1 vs. 23.3% in Group 5, p  = 0.05). Complications decreased from Group 1 to Group 2 (20%–3%), but increased again as more complex procedures were performed (3% in Group 2–13% in Group 5). Similar improvement/regression patterns were observed for operative time and EBL. Discussion: The "true" learning curve for LLR is more appropriately described as alternating periods of improvement and regression until mastery is achieved. Surgeons should understand the true learning curves of procedures they perform, recognizing and mitigating the increased risk they assume by taking on more complex procedures.