Complexity-Adjusted Learning Curves for Robotic and Laparoscopic Liver Resection: A Word of Caution

BACKGROUND: Minimally invasive liver surgery (MILS) has a high variance in the type of resection and complexity, which has been underestimated in learning curve studies in the past. The aim of this work was to evaluate complexity-adjusted learning curves over time for laparoscopic liver resection (LLR) and robotic liver resection (RLR). METHODS: Cumulative sum analysis (CUSUM) and complexity adjustment were performed using the Iwate score for LLR and RLR (n = 647). Lowest point of smoothed data was used to capture the cutoff of the increase in complexity. Data were collected retrospectively at the Department of Surgery of the Charité-Universitätsmedizin Berlin. RESULTS: A total of 132 RLR and 514 LLR were performed. According to the complexity-adjusted CUSUM analysis, the initial learning phase was reached after 117 for LLR and 93 procedures for RLR, respectively. With increasing experience, the rate of (extended) right hemihepatectomy multiplied from 8.4% to 18.9% for LLR (P = 0.031) and from 21.6% to 58.3% for RLR (P < 0.001). Complication rates remained comparable between both episodes for LLR and RLR (T(1) vs T(2), P > 0.05). The complexity-adjusted CUSUM analysis demonstrated for blood transfusion, conversion, and operative time an increase during the learning phase (T(1)), while a steady state was reached in the following (T(2)). CONCLUSIONS: The learning phase for MILS after adjusting for complexity is about 4 times longer than assumed in previous studies, which should urge caution.