Learning curves in minimally invasive hepatectomy: systematic review and meta-regression analysis

Abstract

Abstract Background Minimally invasive hepatectomy (MIH) has become an important option for the treatment of various liver tumours. A major concern is the learning curve required. The aim of this study was to perform a systematic review and summarize current literature analysing the learning curve for MIH. Methods A systematic review of the literature pertaining to learning curves in MIH to July 2019 was performed using PubMed and Scopus databases. All original full-text articles published in English relating to learning curves for both laparoscopic liver resection (LLR), robotic liver resection (RLR), or a combination of these, were included. To explore quantitatively the learning curve for MIH, a meta-regression analysis was performed. Results Forty studies relating to learning curves in MIH were included. The median overall number of procedures required in studies utilizing cumulative summative (CUSUM) methodology for LLR was 50 (range 25–58) and for RLR was 25 (16–50). After adjustment for year of adoption of MIH, the CUSUM-derived caseload to surmount the learning curve for RLR was 47.1 (95 per cent c.i. 1.2 to 71.6) per cent; P = 0.046) less than that required for LLR. A year-on-year reduction in the number of procedures needed for MIH was observed, commencing at 48.3 cases in 1995 and decreasing to 23.8 cases in 2015. Conclusion The overall learning curve for MIH decreased steadily over time, and appeared less steep for RLR compared with LLR.