Learning Curve and Case Selection in Laparoscopic Colorectal Surgery

Abstract

The learning curve for laparoscopic colorectal surgery has not been conclusively analyzed. No reliable framework for case selection during training is available. The aim of this study was to analyze the length of the learning curve of laparoscopic colorectal surgeons and to recommend a case selection framework at the early stage of independent practice. Medline (1988-2010, October week 4) and Embase (1988-2010) were used for the literature review, databases were retrieved from the authors, and expert opinion was surveyed. Studies describing the learning curve of laparoscopic or laparoscopically assisted colorectal surgery were selected. No interventions were performed. Learning curves were analyzed by using risk-adjusted, bootstrapped cumulative sum curves. Conversions and complications were independent variables in a multilevel random-effects regression model. Recommendations are based on analysis of ORs and a structured expert opinion gauging process. Twenty-three studies were identified, showing great disparity on the length of the learning curve. Seven studies, representing 4852 cases (19 surgeons), were analyzed. Risk-adjusted cumulative sum charts demonstrated the length of the learning curves to be 152 cases for conversions, 143 for complications, 96 for operating time, 87 for blood loss, and 103 for length of stay. Body mass index and pelvic dissection (rectum), especially in male patients, independently increased the risk of complication and conversion. The expert survey revealed that increasing T stage and complicated inflammatory disease are likely to increase the complexity of the case. Based on this evidence, a framework for case selection in training was proposed. The generalizability of the study results maybe reduced because of inconsistent data quality and individual variations in the length of the learning curve This multicenter database suggests a length of the learning curve of 88 to 152 cases. The use of the suggested framework may prevent high conversion and complication rates during the learning curve.