Self-reported prevalence of injury and discomfort experienced by surgeons performing traditional and robot-assisted laparoscopic surgery: a meta-analysis demonstrating the value of RALS for surgeons

Background: The negative impact of traditional laparoscopic surgery (TLS) on surgeons has been well-established. Prevalence rates of discomfort and injury between 73% and 90% are regularly cited to support this, with robot-assisted laparoscopic surgery (RALS) often being presented as the solution. The purpose of this study was to systematically review pain studies of TLS and RALS surgeons, to consider the difference in the reported strain in general and concerning specific sites of the body. Methods: PubMed, Embase, and Cochrane databases were searched in October 2019. The resulting articles were screened to ensure the full text was available in English, original data were presented, the study contained pain statistics for TLS or RALS, and the study had a long-term rather than an intra-operative focus. Quality was assessed using the SUrvey Reporting GuidelinE (SURGE). Results from studies were analyzed in two stages for TLS and RALS according to each anatomic region. Results: A total of 1354 papers were found, from which 28 papers were chosen for inclusion. The average quality score of the included articles was 14.8. The risk ratio of experiencing symptoms related to TLS in comparison to RALS was 1.29; however, this was not significant. Discomfort was significantly more likely to be experienced in the back, elbows, and wrists/hands for those practicing TLS in comparison with RALS. The regions associated with the highest risk of injury for TLS and RALS were the back and neck, respectively. Conclusions: There is limited evidence in this study for the possibility that RALS is ergonomically more beneficial for the surgeon in comparison with TLS. Further analysis would be improved with the publication of larger, high-quality, homogenous studies, especially concerning injuries experienced by RALS surgeons, to overcome the limitations of heterogeneity and bias.