Learning Curve for Robotic-Assisted Laparoscopic Retroperitoneal Lymph Node Dissection

Abstract

Introduction: Robotic retroperitoneal lymph node dissection (R-RPLND) is a challenging procedure. We hypothesized that surgical times and operative complications would decrease as surgeons became more facile with R-RPLND. Materials and Methods: We retrospectively reviewed 121 consecutive R-RPLNDs performed at Naval Medical Center San Diego and Mayo Clinic Arizona by 4 fellowship trained robotic surgeons between 2008 and 2018. Linear regression was used to analyze independent predictors of setup time, operative time, and lymph node counts. Logistic regression was used to analyze open conversions, overall complications, and high-grade complications. Variables included as independent predictors were: sequential case number, surgeon, clinical stage, chemotherapy status, RPLND template, and body mass index. Univariate and multivariate analyses were conducted. Statistical significance was established at α = 0.05. Results: There was no change in setup time with case number (p = 0.317), but differences were noted between surgeons. Operative times decreased with increasing case number (p < 0.001) but were negatively affected by clinical stage III testis cancer (p = 0.029) and history of chemotherapy exposure (p = 0.050). Surgical times are predicted to decrease by 1 hour after 44 cases. Lymph node counts were dependent only on the surgeon. No factors were predictive of open conversions. Fewer overall complications occurred as experience was gained (p = 0.001), but high-grade complications could not be predicted. Conclusions: Consistent with the learning curves shown for other technologically advanced surgical techniques, experience appears to improve surgical times and lower complication rates for R-RPLND.