656 Robotic Prostatectomy Learning Curves- An Independent Analysis of 13 Unrelated Surgeons Over 5 Years

You have accessJournal of UrologyProstate Cancer: Localized1 Apr 2011656 ROBOTIC PROSTATECTOMY LEARNING CURVES- AN INDEPENDENT ANALYSIS OF 13 UNRELATED SURGEONS OVER 5 YEARS Dennis Gyomber, Gregory Jack, Frank Parker, Paul Ruljancich, and Damien Bolton Dennis GyomberDennis Gyomber Melbourne, Australia , Gregory JackGregory Jack Melbourne, Australia , Frank ParkerFrank Parker Melbourne, Australia , Paul RuljancichPaul Ruljancich Melbourne, Australia , and Damien BoltonDamien Bolton Melbourne, Australia View All Author Informationhttps://doi.org/10.1016/j.juro.2011.02.1572AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES A prospective database was established capturing 100% of robotic assisted laparoscopic prostatectomy (RALP) procedures performed on 1 of 2 shared da Vinci Surgical Systems in the state of Victoria, Australia. In this study, we attempted to use logistic regression and weighted means to establish learning-curve models using positive surgical margins (PSM) as surrogates for learning curve outcome. METHODS Patient demographics, PSA, biopsy results, procedural time, blood loss, pathology results, margin status, length of stay and post operative complications were captured. Complications were recorded using the Clavien classification system. Multivariate modeling was performed with STATA. RESULTS 13 unrelated robotic surgeons performed 1575 RALP cases between Aug 2005 to Aug 2010. The mean number of RALP increased from 6 to 40/ month over the 5 years of the study. As a group, the overall positive margin rate 19%. The pT2 and pT3 PSM rates were 12% and 37% respectively. While individual surgeons showed significant variability in PSM rates, all surgeons had similar shaped "learning curves". When analyzing individual surgeon PSM results, a surgeon's rate of improvement in PSM was statistically no different in their initial 25 cases compared to the 2nd 25 cases (p=0.808). However, when the rate of improvement in the 1st 50 cases was compared to the 2nd 50 cases, there was a significant statistical improvement in PSM improvement rate (p<0.00001). This implies the major transition point or "learning curve" occurs somewhere between cases 25 to 50. When logistic regression was applied, factoring PSA, Gleason score, prostate size and patient age, there was a 76% greater likely hood of a PSM in the 1st 25 cases compared to all subsequent cases. This risk was not apparent when the 1st 50 cases were compared to all subsequent cases, again implying a rate of change transition (learning-curve) between cases 25–50. CONCLUSIONS We conclude that our surgeons have statistically different incidence of PSM, however, the time frame in which they improve and reach their individual plateau is similar. We calculated that a surgeon's PSM improvement rate significantly improves somewhere between cases 25 to 50 on average. After 50 cases, PSM rate may improve, however, the rate of improvement is not statistically significant and eventually the rate plateaus. The absolute PSM rate achieved appears to be a function of the individual surgeon. © 2011 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 185 Issue 4S April 2011 Page: e265-e266 Advertisement Copyright & Permissions© 2011 by American Urological Association Education and Research, Inc.Metrics Author Information Dennis Gyomber Melbourne, Australia More articles by this author Gregory Jack Melbourne, Australia More articles by this author Frank Parker Melbourne, Australia More articles by this author Paul Ruljancich Melbourne, Australia More articles by this author Damien Bolton Melbourne, Australia More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...