AIM: The aim of this study was to define the learning curve for positive surgical margin (PSM) rate and operative time (OT) for robotic assisted laparoscopic radical prostatectomy (RALP); while the learning curve appears shorter for surgical safety for RALP compared to other surgical modalities, this has not been well established for the above parameters. METHODS: We performed a retrospective cohort study of 3794 patients who underwent RALP between Jan 2003 and Sep 2009 by three surgeons (DL, PW, AKT) from three centers (UPenn, Karolinska, Cornell). Mean overall PSM rates and mean overall OT were calculated for all three surgeons at intervals of 50 RALPs per surgeon, and learning curves for these means were fit using a loess method. R version 2.71 was used for all statistical analysis. RESULTS: The learning curve for PSM rates for all patients demonstrated improvements continued with increasing surgeon experience, with over 1600 cases required to get a PSM rate <10%. When pT3 patients were evaluated, the learning curve started to plateau after 1000-1500 cases. Mean OT plateaued after 750 cases though with further surgical experience the OTs started to climb again. CONCLUSION: The learning curve for RALP is not as short as previously thought, and a large number of cases are needed to get PSM rates and OTs to a minimum. This suggests that RALP should be performed by high volume surgeons in order to optimize patient outcomes.
AIM: The aim of this study was to define the learning curve for positive surgical margin (PSM) rate and operative time (OT) for robotic assisted laparoscopic radical prostatectomy (RALP); while the learning curve appears shorter for surgical safety for RALP compared to other surgical modalities, this has not been well established for the above parameters. METHODS: We performed a retrospective cohort study of 3794 patients who underwent RALP between Jan 2003 and Sep 2009 by three surgeons (DL, PW, AKT) from three centers (UPenn, Karolinska, Cornell). Mean overall PSM rates and mean overall OT were calculated for all three surgeons at intervals of 50 RALPs per surgeon, and learning curves for these means were fit using a loess method. R version 2.71 was used for all statistical analysis. RESULTS: The learning curve for PSM rates for all patients demonstrated improvements continued with increasing surgeon experience, with over 1600 cases required to get a PSM rate <10%. When pT3 patients were evaluated, the learning curve started to plateau after 1000-1500 cases. Mean OT plateaued after 750 cases though with further surgical experience the OTs started to climb again. CONCLUSION: The learning curve for RALP is not as short as previously thought, and a large number of cases are needed to get PSM rates and OTs to a minimum. This suggests that RALP should be performed by high volume surgeons in order to optimize patient outcomes.
Authors: Sergey Sarychev; Jorn H Witt; Christian Wagner; Matthias Oelke; Andreas Schuette; Nikolaos Liakos; Theodoros Karagiotis; Mikolaj Mendrek; Mykyta Kachanov; Markus Graefen; Malte W Vetterlein; Christian P Meyer; Zhe Tian; Sami-Ramzi Leyh-Bannurah Journal: World J Urol Date: 2022-03-27 Impact factor: 4.226
Authors: Low Wei Xiang Alvin; Sim Hong Gee; Huang Hong Hong; Cheng Wai Sam Christopher; Ho Sien Sun Henry; Lau Kam On Weber; Tan Puay Hoon; Lee Lui Shiong Journal: J Robot Surg Date: 2015-07-07
Authors: James P C Ryan; Olwyn Lynch; Mark P Broe; Niall Swan; Diarmaid Moran; Barry McGuire; David Mulvin Journal: Ir J Med Sci Date: 2021-02-27 Impact factor: 1.568