Adding a newly trained surgeon into a high-volume robotic prostatectomy group: are outcomes compromised?

Luchen Wang, Mireya Diaz, Hans Stricker, James O Peabody, Mani Menon, Craig G Rogers
Author Information
  1. Luchen Wang: Wayne State University School of Medicine, 540 East Canfield St, Detroit, MI, 48201, USA.
  2. Mireya Diaz: Vattikuti Urology Institute, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA.
  3. Hans Stricker: Vattikuti Urology Institute, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA.
  4. James O Peabody: Vattikuti Urology Institute, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA.
  5. Mani Menon: Vattikuti Urology Institute, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA.
  6. Craig G Rogers: Vattikuti Urology Institute, Henry Ford Health System, 2799 West Grand Blvd, Detroit, MI, 48202, USA. crogers2@hfhs.org.

Abstract

This study evaluates whether a new staff surgeon early in the learning curve can be integrated into a high-volume robotic practice with an established robotic team and mentorship without compromising robot-assisted radical prostatectomy (RARP) outcomes of the practice. We analyzed outcomes of 3064 patients who underwent RARP from 2007 to 2012 at a high-volume tertiary center by a robotic practice comprising three experienced robotic surgeons (2846 patients) and a newly hired surgeon (218 patients) immediately out of training (residency and oncology fellowship with 2 years of RARP exposure). The new surgeon performed RARP with intraoperative mentorship by the senior surgeons during the first year. Complications, biochemical recurrence (BCR), positive surgical margins rate (PSM), operating time (OR time), estimated blood loss (EBL) for the new and senior surgeons were compared. Multivariable linear, logistic and exact logistic regression adjusting for disease and patient characteristics were performed. On regression analyses, case number was the most significant predictor of decrease in probability of major complications (p = 0.025) and BCR (p = 0.004) for the new surgeon. Increasing case number was not associated with decrease in minor complications, PSM, OR time, or EBL (p > 0.05). Inclusion of the new surgeon's outcomes did not adversely impact outcomes of the practice. In conclusion, a new surgeon joining a high-volume robotic prostatectomy program with an established robotic team and mentorship can progress through the learning curve without compromising overall outcomes of the practice. Our results may be relevant for programs hiring newly trained staff to join an established robotic practice.

Keywords

References

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MeSH Term

Clinical Competence
Humans
Learning Curve
Male
Middle Aged
Neoplasm Recurrence, Local
Postoperative Complications
Prostatectomy
Prostatic Neoplasms
Robotic Surgical Procedures
Treatment Outcome

Word Cloud

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