A multi-surgeon learning curve analysis of overall and site-specific positive surgical margins after RARP and implications for training.
Carlo GandiAngelo TotaroRiccardo BientinesiFilippo MarinoFrancesco PiercontiMaurizio MartiniAndrea RussoMarco RacioppiPierFrancesco BassiEmilio SaccoPublished in: Journal of robotic surgery (2022)
Robot-assisted radical prostatectomy (RARP) is the most adopted treatment for localized prostate cancer. The aim of this study was to explore the learning curves (LC) for overall and site-specific positive surgical margins (PSM) occurrence after RARP of multiple surgeons within a step-structured mentor-initiated training program. The study included consecutive patients undergoing RARP between January 2013 and March 2020, by three surgeons: a mentor and his two trainees. Prospectively collected patients' data were retrospectively analyzed. The cumulative summation (CUSUM) method was used to generate the LCs, with turning points indicating the number of cases to reach proficiency levels. Furthermore, the association between PSM and surgical experience was evaluated, adjusting for case mix. A total of 761 consecutive patients were included, 370 treated by the Mentor surgeon, 247 and 144 treated, respectively, by the two Trainees. Mentor and Trainees had similar PSM rates (31.6% vs 28.0% vs 31.3%, p = 0.6). CUSUM charts showed different LC shapes for different PSM locations (postero-lateral, bladder neck, apex, and multifocal/> 3 mm). Surgical experience was significantly associated with overall, postero-lateral, and multifocal/> 3 mm PSMs, in the Mentor series only. Trainees reached their turning points after far fewer cases then the Mentor, both for overall (12 and 31 vs 153), postero-lateral (24 and 30 vs 120), and multifocal/> 3 mm PSMs (9 and 31 vs 153). The achievement of stable SM proficiency takes involved different LCs depending on the prostatic location being considered. Monitoring site-specific LC can indicate the surgical steps for which there may be still room for further technical refinements, even when an apparent proficiency status seems achieved.
Keyphrases
- prostate cancer
- radical prostatectomy
- robot assisted
- end stage renal disease
- newly diagnosed
- minimally invasive
- patients undergoing
- ejection fraction
- peritoneal dialysis
- prognostic factors
- mass spectrometry
- general practice
- risk assessment
- machine learning
- computed tomography
- patient reported outcomes
- quality improvement
- patient reported