Evaluating the trainee impact on robotic surgery learning curves: a CUSUM analysis of large series pediatric
Jin Kyu Kim1, Nikhil V Batra2, Konrad M Szymanski2
1Department of Pediatric Urology, Riley Hospital for Children, Indiana University Health, 702 Barnhill Drive, Indianapolis, IN, 46202, USA. kimjink@iu.edu.
Abstract:
Robot-assisted laparoscopic pyeloplasty (RALP) is a minimally invasive treatment for ureteropelvic junction obstruction (UPJO). The learning curves for RALP have been well-documented, primarily in post-fellowship surgeons, but the impact of trainee involvement during fellowship training remains unclear. This study aims to (1) establish a large series post-fellowship single surgeon learning curve and (2) analyze the impact of annual trainee involvement on surgical proficiency using cumulative sum (CUSUM) analysis. A retrospective review of pediatric RALP cases from July 2012 to June 2023 at a high-volume institution was conducted. Cases performed by a single post-fellowship surgeon were included. Concomitant procedures and redo surgeries were excluded. Operative time (OT) and complication rates (CR) were tracked using CUSUM analysis. The learning curves were categorized into phases: learning, competency, case-mix, and proficiency. The effect of annual fellowship trainees was analyzed by constructing normalized learning curves per academic year. A total of 220 cases were analyzed. The post-fellowship surgeon reached competency after 29 cases, with proficiency achieved after 99 cases. Trainee involvement demonstrated a characteristic "down-up-down" pattern in OT and was not associated with increased CR during each of 6 years analyzed, reflecting an initial observation phase, increased operative time during hands-on learning, and eventual improvement. In contrast, the single year without a trainee showed a continuous downward OT trend. The complication rates remained below unacceptable thresholds and case complexity influenced OT trends. CUSUM-based analysis effectively maps the RALP learning curve, revealing distinct phases of surgical proficiency and the impact of trainee involvement. The reproducible trainee learning pattern provides insights into optimizing robotic surgery education while maintaining patient safety.
