Learning curve and surgical time predictors in robot-assisted transvaginal natural orifice transluminal endoscopic surgery: a risk-adjusted cumulative sum analysis

To identify factors associated with operative time and evaluate the learning curve of robot-assisted transvaginal natural orifice transluminal endoscopic surgery (RA-vNOTES) using conventional and risk-adjusted cumulative sum (CUSUM) analyses. In this single-surgeon retrospective study, a total of 116 patients who underwent RA-vNOTES for benign gynecologic disease between December 2021 and August 2024 were included. To evaluate the learning curve, both conventional CUSUM and RA-CUSUM analyses were performed. Expected operative time for each case was estimated using a multivariable linear regression model including body mass index (BMI), uterine weight (per 100 g), and parity. RA-CUSUM was calculated as the cumulative sum of observed minus expected operative time. Multivariable linear regression models identified higher BMI (p = 0.001) and greater uterine weight (p < 0.001) as independent predictors of prolonged total operative time. No significant predictors were identified for docking time or console time. Transition plots and conventional CUSUM demonstrated progressive procedural improvement over time. RA-CUSUM analysis identified peak learning points at approximately 31 cases for docking time, 32 cases for console time, and 30 cases for total operative time, indicating stabilization after approximately 30–32 cases. Operative time stabilization in RA-vNOTES was achieved after approximately 30 to 32 cases in this single-surgeon experience. Higher BMI and greater uterine weight are significant predictors of longer total operative time. RA-CUSUM provides a more accurate assessment of the learning curve by accounting for patient-related variability, which may inform surgical training strategies and case selection during the implementation of RA-vNOTES.