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OBJECTIVES: Structured mentor-led training programmes permit the safe introduction of novice trainees to robotic-assisted laparoscopic prostatectomy (RALP). We outline the first description of parallel learning curves for individual surgical steps and quantify the relative difficulty of each step to propose an order of training in our structured mentoring programme. PATIENTS AND METHODS: A prospective ethically approved database was used to evaluate the operating times of each individual surgical step, in the first 150 RALP cases performed independently by a robotic-naive laparoscopic surgeon. Linear regression analysis was used to quantify the effect of surgeon experience on the operating time for each individual surgical step. RESULTS: Univariate linear regression analysis revealed significant reductions in operating time over the first 150 cases for all of the RALP steps, with the exception of the Rocco stitch. Multivariate linear regression analysis compensated for confounding variables and led to the identification of five surgical steps in which the operating time of each was significantly influenced by experience of the procedure. The most substantial improvement in operating time was seen in the bladder take down step. After taking into account the multivariate regression model, standardized univariate coefficients allowed an order of training to be identified for future RALP novices, of increasing complexity rather than order of surgery, beginning with the bladder take down step and ending with the vesico-urethral anastomosis. CONCLUSIONS: We can begin the training of new robotic-naive surgeons at simpler surgical steps, in which the greatest gains in expediency are made. We anticipate that identifying the more challenging surgical steps from this study and targeting training towards them may expedite our future trainees' proficiency at RALP.

Original publication




Journal article



Publication Date





1074 - 1080


General Surgery, Humans, Laparoscopy, Learning Curve, Male, Middle Aged, Prostatectomy, Robotics, Time Factors