Factors influencing performance during robotic surgery training: Results from the EAU Robotic Urology Section HOT-program

INTRODUCTION & OBJECTIVES: Robotic surgery has been widely adopted in the field of urology over the last decade. A structured
training program is needed to ensure better surgical outcomes and patient safety that have to be preserved during the learning process.
This study aimed to identify the factors that may influence the training outcomes of robot-assisted surgery.
MATERIAL & METHODS: This prospective and observational study included 110 participants who were recruited from four European
congresses (namely, EMUC-2013, ESOU-2014, EAU-2014, and ERUS-2014) between November 2013 and September 2014. Overall, 851
dry lab training exercises were completed during the courses. Participants’ data included age, gender, degree (resident vs. urologist), as
well as number of previous bedside assistance, laparoscopic, and robotic surgical procedures. The evaluated outcome was the total score
achieved by the participants. Multivariable linear regression analysis was used to predict the overall exercise score. Predictors consisted of
participant age, gender (male vs female), degree (resident vs. urologist), previous robotic training (no vs. yes), as well as bedside assistant,
laparoscopic, and robotic surgical experience. Locally weighted scatterplot smoothing (Lowess) methods were used to graphically explore
the relationship between overall score and the following variables: participant’s age, number of previous bedside assistant, laparoscopic,
and robotic surgical procedures.
RESULTS: Median participant age was 34 (inter-quartile range (IQR): 30, 40) years. Medians (IQR) of bedside assistance, laparoscopic,
and robotic surgical experience were 20 (10, 50), 30 (10, 90), and 3 (2, 12), respectively. Median overall exercise score was 501 (IQR: 320,
618). At multivariable linear regression analysis, participant age (coefficient (coeff): -5.16; 95% confidence interval (CI): -8.67, -1.65),
p=0.004) was inversely associated with overall performance. On the other hand, robotic surgical experience emerged as a significant
predictor (coeff: 1.43; 95% CI: 0.69, 2.18; p=0.0002) of the overall score. Using Lowess methods, we observed a progressive decrease of
the median score when plotted against participant age. Specifically, median score dropped from 550 to 350 for age ranging from 25 to 50
years.
CONCLUSIONS: The overall performance score during dry lab robotic training is greatly influenced by participant age. Young participants
reach better results compared to their older counterparts during the training sessions. Therefore, trainee’s age should be taken into account
when enrollment in robotic training programs is planned. Although prior robotic experience was associated with improved exercise scores,
laparoscopic surgical experience was not.