231 Identifying surgical factors predicting postoperative urinary continence in robot-assisted radical prostatectomy Introduction The introduction of laparoscopy and robot assisted laparoscopy facilitates the recording of intra-corporal surgical videos.1,2 Analysis of these videos offers the opportunity to gain insight into past performance and review previous adverse postoperative outcomes to learn for the future.3,4 Effective training and assessment of performance are fundamental ensuring that surgeons reach their intended goal and operate safely.4,5 Different template-based video assessment methods have been developed in order to assess surgical skill in Robot Assisted Radical Prostatectomy (RARP). The Prostatectomy Assessment and Competency Evaluation (PACE) developed by the group of Hussein et al.6 has its focus on objective and procedure specific assessment of skills. The Global Evaluative Assessment of Robotic Skill (GEARS) method can be used to evaluate both live surgeries and videos of (robot assisted) laparoscopic surgery. The Generic Error Rating Tool (GERT) can be used to score intra-operative errors made by the surgeon. Most of these assessment methods are currently used to assess the effectiveness of training (PACE 7) or the basic surgical skill (GEARS/GERT 8). Multiple groups are performing different types of analysis into surgical skills in order to improve postoperative outcome and reduce complications.9–13 The group of van Basten et al. reviews surgical videos in order to learn from past performance by expert surgeons as part of their cyclical quality improvement analysis in order to reduce complications and improve postoperative outcome11. The group of Goldenberg et al. used the GEARS8 assessment method and (generic error rating tool) GERT14 to assess specific sections of the RARP in order to evaluate if there is a possible correlation between surgical skills and postoperative outcome, mainly the early continence after RARP.9 The group of Hung et al. have used kinematic and events data (automated performance metrics) in order to evaluate surgical skills.10,15,16 In a recent study Hung et al. used automated performance metrics to train Machine Learning algorithms in order to predict clinical outcomes.10 The PRostatectomy video Observation to Evaluate and Score Technical skill (PROTEST) assessment method was developed by our research group using a Delphi method. It can be used to assess both surgical skill and peri-operative events. So, it may help individual surgeons to improve their skills.17 The correlation between the different video assessment methods (GEARS/GERT, PACE, and PROTEST) and postoperative outcome could give more insight into the possible origins of adverse postoperative outcome. Moreover, to gain more insight into which aspects of the surgical skills as assessed
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