Abstract
We sought to develop and validate automated performance metrics to measure surgeon performance of vesicourethral anastomosis during robotic assisted radical prostatectomy. Furthermore, we sought to methodically develop a standardized training tutorial for robotic vesicourethral anastomosis. We captured automated performance metrics for motion tracking and system events data, and synchronized surgical video during robotic assisted radical prostatectomy. Nonautomated performance metrics were manually annotated by video review. Automated and nonautomated performance metrics were compared between experts with 100 or more console cases and novices with fewer than 100 cases. Needle driving gestures were classified and compared. We then applied task deconstruction, cognitive task analysis and Delphi methodology to develop a standardized robotic vesicourethral anastomosis tutorial. We analyzed 70 vesicourethral anastomoses with a total of 1,745 stitches. For automated performance metrics experts outperformed novices in completion time (p <0.01), EndoWrist® articulation (p <0.03), instrument movement efficiency (p <0.02) and camera manipulation (p <0.01). For nonautomated performance metrics experts had more optimal needle to needle driver positioning, fewer needle driving attempts, a more optimal needle entry angle and less tissue trauma (each p <0.01). We identified 14 common robotic needle driving gestures. Random gestures were associated with lower efficiency (p<0.01), more attempts (p <0.04) and more trauma (p <0.01). The finalized tutorial contained 66 statements and figures. Consensus among 8 expert surgeons was achieved after 2 rounds, including among 58 (88%) after round 1 and 8 (12%) after round 2. Automated performance metrics can distinguish surgeon expertise during vesicourethral anastomosis. The expert vesicourethral anastomosis technique was associated with more efficient movement and less tissue trauma. Standardizing robotic vesicourethral anastomosis and using a methodically developed tutorial may help improve robotic surgical training.
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