V08-06: Validation of a full-immersion simulation platform with performance metrics for robotic radical prostatectomy (RARP) using three-dimensional printing and hydrogel molding technology
Surgical education is dependent on live patients for operative exposure and cultivation of surgical technique. Robotic assisted radical prostatectomy is a unique surgery requiring both oncologic and functional outcomes for success. The steep learning curve of nerve-sparing prostatectomy provides a significant need for surgical education outside of the live patient operative room exposure. In our study, we validate a high-fidelity, inanimate robotic assisted nerve-sparing prostatectomy model within a full-immersion simulation environment using Clinically-relevant Performance Metrics (CRPMs).
Anatomically accurate models of the human pelvis, bladder, prostate, urethra, neurovascular bundle (NVB) and relevant adjacent structures were created form a patients MRI using polyvinyl alcohol (PVA) hydrogels and three-dimensional-printed injection molds. Pertinent steps of the nerve-sparing prostatectomy were simulated: bladder neck dissection, seminal vesicle mobilization, nerve-sparing prostatectomy and urethra-vesical anastomosis. Five experts (>500 caseload) and 10 novices were (
Experts achieved faster task specific times: bladder neck dissection (p= 0.003), nerve sparing (p= 0.007) and VUA (p= 0.002). Experts continued to have superior margins (p= 0.011) and VUAs were without leak (p=0.02). Nerve forces applied were significantly lower for experts in maximum force (p=0.011), average force (p=0.011), peak frequency and total energy (p=0.003). Higher force sensitivity (Subcategory of GEARS Score) and Total GEARS Score correlated with lower nerve forces applied with total energy (J) -0.66(0.019) and -0.87(0.000), respectively. VUA leak rate was correlated with RACE score -0.86 (0.000), which was significantly different between novices and experts (p=0.003).
This high-fidelity simulation model for Robot Assisted Radical Prostatectomy, incorporating CRPMs that co-relate with validated objective metrics presents a valid training tool for robotic surgery.