Incorporation and validation of clinically relevant performance metrics of simulation (CRPMS) into a novel full-immersion simulation platform for nerve-sparing robot-assisted radical prostatectomy (NS-RARP) utilizing three-dimensional printing and hydrogel casting technology.

To incorporate and validate clinically relevant performance metrics of simulation (CRPMS) into a hydrogel model for nerve-sparing robot-assisted radical prostatectomy (NS-RARP).Anatomically accurate models of the human pelvis, bladder, prostate, urethra, neurovascular bundle (NVB) and relevant adjacent structures were created from patient MRI by injecting polyvinyl alcohol (PVA) hydrogels into three-dimensionally printed injection molds. The following steps of NS-RARP were simulated: bladder neck dissection; seminal vesicle mobilization; NVB dissection; and urethrovesical anastomosis (UVA). Five experts (caseload >500) and nine novices (caseload <50) completed the simulation. Force applied to the NVB during the dissection was quantified by a novel tension wire sensor system fabricated into the NVB. Post-simulation margin status (assessed by induction of chemiluminescent reaction with fluorescent dye mixed into the prostate PVA) and UVA weathertightness (via a standard 180-mL leak test) were also assessed. Objective scoring, using Global Evaluative Assessment of Robotic Skills (GEARS) and Robotic Anastomosis Competency Evaluation (RACE), was performed by two blinded surgeons. GEARS scores were correlated with forces applied to the NVB, and RACE scores were correlated with UVA leak rates.The expert group achieved faster task-specific times for nerve-sparing (P = 0.007) and superior surgical margin results (P = 0.011). Nerve forces applied were significantly lower for the expert group with regard to maximum force (P = 0.011), average force (P = 0.011), peak frequency (P = 0.027) and total energy (P = 0.003). Higher force sensitivity (subcategory of GEARS score) and total GEARS score correlated with lower nerve forces (total energy in Joules) applied to NVB during the simulation with a correlation coefficient (r value) of -0.66 (P = 0.019) and -0.87 (P = 0.000), respectively. Both total and force sensitivity GEARS scores were significantly higher in the expert group compared to the novice group (P = 0.003). UVA leak rate highly correlated with total RACE score r value = -0.86 (P = 0.000). Mean RACE scores were also significantly different between novices and experts (P = 0.003).We present a realistic, feedback-driven, full-immersion simulation platform for the development and evaluation of surgical skills pertinent to NS-RARP. The correlation of validated objective metrics (GEARS and RACE) with our CRPMS suggests their application as a novel method for real-time assessment and feedback during robotic surgery training. Further work is required to assess the ability to predict live surgical outcomes.