Development of a High-Fidelity Robot-Assisted Kidney Transplant Simulation Platform Using Three-Dimensional Printing...

Despite the adoption of robotic donor nephrectomy, the steep learning curve of robotic recipient transplantation has hindered the implementation of a complete robot-assisted kidney transplantation (RAKT). We sought to develop a high-fidelity perfused full immersion nonbiohazardous platform for RAKT simulation training. A three-dimensional (3D) computer-aided design (CAD) model consisting of a kidney, pelvicaliceal system, renal artery, and vein was created from a CT scan of a donor patient. 3D printed casts designed from the CAD model were injected with various polyvinyl alcohol hydrogel formulations to fabricate an anatomical kidney phantom and surrounding abdominal cavity. The process was repeated using a recipient's CT scan to create the recipient pelvic model containing a bony pelvis, pelvic musculature, iliac arteries and veins, and bladder. Donor and recipient models each contained structures to simulate the perfused vascular and ureterovesical anastomosis. A board-certified transplant surgeon completed a robotic training curriculum, including four RAKT simulation procedures, from procurement of the donor kidney to final retroperitonealization. Metrics from the simulations (e.g., arterial, venous, ureterovesical, and total anastomosis times) were recorded and compared with surgical times from published data. The average time for the nephrectomies was 67.33 (±31.58) minutes. The average total anastomosis time was 60.85 (±9.73) minutes with 20.37 (±3.87), 20.17 (±4) and 15.1 (±2.35) minutes for arterial, venous, and ureterovesical anastomosis, respectively. The recorded arterial and venous anastomosis times were within published times for competency (Δ = 2.47 and Δ = 2.87, respectively), whereas the uterovesical time was within the mastery range (Δ = 0.45). Using a combination of 3D printing and hydrogel casting technologies, a high fidelity perfused full-immersion nonbiohazardous simulation platform for RAKT was developed. The utilization of this platform has the potential to replace the early cases in a learning curve while decreasing the barriers to utilization for transitioning transplant surgeons.