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Objective: To conduct a multi‐institutional validation of a high‐fidelity, perfused, inanimate, simulation platform for robot‐assisted partial nephrectomy (RAPN) using incorporated clinically relevant objective metrics of simulation (CROMS), applying modern validity standards. Materials and Methods: Using a combination of three‐dimensional (3D) printing and hydrogel casting, a RAPN model was developed from the computed tomography scan of a patient with a 4.2‐cm, upper‐pole renal tumour (RENAL nephrometry score 7×). 3D‐printed casts designed from the patient's imaging were used to fabricate and register hydrogel (polyvinyl alcohol) components of the kidney, including the vascular and pelvicalyceal systems. After mechanical and anatomical verification of the kidney phantom, it was surrounded by other relevant hydrogel organs and placed in a laparoscopic trainer. Twenty‐seven novice and 16 expert urologists, categorized according to caseload, from five academic institutions completed the simulation. Results: Clinically relevant objective metrics of simulators, operative complications, and objective performance ratings (Global Evaluative Assessment of Robotic Skills [GEARS]) were compared between groups using Wilcoxon rank‐sum (continuous variables) and parametric chi‐squared (categorical variables) tests. Pearson and point‐biserial correlation coefficients were used to correlate GEARS scores to each CROMS variable. Post‐simulation questionnaires were used to obtain subjective supplementation of realism ratings and training effectiveness. Results: Expert ratings demonstrated the model's superiority to other procedural simulations in replicating procedural steps, bleeding, tissue texture and appearance. A significant difference between groups was demonstrated in CROMS [console time (P < 0.001), warm ischaemia time (P < 0.001), estimated blood loss (P < 0.001)] and GEARS (P < 0.001). Six major intra‐operative complications occurred only in novice simulations. GEARS scores highly correlated with the CROMS. Conclusions: This perfused, procedural model offers an unprecedented realistic simulation platform, which incorporates objective, clinically relevant and procedure‐specific performance metrics. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate automated performance metrics (APMs) and clinical data of experts and super‐experts for four cardinal steps of robot‐assisted radical prostatectomy (RARP): bladder neck dissection; pedicle dissection; prostate apex dissection; and vesico‐urethral anastomosis. Subjects and Methods: We captured APMs (motion tracking and system events data) and synchronized surgical video during RARP. APMs were compared between two experience levels: experts (100–750 cases) and super‐experts (2100–3500 cases). Clinical outcomes (peri‐operative, oncological and functional) were then compared between the two groups. APMs and outcomes were analysed for 125 RARPs using multi‐level mixed‐effect modelling. Results: For the four cardinal steps selected, super‐experts showed differences in select APMs compared with experts (P < 0.05). Despite similar PSA and Gleason scores, super‐experts outperformed experts clinically with regard to peri‐operative outcomes, with a greater lymph node yield of 22.6 vs 14.9 nodes, respectively (P < 0.01), less blood loss (125 vs 130 mL, respectively; P < 0.01), and fewer readmissions at 30 days (1% vs 13%, respectively; P = 0.02). A similar but nonsignificant trend was seen for oncological and functional outcomes, with super‐experts having a lower rate of biochemical recurrence compared with experts (5% vs 15%, respectively; P = 0.13) and a higher continence rate at 3 months (36% vs 18%, respectively; P = 0.14). Conclusion: We found that experts and super‐experts differed significantly in select APMs for the four cardinal steps of RARP, indicating that surgeons do continue to improve in performance even after achieving expertise. We hope ultimately to identify associations between APMs and clinical outcomes to tailor interventions to surgeons and optimize patient outcomes. [ABSTRACT FROM AUTHOR]

An editorial is presented on the use of machine-learning algorithms to predict biochemical recurrence after robot-assisted prostatectomy. It highlights the importance of individualize care to patients who are at high risk of prostate cancer recurrence and progression. It also cites the clinical outcomes of robot-assisted prostatectomy.