A new training model for robot-assisted urethrovesical anastomosis and posterior muscle-fascial reconstruction: the Verona training technique

Initial surgical performance in robot-assisted radical prostatectomy is associated with clinical outcomes and learning curves

BACKGROUND

The association between surgical performance ratings and clinical outcomes in robotic surgery is poorly understood. Additionally, no studies have reported on the relationship between the surgeon's initial case-skill evaluation and the learning curve in robot-assisted surgery. We evaluated whether an objective surgical technique evaluation score for initial robot-assisted radical prostatectomy (RARP) was associated with clinical outcomes and surgeons' learning curves.

METHODS

Six surgeons who were trained in and started to perform RARP at our institution were included. Anonymized, unedited videos of each surgeon's 10th RARP case were evaluated by three reviewers, using modified Objective Structured Assessment of Technical Skill (OSATS) scores. We then divided the surgeons into two groups on the basis of these OSATS scores. We retrospectively compared the clinical outcomes and learning curves of the console time of the two groups for consecutive RARPs, performed from March 2018 to July 2023.

RESULTS

We analyzed 258 RARPs (43 cases/surgeon), including 129 cases performed by high-OSATS score surgeons (18.2-19.3 points) and 129 cases performed by low-OSATS score surgeons (11.9-16.0 points). Overall, the high-OSATS score group had significantly shorter operation and console times than the low-OSATS score group did (both P < 0.01) and their patients' rate of continence recovery by 3 months post-RARP was significantly higher (P = 0.03). However, complications, blood loss, and positive margins did not differ between the groups (P = 0.08, P = 0.51, and P = 0.90, respectively). The high-OSATS score group had a significantly shorter console time than the low-OSATS score group did after the 11-20 cases.

CONCLUSIONS

The OSATS score in early RARP cases can predict subsequent surgical outcomes and surgeons' learning curves.

Simulation-based education in urology - an update

Over the past 30 years surgical training, including urology training, has changed from the Halstedian apprenticeship-based model to a competency-based one. Simulation-based education (SBE) is an effective, competency-based method for acquiring both technical and non-technical surgical skills and has rapidly become an essential component of urological education. This article introduces the key learning theory underpinning surgical education and SBE, discussing the educational concepts of mastery learning, deliberate practice, feedback, fidelity and assessment. These concepts are fundamental aspects of urological education, thus requiring clinical educators to have a detailed understanding of their impact on learning to assist trainees to acquire surgical skills. The article will then address in detail the current and emerging simulation modalities used in urological education, with specific urological examples provided. These modalities are part-task trainers and 3D-printed models for open surgery, laparoscopic bench and virtual reality trainers, robotic surgery simulation, simulated patients and roleplay, scenario-based simulation, hybrid simulation, distributed simulation and digital simulation. This article will particularly focus on recent advancements in several emerging simulation modalities that are being applied in urology training such as operable 3D-printed models, robotic surgery simulation and online simulation. The implementation of simulation into training programmes and our recommendations for the future direction of urological simulation will also be discussed.

Development and validation of the objective assessment of robotic suturing and knot tying skills for chicken anastomotic model

BACKGROUND

To improve patient safety, there is an imperative to develop objective performance metrics for basic surgical skills training in robotic surgery.

OBJECTIVE

To develop and validate (face, content, and construct) the performance metrics for robotic suturing and knot tying, using a chicken anastomotic model.

DESIGN, SETTING AND PARTICIPANTS

Study 1: In a procedure characterization, we developed the performance metrics (i.e., procedure steps, errors, and critical errors) for robotic suturing and knot tying, using a chicken anastomotic model. In a modified Delphi panel of 13 experts from four EU countries, we achieved 100% consensus on the five steps, 18 errors and four critical errors (CE) of the task. Study 2: Ten experienced surgeons and nine novice urology surgeons performed the robotic suturing and knot tying chicken anastomotic task. The mean inter-rater reliability for the assessments by two experienced robotic surgeons was 0.92 (95% CI, 0.9-0.95). Novices took 18.5 min to complete the task and experts took 8.2 min. (p = 0.00001) and made 74% more objectively assessed performance errors than the experts (p = 0.000343).

CONCLUSIONS

We demonstrated face, content, and construct validity for a standard and replicable basic anastomotic robotic suturing and knot tying task on a chicken model. Validated, objective, and transparent performance metrics of a robotic surgical suturing and knot tying tasks are imperative for effective and quality assured surgical training.

Current Status of Simulation Training in Urology: A Non-Systematic Review

Simulation has emerged as an effective solution to increasing modern constraints in surgical training. It is recognized that a larger proportion of surgical complications occur during the surgeon's initial learning curve. The simulation takes the learning curve out of the operating theatre and facilitates training in a safe and pressure-free environment whilst focusing on patient safety. The cost of simulation is not insignificant and requires commitment in funding, human resources and logistics. It is therefore important for trainers to have evidence when selecting various simulators or devices. Our non-systematic review aims to provide a comprehensive up-to-date picture on urology simulators and the evidence for their validity. It also discusses emerging technologies and future directions. Urologists should embed evidence-based simulation in training programs to shorten learning curves while maintaining patient safety and work should be directed toward a validated and agreed curriculum.

Design and Validation of a Low-Cost, High-Fidelity Model for Urethrovesical Anastomosis in Radical Prostatectomy

OBJECTIVE

We sought to develop and validate a low-cost, high-fidelity robotic surgical model for the urethrovesical anastomosis component of the robot-assisted laparoscopic radical prostatectomy.

MATERIALS AND METHODS

A novel simulation model was constructed using a 3D-printed model of the male bony pelvis from CT scan data and silicone molds to recreate the soft tissue aspects. Using a da Vinci Si surgical robot, urology faculty and trainees performed simulated urethrovesical anastomosis. Each participant was given 12 minutes to complete the simulation. A survey established face validity, content validity, and acceptability. Simulation runs were evaluated by three blinded reviewers. The anastomosis was graded by two reviewers for suture placement accuracy and anastomosis quality. These factors were compared with robotic experience to establish construct validity.

RESULTS

Twenty participants took part in the initial validation of this model. Groups were defined as experts (surgical faculty), intermediate (fellows and chief residents), and novices (junior residents). Likert scores (1-5 scale, top score 5) examining face validity, content validity, and acceptability were 3.49 ± 0.43, 4.15 ± 0.23, and 4.02 ± 0.19, respectively. Construct validity was excellent based on the model's ability to stratify groups. All evaluated metrics were statistically different between the three levels of training. Total material cost was $2.50 per model.

CONCLUSIONS

We developed a novel low-cost robotic simulation of the urethrovesical anastomosis for robot-assisted radical prostatectomy. The model discerns robotic skill level across all levels of training and was found favorable by participants showing excellent face, content, and construct validities.