Kinematics effectively delineate accomplished users of endovascular robotics with a physical training model.

OBJECTIVE: Endovascular robotics systems, now approved for clinical use in the United States and Europe, are seeing rapid growth in interest. Determining who has sufficient expertise for safe and effective clinical use remains elusive. Our aim was to analyze performance on a robotic platform to determine what defines an expert user. METHODS: During three sessions, 21 subjects with a range of endovascular expertise and endovascular robotic experience (novices <2 hours to moderate-extensive experience with >20 hours) performed four tasks on a training model. All participants completed a 2-hour training session on the robot by a certified instructor. Completion times, global rating scores, and motion metrics were collected to assess performance. Electromagnetic tracking was used to capture and to analyze catheter tip motion. Motion analysis was based on derivations of speed and position including spectral arc length and total number of submovements (inversely proportional to proficiency of motion) and duration of submovements (directly proportional to proficiency). RESULTS: Ninety-eight percent of competent subjects successfully completed the tasks within the given time, whereas 91% of noncompetent subjects were successful. There was no significant difference in completion times between competent and noncompetent users except for the posterior branch (151 s:105 s; P = .01). The competent users had more efficient motion as evidenced by statistically significant differences in the metrics of motion analysis. Users with >20 hours of experience performed significantly better than those newer to the system, independent of prior endovascular experience. CONCLUSIONS: This study demonstrates that motion-based metrics can differentiate novice from trained users of flexible robotics systems for basic endovascular tasks. Efficiency of catheter movement, consistency of performance, and learning curves may help identify users who are sufficiently trained for safe clinical use of the system. This work will help identify the learning curve and specific movements that translate to expert robotic navigation.

The model for Fundamentals of Endovascular Surgery (FEVS) successfully defines the competent endovascular surgeon.

OBJECTIVE: Fundamental skills testing is now required for certification in general surgery. No model for assessing fundamental endovascular skills exists. Our objective was to develop a model that tests the fundamental endovascular skills and differentiates competent from noncompetent performance. METHODS: The Fundamentals of Endovascular Surgery model was developed in silicon and virtual-reality versions. Twenty individuals (with a range of experience) performed four tasks on each model in three separate sessions. Tasks on the silicon model were performed under fluoroscopic guidance, and electromagnetic tracking captured motion metrics for catheter tip position. Image processing captured tool tip position and motion on the virtual model. Performance was evaluated using a global rating scale, blinded video assessment of error metrics, and catheter tip movement and position. Motion analysis was based on derivations of speed and position that define proficiency of movement (spectral arc length, duration of submovement, and number of submovements). RESULTS: Performance was significantly different between competent and noncompetent interventionalists for the three performance measures of motion metrics, error metrics, and global rating scale. The mean error metric score was 6.83 for noncompetent individuals and 2.51 for the competent group (P < .0001). Median global rating scores were 2.25 for the noncompetent group and 4.75 for the competent users (P < .0001). CONCLUSIONS: The Fundamentals of Endovascular Surgery model successfully differentiates competent and noncompetent performance of fundamental endovascular skills based on a series of objective performance measures. This model could serve as a platform for skills testing for all trainees.