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Assessing simulation‐based interventional angiography training in novices
Author(s) -
Zaika Oleksiy,
Boulton Mel,
Eagleson Roy,
Ribaupierre Sandrine
Publication year - 2016
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.30.1_supplement.787.4
Subject(s) - fluoroscopy , task (project management) , medical physics , haptic technology , computer science , medicine , training (meteorology) , simulation , radiology , systems engineering , physics , meteorology , engineering
Laparoscopic surgical procedures require visual‐spatial coordination in workspaces with restricted views, involving constrained motions. The development of the skills needed for these procedures can be facilitated by 3D simulator‐based training. Surgical training programs are introducing additional requirements of competency‐based simulation in their curricula. Cerebral angiography (CA), however, has lagged behind in this regard, relying strictly on clinical case exposure frequency as a means of assessing proficiency. Simulation in CA training has encountered some roadblocks, possibly due to lack of validation studies that would support more widespread acceptance. The AngioMENTOR visual‐haptic simulator has been regarded as an effective training tool, increasing performance in diagnostic CA. However, this simulator has not been tested thoroughly for training interventional skills in CA. In our current study, neurosurgery and radiology residents will be trained in diagnostic CA, and later assessed on their interventional performance on a variety of cases using AngioMENTOR. The participants’ spatial abilities are used as a baseline measure for comparative assessment of their procedure times, fluoroscopy use, contrast injection, and error correction. We hypothesize that individuals with higher spatial ability would perform the procedure more quickly, using less fluoroscopy and spending less time in incorrect vessels. These findings would complement our initial work on diagnostic CA simulation training, and provide quantitative metrics of performance on this simulator‐based task.