A randomized controlled trial of simulation training in teaching coronary angiographic views
Posted on 2022-08-27 - 09:29
Abstract Introduction Simulation technology has an established role in teaching technical skills to cardiology fellows, but its impact on teaching trainees to interpret coronary angiographic (CA) images has not been systematically studied. The aim of this randomized controlled study was to test whether structured simulation training, in addition to traditional methods would improve CA image interpretation skills in a heterogeneous group of medical trainees. Methods We prospectively randomized a convenience sample of 105 subjects comprising of medical students (N = 20), residents (N = 68) and fellows (N = 17) from the University of Arizona. Subjects were randomized in a stratified fashion into a simulation training group which received simulation training in addition to didactic teaching (n = 53) and a control training group which received didactic teaching alone (n = 52). The change in pre and post-test score (delta score) was analyzed by a two-way ANOVA for education status and training arm. Results Subjects improved in their post-test scores with a mean change of 4.6 ± 4.0 points. Subjects in the simulation training arm had a higher delta score compared to control (5.4 ± 4.2 versus 3.8 ± 3.7, p = 0.04), with greatest impact for residents (6.6 ± 4.0 versus 3.5 ± 3.4) with a p = 0.02 for interaction of training arm and education status. Conclusions Simulation training complements traditional methods to improve CA interpretation skill, with greatest impact on residents. This highlights the importance of incorporating high-fidelity simulation training early in cardiovascular fellowship curricula.
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Lee, Kwan S.; Natarajan, Balaji; Wong, Wei X.; Yousman, Wina; Koester, Stefan; Nyotowidjojo, Iwan; et al. (2022): A randomized controlled trial of simulation training in teaching coronary angiographic views. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.6169087.v1
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Kwan S. Lee
Wei X. Wong
Justin Z. Lee
Karl B. Kern
Julia H. Indik