A randomized controlled trial of simulation training in teaching coronary angiographic views.

Kwan S Lee, Balaji Natarajan, Wei X Wong, Wina Yousman, Stefan Koester, Iwan Nyotowidjojo, Justin Z Lee, Karl B Kern, Deepak Acharya, David Fortuin, Olivia Hung, Wolfram Voelker, Julia H Indik
Author Information
  1. Kwan S Lee: Sarver Heart Center, University of Arizona, 1501 North Campbell Avenue, Tucson, AZ, 85724, USA.
  2. Balaji Natarajan: University of California Riverside School of Medicine, Riverside, CA, USA.
  3. Wei X Wong: Sarver Heart Center, University of Arizona, 1501 North Campbell Avenue, Tucson, AZ, 85724, USA.
  4. Wina Yousman: Sarver Heart Center, University of Arizona, 1501 North Campbell Avenue, Tucson, AZ, 85724, USA.
  5. Stefan Koester: Sarver Heart Center, University of Arizona, 1501 North Campbell Avenue, Tucson, AZ, 85724, USA.
  6. Iwan Nyotowidjojo: Sarver Heart Center, University of Arizona, 1501 North Campbell Avenue, Tucson, AZ, 85724, USA.
  7. Justin Z Lee: Mayo Clinic Arizona, Phoenix, AZ, USA.
  8. Karl B Kern: Sarver Heart Center, University of Arizona, 1501 North Campbell Avenue, Tucson, AZ, 85724, USA.
  9. Deepak Acharya: Sarver Heart Center, University of Arizona, 1501 North Campbell Avenue, Tucson, AZ, 85724, USA.
  10. David Fortuin: Mayo Clinic Arizona, Phoenix, AZ, USA.
  11. Olivia Hung: Sarver Heart Center, University of Arizona, 1501 North Campbell Avenue, Tucson, AZ, 85724, USA.
  12. Wolfram Voelker: University Medical Center Wuerzburg, Wuerzburg, Germany.
  13. Julia H Indik: Sarver Heart Center, University of Arizona, 1501 North Campbell Avenue, Tucson, AZ, 85724, USA. jindik@shc.arizona.edu.

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.

Keywords

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MeSH Term

Clinical Competence
Computer Simulation
Curriculum
Humans
Internship and Residency
Simulation Training
Students, Medical
Teaching

Word Cloud

Created with Highcharts 10.0.0trainingsimulationteachingrandomizedSimulationimpactCAgroupresidentsSubjectsscorearmskillsfellowstraineescoronaryangiographiccontrolledadditiontraditionalmethodsimproveinterpretationmedicalreceiveddidacticcontrolchangepost-testdeltaeducationstatus46 ± 40versus3p = 0greatestINTRODUCTION:technologyestablishedroletechnicalcardiologyinterpretimagessystematicallystudiedaimstudytestwhetherstructuredimageheterogeneousMETHODS:prospectivelyconveniencesample105subjectscomprisingstudentsN = 20N = 68N = 17UniversityArizonastratifiedfashionn = 53alonen = 52preanalyzedtwo-wayANOVARESULTS:improvedscoresmeanpointshighercompared54 ± 428 ± 370465 ± 302interactionCONCLUSIONS:complementsskillhighlightsimportanceincorporatinghigh-fidelityearlycardiovascularfellowshipcurriculatrialviewsClinicalcompetenceDiagnosticangiography

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