Chest · 2010
Simulation training improves diagnostic performance on a real patient with similar clinical findings.
СтатьяРандомизированное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Fraser Kristin, Wright Bruce, Girard Louis, Tworek Janet, Paget Mike, Welikovich Lisa, McLaughlin Kevin
Аннотация
BACKGROUND: Training on a cardiopulmonary simulator improves subsequent diagnostic performance on the same simulator. But data are lacking on transfer of learning. The objective of this study was to determine whether training on a cardiorespiratory simulator improves diagnostic performance on a real patient.
METHODS: We randomly allocated first-year medical students at the University of Calgary to simulator training in one of three clinical scenarios of acute-onset chest pain: pulmonary embolism with right ventricular strain but no murmur, symptomatic aortic stenosis, or myocardial ischemia causing mitral regurgitation. Simulation sessions ran for 20 min, after which participants had a standardized debriefing session and reviewed the physical findings. Immediately following the training sessions, students assessed the auscultatory findings of a real patient with mitral regurgitation. Our outcome measures were accuracy of identifying abnormal auscultatory findings and diagnosing the underlying cardiac abnormality (mitral regurgitation).
RESULTS: Eighty-six students participated in the study. Students trained on mitral regurgitation were more likely to identify and diagnose these findings on a real patient with mitral regurgitation than those who had trained on aortic stenosis or a scenario with no cardiac murmur. The accuracy (SD) of identifying clinical features of mitral regurgitation for these three groups was 74.0 (36.4) vs 56.2 (34.3) vs 36.8 (33.1), respectively (P = .0005), and for diagnosing mitral regurgitation, the accuracy was 68.0 (45.4) vs 51.6 (50.0) vs 29.9 (40.7), respectively (P = .01).
CONCLUSIONS: Simulator training on mitral regurgitation increases the likelihood of diagnosing this abnormality on a real patient.
METHODS: We randomly allocated first-year medical students at the University of Calgary to simulator training in one of three clinical scenarios of acute-onset chest pain: pulmonary embolism with right ventricular strain but no murmur, symptomatic aortic stenosis, or myocardial ischemia causing mitral regurgitation. Simulation sessions ran for 20 min, after which participants had a standardized debriefing session and reviewed the physical findings. Immediately following the training sessions, students assessed the auscultatory findings of a real patient with mitral regurgitation. Our outcome measures were accuracy of identifying abnormal auscultatory findings and diagnosing the underlying cardiac abnormality (mitral regurgitation).
RESULTS: Eighty-six students participated in the study. Students trained on mitral regurgitation were more likely to identify and diagnose these findings on a real patient with mitral regurgitation than those who had trained on aortic stenosis or a scenario with no cardiac murmur. The accuracy (SD) of identifying clinical features of mitral regurgitation for these three groups was 74.0 (36.4) vs 56.2 (34.3) vs 36.8 (33.1), respectively (P = .0005), and for diagnosing mitral regurgitation, the accuracy was 68.0 (45.4) vs 51.6 (50.0) vs 29.9 (40.7), respectively (P = .01).
CONCLUSIONS: Simulator training on mitral regurgitation increases the likelihood of diagnosing this abnormality on a real patient.