Background: The ability of medical students to recognize heart murmurs is poor (20%), and does not improve with subsequent years of training. A teaching method to improve this skill would be useful.
Study Objectives: To determine whether intensive repetition of four basic cardiac murmurs improves auscultatory proficiency in medical students.
Design: Controlled intervention study.
Subjects: Fifty-one second-year medical students in an east coast medical school.
Interventions: Subjects were classified into three groups: (1) a monitored group, who listened to 500 repetitions of each murmur in a monitored setting, (2) an unmonitored group, who listened to 500 repetitions of each murmur in an unmonitored setting, and (3) a control group. All three groups were tested using a pretest and posttest methodology.
Measurements And Results: The 20 subjects in the monitored group improved from 13.5 +/- 9.8 to 85 +/- 17.6% following the intervention (mean +/- SD). Similarly, 21 students in the unmonitored group improved from 20.9 +/- 10.9 to 86.1 +/- 15.6%. Ten control students showed no significant improvement (24 +/- 21.7 to 32 +/- 22.5%). The differences between the two intervention groups and the control subjects was significant at p < 0.001 by analysis of variance.
Conclusion: Five hundred repetitions of four basic cardiac murmurs significantly improved auscultatory proficiency in recognizing basic cardiac murmurs by medical students. These results suggest that cardiac auscultation is, in part, a technical skill.
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http://dx.doi.org/10.1378/chest.126.2.470 | DOI Listing |
Animals (Basel)
January 2025
Department of Veterinary Clinical Medicine, College of Veterinary Medicine, University of Illinois, 1008 West Hazelwood Drive, Urbana, IL 61802, USA.
Cardiac troponin-I (cTnI) is a highly sensitive and specific marker of myocardial injury detectable in plasma by immunoassay techniques. Inclusion criteria over a 3-year period required a diagnosis of cardiac disease accompanied by electrocardiographic (ECG) and cardiac ultrasound examinations (n = 23) in adult horses (≥2 years of age). A second group of normal adult ponies (n = 12) was studied as a reference group.
View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Fetal phonocardiography is a well-known auscultation technique for evaluation of fetal health. However, murmurs that are synchronous with the maternal heartbeat can often be heard while listening to fetal heart sounds. Maternal placental murmurs (MPM) could be used to detect maternal cardiovascular and placental abnormalities, but the recorded MPMs are often contaminated by ambient interference and noise.
View Article and Find Full Text PDFHeart
December 2024
Interventional Center of Valvular Heart Disease, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing, China
Background: Subclinical leaflet thrombosis (SLT) is a common complication after transcatheter aortic valve replacement (TAVR). Multidimensional CT (MDCT) is the main imaging mortality for the diagnosis of SLT but it enhances the risk of contrast-induced nephropathy. Our study aimed to use an innovative wearable acoustic cardiography (ACG) device to diagnose SLT as an alternative option.
View Article and Find Full Text PDFPediatr Cardiol
January 2025
Department of Medical Biology, Faculty of Medicine, Mersin University, Mersin, Turkey.
Studies on the genetic basis of bicuspid aortic valve (BAV), characterized by a configuration of the aortic valve with two leaflets instead of three, are insufficient. This study aimed to elucidate the possible relationship between BAV and TGF-β1 gene expression levels. Forty-eight pediatric patients diagnosed with isolated BAV and 50 healthy children with innocent heart murmurs were included in the study.
View Article and Find Full Text PDFBiomed Phys Eng Express
December 2024
Department of Electronic and Telecommunication Engineering, University of Moratuwa, Katubedda, Moratuwa, 10400, SRI LANKA.
Cardiovascular diseases rank among the leading causes of mortality worldwide and the early identification of diseases is of paramount importance. This work focuses on developing a novel machine learning-based framework for early detection and classification of heart murmurs by analysing phonocardiogram signals. Our heart murmur detection and classification pipeline encompasses three classification settings.
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