Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods and Results Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate‐to‐severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate‐to‐severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. Conclusions The deep learning algorithm’s ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front‐line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. Registration URL: https://clinicaltrials.gov ; Unique Identifier: NCT03458806.

Coronary artery fistulas represent rare congenital or acquired defects in the coronary circulation. We describe a case of bilateral coronary to pulmonary artery fistulas resulting in coronary artery steal syndrome in a patient with a history of valve-sparing aortic repair surgery.

Objectives. is systematic review and meta-analysis evaluates the safety and efficacy of dual antiplatelet therapy (DAPT) in elderly patients with acute coronary syndrome (ACS). Background. e safety and efficacy of DAPT in elderly patients with ACS is not well characterized. Methods. We performed a systematic literature review to identify clinical studies that reported safety and efficacy outcomes after DAPT for ACS in elderly patients. e primary outcomes of primary efficacy endpoint rates and bleeding event rates were reported as random effects risk ratio (RR) with 95% confidence interval. No prior ethical approval was required since all data are public. Results. Our search yielded 660 potential studies. We included 8 studies reporting on 29,217 patients.ere was a higher risk of bleeding event rates in elderly patients treated with prasugrel or ticagrelor when compared to clopidogrel with a risk ratio of 1.17 (95% CI 1.08 to 1.27, p < 0.05). ere was no difference in primary efficacy endpoint rates between elderly patients treated with prasugrel or ticagrelor when compared to clopidogrel with a risk ratio of 0.85 (95% CI 0.68 to 1.07, p � 0.17). Conclusions. is systematic review and meta-analysis suggests that DAPT with prasugrel or ticagrelor compared to clopidogrel is associated with a higher risk of bleeding events in elderly patients with ACS. ere was no difference in the primary efficacy endpoints between the two treatment groups.

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