Assessment of left ventricular global longitudinal strain in patients with hypertrophic cardiomyopathy and coronary artery disease.

Impaired left ventricular global longitudinal strain (GLS) and coronary artery disease (CAD) each confer adverse prognosis in hypertrophic cardiomyopathy (HCM). Despite their prevalence, data on GLS in co-existent HCM and CAD is lacking. Ninety-six patients with HCM and CAD were retrospectively identified between 2005 and 2021, and analyzed using 2D speckle-tracking echocardiography. Obstructive and non-obstructive CAD patients were compared, multivariate linear regression tested associations between clinical and echocardiographic variables with GLS, and Receiver Operating Characteristic Curve assessed the utility of GLS to predict all-cause mortality at follow-up. Mean age was 71 ± 12.2 years, 41% had obstructive HCM, 78% had obstructive CAD, and 75% had prior acute coronary syndrome. At 4.8-year follow-up, GLS decreased compared with baseline (- 12.5 ± 4.5 vs. - 14 ± 4.2%, p = 0.007), with basal segments experiencing the greatest impairment. GLS was lower in obstructive versus non-obstructive CAD patients at follow-up, although the magnitude was attenuated (baseline: - 13.2 vs. - 17.1%, p < 0.001; follow-up: - 12 vs. - 14.1%, p = 0.05). Interventricular septal thickness (ß = 0.54), apical HCM (ß = 0.48), and right ventricular systolic pressure (ß = 0.39) were associated with more impaired GLS (all p < 0.001), independent of obstructive CAD (ß = 0.09, p = 0.44). There were 9 follow-up deaths, with baseline GLS > - 13.5% being a good predictor of all-cause mortality (AUC 0.78, 95% CI 0.64-0.92, sensitivity 88%, specificity 57%, p = 0.01). Patients with HCM and CAD experience progressive GLS impairment over long-term follow-up, with GLS > - 13.5% appearing to be a threshold for predicting all-cause mortality. Apical HCM phenotype is independently associated with worse GLS.

Coronary Steal Syndrome Secondary to Large Coronary to Pulmonary Artery Fistulas.

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.

Dual Antiplatelet Therapy in Patients Aged 75 Years and Older with Coronary Artery Disease: A Meta-Analysis and Systematic Review.

Objectives: This systematic review and meta-analysis evaluates the safety and efficacy of dual antiplatelet therapy (DAPT) in elderly patients with acute coronary syndrome (ACS). Background: The 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. The 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. There 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). There 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: This 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. There was no difference in the primary efficacy endpoints between the two treatment groups.

Positive Regadenoson Stress Test in a Patient on Phentermine With Normal Coronaries.

Obesity is a well-established cardiovascular (CV) risk factor with greater mortality and morbidity rates than the general population. Phentermine is a weight loss medication that is approved for short-term obesity treatment in conjunction with lifestyle modifications to decrease CV risk. A 51-year-old female with Raynaud's phenomenon who was started on phentermine one week prior presented with a one-day history of palpitations. Subsequent workup revealed non-ST elevation myocardial infarction (NSTEMI) on presentation and worsening ST segment depressions following regadenoson injection during pharmacological stress testing. Although current evidence suggests that the use of phentermine is safe and may even reduce the risk of CV disease in obese patients, it still may pose adverse CV effects. A detailed medical history, including medications used and predisposing conditions, is crucial to help identify and possibly prevent exacerbation of such CV side effects.

Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform.

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.