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9±48.9; P=0.002; Framingham, 351.0±40.1 versus 406.2±46.1; P=0.002; Bazett, 423.8±38.3 versus 507.7±56.6; P less then 0.0001; men Fridericia, 403.8±30.4 versus 471.0±47.1; P less then 0.0001; Framingham, 342.7±36.4 versus 409.4±45.8; P less then 0.0001; Bazett, 439.3±38.6 versus 506.2±56.8; P less then 0.0001). QT dispersion and Tpeak-Tend intervals were comparable between groups. We also observed abnormal ST-segment elevation in 5 patients. Importantly, no patients showed fatal arrhythmias during or after seizures. Conclusions Our study demonstrated that brain abnormalities can be associated with abnormal cardiac repolarization after seizures, which might be a manifestation of electrophysiological remodeling in the brain.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.Cardiac amyloidosis (CA) is an increasingly recognized cause of heart failure, arrhythmias, and sudden cardiac death. While CA was previously rapidly fatal, recent advances in diagnosis and treatment have significantly improved outcomes. Advances in cardiac imaging and biomarkers have critically improved the accuracy and efficiency with which CA is diagnosed, even allowing for the noninvasive diagnosis of transthyretin CA. Cardiac magnetic resonance imaging, technetium nuclear imaging, echocardiography, and blood-based biomarkers have established important and complementary roles in the management and advancement of care. At the same time, the development of novel targeted amyloid therapies has allowed patients with CA to live longer and potentially achieve better quality of life. Still, despite this significant progress, there remain critical ongoing questions in the field. Accordingly, within this review we will highlight recent advances in cardiac imaging and therapeutics for CA, while focusing on key opportunities for further optimization of care and outcomes among this growing population. Specifically, we will discuss ongoing debates in the diagnosis of CA, including the interpretation of indeterminate cardiac imaging findings, the best technique to screen asymptomatic transthyretin amyloidosis gene mutation carriers for cardiac involvement, and the ideal method for monitoring response to CA treatment. We will additionally focus on recent advances in treatment for transthyretin amyloidosis-CA, including a discussion of available agents as well as highlighting ongoing clinical trials. PD0166285 mouse Together, these data will allow clinicians to emerge with a greater understanding of the present and future of diagnosis, management, and potentially enhanced outcomes in this rapidly advancing field.Background Premature discontinuation of dual antiplatelet therapy (DAPT) after percutaneous coronary intervention is related to higher short-term risks of adverse outcomes. Whether these risks persist in the long-term is uncertain. Methods and Results We assessed all patients having percutaneous coronary intervention with coronary second- or first-generation drug-eluting stents in the Veterans Affairs healthcare system between 2006 and 2012 who were free of major ischemic or bleeding events in the first 12 months. The characteristics of patients who stopped DAPT prematurely (1-9 months duration), compared with >9 to 12 months, or extended duration (>12 months) were assessed by odds ratios (ORs) from multivariable logistic models. The risk of adverse clinical outcomes over a mean 5.1 years in patients who stopped DAPT prematurely was assessed by hazard ratios (HRs) and 95% CIs from Cox regression models. A total of 14 239 had second-generation drug-eluting stents, and 8583 had first-generation drug-eluting stents. Premature discontinuation of DAPT was more likely in Black patients (OR, 1.54; 95% CI, 1.40-1.68), patients with greater frailty (OR, 1.04; 95% CI, 1.03-1.05), and patients with higher low-density lipoprotein cholesterol, and less likely in patients on statins (OR, 0.87; 95% CI, 0.80-0.95). Patients who stopped DAPT prematurely had higher long-term risks of death (second-generation drug-eluting stents HR, 1.35; 95% CI, 1.19-1.56), myocardial infarction (second-generation drug-eluting stents HR, 1.46; 95% CI, 1.22-1.74), and repeated coronary revascularization (second-generation drug-eluting stents HR, 1.24; 95% CI, 1.08-1.41). Conclusions Patients who stop DAPT prematurely have features that reflect greater frailty, poorer medication use, and other social factors. They continue to have higher risks of major adverse outcomes over the long-term and may require more intensive surveillance many years after percutaneous coronary intervention.