Complete revascularization with routine percutaneous coronary intervention of nonculprit lesions after primary percutaneous coronary intervention improves outcomes in ST-segment-elevation myocardial infarction. Whether this benefit is associated with nonculprit lesion vulnerability is unknown.

Large data comparing the performance of new-generation self-expandable versus balloon-expandable transcatheter heart valves in bicuspid aortic stenosis are lacking. We aim to compare the safety and performance of balloon-expandable and self-expandable transcatheter heart valves in the treatment of bicuspid aortic stenosis.

Cardiac hypertrophic growth is mediated by robust changes in gene expression and changes that underlie the increase in cardiomyocyte size. The former is regulated by RNA polymerase II (pol II) de novo recruitment or loss; the latter involves incremental increases in the transcriptional elongation activity of pol II that is preassembled at the transcription start site. The differential regulation of these distinct processes by transcription factors remains unknown. Forkhead box protein O1 (FoxO1) is an insulin-sensitive transcription factor that is also regulated by hypertrophic stimuli in the heart. However, the scope of its gene regulation remains unexplored.

NEXN (nexilin) is a protein of the junctional membrane complex required for development of cardiac T-tubules. Global and cardiomyocyte-specific loss of Nexn in mice leads to a rapidly progressive dilated cardiomyopathy and premature death. Therefore, little is known as to the role of NEXN in adult cardiomyocytes. Transverse-axial tubular system remodeling are well-known features in heart failure. Although NEXN is required during development for T-tubule formation, its role, if any, in mature T-tubules remains to be addressed.

Black patients have higher rates of hospitalization for acute heart failure than other race/ethnic groups. We sought to determine whether diuretic efficiency is associated with racial differences in risk for rehospitalization after acute heart failure.

Normative ECG values for children are based on relatively few subjects and are not standardized, resulting in interpersonal variability of interpretation. Recent advances in digital technology allow a more quantitative, reproducible assessment of ECG variables. Our objective was to create the foundation of normative ECG standards in the young utilizing Z-scores.

Endocardial-epicardial dissociation and focal breakthroughs in humans with atrial fibrillation (AF) have been recently demonstrated using activation mapping of short 10-second AF segments. In the current study, we used simultaneous endo-epi phase mapping to characterize endo-epi activation patterns on long segments of human persistent AF.

Advances in ablation for atrial fibrillation (AF) continue to be hindered by ambiguities in mapping, even between experts. We hypothesized that convolutional neural networks (CNN) may enable objective analysis of intracardiac activation in AF, which could be applied clinically if CNN classifications could also be explained.

Clinical factors associated with development of intravascular lead adherence (ILA) are unreliable predictors. Because vascular injury in the superior vena cava-right atrium during transvenous lead extraction is more likely to occur in segments with higher degrees of ILA, reliable and accurate assessment of ILA is warranted. We hypothesized that intravascular ultrasound (IVUS) could accurately visualize and quantify ILA and degree of ILA correlates with transvenous lead extraction difficulty.

Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.