Objectives We hypothesized that human atrial fibrillation (AF) may be sustained by localized sources (electrical rotors and focal impulses), whose elimination (Focal Impulse and Rotor Modulation, FIRM) may improve outcome from AF ablation. Background Catheter ablation for AF is a promising therapy, whose success is limited in part by uncertainty in the mechanisms that sustain AF. We developed a computational approach to map whether AF is sustained by several meandering waves (the prevailing hypothesis) or localized sources, then prospectively tested whether targeting patient-specific mechanisms revealed by mapping would improve AF ablation outcome. Methods We recruited 92 individuals during 107 consecutive ablation procedures for paroxysmal or persistent (72%) AF. Cases were prospectively treated, in a 2-arm 1:2 design, by ablation at sources (FIRM-Guided) followed by conventional ablation (n=36), or conventional ablation alone (n=71; FIRM-Blinded). Results Localized rotors or focal impulses were detected in 98 (97%) of 101 cases with sustained AF, each exhibiting 2.1±1.0 sources. The acute endpoint (AF termination or consistent slowing) was achieved in 86% of FIRM-guided versus 20% of FIRM-Blinded cases (p<0.001). FIRM ablation alone at the primary source terminated AF in 2.5 minutes (median; IQR 1.0–3.1). Total ablation time did not differ between groups (57.8±22.8 versus 52.1±17.8 minutes, p=0.16). During 273 days (median; IQR 132–681 days) after a single procedure, FIRM-Guided cases had higher freedom from AF (82.4% versus 44.9%; p<0.001) after a single procedure than FIRM-blinded cases with rigorous, often implanted, ECG monitoring. Adverse events did not differ between groups. CONCLUSIONS Localized electrical rotors and focal impulse sources are prevalent sustaining-mechanisms for human AF. FIRM ablation at patient-specific sources acutely terminated or slowed AF, and improved outcome. These results offer a novel mechanistic framework and treatment paradigm for AF. (ClinicalTrials.gov number, NCT01008722)

T-wave alternans (TWA) reflects beat-to-beat fluctuations in the electrocardiographic T-wave, and is associated with dispersion of repolarization and the mechanisms for sudden cardiac arrest (SCA). This review examines the bench-to-bedside literature that, over decades, has linked alternans of repolarization in cellular, whole-heart, and human studies with spatial dispersion of repolarization, alternans of cellular action potential, and fluctuations in ionic currents that may lead to ventricular arrhythmias. Collectively, these studies provide a foundation for the clinical use of TWA to reflect susceptibility to ventricular arrhythmias in several disease states. This review then provides a contemporary evidence-based framework for the use of TWA to enhance risk stratification for SCA, identifying populations for whom TWA is best established, those for whom further studies are required, and areas for additional investigation.

Introduction The perpetuating mechanisms for human AF remain undefined. Localized rotors and focal beat sources may sustain AF in elegant animal models, but there has been no direct evidence for localized sources in human AF using traditional methods. We developed a clinical computational mapping approach, guided by human atrial tissue physiology, to reveal sources of human AF. Methods and Results In 49 AF patients referred for ablation (62±9 years; 30 persistent), we defined repolarization dynamics using monophasic action potentials (MAP) and recorded AF activation from 64-pole basket catheters in left atrium and, in n=20 patients, in both atria. Careful positioning of basket catheters was required for optimal mapping. AF electrograms at 64-128 electrodes were combined with repolarization and conduction dynamics to construct spatiotemporal AF maps. We observed sustained sources in 47/49 patients, in the form of electrical rotors (n=57) and focal beats (n=11) that controlled local atrial activation with peripheral wavebreak (‘fibrillatory conduction’). Patients with persistent AF had more sources than those with paroxysmal AF (2.1±1.0 vs 1.5±0.8, p=0.02), related to shorter cycle length (163±19 vs 187±25 ms, p<0.001). Approximately one-quarter of sources lay in the right atrium. Conclusions Physiologically-guided computational mapping revealed sustained electrical rotors and repetitive focal beats during human AF for the first time. These localized sources were present in 96% of AF patients, and controlled AF activity. These results provide novel mechanistic insights into human AF and lay the foundation for mechanistically-tailored approaches to AF ablation.

Background The substrates for human atrial fibrillation (AF) are poorly understood but involve abnormal repolarization (action potential duration, APD). We hypothesized that beat-to-beat oscillations in APD may explain AF substrates and why vulnerability to AF forms a spectrum from control subjects without AF to patients with paroxysmal then persistent AF. Methods and Results In 33 subjects (12 persistent AF, 13 paroxysmal AF, 8 controls without AF), we recorded left (n=33) and right (n=6) atrial APD on pacing from cycle lengths (CL) 600–500 ms (100–120 beats/min) to AF. APD alternans required progressively faster rates for patients with persistent AF, paroxysmal AF and controls (CL 411±94 vs 372±72 vs 218±33 ms; p<0.01). In AF patients, APD alternans occurred at rates as slow as 100–120 beats/min, unrelated to APD restitution (p=NS). In this milieu, spontaneous ectopy initiated AF. At fast rates, APD alternans disorganized to complex oscillations en route to AF. Complex oscillations also arose at progressively faster rates for persistent AF, paroxysmal AF and controls (CL: 316±99 vs 266±19 vs 177±16 ms; p=0.02). In paroxysmal AF, APD oscillations amplified prior to AF (p<0.001). In controls, APD alternans arose only at very fast rates (CL < 250 ms; p<0.001 vs AF groups) just prior to AF. In n=4 AF patients in whom rapid pacing did not initiate AF, APD alternans arose transiently then extinguished. Conclusions Atrial APD alternans reveals dynamic substrates for AF, arising most readily (at lower rates and higher magnitudes) in persistent AF then paroxysmal AF, and least readily in controls. APD alternans preceded all AF episodes, and was absent when AF did not initiate. The cellular mechanisms for APD alternans near resting heart rates require definition.

Recent surveys and reports suggest that many athletes and bodybuilders abuse anabolic-androgenic steroids (AAS). However, scientific data on the cardiac and metabolic complications of AAS abuse are divergent and often conflicting. A total of 49 studies describing 1,467 athletes were reviewed to investigate the cardiovascular effects of the abuse of AAS. Although studies were typically small and retrospective, some associated AAS abuse with unfavorable effects. Otherwise healthy young athletes abusing AAS may show elevated levels of low-density lipoprotein and low levels of high-density lipoprotein. Although data are conflicting, AAS have also been linked with elevated systolic and diastolic blood pressure and with left ventricular hypertrophy that may persist after AAS cessation. Finally, in small case studies, AAS abuse has been linked with acute myocardial infarction and fatal ventricular arrhythmias. In conclusion, recognition of these adverse effects may improve the education of athletes and increase vigilance when evaluating young athletes with cardiovascular abnormalities.

Patient-specific models of cardiac function have the potential to improve diagnosis and management of heart disease by integrating medical images with heterogeneous clinical measurements subject to constraints imposed by physical first principles and prior experimental knowledge. We describe new methods for creating three-dimensional patient-specific models of ventricular biomechanics in the failing heart. Three-dimensional bi-ventricular geometry is segmented from cardiac CT images at end-diastole from patients with heart failure. Human myofiber and sheet architecture is modeled using eigenvectors computed from diffusion tensor MR images from an isolated, fixed human organ-donor heart and transformed to the patient-specific geometric model using large deformation diffeomorphic mapping. Semi-automated methods were developed for optimizing the passive material properties while simultaneously computing the unloaded reference geometry of the ventricles for stress analysis. Material properties of active cardiac muscle contraction were optimized to match ventricular pressures measured by cardiac catheterization, and parameters of a lumped-parameter closed-loop model of the circulation were estimated with a circulatory adaptation algorithm making use of information derived from echocardiography. These components were then integrated to create a multi-scale model of the patient-specific heart. These methods were tested in five heart failure patients from the San Diego Veteran’s Affairs Medical Center who gave informed consent. The simulation results showed good agreement with measured echocardiographic and global functional parameters such as ejection fraction and peak cavity pressures.

The pacing site is a primary determinant of the hemodynamic response to LV pacing in patients with nonischemic dilated cardiomyopathy. Pacing at the best LV site is associated acutely with fewer nonresponders and twice the improvement in +dP/dT(max) observed with CS pacing.

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via multi-layered neural networks. Deep learning has accelerated by recent advances in computer hardware and algorithms and is increasingly applied in e-commerce, finance, and voice and image recognition to learn and classify complex datasets. The current medical literature shows both strengths and limitations of DL. Strengths of DL include its ability to automate medical image interpretation, enhance clinical decision-making, identify novel phenotypes, and select better treatment pathways in complex diseases. Deep learning may be well-suited to cardiovascular medicine in which haemodynamic and electrophysiological indices are increasingly captured on a continuous basis by wearable devices as well as image segmentation in cardiac imaging. However, DL also has significant weaknesses including difficulties in interpreting its models (the ‘black-box’ criticism), its need for extensive adjudicated (‘labelled’) data in training, lack of standardization in design, lack of data-efficiency in training, limited applicability to clinical trials, and other factors. Thus, the optimal clinical application of DL requires careful formulation of solvable problems, selection of most appropriate DL algorithms and data, and balanced interpretation of results. This review synthesizes the current state of DL for cardiovascular clinicians and investigators, and provides technical context to appreciate the promise, pitfalls, near-term challenges, and opportunities for this exciting new area.