Atrial fibrillation (AF) is a major cause of heart failure and stroke. The early maintenance of sinus rhythm has been shown to reduce major cardiovascular endpoints, yet is difficult to achieve. For instance, it is unclear how discoveries at the genetic and cellular level can be used to tailor pharmacotherapy. For non-pharmacologic therapy, pulmonary vein isolation (PVI) remains the cornerstone of rhythm control, yet has suboptimal success. Improving these therapies will likely require a multifaceted approach that personalizes therapy based on mechanisms measured in individuals across biological scales. We review AF mechanisms from cell-to-organ-to-patient from this perspective of personalized medicine, linking them to potential clinical indices and biomarkers, and discuss how these data could influence therapy. We conclude by describing approaches to improve ablation, including the emergence of several mapping systems that are in use today.

Aims There is a clinical spectrum for atrial tachyarrhythmias wherein most patients with atrial tachycardia (AT) and some with atrial fibrillation (AF) respond to ablation, while others do not. It is undefined if this clinical spectrum has pathophysiological signatures. This study aims to test the hypothesis that the size of spatial regions showing repetitive synchronized electrogram (EGM) shapes over time reveals a spectrum from AT, to AF patients who respond acutely to ablation, to AF patients without acute response. Methods and results We studied n = 160 patients (35% women, 65.0 ± 10.4 years) of whom (i) n = 75 had AF terminated by ablation propensity matched to (ii) n = 75 without AF termination and (iii) n = 10 with AT. All patients had mapping by 64-pole baskets to identify areas of repetitive activity (REACT) to correlate unipolar EGMs in shape over time. Synchronized regions (REACT) were largest in AT, smaller in AF termination, and smallest in non-termination cohorts (0.63 ± 0.15, 0.37 ± 0.22, and 0.22 ± 0.18, P < 0.001). Area under the curve for predicting AF termination in hold-out cohorts was 0.72 ± 0.03. Simulations showed that lower REACT represented greater variability in clinical EGM timing and shape. Unsupervised machine learning of REACT and extensive (50) clinical variables yielded four clusters of increasing risk for AF termination (P < 0.01, χ2), which were more predictive than clinical profiles alone (P < 0.001). Conclusion The area of synchronized EGMs within the atrium reveals a spectrum of clinical response in atrial tachyarrhythmias. These fundamental EGM properties, which do not reflect any predetermined mechanism or mapping technology, predict outcome and offer a platform to compare mapping tools and mechanisms between AF patient groups.

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Introduction: Segmenting cardiac computed tomography (CT) to provide anatomic guidance for Atrial Fibrillation (AF) ablation is routinely applied, but is time-consuming and prone to error. Machine learning (ML) is a powerful approach that could automate this approach, but is hindered by the small size of available labeled datasets. Hypothesis: We hypothesized that a new computational pipeline, in which an ML model is trained mathematically in a small cohort (N=20) using geometrical heart avatars derived from computer graphics imaging (CGI), rather than on manually-segmented data, would enable rapid expert-level segmentation of raw cardiac CT scans. Methods: We first encoded anatomical knowledge with generic geometrical avatars and derived a “virtual dissection” method to geometrically parse the heart (Fig A). An ML model trained by virtual dissection using 20 cases was able to rapidly and accurately segment the pulmonary veins (PVs), left atrial appendage (LAA), and left atrium (LA) from cardiac CT scans (Fig B), which we tested in a retrospective study of N=100 patients (30% women, 64.7±10.1Y) and in a prospective clinical trial of N=42 patients (42.9% women, 65.2±10.8Y) undergoing AF ablation, against a panel of 3 experts (Fig C). Results: In a retrospective study (N=100), ML achieved median Dice scores of 96.6% (IQR: 95.1% to 97.5%), similar to experts (p<0.05). In a prospective study (N=42), this pipeline reduced segmentation time (2.3±0.8 vs 15.0±6.9 minutes; p < 0.00001; Fig D), but achieved similar Dice scores (93.9% (IQR: 93.0% to 94.6%) vs 94.4% (IQR: 92.8% to 95.7%); p<0.05; Fig E). Conclusions: In our prospective trial, virtual dissection (machine learning of cardiac structures based on mathematical cardiac geometry) accelerated cardiac segmentation prior to AF ablation. In general, this approach may reduce the dependence on large training datasets for machine learning, and could be applied to other organ systems for diverse therapeutic strategies.