BACKGROUND: The rotational activation created by spiral waves may be a mechanism for atrial fibrillation (AF), yet it is unclear how activation patterns obtained from endocardial baskets are influenced by the 3D geometric curvature of the atrium or unfolding into 2D maps. We develop algorithms that can visualize spiral waves and their tip locations on curved atrial geometries. We use these algorithms to quantify differences in AF maps and spiral tip locations between 3D basket reconstructions, projection onto 3D anatomical shells and unfolded 2D surfaces. METHODS: We tested our algorithms in N = 20 patients in whom AF was recorded from 64-pole baskets (Abbott, CA). Phase maps were generated by non-proprietary software to identify the tips of spiral waves, indicated by phase singularities. The number and density of spiral tips were compared in patient-specific 3D shells constructed from the basket, as well as 3D maps from clinical electroanatomic mapping systems and 2D maps. RESULTS: Patients (59.4±12.7 yrs, 60% M) showed 1.7±0.8 phase singularities/patient, in whom ablation terminated AF in 11/20 patients (55%). There was no difference in the location of phase singularities, between 3D curved surfaces and 2D unfolded surfaces, with a median correlation coefficient between phase singularity density maps of 0.985 (0.978-0.990). No significant impact was noted by phase singularities location in more curved regions or relative to the basket location (p>0.1). CONCLUSIONS: AF maps and phase singularities mapped by endocardial baskets are qualitatively and quantitatively similar whether calculated by 3D phase maps on patient-specific curved atrial geometries or in 2D. Phase maps on patient-specific geometries may be easier to interpret relative to critical structures for ablation planning.

Background: Termination of atrial fibrillation (AF), the most common arrhythmia in the United States, during catheter ablation is an attractive procedural endpoint, which has been associated with improved long-term outcome in some studies. It is not clear, however, whether it is possible to predict termination using clinical data. We developed and applied three quantitative indices in global multielectrode recordings of AF prior to ablation: average dominant frequency (ADF), spectral power index (SPI), and electrogram quality index (EQI). Methods: In N = 42 persistent AF patients (65 ± 9 years, 14% female) we collected unipolar electrograms from 64-pole baskets (Abbott, CA). We studied N = 17 patients in whom AF terminated during ablation ("Term") and N = 25 in whom it did not ("Non-term"). For each index, we determined its ability to predict ablation by computing receiver operating characteristic (ROC) and calculated the area under the curve (AUC). Results: The ADF did not differ for Term and Non-term patients at 5.28 ± 0.82 Hz and 5.51 ± 0.81 Hz, respectively (p = 0.34). Conversely, the SPI for these two groups was. 0.85 (0.80-0.92) and 0.97 (0.93-0.98) and the EQI was 0.61 (0.58-0.64) and 0.56 (0.55-0.59) (p < 0.0001). The AUC for predicting AF termination for the SPI was 0.85 ([0.68, 0.95] 95% CI), and for the EQI, 0.86 ([0.72, 0.95] 95% CI). Conclusion: Both the EQI and the SPI may provide a useful clinical tool to predict procedural ablation outcome in persistent AF patients. Future studies are required to identify which physiological features of AF are revealed by these indices and hence linked to AF termination or non-termination.