Ablation of persistent atrial fibrillation (persAF) targeting complex fractionated atrial electrograms (CFAEs) detected by automated algorithms has produced conflicting outcomes in previous electrophysiological studies. We hypothesize that the differences in these algorithms could lead to discordant CFAE classifications by the available mapping systems, giving rise to potential disparities in CFAE-guided ablation. This study reports the results of a head-to-head comparison of CFAE detection performed by NavX (St. Jude Medical) versus CARTO (Biosense Webster) on the same bipolar electrogram data (797 electrograms) from 18 persAF patients. We propose revised thresholds for both primary and complementary indices to minimize the differences in CFAE classification performed by either system. Using the default thresholds [NavX: CFE-Mean ≤ 120 ms; CARTO: ICL ≥ 7], NavX classified 70 % of the electrograms as CFAEs, while CARTO detected 36 % (Cohen’s kappa κ ≈ 0.3, P < 0.0001). Using revised thresholds found using receiver operating characteristic curves [NavX: CFE-Mean ≤ 84 ms, CFE-SD ≤ 47 ms; CARTO: ICL ≥ 4, ACI ≤ 82 ms, SCI ≤ 58 ms], NavX classified 45 %, while CARTO detected 42 % (κ ≈ 0.5, P < 0.0001). Our results show that CFAE target identification is dependent on the system and thresholds used by the electrophysiological study. The thresholds found in this work counterbalance the differences in automated CFAE classification performed by each system. This could facilitate comparisons of CFAE ablation outcomes guided by either NavX or CARTO in future works.Electronic supplementary materialThe online version of this article (doi:10.1007/s11517-016-1456-2) contains supplementary material, which is available to authorized users.
Multiple PS points drifting over the LA were identified with their clusters correlating spatially with the DF regions. After pulmonary vein isolation, the PS's complexity was reduced, which supports the notion that PS sites represent areas of relevance to the atrial substrate.
Electrocardiographic imaging (ECGI) is a technique to reconstruct non-invasively the electrical activity on the heart surface from body-surface potential recordings and geometric information of the torso and the heart. ECGI has shown scientific and clinical value when used to characterize and treat both atrial and ventricular arrhythmias. Regarding atrial fibrillation (AF), the characterization of the electrical propagation and the underlying substrate favoring AF is inherently more challenging than for ventricular arrhythmias, due to the progressive and heterogeneous nature of the disease and its manifestation, the small volume and wall thickness of the atria, and the relatively large role of microstructural abnormalities in AF. At the same time, ECGI has the advantage over other mapping technologies of allowing a global characterization of atrial electrical activity at every atrial beat and non-invasively. However, since ECGI is time-consuming and costly and the use of electrical mapping to guide AF ablation is still not fully established, the clinical value of ECGI for AF is still under assessment. Nonetheless, AF is known to be the manifestation of a complex interaction between electrical and structural abnormalities and therefore, true electro-anatomical-structural imaging may elucidate important key factors of AF development, progression, and treatment. Therefore, it is paramount to identify which clinical questions could be successfully addressed by ECGI when it comes to AF characterization and treatment, and which questions may be beyond its technical limitations. In this manuscript we review the questions that researchers have tried to address on the use of ECGI for AF characterization and treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance.
Atrial fibrillation (AF) is regarded as a complex arrhythmia, with one or more co-existing mechanisms, resulting in an intricate structure of atrial activations. Fractionated atrial electrograms (AEGs) were thought to represent arrhythmogenic tissue and hence have been suggested as targets for radiofrequency ablation. However, current methods for ablation target identification have resulted in suboptimal outcomes for persistent AF (persAF) treatment, possibly due to the complex spatiotemporal dynamics of these mechanisms. In the present work, we sought to characterize the dynamics of atrial tissue activations from AEGs collected during persAF using recurrence plots (RPs) and recurrence quantification analysis (RQA). 797 bipolar AEGs were collected from 18 persAF patients undergoing pulmonary vein isolation (PVI). Automated AEG classification (normal vs. fractionated) was performed using the CARTO criteria (Biosense Webster). For each AEG, RPs were evaluated in a phase space estimated following Takens' theorem. Seven RQA variables were obtained from the RPs: recurrence rate; determinism; average diagonal line length; Shannon entropy of diagonal length distribution; laminarity; trapping time; and Shannon entropy of vertical length distribution. The results show that the RQA variables were significantly affected by PVI, and that the variables were effective in discriminating normal vs. fractionated AEGs. Additionally, diagonal structures associated with deterministic behavior were still present in the RPs from fractionated AEGs, leading to a high residual determinism, which could be related to unstable periodic orbits and suggesting a possible chaotic behavior. Therefore, these results contribute to a nonlinear perspective of the spatiotemporal dynamics of persAF.
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