Modelling methodology of atrial fibrosis affects rotor dynamics and electrograms.

AIMS: Catheter ablation is an effective technique for terminating atrial arrhythmia. However, given a high atrial fibrillation (AF) recurrence rate, optimal ablation strategies have yet to be defined. Computer modelling can be a powerful aid but modelling of fibrosis, a major factor associated with AF, is an open question. Several groups have proposed methodologies based on imaging data, but no comparison to determine which methodology best corroborates clinically observed reentrant behaviour has been performed. We examined several methodologies to determine the best method for capturing fibrillation dynamics. METHODS AND RESULTS: Patient late gadolinium-enhanced magnetic resonance imaging data were transferred onto a bilayer atrial computer model and used to assign fibrosis distributions. Fibrosis was modelled as conduction disturbances (lower conductivity, edge splitting, or percolation), transforming growth factor-ß1 ionic channel effects, myocyte-fibroblast coupling, and combinations of the preceding. Reentry was induced through pulmonary vein ectopy and the ensuing rotor dynamics characterized. Non-invasive electrocardiographic imaging data of the patients in AF was used for comparison. Electrograms were computed and the fractionation durations measured over the surface. Edge splitting produced more phase singularities from wavebreaks than the other representations. The number of phase singularities seen with percolation was closer to the clinical values. Addition of fibroblast coupling had an organizing effect on rotor dynamics. Simple tissue conductivity changes with ionic changes localized rotors over fibrosis which was not observed with clinical data. CONCLUSION: The specific representation of fibrosis has a large effect on rotor dynamics and needs to be carefully considered for patient specific modelling.

Atrial fibrillation fingerprinting; spotting bio-electrical markers to early recognize atrial fibrillation by the use of a bottom-up approach (AFFIP): Rationale and design.

BACKGROUND: The exact pathophysiology of atrial fibrillation (AF) remains incompletely understood and treatment of AF is associated with high recurrence rates. Persistence of AF is rooted in the presence of electropathology, defined as complex electrical conduction disorders caused by structural damage of atrial tissue. The atrial fibrillation fingerprinting (AFFIP) study aims to characterize electropathology, enabling development of a novel diagnostic instrument to predict AF onset and early progression. HYPOTHESES: History of AF, development of post-operative AF, age, gender, underlying heart disease, and other clinical characteristics impact the degree of electropathology. METHODS: This study is a prospective observational study with a planned duration of 48 months. Three study groups are defined: (1) patients with (longstanding) persistent AF, (2) patients with paroxysmal AF, and (3) patients without a history of AF, all undergoing open-chest cardiac surgery. Intra-operative high-resolution epicardial mapping is performed to identify the patient-specific electrical profile, whereas the patient-specific biological profile is assessed by evaluating proteostasis markers in blood samples and atrial appendage tissue samples. Post-operative continuous rhythm monitoring is performed for detection of early post-operative AF. Late post-operative AF (during 5-year follow-up) is documented by either electrocardiogram or 24-hour Holter registration. RESULTS: The required sample size for this study is estimated at 447 patients. Up till now, 105 patients were included, of whom 36 have a history of AF. CONCLUSION: The AFFIP study will elucidate whether electrophysiological and structural characteristics represent a novel diagnostic tool, the AF fingerprint, to predict onset and early progression of AF in cardiac surgery patients.

Inhomogeneity and complexity in defining fractionated electrograms.

Ablation strategies targeting areas of complex fractionated atrial electrograms are not successful for treatment of atrial fibrillation. Fractionation of atrial electrograms may have multiple causes of both pathologic and nonpathologic origin. In order to gain insight into the definitions used for determining areas of fractionation, a literature search was performed using a systematic approach. A PubMed search for studies describing fractionation during human atrial electrophysiologic measurements resulted in 348 articles that were screened for new definitions of fractionation. The 24 studies remaining after screening described 11 different visual definitions for fractionation, 3 automated complex fractionated atrial electrogram detection programs, and 7 new parameters for measuring fractionation. Five different definitions for continuous electrical activity were presented. Electrode properties were often not described, and endocardial bipolar recordings in recent studies used electrode diameters ranging from 1 to 8 mm with interelectrode distance of 2-5 mm. In summary, no uniform definition or recording method is used for measuring fractionation of cardiac atrial electrograms. The different electrophysiologic causes of fractionation and the influence of recording device properties on fractionation complicate identification of true pathologic inhomogeneous conduction. The first step in discrimination between origins of fractionation may be accomplished by relating electrogram morphology to spatial patterns of activation. Before revisiting ablation of areas with fractionated electrograms, we need to determine the correct method for identifying pathologic fractionation.

Spatiotemporal characteristics of atrial fibrillation electrograms: A novel marker for arrhythmia stability and termination.

BACKGROUND: Sequentially mapped complex fractionated atrial electrograms (CFAE) and dominant frequency (DF) sites have been targeted during catheter ablation for atrial fibrillation (AF). However, these strategies have yielded variable success and have not been shown to correlate consistently with AF dynamics. Here, we evaluated whether the spatiotemporal stability of CFAE and DF may be a better marker of AF sustenance and termination. METHODS: Eighteen sheep with 12 weeks of "one-kidney, one-clip" hypertension underwent open-chest studies. A total of 42 self-terminating (28-100 s) and 6 sustained (>15 min) AF episodes were mapped using a custom epicardial plaque and analyzed in 4-s epochs for CFAE, using the NavX CFE-m algorithm, and DF, using a Fast Fourier Transform. The spatiotemporal stability index (STSI) was calculated using the intraclass correlation coefficient of consecutive AF epochs. RESULTS: A total of 67,733 AF epochs were analyzed. During AF initiation, mean CFE-m and the STSI of CFE-m/DF were similar between sustained and self-terminating episodes, although median DF was higher in sustained AF (p=0.001). During sustained AF, the STSI of CFE-m increased significantly (p=0.02), whereas mean CFE-m (p=0.5), median DF (p=0.07), and the STSI of DF remained unchanged (p=0.5). Prior to AF termination, the STSI of CFE-m was significantly lower (p<0.001), with a physiologically non-significant decrease in median DF (-0.3 Hz, p=0.006) and no significant changes in mean CFE-m (p=0.14) or the STSI of DF (p=0.06). CONCLUSIONS: Spatiotemporal stabilization of CFAE favors AF sustenance and its destabilization heralds AF termination. The STSI of CFE-m is more representative of AF dynamics than are the STSI of DF, sequential mean CFE-m, or median DF.

Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation.

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.

Indices of bipolar complex fractionated atrial electrograms correlate poorly with each other and atrial fibrillation substrate complexity.

BACKGROUND: The pathophysiological relevance of complex fractionated atrial electrograms (CFAE) in atrial fibrillation (AF) remains poorly understood. OBJECTIVE: The aim of this study was to comprehensively investigate how bipolar CFAE correlates with unipolar electrogram fractionation and the underlying electrophysiological substrate of AF. METHODS: Ten-second unipolar AF electrograms were recorded using a high-density electrode from the left atrium of 20 patients with AF (10 with persistent AF and 10 with paroxysmal AF) undergoing cardiac surgery. Semiautomated bipolar CFAE algorithms: complex fractionated electrogram-mean, interval confidence interval, continuous electrical activity, average complex interval, and shortest complex interval were evaluated against AF substrate complexity measures following fibrillation wave reconstruction derived from local unipolar activation time. The effect of interelectrode spacing and electrode orientation on bipolar CFAE was also examined. RESULTS: All 5 semiautomated bipolar CFAE algorithms showed poor correlation with each other and AF substrate complexity measures (conduction velocity, number of waves or breakthroughs per AF cycle, and electrical dissociation). Bipolar CFAE also correlated poorly with fractionation index derived from unipolar electrograms. Increased interelectrode spacing resulted in an increase in bipolar CFAE detected except for the interval confidence interval algorithm. CFAE appears unaffected by bipolar electrode orientation (vertical vs horizontal). By contrast, unipolar fractionation index correlated well with AF substrate complexity measures and can be regarded as a marker for conduction block. CONCLUSION: The lack of pathophysiological relevance of bipolar CFAE analysis may in part contribute to the divergent and limited success rates of catheter ablation strategies targeting CFAE.

Inverse relationship between fractionated electrograms and atrial fibrosis in persistent atrial fibrillation: combined magnetic resonance imaging and high-density mapping.

OBJECTIVES: This study sought to evaluate the relationship between fibrosis imaged by delayed-enhancement (DE) magnetic resonance imaging (MRI) and atrial electrograms (Egms) in persistent atrial fibrillation (AF). BACKGROUND: Atrial fractionated Egms are strongly related to slow anisotropic conduction. Their relationship to atrial fibrosis has not yet been investigated. METHODS: Atrial high-resolution MRI of 18 patients with persistent AF (11 long-lasting persistent AF) was registered with mapping geometry (NavX electro-anatomical system (version 8.0, St. Jude Medical, St. Paul, Minnesota)). DE areas were categorized as dense or patchy, depending on their DE content. Left atrial Egms during AF were acquired using a high-density, 20-pole catheter (514 ± 77 sites/map). Fractionation, organization/regularity, local mean cycle length (CL), and voltage were analyzed with regard to DE. RESULTS: Patients with long-lasting persistent versus persistent AF had larger left atrial (LA) surface area (134 ± 38 cm(2) vs. 98 ± 9 cm(2), p = 0.02), a higher amount of atrial DE (70 ± 16 cm(2) vs. 49 ± 10 cm(2), p = 0.01), more complex fractionated atrial Egm (CFAE) extent (54 ± 16 cm(2) vs. 28 ± 15 cm(2), p = 0.02), and a shorter baseline AF CL (147 ± 10 ms vs. 182 ± 14 ms, p = 0.01). Continuous CFAE (CFEmean [NavX algorithm that quantifies Egm fractionation] <80 ms) occupied 38 ± 19% of total LA surface area. Dense DE was detected at the left posterior left atrium. In contrast, the right posterior left atrium contained predominantly patchy DE. Most CFAE (48 ± 14%) occurred at non-DE LA sites, followed by 41 ± 12% CFAE at patchy DE and 11 ± 6% at dense DE regions (p = 0.005 and p = 0.008, respectively); 19 ± 6% CFAE sites occurred at border zones of dense DE. Egms were less fractionated, with longer CL and lower voltage at dense DE versus non-DE regions: CFEmean: 97 ms versus 76 ms, p < 0.0001; local CL: 153 ms versus 143 ms, p < 0.0001; mean voltage: 0.63 mV versus 0.86 mV, p < 0.0001. CONCLUSIONS: Atrial fibrosis as defined by DE MRI is associated with slower and more organized electrical activity but with lower voltage than healthy atrial areas. Ninety percent of continuous CFAE sites occur at non-DE and patchy DE LA sites. These findings are important when choosing the ablation strategy in persistent AF.