Rhythm Control in Heart Failure Patients With Atrial Fibrillation Contemporary Challenges Including the Role of Ablation

AbstractBecause nonpharmacological interventions likely alter the risks and benefits associated with rhythm control, this paper reviews the role of current rhythm control strategies in atrial fibrillation. This report also focuses on the specific limitations of pharmacological interventions and the utility of percutaneous ablation in this growing population of patients with concomitant atrial fibrillation and heart failure

Future Directions for Mapping Atrial Fibrillation

Mapping for AF focuses on the identification of regions of interest that may guide management and – in particular – ablation therapy. Mapping may point to specific mechanisms associated with localised scar or fibrosis, or electrical features, such as localised repetitive, rotational or focal activation. In patients in whom AF is caused by disorganised waves with no spatial predilection, as proposed in the multiwavelet theory for AF, mapping would be of less benefit. The role of AF mapping is controversial at the current time in view of the debate over the underlying mechanisms. However, recent clinical expansions of mapping technologies confirm the importance of understanding the state of the art, including limitations of current approaches and potential areas of future development

Predicting acute termination and non-termination during ablation of human atrial fibrillation using quantitative indices

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

Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy. Objective: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures. Methods: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data. Results: DL identified AF with AUC of 0.97 ± 0.04 (unipolar) and 0.92 ± 0.09 (bipolar). Rule-based classifiers misclassified ∼10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p 15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL < 190 ms (p < 0.001). Conclusions: Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF

Three dimensional reconstruction to visualize atrial fibrillation activation patterns on curved atrial geometry

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

Contribution of Left and Right Atrial Appendage Activities to ECG Fibrillation Waves

It was recently shown that atrial fibrillation (AF) waves on chest lead V1 adequately reflect right atrial appendage (RAA) activity during long standing persistent AF (pers-AF). The contribution of the left atrial (LA) activity to chest leads AF waves, however, remains unknown. Our study is aimed at evaluating the respective contribution of the RA and LA depolarization to ECG chest leads AF waves during pers-AF. Methods: Catheters (CAT) were introduced in 10 consecutive patients (60±5 y, AF duration 22±14 m) prior to ablation: 1) a quadripolar CAT in the RAA, 2) a decapolar CAT in the coronary sinus (CS) and 3) a duodecapolar CAT in the LA appendage (LAA). Local activation times were extracted from bipolar recordings using sliding windows. Chest lead V6 was placed in the back (V6b). Mean AF cycle length (AFCL) of leads V1 to V6b were computed as the inverse of the dominant frequency of ECG spectra after QRST cancellation, and compared to intracardiac RAA, LAA and CS AFCL using Pearson’s correlation coefficient. Results: The figure shows that the correlation between RAA and chest leads AFCL was maximal for V1 and progressively dropped till V5, with a moderate rise for V6b. LAA AFCL showed the opposite pattern with the highest correlation in V6B and the lowest one in V2. The correlation of CS AFCL was similar to the LAA one, but of lower magnitude. Conclusion: Our preliminary results suggest that the respective contribution of RAA and LAA activities can be estimated using a modified surface ECG. Whether this technique has the potential to guide ablation of LA and RA drivers in pers-AF needs further validation

Immediate and Delayed Response of Simulated Human Atrial Myocytes to Clinically-Relevant Hypokalemia

Although plasma electrolyte levels are quickly and precisely regulated in the mammalian cardiovascular system, even small transient changes in K+, Na+, Ca2+, and/or Mg2+ can significantly alter physiological responses in the heart, blood vessels, and intrinsic (intracardiac) autonomic nervous system. We have used mathematical models of the human atrial action potential (AP) to explore the electrophysiological mechanisms that underlie changes in resting potential (Vr) and the AP following decreases in plasma K+, [K+]o, that were selected to mimic clinical hypokalemia. Such changes may be associated with arrhythmias and are commonly encountered in patients (i) in therapy for hypertension and heart failure; (ii) undergoing renal dialysis; (iii) with any disease with acid-base imbalance; or (iv) post-operatively. Our study emphasizes clinically-relevant hypokalemic conditions, corresponding to [K+]o reductions of approximately 1.5 mM from the normal value of 4 to 4.5 mM. We show how the resulting electrophysiological responses in human atrial myocytes progress within two distinct time frames: (i) Immediately after [K+]o is reduced, the K+-sensing mechanism of the background inward rectifier current (IK1) responds. Specifically, its highly non-linear current-voltage relationship changes significantly as judged by the voltage dependence of its region of outward current. This rapidly alters, and sometimes even depolarizes, Vr and can also markedly prolong the final repolarization phase of the AP, thus modulating excitability and refractoriness. (ii) A second much slower electrophysiological response (developing 5–10 minutes after [K+]o is reduced) results from alterations in the intracellular electrolyte balance. A progressive shift in intracellular [Na+]i causes a change in the outward electrogenic current generated by the Na+/K+ pump, thereby modifying Vr and AP repolarization and changing the human atrial electrophysiological substrate. In this study, these two effects were investigated quantitatively, using seven published models of the human atrial AP. This highlighted the important role of IK1 rectification when analyzing both the mechanisms by which [K+]o regulates Vr and how the AP waveform may contribute to “trigger” mechanisms within the proarrhythmic substrate. Our simulations complement and extend previous studies aimed at understanding key factors by which decreases in [K+]o can produce effects that are known to promote atrial arrhythmias in human hearts

Morphological Study of Intracardiac Signals as a New Tool to Track the Efficiency of Stepwise Ablation of Persistent Atrial Fibrillation

Intracardiac organization indices such as atrial fibrillation (AF) cycle length (AFCL) have been used to track the efficiency of stepwise catheter ablation (step-CA) of longstanding persistent AF, however with limited success. The morphology of AF activation waves reflects the underlying activation patterns. Its temporal evolution is a local organization indicator that could be potentially used for tracking the efficiency of step-CA. We report a new method for characterizing the structure of the temporal evolution of activation wave morphology. Using recurrence plots, novel organization indices are proposed. By computing their relative evolution during the first step of ablation vs baseline, we found that these new parameters are superior to AFCL to track the effect of step-CA “en route” to AF termination

Non-invasive Spatial Mapping of Frequencies in Atrial Fibrillation: Correlation With Contact Mapping

Introduction: Regional differences in activation rates may contribute to the electrical substrates that maintain atrial fibrillation (AF), and estimating them non-invasively may help guide ablation or select anti-arrhythmic medications. We tested whether non-invasive assessment of regional AF rate accurately represents intracardiac recordings. Methods: In 47 patients with AF (27 persistent, age 63 ± 13 years) we performed 57-lead non-invasive Electrocardiographic Imaging (ECGI) in AF, simultaneously with 64-pole intracardiac signals of both atria. ECGI was reconstructed by Tikhonov regularization. We constructed personalized 3D AF rate distribution maps by Dominant Frequency (DF) analysis from intracardiac and non-invasive recordings. Results: Raw intracardiac and non-invasive DF differed substantially, by 0.54 Hz [0.13 - 1.37] across bi-atrial regions (R2 = 0.11). Filtering by high spectral organization reduced this difference to 0.10 Hz (cycle length difference of 1 - 11 ms) [0.03 - 0.42] for patient-level comparisons (R2 = 0.62), and 0.19 Hz [0.03 - 0.59] and 0.20 Hz [0.04 - 0.61] for median and highest DF, respectively. Non-invasive and highest DF predicted acute ablation success (p = 0.04). Conclusion: Non-invasive estimation of atrial activation rates is feasible and, when filtered by high spectral organization, provide a moderate estimate of intracardiac recording rates in AF. Non-invasive technology could be an effective tool to identify patients who may respond to AF ablation for personalized therapy