Focal Impulse And Rotor Mapping (FIRM): Conceptualizing And Treating Atrial Fibrillation

Catheter Ablation of Persistent AF-Where are We Now?

Persistent atrial fibrillation (AF) is a diverse condition that includes various subtypes and underlying causes of arrhythmia. Progress made in catheter ablation technology in recent years has significantly enhanced the durability of ablation. Despite these advances however, the effectiveness of ablation in treating persistent AF is still relatively modest. Studies exploring the mechanisms behind persistent AF have identified substrate-driven focal and re-entrant sources within the atrial body as crucial in sustaining AF among individuals with persistent AF. Furthermore, the widespread adoption of atrial late gadolinium enhancement cardiac magnetic resonance (CMR) imaging and the ongoing refinement of invasive voltage mapping techniques have allowed for detailed assessment of fibrotic remodelling prior to or at the time of procedure. Translation into clinical practice, however, has yielded overall disappointing results. The clinical application of AF mapping in ablation procedures has not shown any substantial advantages beyond the use of pulmonary vein isolation (PVI) alone and adjunct ablation of fibrotic areas has yielded conflicting results in recent randomized trials. The emergence of pulsed field ablation represents a welcome development in the field and several studies have demonstrated an enhanced safety profile and increased procedural efficiency with this non-thermal energy modality. Pulsed field ablation also holds promise for safe and efficient substrate ablation beyond the pulmonary veins, but further trials are needed to assess its impact on longer term success rates. Continued advancements in our comprehension of AF mechanisms, alongside ongoing developments in catheter technology aimed at safe formation of transmural lesions, are essential for achieving better clinical outcomes for patients with persistent AF.

Catheter Ablation in Persistent AF, the Evolution towards a More Pragmatic Strategy

Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide and represents a heterogeneous disorder with a complex pathological basis. While significant technological advances have taken place over the last decade in the field of catheter ablation of AF, response to ablation varies and long-term success rates in those with persistent AF remain modest. Mechanistic studies have highlighted potentially different sustaining factors for AF in the persistent AF population with substrate-driven focal and re-entrant sources in the body of the atria identified on invasive and non-invasive mapping studies. Translation to clinical practice, however, remains challenging and the application of such mapping techniques to clinical ablation has yet to demonstrate a significant benefit beyond pulmonary vein isolation (PVI) alone in the persistent AF cohort. Recent advances in catheter and ablation technology have centered on improving the durability of ablation lesions at index procedure and although encouraging results have been demonstrated with early studies, large-scale trials are awaited. Further meaningful improvement in clinical outcomes in the persistent AF population requires ongoing advancement in the understanding of AF mechanisms, coupled with continuing progress in catheter technology capable of delivering durable transmural lesions.

Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG

BACKGROUND

Atrial fibrillation (AF) is the most common supraventricular arrhythmia, characterized by disorganized atrial electrical activity, maintained by localized arrhythmogenic atrial drivers. Pulmonary vein isolation (PVI) allows to exclude PV-related drivers. However, PVI is less effective in patients with additional extra-PV arrhythmogenic drivers.

OBJECTIVES

To discriminate whether AF drivers are located near the PVs vs extra-PV regions using the noninvasive 12-lead electrocardiogram (ECG) in a computational and clinical framework, and to computationally predict the acute success of PVI in these cohorts of data.

METHODS

AF drivers were induced in 2 computerized atrial models and combined with 8 torso models, resulting in 1128 12-lead ECGs (80 ECGs with AF drivers located in the PVs and 1048 in extra-PV areas). A total of 103 features were extracted from the signals. Binary decision tree classifier was trained on the simulated data and evaluated using hold-out cross-validation. The PVs were subsequently isolated in the models to assess PVI success. Finally, the classifier was tested on a clinical dataset (46 patients: 23 PV-dependent AF and 23 with additional extra-PV sources).

RESULTS

The classifier yielded 82.6% specificity and 73.9% sensitivity for detecting PV drivers on the clinical data. Consistency analysis on the 46 patients resulted in 93.5% results match. Applying PVI on the simulated AF cases terminated AF in 100% of the cases in the PV class.

CONCLUSION

Machine learning-based classification of 12-lead-ECG allows discrimination between patients with PV drivers vs those with extra-PV drivers of AF. The novel algorithm may aid to identify patients with high acute success rates to PVI.

The Impact of Filter Settings on Morphology of Unipolar Fibrillation Potentials

Using unipolar atrial electrogram morphology as guidance for ablative therapy is regaining interest. Although standardly used in clinical practice during ablative therapy, the impact of filter settings on morphology of unipolar AF potentials is unknown. Thirty different filters were applied to 2,557,045 high-resolution epicardial AF potentials recorded from ten patients. Deflections with slope ≤ - 0.05 mV/ms and amplitude ≥ 0.3 mV were marked. High-pass filtering decreased the number of detected potentials, deflection amplitude, and percentage of fractionated potentials (≥ 2 deflections) as well as fractionation delay time (FDT) and increased percentage of single potentials. Low-pass filtering decreased the number of potentials, percentage of fractionated potentials, whereas deflection amplitude, percentage of single potentials, and FDT increased. Notch filtering (50 Hz) decreased the number of potentials and deflection amplitude, whereas the percentage of complex fractionated potentials (≥ 3 deflections) increased. Filtering significantly impacted morphology of unipolar fibrillation potentials, becoming a potential source of error in identification of ablative targets. Graphical Abstract Impact of filtering on morphology of unipolar AF potentials. High-pass, low-pass and notch filters were applied to 2,557,045 high-resolution epicardial AF potentials recorded from ten patients. Filtering significantly impacted AF potential morphology, i.e., number of detected potentials, peak-to-peak amplitude, number of deflections, and fractionation delay time. CFP, complex fractionated potential (≥ 3 deflections); DP, double potential (two deflections); FDT, fractionation delay time; SP, single potential (one deflection).

Atrial fibrillation observed on surface ECG can be atrial flutter or atrial tachycardia

BACKGROUND

Differentiating between atrial fibrillation (AF) and atrial tachycardia (AT) or atrial flutter (AFL) on surface ECG can be challenging. The same problem arises in animal models of AF, in which atrial arrhythmias are induced by pacing or pharmacological intervention with the goal of making mechanistic determinations. Some of these induced arrhythmias can be AFL or AT, even though it might appear as AF on the body-surface ECG based on irregular R-R intervals. We hypothesize that a dominant frequency (DF) analysis of the ECG can differentiate between the two distinct arrhythmias, even when it is not evident by the presence of flutter waves or beat-to-beat regularity when looking at brief recordings.

METHODS

Canine model (n = 15, 10 controls and 5 Persistent AF animals with >6 months of AF) was used to test the hypothesis. Atrial arrhythmia was induced by rapid atrial pacing. Five blinded observers evaluated the 3‑lead surface ECGs recorded during atrial arrhythmia and classified the rhythm as AFL/AT or AF. The 64-electrode Constellation (Boston Scientific) catheter was used to acquire left (entire group) and right (7 of 10 controls) atrial intracardiac electrograms. For the surface ECG and the intracardiac electrograms, Welch method with a hamming window and 50% overlap was used to calculate DF of two-minute segments. Mean of standard deviations of the DF values were calculated for both ECGs and intracardiac EGMs. Ground truth came from activations maps and DF analysis derived from the intracardiac electrograms recorded in the two chambers.

RESULTS

Rapid pacing induced atrial arrhythmias in all the control animals. The ECG in 8 of the 10 control cases was read as AF by at least 80% percent of observers even though the EGMs from the Constellation showed organized activation and consistent DF (STD of DF < 0.001) in all the electrodes confirming the arrhythmia as AFL in 10/10 cases. In the persistent AF group, the DF from the three lead ECGs were significantly different (Mean of STDs = 2.65 ± 0.99) whereas the DF in the control animals with AFL was consistent across all ECG channels (Mean of STDs < 0.001), and the DF in the control animals ECGs was in agreement with the DF of the intracardiac electrograms.

CONCLUSION

Surface ECG recordings can mimic AF even when the underlying atrial arrhythmia is AFL in control canine models. DF variation of the signals from multiple surface ECG leads can help differentiate between the AF and AFL.