Useful Electrocardiographic Features to Help Identify the Mechanism of Atrial Tachycardia Occurring After Persistent Atrial Fibrillation Ablation

Detailed analysis of tachycardia cycle length aids diagnosis of the mechanism and location of atrial tachycardias

AIMS

Although the mechanism of an atrial tachycardia (AT) can usually be elucidated using modern high-resolution mapping systems, it would be helpful if the AT mechanism and circuit could be predicted before initiating mapping.

OBJECTIVE

We examined if the information gathered from the cycle length (CL) of the tachycardia can help predict the AT-mechanism and its localization.

METHODS

One hundred and thirty-eight activation maps of ATs including eight focal-ATs, 94 macroreentrant-ATs, and 36 localized-ATs in 95 patients were retrospectively reviewed. Maximal CL (MCL) and minimal CL (mCL) over a minute period were measured via a decapolar catheter in the coronary sinus. CL-variation and beat-by-beat CL-alternation were examined. Additionally, the CL-respiration correlation was analysed by the RhythmiaTM system. : Both MCL and mCL were significantly shorter in macroreentrant-ATs [MCL = 288 (253-348) ms, P = 0.0001; mCL = 283 (243-341) ms, P = 0.0012], and also shorter in localized-ATs [MCL = 314 (261-349) ms, P = 0.0016; mCL = 295 (248-340) ms, P = 0.0047] compared to focal-ATs [MCL = 506 (421-555) ms, mCL = 427 (347-508) ms]. An absolute CL-variation (MCL-mCL) < 24 ms significantly differentiated re-entrant ATs from focal-ATs with a sensitivity = 96.9%, specificity = 100%, positive predictive value (PPV) = 100%, and negative predictive value (NPV) = 66.7%. The beat-by-beat CL-alternation was observed in 10/138 (7.2%), all of which showed the re-entrant mechanism, meaning that beat-by-beat CL-alternation was the strong sign of re-entrant mechanism (PPV = 100%). Although the CL-respiration correlation was observed in 28/138 (20.3%) of ATs, this was predominantly in right-atrium (RA)-ATs (24/41, 85.7%), rather than left atrium (LA)-ATs (4/97, 4.1%). A positive CL-respiration correlation highly predicted RA-ATs (PPV = 85.7%), and negative CL-respiration correlation probably suggested LA-ATs (NPV = 84.5%).

CONCLUSION

Detailed analysis of the tachycardia CL helps predict the AT-mechanism and the active AT chamber before an initial mapping.

Artificial intelligence-enabled electrocardiogram to distinguish cavotricuspid isthmus dependence from other atrial tachycardia mechanisms. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022;3:405-414. [PMID: 36712163 PMCID: PMC9708023 DOI: 10.1093/ehjdh/ztac042]

Aims

Accurately determining atrial arrhythmia mechanisms from a 12-lead electrocardiogram (ECG) can be challenging. Given the high success rate of cavotricuspid isthmus (CTI) ablation, identification of CTI-dependent typical atrial flutter (AFL) is important for treatment decisions and procedure planning. We sought to train a convolutional neural network (CNN) to classify CTI-dependent AFL vs. non-CTI dependent atrial tachycardia (AT), using data from the invasive electrophysiology (EP) study as the gold standard.

Methods and results

We trained a CNN on data from 231 patients undergoing EP studies for atrial tachyarrhythmia. A total of 13 500 five-second 12-lead ECG segments were used for training. Each case was labelled CTI-dependent AFL or non-CTI-dependent AT based on the findings of the EP study. The model performance was evaluated against a test set of 57 patients. A survey of electrophysiologists in Europe was undertaken on the same 57 ECGs. The model had an accuracy of 86% (95% CI 0.77-0.95) compared to median expert electrophysiologist accuracy of 79% (range 70-84%). In the two thirds of test set cases (38/57) where both the model and electrophysiologist consensus were in agreement, the prediction accuracy was 100%. Saliency mapping demonstrated atrial activation was the most important segment of the ECG for determining model output.

Conclusion

We describe the first CNN trained to differentiate CTI-dependent AFL from other AT using the ECG. Our model matched and complemented expert electrophysiologist performance. Automated artificial intelligence-enhanced ECG analysis could help guide treatment decisions and plan ablation procedures for patients with organized atrial arrhythmias.

Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram

Aims

Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG).

Methods and results

Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients—three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%.

Conclusion

Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments.

Mapping and ablation of post-AF atrial tachycardias

Atrial tachycardias are commonly encountered in patients undergoing catheter ablation of persistent atrial fibrillation (AF). Unlike typical atrial flutter that can be readily recognized and ablated, these post-AF tachycardias can arise from a wide variety of locations and involve a multiplicity of mechanisms. Apart from diagnostic challenges, radiofrequency ablation to eliminate the tachycardias may require multiple approaches. In addition, specialized techniques such as epicardial and chemical ablation may be required for definitive treatment. This review describes the various mechanisms and approaches to mapping and ablation of these challenging tachycardias.

Atrial Tachycardias After Atrial Fibrillation Ablation: How to Manage?

With catheter ablation becoming effective for non-pharmacological management of AF, many cases of atrial tachycardia (AT) after AF ablation have been reported in the past decade. These arrhythmias are often symptomatic and respond poorly to medical therapy. Post-AF-ablation ATs can be classified into the following three categories: focal, macroreentrant and microreentrant ATs. Mapping these ATs is challenging because of atrial remodelling and its complex mechanisms, such as double ATs and multiple-loop ATs. High-density mapping can achieve precise identification of the circuits and critical isthmuses of ATs and improve the efficacy of catheter ablation. The purpose of this article is to review the mechanisms, mapping and ablation strategy, and outcome of ATs after AF ablation.

Noninvasive localization of cardiac arrhythmias using electromechanical wave imaging

Cardiac arrhythmias are a major cause of morbidity and mortality worldwide. The 12-lead electrocardiogram (ECG) is the current noninvasive clinical tool used to diagnose and localize cardiac arrhythmias. However, it has limited accuracy and is subject to operator bias. Here, we present electromechanical wave imaging (EWI), a high-frame rate ultrasound technique that can noninvasively map with high accuracy the electromechanical activation of atrial and ventricular arrhythmias in adult patients. This study evaluates the accuracy of EWI for localization of various arrhythmias in all four chambers of the heart before catheter ablation. Fifty-five patients with an accessory pathway (AP) with Wolff-Parkinson-White (WPW) syndrome, premature ventricular complexes (PVCs), atrial tachycardia (AT), or atrial flutter (AFL) underwent transthoracic EWI and 12-lead ECG. Three-dimensional (3D) rendered EWI isochrones and 12-lead ECG predictions by six electrophysiologists were applied to a standardized segmented cardiac model and subsequently compared to the region of successful ablation on 3D electroanatomical maps generated by invasive catheter mapping. There was significant interobserver variability among 12-lead ECG reads by expert electrophysiologists. EWI correctly predicted 96% of arrhythmia locations as compared with 71% for 12-lead ECG analyses [unadjusted for arrhythmia type: odds ratio (OR), 11.8; 95% confidence interval (CI), 2.2 to 63.2; P = 0.004; adjusted for arrhythmia type: OR, 12.1; 95% CI, 2.3 to 63.2; P = 0.003]. This double-blinded clinical study demonstrates that EWI can localize atrial and ventricular arrhythmias including WPW, PVC, AT, and AFL. EWI when used with ECG may allow for improved treatment for patients with arrhythmias.

Overdrive pacing mapping: An alternative approach used in scar associated localized atrial tachycardia

BACKGROUND

Mapping and ablation of localized reentry atrial tachycardia (AT) can be challenging, especially in those with varying cycle length (CL).

OBJECTIVE

We attempted to use the traditional maneuver of overdrive pacing to facilitate AT mapping.

METHODS

Data were collected from 12 patients with localized ATs. All patients had prior cardiac surgery or prior atrial fibrillation ablation. Overdrive pacing mapping (ODPM) was performed to find independent local activity (ILA) and compared with conventional activation mapping (CAM) during ongoing AT to determine its accuracy and efficacy. Patients with macro-reentry AT around the tricuspid or mitral annulus were excluded.

RESULTS

Twelve patients with 14 localized ATs were included. All 14 ATs including 4 (29%) with varying CL successfully completed ODPM and had the ILA, although two ATs terminated during ODP and required repeated mapping. Radiofrequency ablation focused on critical sites with ILA was successful in all 12 patients. Using CAM, however, 6 of 14 ATs (43%) mapping attempts were aborted due to AT termination (2 ATs) or varying CL (4 ATs), and only 5 of 8 (63%) located "critical sites" were ultimately confirmed by entrainment and ablation results. After 25 ± 9 months of follow-up, no patient had AT recurrence.

CONCLUSION

Our preliminary results demonstrated that ODPM is superior to CAM in ATs that were poorly sustained or with varying CL and is a useful supplement to CAM.

Atrial Tachycardias and Atypical Atrial Flutters: Mechanisms and Approaches to Ablation

Atrial tachycardias (ATs) may be classified into three broad categories: focal ATs, macroreentry and localised reentry – also known as ‘microreentry’. Features that distinguish these AT mechanisms include electrogram characteristics, responses to entrainment and pharmacological sensitivities. Focal ATs may occur in structurally normal hearts but can also occur in patients with structural heart disease. These typically arise from preferential sites such as the valve annuli, crista terminalis and pulmonary veins. Macro-reentrant ATs occur in the setting of atrial fibrosis, often after prior catheter ablation or post atriotomy, but also de novo in patients with atrial myopathy. High-resolution mapping techniques have defined details of macro-reentrant circuits, including zones of conduction block, scar and slow conduction. Localised reentry occurs in the setting of diseased atrial myocardium that supports very slow conduction. A characteristic feature of localised reentry is highly fractionated, low-amplitude electrograms that encompass most of the tachycardia cycle length over a small diameter. Advances in understanding the mechanisms of ATs and their signature electrogram characteristics have improved the efficacy and efficiency of catheter ablation.

Pulmonary vein-gap re-entrant atrial tachycardia following atrial fibrillation ablation: an electrophysiological insight with high-resolution mapping

Aims

The circuit of pulmonary vein-gap re-entrant atrial tachycardia (PV-gap RAT) after atrial fibrillation ablation is sometimes difficult to identify by conventional mapping. We analysed the detailed circuit and electrophysiological features of PV-gap RATs using a novel high-resolution mapping system.

Methods and results

This multicentre study investigated 27 (7%) PV-gap RATs in 26 patients among 378 atrial tachycardias (ATs) mapped with Rhythmia™ system in 281 patients. The tachycardia cycle length (TCL) was 258 ± 52 ms with P-wave duration of 116 ± 28 ms. Three types of PV-gap RAT circuits were identified: (A) two gaps in one pulmonary vein (PV) (unilateral circuit) (n = 17); (B) two gaps in the ipsilateral superior and inferior PVs (unilateral circuit) (n = 6); and (C) two gaps in one PV with a large circuit around contralateral PVs (bilateral circuit) (n = 4). Rhythmia™ mapping demonstrated two distinctive entrance and exit gaps of 7.6 ± 2.5 and 7.9 ± 4.1 mm in width, respectively, the local signals of which showed slow conduction (0.14 ± 0.18 and 0.11 ± 0.10m/s) with fragmentation (duration 86 ± 27 and 78 ± 23 ms) and low-voltage (0.17 ± 0.13 and 0.17 ± 0.21 mV). Twenty-two ATs were terminated (mechanical bump in one) and five were changed by the first radiofrequency application at the entrance or exit gap. Moreover, the conduction time inside the PVs (entrance-to-exit) was 138 ± 60 ms (54 ± 22% of TCL); in all cases, this resulted in demonstrating P-wave with an isoelectric line in all leads.

Conclusion

This is the first report to demonstrate the detailed mechanisms of PV-gap re-entry that showed evident entrance and exit gaps using a high-resolution mapping system. The circuits were variable and Rhythmia™-guided ablation targeting the PV-gap can be curative.

Coronary Sinus Activation and ECG Characteristics of Roof-Dependent Left Atrial Flutter After Pulmonary Vein Isolation

BACKGROUND

The electrocardiographic and intracardiac activation features of left atrial roof-dependent macroreentrant flutter have been incompletely characterized.

METHODS

Patients post-pulmonary vein (PV) isolation with roof-dependent atrial flutter based on activation and entrainment mapping were included. ECG and coronary sinus activation were compared with mitral annular (MA) flutter.

RESULTS

The roof-dependent left atrial flutter circled the right PVs in 32 of 33 cases. Two forms of roof flutters were identified, posteroanterior, ascendant on posterior wall and descendant on anterior wall (n=24); and anteroposterior, ascendant on the anterior wall and descendent on the posterior wall (n=9). Both forms had positive large amplitude P waves in V₁ through V₂ with decreasing amplitude in V₃ through V₆. Posteroanterior roof flutters had positive P wave in the inferior and negative P wave in leads I and aVL similar to counterclockwise MA flutter, but coronary sinus activation was simultaneous for roof and proximal to distal for counterclockwise. Anteroposterior roof flutters were similar to clockwise MA flutter with negative P in inferior leads and transition to flat or negative P in V₃ through V₆. Coronary sinus activation time ≤39 ms identified roof versus MA flutter (sensitivity: 100% and specificity: 97%).

CONCLUSIONS

Roof-dependent flutter around right PVs is more common than around left PVs. The ECG pattern for roof-dependent flutter around right PVs is similar to MA flutter with frontal plane axis dictated by septal activation. Roof-dependent flutter can be distinguished from MA flutter by more simultaneous rather than sequential coronary sinus activation.