The His-Purkinje system is a network of bundles and fibres comprised of specialised cells that allow for coordinated, synchronous activation of the ventricles. Although the histology and physiology of the His-Purkinje system have been studied for more than a century, its role in ventricular arrhythmias has recently been discovered with the ongoing elucidation of the mechanisms leading to both benign and life-threatening arrhythmias. Studies of Purkinje-cell electrophysiology show multiple mechanisms responsible for ventricular arrhythmias, including enhanced automaticity, triggered activity and reentry. The variation in functional properties of Purkinje cells in different areas of the His-Purkinje system underlie the propensity for reentry within Purkinje fibres in structurally normal and abnormal hearts. Catheter ablation is an effective therapy in nearly all forms of reentrant arrhythmias involving Purkinje tissue. However, identifying those at risk of developing fascicular arrhythmias is not yet possible. Future research is needed to understand the precise molecular and functional changes resulting in these arrhythmias.

Supraventricular tachycardia (SVT) with ventriculoatrial (VA) block can represent a diagnostic challenge. We present a case of SVT where His-His interval shortening was repeatedly observed during episodes of VA block. This novel observation is more diagnostically suggestive of atrioventricular nodal re-entrant tachycardia, as opposed to orthodromic re-entry using a nodofascicular or nodoventricular pathway where a constant His-His is recorded during episodes of VA block. (Level of Difficulty: Intermediate.).

Introduction

The surface electrocardiography of typical atrioventricular nodal reentrant tachycardia (AVNRT) shows simultaneous ventricular-atrial (RP) activation with pseudo R' in V1 and typical heart rates ranging from 150 to 220/min. Slower rates are suspicious for junctional tachycardia (JT). However, occasionally we encounter typical AVNRT with slow ventricular rates. We describe a series of typical AVNRT cases with heart rates under 110/min.

Methods

A total of 1972 patients with AVNRT who underwent slow pathway ablation were analyzed. Typical AVNRT was diagnosed when; (1) evidence of dual atrioventricular nodal conduction, (2) tachycardia initiation by atrial drive train with atrial-His-atrial response, (3) short septal ventriculoatrial time, and (4) ventricular-atrial-ventricular (V-A-V) response to ventricular overdrive (VOD) pacing with corrected post pacing interval-tachycardia cycle length (cPPI-TCL) > 110 ms. JT was excluded by either termination or advancement of tachycardia by atrial extrastimuli (AES) or atrial overdrive (AOD) pacing.

Results

We found 11 patients (age 20-78 years old, six female) who met the above-mentioned criteria. The TCL ranged from 560 to 782 ms. Except for one patient showing tachycardia termination, all patients demonstrated a V-A-V response and cPPI-TCL over 110 ms with VOD. AES or AOD pacing successfully excluded JT by either advancing the tachycardia in 10 patients or by tachycardia termination in one patient. Slow pathway was successfully ablated, and tachycardia was not inducible in all patients.

Conclusions

This case series describes patients with typical AVNRT with slow ventricular rate (less than 110/min) who may mimic JT. We emphasize the importance of using pacing maneuvers to exclude JT.

BACKGROUND: It remains difficult to definitively distinguish supraventricular tachycardia (SVT) mechanisms using a 12-lead electrocardiogram (ECG) alone. Machine learning may identify visually imperceptible changes on 12-lead ECGs and may improve ability to determine SVT mechanisms. OBJECTIVE: We sought to develop a convolutional neural network (CNN) that identifies the SVT mechanism according to the gold standard of SVT ablation and to compare CNN performance against experienced electrophysiologists among patients with atrioventricular nodal re-entrant tachycardia (AVNRT), atrioventricular reciprocating tachycardia (AVRT), and atrial tachycardia (AT). METHODS: All patients with 12-lead surface ECG during sinus rhythm and SVT and had successful SVT ablation from 2013 to 2020 were included. A CNN was trained using data from 1505 surface ECGs that were split into 1287 training and 218 test ECG datasets. We compared the CNN performance against independent adjudication by 2 experienced cardiac electrophysiologists on the test dataset. RESULTS: Our dataset comprised 1505 ECGs (368 AVNRT, 304 AVRT, 95 AT, and 738 sinus rhythm) from 725 patients. The CNN areas under the receiver-operating characteristic curve for AVNRT, AVRT, and AT were 0.909, 0.867, and 0.817, respectively. When fixing the specificity of the CNN to the electrophysiologist adjudicators specificity, the CNN identified all SVT classes with higher sensitivity: (1) AVNRT (91.7% vs 65.9%), (2) AVRT (78.4% vs 63.6%), and (3) AT (61.5% vs 50.0%). CONCLUSION: A CNN can be trained to differentiate SVT mechanisms from surface 12-lead ECGs with high overall performance, achieving similar performance to experienced electrophysiologists at fixed specificities.

Background

The short-coupled variant of torsade de pointes (sc-TdP) is a malignant arrhythmia that frequently presents with ventricular fibrillation (VF) electrical storm. Verapamil is considered the first-line therapy of sc-TdP while catheter ablation is not widely adopted. The aim of this study was to determine the origin of sc-TdP and to assess the outcome of catheter ablation using 3D-mapping.

Methods and results

We retrospectively analyzed five patients with sc-TdP who underwent 3D-mapping and ablation of sc-TdP at five different institutions. Four patients initially presented with sudden cardiac arrest, one patient experienced recurrent syncope as the first manifestation. All patients demonstrated a monomorphic premature ventricular contraction (PVC) with late transition left bundle branch block pattern, superior axis, and a coupling interval of less than 300 ms. triggering recurrent TdP and VF. In four patients, the culprit PVC was mapped to the free wall insertion of the moderator band (MB) with a preceding Purkinje potential in two patients. Catheter ablation using 3D-mapping and intracardiac echocardiography eliminated sc-TdP in all patients, with no recurrence at mean 2.7 years (range 6 months to 8 years) of follow-up.

Conclusion

3D-mapping and intracardiac echocardiography demonstrate that sc-TdP predominantly originates from the MB free wall insertion and its Purkinje network. Catheter ablation of the culprit PVC at the MB free wall junction leads to excellent short- and long-term results and should be considered as first-line therapy in recurrent sc-TdP or electrical storm.

Purpose To clarify the predominant causative plaque constituent for periprocedural myocardial injury (PMI) following percutaneous coronary intervention: (a) erythrocyte-derived materials, indicated by a high plaque-to-myocardium signal intensity ratio (PMR) at coronary atherosclerosis T1-weighted characterization (CATCH) MRI, or (b) lipids, represented by a high maximum 4-mm lipid core burden index (maxLCBI4 mm) at near-infrared spectroscopy intravascular US (NIRS-IVUS). Materials and Methods This retrospective study included consecutive patients who underwent CATCH MRI before elective NIRS-IVUS-guided percutaneous coronary intervention at two facilities. PMI was defined as post-percutaneous coronary intervention troponin T values greater than five times the upper reference limit. Multivariable analysis was performed to identify predictors of PMI. Finally, the predictive capabilities of MRI, NIRS-IVUS, and their combination were compared. Results A total of 103 lesions from 103 patients (median age, 72 years [IQR, 64-78]; 78 male patients) were included. PMI occurred in 36 lesions. In multivariable analysis, PMR emerged as the strongest predictor (P = .001), whereas maxLCBI4 mm was not a significant predictor (P = .07). When PMR was excluded from the analysis, maxLCBI4 mm emerged as the sole independent predictor (P = .02). The combination of MRI and NIRS-IVUS yielded the largest area under the receiver operating curve (0.86 [95% CI: 0.64, 0.83]), surpassing that of NIRS-IVUS alone (0.75 [95% CI: 0.64, 0.83]; P = .02) or MRI alone (0.80 [95% CI: 0.68, 0.88]; P = .30). Conclusion Erythrocyte-derived materials in plaques, represented by a high PMR at CATCH MRI, were strongly associated with PMI independent of lipids. MRI may play a crucial role in predicting PMI by offering unique pathologic insights into plaques, distinct from those provided by NIRS. Keywords: Coronary Plaque, Periprocedural Myocardial Injury, MRI, Near-Infrared Spectroscopy Intravascular US Supplemental material is available for this article. © RSNA, 2024.

High-intensity plaque (HIP) on magnetic resonance imaging (MRI) has been documented as a powerful predictor of periprocedural myocardial injury (PMI) following percutaneous coronary intervention (PCI). Despite the recent proposal of three-dimensional HIP quantification to enhance the predictive capability, the conventional pulse sequence, which necessitates the separate acquisition of anatomical reference images, hinders accurate three-dimensional segmentation along the coronary vasculature. Coronary atherosclerosis T1-weighted characterization (CATCH) enables the simultaneous acquisition of inherently coregistered dark-blood plaque and bright-blood coronary artery images. We aimed to develop a novel HIP quantification approach using CATCH and to ascertain its superior predictive performance compared to the conventional two-dimensional assessment based on plaque-to-myocardium signal intensity ratio (PMR). In this prospective study, CATCH MRI was conducted before elective stent implantation in 137 lesions from 125 patients. On CATCH images, dedicated software automatically generated tubular three-dimensional volumes of interest on the dark-blood plaque images along the coronary vasculature, based on the precisely matched bright-blood coronary artery images, and subsequently computed PMR and HIP volume (HIPvol). Specifically, HIPvol was calculated as the volume of voxels with signal intensity exceeding that of the myocardium, weighted by their respective signal intensities. PMI was defined as post-PCI cardiac troponin-T >5× the upper reference limit. The entire analysis process was completed within 3minutes per lesion. PMI occurred in 44 lesions. Based on the receiver operating characteristic curve analysis, HIPvol outperformed PMR for predicting PMI (C-statistics, 0.870 [95% CI, 0.805-0.936] vs. 0.787 [95% CI, 0.706-0.868]; p = 0.001). This result was primarily driven by the higher sensitivity HIPvol offered: 0.886 (95% CI, 0.754-0.962) vs. 0.750 for PMR (95% CI, 0.597-0.868; p = 0.034). Multivariable analysis identified HIPvol as an independent predictor of PMI (odds ratio, 1.15 per 10-μL increase; 95% CI, 1.01-1.30, p = 0.035). Our semi-automated method of analyzing coronary plaque using CATCH MRI provided rapid HIP quantification. Three-dimensional assessment using this approach had a better ability to predict PMI than conventional two-dimensional assessment.