Electrocardiographic Criteria for Differentiating Left from Right Idiopathic Outflow Tract Ventricular Arrhythmias

RV1+RV3 Index to Differentiate Idiopathic Ventricular Arrhythmias Arising From Right Ventricular Outflow Tract and Aortic Sinus of Valsalva: A Multicenter Study

BACKGROUND

This study aimed to investigate the predictive value of parameters of every precordial lead and their combinations in differentiating between idiopathic ventricular arrhythmias (IVAs) from the right ventricular outflow tract and aortic sinus of Valsalva (ASV).

METHODS AND RESULTS

Between March 1, 2018, and December 1, 2021, consecutive patients receiving successful ablation of right ventricular outflow tract or ASV IVAs were enrolled. The amplitude and duration of the R wave and S wave were measured in every precordial lead during IVAs. These parameters were either summed, subtracted, multiplied, or divided to create different indexes. The index with the highest area under the curve to predict ASV IVAs was developed, compared with established indexes, and validated in an independent prospective multicenter cohort. A total of 150 patients (60 men; mean age, 45.3±16.4 years) were included in the derivation cohort. The RV1+RV3 index (summed R-wave amplitude in leads V1 and V3) had the highest area under the curve (0.942) among the established indexes. An RV1+RV3 index >1.3 mV could predict ASV IVAs with a sensitivity of 95% and a specificity of 83%. Its predictive performance was maintained in the validation cohort (N=109). In patients with V3 R/S transition, an RV1+RV3 index >1.3 mV could predict ASV IVAs, with an area under the curve of 0.892, 93% sensitivity, and 75% specificity.

CONCLUSIONS

The RV1+RV3 index is a simple and novel criterion that accurately differentiates between right ventricular outflow tract and ASV IVAs. Its performance outperformed established indexes, making it a valuable tool in clinical practice.

Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data

The treatment of outflow tract ventricular arrhythmias (OTVA) through radiofrequency ablation requires the precise identification of the site of origin (SOO). Pinpointing the SOO enhances the likelihood of a successful procedure, reducing intervention times and recurrence rates. Current clinical methods to identify the SOO are based on qualitative analysis of pre-operative electrocardiograms (ECG), heavily relying on physician's expertise. Although computational models and machine learning (ML) approaches have been proposed to assist OTVA procedures, they either consume substantial time, lack interpretability or do not use clinical information. Here, we propose an alternative strategy for automatically predicting the ventricular origin of OTVA patients using ML. Our objective was to classify ventricular (left/right) origin in the outflow tracts (LVOT and RVOT, respectively), integrating ECG and clinical data from each patient. Extending beyond differentiating ventricle origin, we explored specific SOO characterization. Utilizing four databases, we also trained supervised learning models on the QRS complexes of the ECGs, clinical data, and their combinations. The best model achieved an accuracy of 89%, highlighting the significance of precordial leads V1-V4, especially in the R/S transition and initiation of the QRS complex in V2. Unsupervised analysis revealed that some origins tended to group closer than others, e.g., right coronary cusp (RCC) with a less sparse group than the aortic cusp origins, suggesting identifiable patterns for specific SOOs.

The significance of heart rate variability in patients with frequent premature ventricular complex originating from the ventricular outflow tract

BACKGROUND

As an indicator of cardiac autonomic nervous activity, heart rate variability (HRV) is closely linked to premature ventricular complexes (PVCs). However, its role in patients with frequent PVCs originating from the ventricular outflow tract remains unclear.

HYPOTHESIS

Here, we hypothesize that there may be alterations in HRV among patients with frequent PVCs originating from the ventricular outflow tract, which could play significant roles in the management of such patients.

METHODS

A retrospective study was conducted, including 106 patients with frequent outflow tract PVCs and 106 healthy participants as controls. HRV was assessed based on the 24-hour Holter recording. The originating foci of PVCs were identified during radiofrequency catheter ablation.

RESULTS

Patients with frequent outflow tract PVCs exhibited decreased levels of high frequency (HF), standard deviation of all NN intervals, and standard deviation of the average NN intervals, but increased ratios of low frequency to HF (LF/HF ratio), even after propensity score-matched analysis. Further investigation revealed that patients with PVCs originating from right ventricular outflow tract (RVOT) had much higher LF/HF ratios. Multivariate logistic regression analysis demonstrated that the LF/HF ratio was independently associated with PVCs originating from RVOT. Receiver operating characteristics curve indicated that the LF/HF ratio effectively determined the origin of PVCs (the area under the curve = 0.75, p < .001).

CONCLUSIONS

Patients with frequent outflow tract PVCs exhibited impaired HRV. Additionally, the LF/HF ratio played a significant role in determining the origin of outflow tract PVCs.

Electrical and Structural Insights into Right Ventricular Outflow Tract Arrhythmogenesis

The right ventricular outflow tract (RVOT) is the major origin of ventricular arrhythmias, including premature ventricular contractions, idiopathic ventricular arrhythmias, Brugada syndrome, torsade de pointes, long QT syndrome, and arrhythmogenic right ventricular cardiomyopathy. The RVOT has distinct developmental origins and cellular characteristics and a complex myocardial architecture with high shear wall stress, which may lead to its high vulnerability to arrhythmogenesis. RVOT myocytes are vulnerable to intracellular sodium and calcium overload due to calcium handling protein modulation, enhanced CaMKII activity, ryanodine receptor phosphorylation, and a higher cAMP level activated by predisposing factors or pathological conditions. A reduction in Cx43 and Scn5a expression may lead to electrical uncoupling in RVOT. The purpose of this review is to update the current understanding of the cellular and molecular mechanisms of RVOT arrhythmogenesis.

[Catheter ablation of ventricular tachycardia-Update 2023]

The management of ventricular tachycardias (VT), which are often associated with severe cardiac disease, is a challenging clinical task. The structural damage to the myocardium associated with cardiomyopathy is crucial to the occurrence of VT and plays a fundamental role in arrhythmia mechanisms. The goal of catheter ablation is to develop an accurate understanding of the patient-specific arrhythmia mechanism as a first procedural step. As a second step, the ventricular areas that maintain the arrhythmia mechanism can be ablated and thereby electrically inactivated. Catheter ablation thereby enables causal therapy of VT by modifying the areas of the affected myocardium in such a way that VT can no longer be triggered. The procedure is an effective treatment option for affected patients.

The scar: the wind in the perfect storm-insights into the mysterious living tissue originating ventricular arrhythmias

BACKGROUND

Arrhythmic death is very common among patients with structural heart disease, and it is estimated that in European countries, 1 per 1000 inhabitants yearly dies for sudden cardiac death (SCD), mainly as a result of ventricular arrhythmias (VA). The scar is the result of cardiac remodelling process that occurs in several cardiomyopathies, both ischemic and non-ischemic, and is considered the perfect substrate for re-entrant and non-re-entrant arrhythmias.

METHODS

Our aim was to review published evidence on the histological and electrophysiological properties of myocardial scar and to review the central role of cardiac magnetic resonance (CMR) in assessing ventricular arrhythmias substrate and its potential implication in risk stratification of SCD.

RESULTS

Scarring process affects both structural and electrical myocardial properties and paves the background for enhanced arrhythmogenicity. Non-uniform anisotropic conduction, gap junctions remodelling, source to sink mismatch and refractoriness dispersion are some of the underlining mechanisms contributing to arrhythmic potential of the scar. All these mechanisms lead to the initiation and maintenance of VA. CMR has a crucial role in the evaluation of patients suffering from VA, as it is considered the gold standard imaging test for scar characterization. Mounting evidences support the use of CMR not only for the definition of gross scar features, as size, localization and transmurality, but also for the identification of possible conducting channels suitable of discrete ablation. Moreover, several studies call out the CMR-based scar characterization as a stratification tool useful in selecting patients at risk of SCD and amenable to implantable cardioverter-defibrillator (ICD) implantation.

CONCLUSIONS

Scar represents the substrate of ventricular arrhythmias. CMR, defining scar presence and its features, may be a useful tool for guiding ablation procedures and for identifying patients at risk of SCD amenable to ICD therapy.

Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.