Lead I R‐wave amplitude to differentiate idiopathic ventricular arrhythmias with left bundle branch block right inferior axis originating from the left versus right ventricular outflow tract

Explainable localization of premature ventricular contraction using deep learning-based semantic segmentation of 12-lead electrocardiogram

Background

Predicting the origin of premature ventricular contraction (PVC) from the preoperative electrocardiogram (ECG) is important for catheter ablation therapies. We propose an explainable method that localizes PVC origin based on the semantic segmentation result of a 12-lead ECG using a deep neural network, considering suitable diagnosis support for clinical application.

Methods

The deep learning-based semantic segmentation model was trained using 265 12-lead ECG recordings from 84 patients with frequent PVCs. The model classified each ECG sampling time into four categories: background (BG), sinus rhythm (SR), PVC originating from the left ventricular outflow tract (PVC-L), and PVC originating from the right ventricular outflow tract (PVC-R). Based on the ECG segmentation results, a rule-based algorithm classified ECG recordings into three categories: PVC-L, PVC-R, as well as Neutral, which is a group for the recordings requiring the physician's careful assessment before separating them into PVC-L and PVC-R. The proposed method was evaluated with a public dataset which was used in previous research.

Results

The evaluation of the proposed method achieved neutral rate, accuracy, sensitivity, specificity, F1-score, and area under the curve of 0.098, 0.932, 0.963, 0.882, 0.945, and 0.852 on a private dataset, and 0.284, 0.916, 0.912, 0.930, 0.943, and 0.848 on a public dataset, respectively. These quantitative results indicated that the proposed method outperformed almost all previous studies, although a significant number of recordings resulted in requiring the physician's assessment.

Conclusions

The feasibility of explainable localization of premature ventricular contraction was demonstrated using deep learning-based semantic segmentation of 12-lead ECG.Clinical trial registration: M26-148-8.

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.

A hybrid clinical and electrocardiographic score to predict the origin of outflow tract ventricular arrhythmias

BACKGROUND

To predict the outflow tract ventricular arrhythmias (OTVA) site of origin (SOO) before the ablation procedure has important practical implications. The present study sought to prospectively evaluate the accuracy of a clinical and electrocardiographic hybrid algorithm (HA) for the prediction of OTVAs-SOO, and at the same time to develop and to prospectively validate a new score with improved discriminatory capacity.

METHODS

In this multicenter study, we prospectively enrolled consecutive patients referred for OTVA ablation (N = 202), and we divided them in a derivation sample and a validation cohort. Surface ECGs during OTVA were analyzed to compare previous published ECG-only criteria and to develop a new score.

RESULTS

In the derivation sample (N = 105), the correct prediction rate of HA and ECG-only criteria ranged from 74 to 89%. R-wave amplitude in V3 was the best ECG parameter for discriminating LVOT origin in V3 precordial transition (V3PT) patients, and was incorporated to the novel weighted hybrid score (WHS). WHS correctly classified 99 (94.2%) patients, presenting 90% sensitivity and 96% specificity (AUC 0.97) in the entire population; WHS mantained a 87% sensitivity and 91% specificity (AUC 0.95) in patients with V3PT subgroup. The high discriminatory capacity was confirmed in the validation sample (N = 97): the WHS exhibited an AUC (0.93), and a WHS ≥ 2 allowed a correct prediction of LVOT origin in 87 (90.0%) cases, yielding a sensitivity of 87% and specificity of 90%; moreover, the V3PT subgroup showed an AUC of 0.92, and a punctuation ≥ 2 predicted an LVOT origin with a sensitivity of 94% and specificity of 78%.

CONCLUSIONS

The novel hybrid score has proved to accurately anticipate the OTVA's origin, even in those with a V3 precordial transition. A Weighted hybrid score. B Typical examples of the use of the weighted hybrid score. C ROC analysis of WHS and previous ECG criteria for prediction of LVOT origin in the derivation cohort. D ROC analysis of WHS and previous ECG criteria for prediction of LVOT origin in the V3 precordial transition OTVA subgroup.

Validation of a clinical model for predicting left versus right ventricular outflow tract origin of idiopathic ventricular arrhythmias

BACKGROUND

Prediction of the chamber of origin in patients with outflow tract ventricular arrhythmias (OTVA) remains challenging. A clinical risk score based on age, sex and presence of hypertension was associated with a left ventricular outflow tract (LVOT) origin. We aimed to validate this clinical score to predict an LVOT origin in patients with OTVA.

METHODS

In a two-center observational cohort study, unselected patients undergoing catheter ablation (CA) for OTVA were enrolled. All procedures were performed using an electroanatomical mapping system. Successful ablation was defined as a ≥80% reduction of the initial overall PVC burden after 3 months of follow-up. Patients with unsuccessful ablation were excluded from this analysis.

RESULTS

We included 187 consecutive patients with successful CA of idiopathic OTVA. Mean age was 52 ± 15 years, 102 patients (55%) were female, and 74 (40%) suffered from hypertension. A LVOT origin was found in 64 patients (34%). A score incorporating age, sex and presence of hypertension reached 73% sensitivity and 67% specificity for a low (0-1) and high (2-3) score, to predict an LVOT origin. The combination of one ECG algorithm (V2 S/V3 R-index) with the clinical score resulted in a sensitivity and specificity of 81% and 70% for PVCs with R/S transition at V3 .

CONCLUSION

The published clinical score yielded a lower sensitivity and specificity in our cohort. However, for PVCs with R/S transition at V3, the combination with an existing ECG algorithm can improve the predictability of LVOT origin.

Electrocardiographic Characteristics, Identification, and Management of Frequent Premature Ventricular Contractions

Premature ventricular complexes (PVCs) are frequently encountered in clinical practice. The association of PVCs with adverse cardiovascular outcomes is well established in the context of structural heart disease, yet not so much in the absence of structural heart disease. However, cardiac magnetic resonance (CMR) seems to contribute prognostically in the latter subgroup. PVC-induced myocardial dysfunction refers to the impairment of ventricular function due to PVCs and is mostly associated with a PVC burden > 10%. Surface 12-lead ECG has long been used to localize the anatomic site of origin and multiple algorithms have been developed to differentiate between right ventricular and left ventricular outflow tract (RVOT and LVOT, respectively) origin. Novel algorithms include alternative ECG lead configurations and, lately, sophisticated artificial intelligence methods have been utilized to determine the origins of outflow tract arrhythmias. The decision to therapeutically address PVCs should be made upon the presence of symptoms or the development of PVC-induced myocardial dysfunction. Therapeutic modalities include pharmacological therapy (I-C antiarrhythmic drugs and beta blockers), as well as catheter ablation, which has demonstrated superior efficacy and safety.

A Four-Stepwise Electrocardiographic Algorithm for Differentiation of Ventricular Arrhythmias Originated from Left Ventricular Outflow Tract

Several electrocardiographic algorithms have been proposed to identify the site of origin for the ventricular arrhythmias (VAs) from the left ventricular outflow tract (LVOT) versus right ventricular outflow tract. However, the electrocardiographic criteria for distinguishing VAs originated from the different sites of LVOT is lacking. We aimed to develop a simple and efficient ECG algorithm to differentiate LVOT VAs originated from the aortic root, AMC and LV summit. We analyzed 12-lead ECG characteristics of 68 consecutive patients who underwent successful radiofrequency catheter ablation of symptomatic VAs from LVOT. Patients were divided into RCC (right coronary cusp) group (n = 8), the L-RCC (the junction between the LCC and RCC) group (n = 21), the LCC (left coronary cusp) group (n = 24), the aortomitral continuity (AMC) group (n = 9) and the LV summit group (n = 6) according to the final ablation sites. Measurements with the highest diagnostic performance were modeled into a 4-stepwise algorithm to discriminate LVOT VAs. The performance of this novel algorithm was prospectively tested in a validation cohort of 43 consecutive patients undergoing LVOT VAs ablation. Based on the accuracy of AUC, a 4-stepwise ECG algorithm was developed. First, the QS duration in aVL > 134 ms was used to distinguish VAs from AMC, LV summit and VAs from aortic root (80% sensitivity and 76% specificity). Second, the R duration in II > 155 ms was used to differentiate VAs from LV summit and VAs from AMC (67% sensitivity and 56% specificity). Third, the ratio of III/II < 0.9 was used to discriminate VAs from RCC and VAs from LCC, L-RCC (82% sensitivity and 63% specificity). Fourth, the QS duration of aVR > 130 ms was used to discern VAs from LCC and VAs from L-RCC (75% sensitivity and 62% specificity). In the prospective evaluation, our 4-stepwise ECG algorithm exhibited a good predictive value. We have developed a novel and simple 4-stepwise ECG algorithm with good predictive value to discriminate the AVs from different sites of LVOT.

A novel and effective ECG method to differentiate right from left ventricular outflow tract arrhythmias: Angle-corrected V2S

Background and aims

Standard 12-lead electrocardiogram (ECG) patterns combined with the anatomical cardiac long-axis angle revealed by chest X-ray can prevent the influence of cardiac rotation, physical shape, and lead position, so it may be an ideal means to predict the origin of the outflow tract (OT) ventricular arrhythmias (OTVAs) for ablation procedures. The study explores the value of this strategy in identifying the origin of OTVA.

Methods

This study was conducted using a retrospective cohort and a prospective cohort of consecutive patients at two centers. The anatomical cardiac long-axis angle was calculated by measuring the angle between the cardiac long-axis (a line joining the apex to the midpoint of the mitral annulus) and the horizontal plane on a chest X-ray. The V2S angle was calculated as the V2S amplitude times the angle. We ultimately enrolled 147 patients with symptomatic OTVAs who underwent successful radiofrequency catheter ablation (RFCA) (98 women (66.7%); mean age 46.9 ± 14.7 years; 126 right ventricular OT (RVOT) origins, 21 left ventricular OT (LVOT) origins) as a development cohort. The new algorithm was validated in 48 prospective patients (12 men (25.0%); mean age 48.0 ± 15.8 years; 36 RVOT, 12 LVOT origins).

Results

Patients with RVOT VAs had greater V2S, long-axis angle, and V2S angle than patients with LVOT VA (all P < 0.001). The cut-off V2S angle obtained by receiver operating characteristic (ROC) curve analysis was 58.28 mV° for the prediction of RVOT origin (sensitivity: 85.7%; specificity: 95.2%; positive predictive value: 99.1%; negative predictive value: 52.6%). The AUC achieved using the V2S angle was 0.888 (P < 0.001), which was the highest among all indexes (V2S/V3R: 0.887 (P < 0.016); TZ index: 0.858 (P < 0.001); V1-2 SRd: 0.876 (P < 0.001); V3 transition: 0.651 (P < 0.001)). In the prospective cohort, the V2S angle had a high overall accuracy of 93.8% and decreased the procedure time (P = 0.002).

Conclusion

V2S angle can be a novel measure that can be used to accurately differentiate RVOT from LVOT origins. It could help decrease ablation duration and radiation exposure.

A High-Precision Deep Learning Algorithm to Localize Idiopathic Ventricular Arrhythmias

Background: An accurate prediction of ventricular arrhythmia (VA) origins can optimize the strategy of ablation, and facilitate the procedure. Objective: This study aimed to develop a machine learning model from surface ECG to predict VA origins. Methods: We obtained 3628 waves of ventricular premature complex (VPC) from 731 patients. We chose to include all signal information from 12 ECG leads for model input. A model is composed of two groups of convolutional neural network (CNN) layers. We chose around 13% of all the data for model testing and 10% for validation. Results: In the first step, we trained a model for binary classification of VA source from the left or right side of the chamber with an area under the curve (AUC) of 0.963. With a threshold of 0.739, the sensitivity and specification are 90.7% and 92.3% for identifying left side VA. Then, we obtained the second model for predicting VA from the LV summit with AUC is 0.998. With a threshold of 0.739, the sensitivity and specificity are 100% and 98% for the LV summit. Conclusions: Our machine learning algorithm of surface ECG facilitates the localization of VPC, especially for the LV summit, which might optimize the ablation strategy.

Development of a Visualization Deep Learning Model for Classifying Origins of Ventricular Arrhythmias

BACKGROUND

Several algorithms have been proposed for differentiating the right and left outflow tracts (RVOT/LVOT) arrhythmia origins from 12-lead electrocardiograms (ECGs); however, the procedure is complicated. A deep learning (DL) model, a form of artificial intelligence, can directly use ECGs and depict the importance of the leads and waveforms. This study aimed to create a visualized DL model that could classify arrhythmia origins more accurately.Methods and Results: This study enrolled 80 patients who underwent catheter ablation. A convolutional neural network-based model that could classify arrhythmia origins with 12-lead ECGs and visualize the leads that contributed to the diagnosis using a gradient-weighted class activation mapping method was developed. The average prediction results of the origins by the DL model were 89.4% (88.2-90.6) for accuracy and 95.2% (94.3-96.2) for recall, which were significantly better than when a conventional algorithm is used. The ratio of the contribution to the prediction differed between RVOT and LVOT origins. Although leads V1 to V3 and the limb leads had a focused balance in the LVOT group, the contribution ratio of leads aVR, aVL, and aVF was higher in the RVOT group.

CONCLUSIONS

This study diagnosed the arrhythmia origins more accurately than the conventional algorithm, and clarified which part of the 12-lead waveforms contributed to the diagnosis. The visualized DL model was convincing and may play a role in understanding the pathogenesis of arrhythmias.

A High Precision Machine Learning-Enabled System for Predicting Idiopathic Ventricular Arrhythmia Origins

Background

Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy.

Method

A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, validation and testing cohorts. We designed four classification schemes responding to different hierarchical levels of the possible IVA origins. For every classification scheme, we compared 98 distinct machine learning models with optimized hyperparameter values obtained through extensive grid search and reported an optimal algorithm with the highest accuracy scores attained on the testing cohorts.

Results

For classification scheme 4, our pioneering study designs and implements a machine learning-based ECG algorithm to predict 21 possible sites of IVA origin with an accuracy of 98.24% on a testing cohort. The accuracy and F1-score for the left three schemes surpassed 99%.

Conclusion

In this work, we developed an algorithm that precisely predicts the correct origins of IVA (out of 21 possible sites) and outperforms the accuracy of all prior studies and human experts.

Reappraisal of electrocardiographic criteria for localization of idiopathic outflow region ventricular arrhythmias

BACKGROUND

Electrocardiographic (ECG) criteria have been proposed to localize the site of origin of outflow region ventricular arrhythmias (VAs). Many factors influence the QRS morphology of VAs and may limit the accuracy of these criteria.

OBJECTIVE

The purpose of this study was to assess the accuracy of ECG criteria that differentiate right from left outflow region VAs and localize VAs within the aortic sinus of Valsalva (ASV).

METHODS

One hundred one patients (mean age 52 ± 16 years; 55 [54%] women) undergoing catheter ablation of right ventricular outflow tract (RVOT) or ASV VAs with a left bundle branch block, inferior axis morphology were studied. ECG measurements including V₂ transition ratio, transition zone index, R-wave duration index, R/S amplitude index, V₂S/V₃R index, V_1-3 QRS morphology, R-wave amplitude in the inferior leads were tabulated for all VAs. Comparisons were made between the predicted site of origin using these criteria and the successful ablation site.

RESULTS

Patients had successful ablation of 71 RVOT and 38 ASV VAs. For the differentiation of RVOT from ASV VAs, the positive predictive values and negative predictive values for all tested ECG criteria ranged from 42% to 75% and from 71% to 82%, respectively, with the V₂S/V₃R index having the largest area under the curve of 0.852. Morphological QRS criteria in leads V₁ through V₃ did not localize ASV VAs. The maximum R-wave amplitude in the inferior leads was the sole criterion demonstrating a significant difference between right ASV, right-left ASV commissure, and left ASV sites.

CONCLUSION

ECG criteria for differentiating right from left ventricular outflow region VAs and for localizing ASV VAs have a limited accuracy.

Electrocardiographic recognition of benign and malignant right ventricular arrhythmias

Ventricular arrhythmias (VAs) can originate from different anatomical locations of the right ventricle. Ventricular arrhythmias originating from right ventricle have unique electrocardiographic (ECG) characteristics that can be utilized to localize the origin of the arrhythmia. This is crucial in pre-procedural planning particularly for ablation treatments. Moreover, non-ischaemic structural heart diseases, such as infiltrative and congenital heart diseases, are associated with the VAs that exhibit particular ECG findings. This article comprehensively reviews discriminatory ECG characteristics of VAs in the right ventricle with and without structural right ventricular diseases.

A High-Precision Machine Learning Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia

Introduction

Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.

Methods

We randomly sampled training, validation, and testing data sets from 420 patients who underwent successful catheter ablation (CA) to treat VT or PVC, containing 340 (81%), 38 (9%), and 42 (10%) patients, respectively. We iteratively trained a machine learning algorithm supplied with 1,600,800 features extracted via our proprietary algorithm from 12-lead ECGs of the patients in the training cohort. The area under the curve (AUC) of the receiver operating characteristic curve was calculated from the internal validation data set to choose an optimal discretization cutoff threshold.

Results

The proposed approach attained the following performance: accuracy (ACC) of 97.62 (87.44–99.99), weighted F1-score of 98.46 (90–100), AUC of 98.99 (96.89–100), sensitivity (SE) of 96.97 (82.54–99.89), and specificity (SP) of 100 (62.97–100).

Conclusions

The proposed multistage diagnostic scheme attained clinical-grade precision of prediction for LVOT and RVOT locations of VT origin with fewer applicability restrictions than prior studies.

A High-Precision Machine Learning Algorithm to Classify Left and Right Outflow Tract Ventricular Tachycardia

Introduction

Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.

Methods

We randomly sampled training, validation, and testing data sets from 420 patients who underwent successful catheter ablation (CA) to treat VT or PVC, containing 340 (81%), 38 (9%), and 42 (10%) patients, respectively. We iteratively trained a machine learning algorithm supplied with 1,600,800 features extracted via our proprietary algorithm from 12-lead ECGs of the patients in the training cohort. The area under the curve (AUC) of the receiver operating characteristic curve was calculated from the internal validation data set to choose an optimal discretization cutoff threshold.

Results

The proposed approach attained the following performance: accuracy (ACC) of 97.62 (87.44-99.99), weighted F1-score of 98.46 (90-100), AUC of 98.99 (96.89-100), sensitivity (SE) of 96.97 (82.54-99.89), and specificity (SP) of 100 (62.97-100).

Conclusions

The proposed multistage diagnostic scheme attained clinical-grade precision of prediction for LVOT and RVOT locations of VT origin with fewer applicability restrictions than prior studies.

Lead I R-wave amplitude to distinguish ventricular arrhythmias with lead V3 transition originating from the left versus right ventricular outflow tract

BACKGROUND

The electrophysiology algorithm for localizing left or right origins of outflow tract ventricular arrhythmias (OT-VAs) with lead V₃ transition still needs further investigation in clinical practice.

HYPOTHESIS

Lead I R-wave amplitude is effective in distinguishing the left or right origin of OT-VAs with lead V₃ transition.

METHODS

We measured lead I R-wave amplitude in 82 OT-VA patients with lead V₃ transition and a positive complex in lead I who underwent successful catheter ablation from the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT). The optimal R-wave threshold was identified, compared with the V₂ S/V₃ R index, transitional zone (TZ) index, and V₂ transition ratio, and validated in a prospective cohort study.

RESULTS

Lead I R-wave amplitude for LVOT origins was significantly higher than that for RVOT origins (0.55 ± 0.13 vs. 0.32 ± 0.15 mV; p < .001). The area under the curve (AUC) for lead I R-wave amplitude as assessed by receiver operating characteristic (ROC) analysis was 0.926, with a cutoff value of ≥0.45 predicting LVOT origin with 92.9% sensitivity and 88.2% specificity, superior to the V₂ S/V₃ R index, TZ index, and V₂ transition ratio. VAs in the LVOT group mainly originated from the right coronary cusp (RCC) and left and right coronary cusp junction (L-RCC). In the prospective study, lead I R-wave amplitude identified the LVOT origin with 92.3% accuracy.

CONCLUSION

Lead I R-wave amplitude provides a useful and simple criterion to identify RCC or L-RCC origin in OT-VAs with lead V₃ transition.

A 12-Lead ECG database to identify origins of idiopathic ventricular arrhythmia containing 334 patients

Cardiac catheter ablation has shown the effectiveness of treating the idiopathic premature ventricular complex and ventricular tachycardia. As the most important prerequisite for successful therapy, criteria based on analysis of 12-lead ECGs are employed to reliably speculate the locations of idiopathic ventricular arrhythmia before a subsequent catheter ablation procedure. Among these possible locations, right ventricular outflow tract and left outflow tract are the major ones. We created a new 12-lead ECG database under the auspices of Chapman University and Ningbo First Hospital of Zhejiang University that aims to provide high quality data enabling detection of the distinctions between idiopathic ventricular arrhythmia from right ventricular outflow tract to left ventricular outflow tract. The dataset contains 334 subjects who successfully underwent a catheter ablation procedure that validated the accurate origins of idiopathic ventricular arrhythmia.

Electrocardiographic features, mapping and ablation of idiopathic outflow tract ventricular arrhythmias

PURPOSE

Idiopathic outflow tract ventricular arrhythmias are ventricular tachycardias or premature ventricular contractions presumably not related to myocardial scar or disorders of ion channels. These arrhythmias have focal origin and display characteristic electrocardiographic features. The purpose of this article is to review the state of the art of diagnosis and treatment of idiopathic outflow tract ventricular arrhythmias.

METHODS

We systematically reviewed scientific literature about idiopathic outflow tract ventricular arrhythmias selecting the most relevant papers on this topic.

RESULTS

The right ventricle outflow tract is the most common site of origin for outflow tract ventricular arrhythmias, but also left ventricle outflow tract can harbour these arrhythmias. Outflow tract ventricular arrhythmias are generally benign and may require treatment if they are symptomatic, incessant or give rise to cardiomyopathy. Radiofrequency catheter ablation is an effective and safe therapeutic strategy. A successful procedure requires a thorough preoperative analysis of the 12-lead electrocardiogram of the spontaneous arrhythmia combined with a detailed electroanatomical mapping and intracardiac echocardiography.

CONCLUSIONS

Idiopathic outflow tract arrhythmias are frequent in daily clinical practice and can be successfully eliminated through discrete radiofrequency catheter ablation with low rates of complications.

Idiopathic Outflow Tract Ventricular Arrhythmia Ablation: Pearls and Pitfalls

Idiopathic outflow tract ventricular arrhythmias (VAs) occur typically in patients without structural heart disease. They are often symptomatic and can sometimes lead to left ventricular systolic dysfunction. Both activation and pace mapping are utilised for successful ablation of these arrhythmias. Pace mapping is particularly helpful when the VA is infrequent and/or cannot be elucidated during the ablation procedure. VAs originating from different sites in the outflow tract region have distinct QRS patterns on the 12-lead ECG and careful analysis of the latter can help predict the site of origin of these arrhythmias. Successful ablation of these VAs requires understanding of the detailed anatomy of the OT region, which can be accomplished through electroanatomic mapping tools and intracardiac echocardiography.