An electrocardiographic diagnostic model for differentiating left from right ventricular outflow tract tachycardia origin

Activation pattern of the coronary sinus facilitates the differentiation for ventricular outflow tract arrhythmias

INTRODUCTION

The accuracy of surface ECG algorithms for predicting the origin of outflow tract ventricular arrhythmias (OT-VAs) might be questioned. Intracardiac electrograms recorded at anatomic landmarks could provide new predictive insights. We aim to evaluate the efficacy of a novel criterion utilizing the activation pattern of the coronary sinus (CS) in localizing OT-VAs, including VAs originating from the right ventricular outflow tract (RVOT), endocardial left ventricular outflow tract (Endo-LVOT), and epicardial left ventricular outflow tract (Epi-LVOT).

METHODS

We measured the ventricular activation time of the mitral annulus (MA) from the onset of the earliest QRS complex of VAs to the initial deflection over the isoelectric line at local signals, namely the QRS-MA interval. The activation at 3 and 12 o'clock of the MA was recorded as the QRS-MA3 and QRS-MA12 intervals, respectively. Their predictive values were compared to previous ECG algorithms.

RESULTS

A total of 68 patients with OT-VAs were enrolled (51 for development and 17 for validation). From early to late, the ventricular activation sequences at MA12 were as follows: Epi-LVOT, Endo-LVOT, and RVOT. In LBBB morphology OT-VAs, the QRS-MA12 interval was significantly earlier for LVOT origins than RVOT origins. In the combined cohort of development and validation cohort, a cut-off value of ≤10 ms predicted the LVOT origin with a sensitivity of 100% and specificity of 78%. The QRS-MA12 interval ≤ -24 ms additionally predicted epicardial LVOT sites of origin.

CONCLUSIONS

The QRS-MA interval could accurately differentiate the OT-VAs localization.

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 novel practical algorithm using machine learning to differentiate outflow tract ventricular arrhythmia origins

INTRODUCTION

Diagnosis of outflow tract ventricular arrhythmia (OTVA) localization by an electrocardiographic complex is key to successful catheter ablation for OTVA. However, diagnosing the origin of OTVA with a precordial transition in lead V3 (V3TZ) is challenging. This study aimed to create the best practical electrocardiogram algorithm to differentiate the left ventricular outflow tract (LVOT) from the right ventricular outflow tract (RVOT) of OTVA origin with V3TZ using machine learning.

METHODS

Of 498 consecutive patients undergoing catheter ablation for OTVA, we included 104 patients who underwent ablation for OTVA with V3TZ and identified the origin of LVOT (n = 62) and RVOT (n = 42) from the results. We analyzed the standard 12-lead electrocardiogram preoperatively and measured 128 elements in each case. The study population was randomly divided into training group (70%) and testing group (30%), and decision tree analysis was performed using the measured elements as features. The performance of the algorithm created in the training group was verified in the testing group.

RESULTS

Four measurements were identified as important features: the aVF/II R-wave ratio, the V2S/V3R index, the QRS amplitude in lead V3, and the R-wave deflection slope in lead V3. Among them, the aVF/II R-wave ratio and the V2S/V3R index had a particularly strong influence on the algorithm. The performance of this algorithm was extremely high, with an accuracy of 94.4%, precision of 91.5%, recall of 100%, and an F1-score of 0.96.

CONCLUSIONS

The novel algorithm created using machine learning is useful in diagnosing the origin of OTVA with V3TZ.

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.

Hourly variability in outflow tract ectopy as a predictor of its site of origin

INTRODUCTION

Before ablation, predicting the site of origin (SOO) of outflow tract ventricular arrhythmia (OTVA), can inform patient consent and facilitate appropriate procedural planning. We set out to determine if OTVA variability can accurately predict SOO.

METHODS

Consecutive patients with a clear SOO identified at OTVA ablation had their prior 24-h ambulatory ECGs retrospectively analysed (derivation cohort). Percentage ventricular ectopic (VE) burden, hourly VE values, episodes of trigeminy/bigeminy, and the variability in these parameters were evaluated for their ability to distinguish right from left-sided SOO. Effective parameters were then prospectively tested on a validation cohort of consecutive patients undergoing their first OTVA ablation.

RESULTS

High VE variability (coefficient of variation ≥0.7) and the presence of any hour with <50 VE, were found to accurately predict RVOT SOO in a derivation cohort of 40 patients. In a validation cohort of 29 patients, the correct SOO was prospectively identified in 23/29 patients (79.3%) using CoV, and 26/29 patients (89.7%) using VE < 50. Including current ECG algorithms, VE < 50 had the highest Youden Index (78), the highest positive predictive value (95.0%) and the highest negative predictive value (77.8%).

CONCLUSION

VE variability and the presence of a single hour where VE < 50 can be used to accurately predict SOO in patients with OTVA. Accuracy of these parameters compares favorably to existing ECG algorithms.

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 Criteria for Differentiating Left from Right Idiopathic Outflow Tract Ventricular Arrhythmias

Idiopathic ventricular arrhythmias are ventricular tachycardias or premature ventricular contractions presumably not related to myocardial scar or disorders of ion channels. Of the ventricular arrhythmias (VAs) without underlying structural heart disease, those arising from the ventricular outflow tracts (OTs) are the most common. The right ventricular outflow tract (RVOT) is the most common site of origin for OT-VAs, but these arrhythmias can, less frequently, originate from the left ventricular outflow tract (LVOT). OT-VAs are focal and have characteristic ECG features based on their anatomical origin. Radiofrequency catheter ablation (RFCA) is an effective and safe treatment strategy for OT-VAs. Prediction of the OT-VA origin according to ECG features is an essential part of the preprocedural planning for RFCA procedures. Several ECG criteria have been proposed for differentiating OT site of origin. Unfortunately, the ECG features of RVOT-VAs and LVOT-VAs are similar and could possibly lead to misdiagnosis. The authors review the ECG criteria used in clinical practice to differentiate RVOT-VAs from LVOT-VAs.

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.

A Simplified Two-Stepwise Electrocardiographic Algorithm to Distinguish Left from Right Ventricular Outflow Tract Tachycardia Origin

BACKGROUND

There are several electrocardiographic algorithms to predict the origin of idiopathic outflow tract ventricular arrhythmias (OT-VAs). This study aimed to develop a more accurate and efficient stepwise electrocardiographic algorithm to discriminate left ventricular outflow tract (LVOT) from right ventricular outflow tract (RVOT) origin.

METHODS AND RESULTS

We analyzed 12-lead electrocardiographic characteristics of 173 consecutive OT-VAs patients who underwent successful radiofrequency catheter ablation in the RVOT (n = 124) or LVOT (n = 49). Based on the areas under the receiver operating characteristic curves, the combination of transitional zone (TZ) index <0 and V2S/V3R index ≤1.5 exhibited 93.5% sensitivity, 85.9% specificity, and 87.3% accuracy. A further analysis was performed in the 71 OT-VAs with a V3-lead precordial transition. The sensitivity, specificity, and accuracy of the integration of V2S/V3R index ≤1.5 and R-wave deflection interval in lead V3 >80 ms were 91.7, 83.1, and 85.9%, respectively. In the prospective evaluation, the combination of TZ index and V2S/V3R index could identify the correct origin sites with 91.2% accuracy in the overall analysis, and the integration of V2S/V3R index ≤1.5 and R-wave deflection interval in lead V3 >80 ms exhibited 94% accuracy in V3-lead precordial transition.

CONCLUSIONS

The combination of TZ index <0 and V2S/V3R index ≤1.5 is a simple and efficient stepwise electrocardiographic algorithm for predicting LVOT origin. For the OT-VAs with a V3-lead precordial transition, the integration of V2S/V3R index ≤1.5 and R-wave deflection interval in lead V3 >80 ms would be a better choice.