S-R difference in V1-V2 is a novel criterion for differentiating the left from right ventricular outflow tract 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.

A novel ECG algorithm to differentiate between ventricular arrhythmia from right versus left ventricular outflow tract

AIM

The aim of this study was to evaluate the accuracy of the diagnostic criteria for determining the origin of outflow tract ventricular arrhythmia (OTVA) and develop an ECG algorithm to predict its origin.

METHOD

We analyzed the ECGs of 100 patients with OTVA who underwent successful ablation. The QRS complex was measured during sinus rhythm and ventricular arrhythmia. After the ECG algorithm was developed, it was validated in an additional 100 patients from two different hospitals.

RESULTS

In this retrospective study, among the parameters without restrictions in the transition lead, the V2S/V3R index (AUC = 0.96) was significantly better in predicting ventricular arrhythmia originating from the right ventricular outflow tract (RVOT). Further, the larger initial r wave surface area (ISA) in V1 and V2 (AUC = 0.06) was significantly better in predicting ventricular arrhythmias originating from the left ventricular outflow tract (LVOT). Among the parameters with the transition lead in V3, the V2S/V3R index (AUC = 0.82) was significantly better in predicting VAs originating from the RVOT. On the contrary, the V3 R-wave deflection interval (AUC = 0.19) was significantly better in predicting ventricular arrhythmias originating from the LVOT. The algorithm combining the V2S/V3R index and the larger ISA in V1 and V2 could predict OTVA origin with an accuracy of 95.00%, a sensitivity of 87.18%, a specificity of 100.00%, a positive predictive value (PPV) of 100.00%, and a negative predictive value (NPV) of 92.42%. In the validation study, the algorithm exhibited excellent accuracy (95.00%) and AUC (AUC = 0.95), with a sensitivity of 94.12%, a specificity of 95.45%, a PPV of 91.43%, and an NPV of 96.92%.

CONCLUSION

Our developed algorithm can reliably predict OTVA origin without restrictions in the transition lead.

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.

Identifying Premature Ventricular Complexes from Outflow Tracts Based on PVC Configuration: A Machine Learning Approach

BACKGROUND

Current inferences about the site of origin (SOO) of premature ventricular complexes (PVC) from the surface ECG have not been subjected to newer data analytic techniques that identify signals that are not recognized by visual inspection.

AIMS

The objective of this study was to apply data analytics to PVC characteristics.

METHODS

PVCs from 12-lead ECGs of a consecutive series of 338 individuals were examined by unsupervised machine learning cluster analysis, and indexes were compared to a composite criterion for SOO.

RESULTS

Data analytics found that V1S plus V2S ≤ 9.25 of the PVC had a LVOT origin (sensitivity 95.4%; specificity 97.5%). V1R + V2R + V3R > 15.0 (a RBBB configuration) likely had a LVOT origin. PVCs with V1S plus V2S > 12.75 (LBBB configuration) likely had a RVOT origin. PVC with V1S plus V2S > 14.25 (LBBB configuration) and all inferior leads positive likely had a RVOT origin.

CONCLUSION

Newer data analytic techniques provide a non-invasive approach to identifying PVC SOO, which should be useful for the clinician evaluating a 12-lead ECG.

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.

The R-S difference index: A new electrocardiographic method for differentiating idiopathic premature ventricular contractions originating from the left and right ventricular outflow tracts presenting a left bundle branch block pattern

Introduction: Differentiating idiopathic premature ventricular contractions (PVCs) originating from the right and left ventricular outflow tracts with a left bundle branch block (LBBB) morphology is relevant to catheter ablation planning and important for lowering the risk of complications. This study established a novel electrocardiographic (ECG) criterion to discriminate PVCs originating from the septum of the right ventricular outflow tract (s-RVOT) and those originating from the aortic sinus cusp of the left ventricular outflow tract (LVOT-ASC).

Methods: A total of 259 patients with idiopathic PVCs originating from ventricular outflow tract with a LBBB pattern who underwent successful catheter ablation were retrospectively included. Among them, the PVCs originated from the s-RVOT in 183 patients and from the LVOT-ASC in 76 patients. The surface ECGs of the PVCs and sinus beats were analyzed using an electronic caliper. The R-S difference index in the precordial leads was calculated as V2R + V3R + V4R − V1S.

Results: PVCs originating from both the s-RVOT and LVOT-ASC displayed an inferior axis (dominant R waves in leads II, III, and aVF). Compared with the s-RVOT group, the R-wave amplitudes on leads II, III, and aVF were significantly larger in the LVOT-ASC group (p < 0.001, p < 0.003, and p < 0.001, respectively). Compared to the LVOT-ASC group, the s-RVOT group showed smaller R-wave amplitudes on leads V1–V6 (p = 0.021, p < 0.001, p < 0.001, p < 0.001, p < 0.001, and p < 0.001, respectively) and larger S-wave amplitudes on leads V1–V3 (p < 0.001, p < 0.001, and p < 0.001, respectively). Lead V3 was the most common transitional lead in both groups. Analysis of the receiver operating characteristic curve showed that the R-wave amplitude on lead V3 had the largest area under the curve (AUC) of 0.856 followed by the R-wave amplitudes on leads V4 (0.834) and V2 (0.806). The AUC of the R-S difference index was 0.867. An R-S difference index greater than 20.9 predicted an LVOT-ASC origin with 73.7% sensitivity and 86.3% specificity. This index is superior to previous criteria in differentiating PVCs with LBBB morphology and inferior axis originating from s-RVOT vs. LVOT-ASC.

Conclusions: The R-S difference index in precordial leads is a useful new ECG criterion for distinguishing LVOT-PVCs from RVOT-PVCs with LBBB morphology.

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.

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.

S-R index in V1/V3 serves as a novel criterion to discriminate idiopathic premature ventricular contractions originating from posteroseptal right ventricular outflow tract versus right coronary cusp

AIM

The current study aimed to establish a novel electrocardiographic (ECG) criterion for discrimination of idiopathic premature ventricular contractions (PVCs) originating from posteroseptal right ventricular outflow tract (sRVOT-p) versus right coronary cusp (RCC).

METHODS

A total of 76 patients with idiopathic PVCs who underwent mapping and successful ablation were retrospectively included. Among them, 37 patients had PVCs from sRVOT-p origin and 39 patients from RCC origin. The surface ECGs during PVCs were recorded. S-R different index in V1/V3 was calculated with the following formula of 0.134*V3R-0.133*V1S.

RESULTS

ECG characteristics showed wider total QRS duration, smaller R-wave amplitude on lead V2-V5, and larger S-wave amplitude on lead V1-V3 in sRVOT-p origin than RCC origin. Lead V3 was the most common transitional lead in two groups. Receiver operating characteristic (ROC) curve analysis showed that S-wave amplitude on lead V1 exhibited the largest AUC of 0.772, followed by the AUC of R-wave amplitude on lead V3 of 0.771. Subsequently, 0.134*V3R-0.133*V1S index was obtained by multiplication, subtraction, sum, and division of these ECG measurements, which exhibited the largest AUC of 0.808. The optimal cut-off value was -0.26 for differentiating RCC from sRVOT-p origin, with the sensitivity of 78.4% and specificity of 77.8%. Moreover, 0.134*V3R-0.133*V1S index was superior to previous criteria in analysis of PVCs originating from sRVOT-p and RCC.

CONCLUSIONS

0.134*V3R-0.133*V1S is a novel ECG criterion to discriminate sRVOT-p from RCC origin in patients with idiopathic PVCs, which may provide guidance for approach of radiofrequency catheter ablation.

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.

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 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.

Amplitude of QRS complex within initial 40 ms in V2 (V2QRSi40): Novel electrocardiographic criterion for predicting accurate localization of outflow tract ventricular arrhythmia origin

BACKGROUND

The initial depolarization vector of outflow tract (OT) ventricular arrhythmia (VA) varies in different origins, which may help to predict OT-VA origin more accurately.

OBJECTIVE

The purpose of this study was to develop a more accurate electrocardiographic (ECG) criterion for differentiating between left and right OT-VA origins.

METHODS

We studied 275 patients with successful ablation in the right ventricular outflow tract (RVOT) (n = 207) or left ventricular outflow tract (LVOT) (n = 68) in the development cohort. Amplitude of the QRS complex within initial 40 ms (QRS_i40) in precordial leads was measured. A novel criterion for identifying OT-VA origin was developed based on the development cohort. Predictive performance of novel criterion was further validated by comparing with previous ECG criteria (V₂S/V₃R index, V₂ transition ratio, and transition zone index) in the validation cohort with 107 patients (RVOT 75; LVOT 32).

RESULTS

QRS_i40 of identical precordial leads were significantly greater in the LVOT group than the RVOT group (P <.05). In the development cohort, QRS_i40 of V₂ (V₂QRS_i40) exhibited the greatest area under the curve of 0.950, with cutoff ≥0.52 mV predicting LVOT origin (sensitivity 86.0%; specificity 94.6%). In the validation cohort, V₂QRS_i40 ≥0.52 mV outperformed previous criteria in predictive performance (accuracy 90.7%; sensitivity 84.4%; specificity 93.3%). This advantage of V₂QRS_i40 over previous criteria also held true for subgroups of transition zone index = 0 and V₃ R/S transition.

CONCLUSION

V₂QRS_i40 is a novel and accurate ECG criterion to predict OT-VA origin that outperforms previous criteria.

S-R difference in V1-V2 is a novel criterion for differentiating the left from right ventricular outflow tract arrhythmias

AIM

The correct estimation of the VA origin as RVOT or LVOT results in reduced ablation duration reduced radiation exposure and decreased number of vascular access. In our study, we aimed to detect the predictive value of S-R difference in V1-V2 for differentiating the left from right ventricular outflow tract arrhythmias.

METHODS

We included 123 patients with symptomatic frequent premature ventricular outflow tract contractions who underwent successful catheter ablation (70 male, 53 female; mean age 46.2 ± 13.9 years, 61 RVOT, 62 LVOT origins). S-R difference in V1-V2 was calculated with this formula on the 12-lead surface ECG: (V1S + V2S) - (V1R + V2R). Conventional ablation was performed in 101 (82.1%) patients, CARTO electroanatomic mapping system was used in 22 (17.9%) patients.

RESULTS

V1-2 SRd was found to be significantly lower for LVOT origins than RVOT origins (p < .001). The cutoff value of V1-2 SRd obtained by ROC curve analysis was 1.625 mV for prediction of RVOT origin (sensitivity: 95.1%, specificity: 85.5%, positive predictive value: 86.5%, negative predictive value: 94.5%). The area under the curve (AUC) was 0.929 (p < .001).

CONCLUSION

S-R difference in V1-V2 is a novel and simple electrocardiographic criterion for accurately differentiating RVOT from LVOT sites of ventricular arrhythmia origins. The use of this simple ECG measurement could improve the accuracy of OTVA localization, could be beneficial for decreasing ablation duration and radiation exposure. Further studies with larger patient population are needed to verify the results of this study.