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

Mariani, Marco Valerio; Piro, Agostino; Rocca, Domenico Giovanni Della; Forleo, Giovanni B.; Pothineni, Naga Venkata K.; Romero, Jorge; Biase, Luigi Di; Fedele, Francesco; Lavalle, Carlo

doi:10.15420/aer.2020.10

Cited by 10 publications

(11 citation statements)

References 36 publications

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“…The obtained classification results had a similar level of accuracy than clinical algorithms used in the SOO prediction (Anderson et al, 2019;Mariani et al, 2021). From these results, we have been able to conclude that, although extracting signal features from ECG seems to be the best approach, there is not a large improvement with respect to simply use raw data as features (the potential of the signal at ten equally spaced time points), provided that all signals are aligned.…”

Section: Discussionsupporting

confidence: 52%

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

et al. 2022

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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.

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Section: Discussionsupporting

confidence: 52%

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

et al. 2022

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“…31,32 However, the complex anatomy of the outflow tract, overlapping common sites of PVC origin, and potential factors such as cardiac rotation, lead positions, and preferential conduction of PVCs may limit the efficacy of typical surface ECG criteria. 31,33 Our study demonstrated significant clinical value of the LF/HF ratio in identifying the originating foci of OT-PVCs, which was independent of cardiac anatomy. Therefore, the LF/HF ratio may provide additional value to existing ECG criteria and algorithms in the differentiation between RVOT-PVCs and LVOT-PVCs.…”

Section: Discussionmentioning

confidence: 65%

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

Zhang,

Yu,

et al. 2023

Clinical Cardiology

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BackgroundAs 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.HypothesisHere, 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.MethodsA 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.ResultsPatients 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).ConclusionsPatients 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.

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“…The differences of vector ratios and QRS morphologies are maximized in an anteroposterior dimension. 12 The electroanatomical considerations can constitute a logical explanation for the principle of most ECG algorithms, including TZ index. 4 However, the posteriorseptal of RVOT is directly anterior to the right coronary cusp (RCC).…”

Section: Discussionmentioning

confidence: 99%

“…This is not surprising given that the electrical cardiac depolarizing vector moving away from (for RVOT) or towards (for LVOT) the anterior chest wall. The differences of vector ratios and QRS morphologies are maximized in an anteroposterior dimension 12 . The electroanatomical considerations can constitute a logical explanation for the principle of most ECG algorithms, including TZ index 4 .…”

Section: Discussionmentioning

confidence: 99%

Interventricular septum angle obtained from cardiac computed tomography for origin differentiation of outflow tract ventricular arrhythmia between left and right

Zhu

Chen

et al. 2022

Pacing Clinical Electrophis

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Aims Our objective was to explore whether the accuracy of the transitional zone index (TZI) for outflow tract ventricular arrhythmias (OT‐VAs) origin is affected by cardiac rotation and the additive value of interventricular septum angle (IVSa) obtained from coronary computed tomography angiography (CCTA). Methods Standard 12‐lead ECGs of OT‐VAs with inferior axis in consecutive patients undergoing both CCTA examination and successful ablation were retrospectively analyzed. The IVSa was defined as an angle between the long axis of IVS and sagittal axis of the body from CCTA. Results 64 patients (31 men; mean age 54.2 ± 11.6 years) were enrolled. The OT‐VAs exhibited right ventricular outflow tract origin in 46 (71.9%) patients and 36 (78.3%) were diagnosed correctly by TZI. The left ventricular outflow tract origin OT‐VAs was observed in 18 (28.1%) patients and 16 (88.9%) were diagnosed correctly by TZI. The patients were then divided into TZI correct group (n = 52) and TZI incorrect group (n = 12). In the TZI incorrect group, 11/12 (91.7%) cases were R/S transition in lead V3 with the TZ score during premature ventricular contractions [2.8(2.5−3.4)], and the TZI between −1.5 and 0. The IVSa was significantly larger in the TZI incorrect group than correct group (52.0 ± 6.9° vs. 39.0 ± 6.1°; p < .0001). The IVSa ≥46° predicted TZI incorrect with 92% sensitivity, 94% specificity, and 94% accuracy. Conclusion The IVSa is a novel cardiac rotation index that reliably improves TZI to differentiate the OT‐VAs origin, especially for the OT‐VAs with lead V3 R/S transition.

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

Cited by 10 publications

References 36 publications

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

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

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

Interventricular septum angle obtained from cardiac computed tomography for origin differentiation of outflow tract ventricular arrhythmia between left and right

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