Approaches for Localizing the Origin of PVCs in the Outflow Tract: Future Perspectives and Challenges
Idiopathic ventricular arrhythmias (IVAs) are a type of arrhythmias with focal origins. The locations of most such arrhythmias have been identified and confirmed. In cases in which pharmacological treatment is ineffective or limited, radiofrequency catheter ablation is a therapeutic option whose suc...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Compuscript Ltd
2025-03-01
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| Series: | Cardiovascular Innovations and Applications |
| Online Access: | https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2025.0006 |
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| Summary: | Idiopathic ventricular arrhythmias (IVAs) are a type of arrhythmias with focal origins. The locations of most such arrhythmias have been identified and confirmed. In cases in which pharmacological treatment is ineffective or limited, radiofrequency catheter ablation is a therapeutic option whose success rate largely depends on accurate IVA localization. The current standard approach for localizing the origin of IVAs involves comparing the normal electrocardiogram (ECG) with the characteristic ECG of the arrhythmia. This comparison includes analysis of parameters such as the QRS wave polarity in different leads, QRS duration, R/S ratio, and S-R difference in precordial leads. Innovation and improvement in the analysis of the relationships among these ECG characteristics would enhance the accuracy of IVA localization. However, the accuracy of this method may be limited by factors including the patient’s body habitus, cardiac rotation, and specific conduction characteristics. To mitigate these influences, combining this approach with imaging modalities such as cardiac MRI, CT, and echocardiography can help identify structural abnormalities at the foci of premature ventricular contractions (PVCs), thereby enhancing the precision of IVA localization. To decrease human error and achieve more efficient PVC localization, algorithmic analysis and anatomical modeling with computer-based methods have emerged as promising new approaches. Recently, with advancements in artificial intelligence, non-invasive localization of IVAs through deep learning has emerged as a research direction. This article reviews the methods currently used for the localization and differentiation of PVCs, compares and analyzes their clinical significance, and explores their potential for combined application. Future directions and trends in this field are also discussed. |
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| ISSN: | 2009-8618 2009-8782 |