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

Penela, Diego; Falasconi, Giulio; Carreño, José Miguel; Soto‐Iglesias, David; Fernández-Armenta, Juan; Acosta, Juan; Martí-Almor, Julio; Benito, Begoña; Bellido, A; Chauca, Alfredo; Scherer, Claudia; Viveros, Daniel; Alderete, José F.; Ordóñez, A; Francisco‐Pascual, Jaume; Rivas-Gándara, Núria; Meca-Santamaria, Julia; Franco, Paula; Lucia, Carmine De; Ali, Hussam; Cappato, Riccardo; Cámara, Óscar; Francia, Pietro; Berruezo, Antonio

doi:10.1007/s10840-023-01507-x

Cited by 2 publications

(19 citation statements)

References 15 publications

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“…Furthermore, an algorithm was designed to automate the identification of the R/S transition from PVC and sinus rhythm beats, thereby improving the precision of the models. Additionally, all thresholds were removed from the original approach proposed by Penela et al ( 9 ).…”

Section: Methodsmentioning

confidence: 99%

“… DS-334: Containing an open-source database with 334 RVOT: 234, LVOT: 100) 12-lead ECG signals published by Zheng et al ( 15 ). DS-114: Consisting 114 (RVOT: 79, LVOT: 35) cases retrieved from Hospital Teknon, Barcelona; these cases were partially used in Penela et al ( 9 ) and included the 12-lead ECG signals, multiple beats from each patient, including the PVC, and clinical data of the patient. Ethical guidelines were followed, with written informed consent obtained from every patient.…”

Section: Methodsmentioning

confidence: 99%

“…DS-114: Consisting 114 (RVOT: 79, LVOT: 35) cases retrieved from Hospital Teknon, Barcelona; these cases were partially used in Penela et al ( 9 ) and included the 12-lead ECG signals, multiple beats from each patient, including the PVC, and clinical data of the patient.…”

Section: Methodsmentioning

confidence: 99%

“…The model utilizing clinical information was designed based on the weighted hybrid algorithm proposed by Penela et al ( 9 ). In that study, researchers employed age and amplitude in V3, binarized with thresholds of 50 years old and 1 mV.…”

Section: Methodsmentioning

confidence: 99%

“…These methods combine ECG visual morphology analysis with patient data to improve SOO classification. For instance, Penela et al ( 9 ) proposed a hybrid algorithm that incorporates clinical features, such as sex, hypertension, and age, along with ECG analysis based on the precordial R/S transition and the amplitude in lead V3. However, this approach relies on manual thresholds for age and V3 amplitude, and it is partially dependent on clinician expertise in determining the R/S transition.…”

Section: Introductionmentioning

confidence: 99%

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Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data

Bocanegra-Pérez,

Piella,

Sebastian

et al. 2024

Front. Cardiovasc. Med.

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The treatment of outflow tract ventricular arrhythmias (OTVA) through radiofrequency ablation requires the precise identification of the site of origin (SOO). Pinpointing the SOO enhances the likelihood of a successful procedure, reducing intervention times and recurrence rates. Current clinical methods to identify the SOO are based on qualitative analysis of pre-operative electrocardiograms (ECG), heavily relying on physician’s expertise. Although computational models and machine learning (ML) approaches have been proposed to assist OTVA procedures, they either consume substantial time, lack interpretability or do not use clinical information. Here, we propose an alternative strategy for automatically predicting the ventricular origin of OTVA patients using ML. Our objective was to classify ventricular (left/right) origin in the outflow tracts (LVOT and RVOT, respectively), integrating ECG and clinical data from each patient. Extending beyond differentiating ventricle origin, we explored specific SOO characterization. Utilizing four databases, we also trained supervised learning models on the QRS complexes of the ECGs, clinical data, and their combinations. The best model achieved an accuracy of 89%, highlighting the significance of precordial leads V1-V4, especially in the R/S transition and initiation of the QRS complex in V2. Unsupervised analysis revealed that some origins tended to group closer than others, e.g., right coronary cusp (RCC) with a less sparse group than the aortic cusp origins, suggesting identifiable patterns for specific SOOs.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data

Bocanegra-Pérez,

Piella,

Sebastian

et al. 2024

Front. Cardiovasc. Med.

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Ablación por cateter de radiofrecuencia de extrasístoles ventriculares con origen en el seno coronario izquierdo

Alderete,

Berruezo,

Centurión

et al. 2024

Mem. Inst. Investig. Cienc. Salud

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Las extrasístoles ventriculares (EEVV) son frecuentemente asintomáticas y no requieren de tratamiento antiarrítmico. Las EEVV de los tractos de salida son las EEVV idiopáticas más frecuentes en la práctica clínica. La anamnésis, el examen físico y el electrocardiograma son fundamentales en el diagnóstico. Aunque las EEVV de los tractos de salidas ventriculares son consideradas benignas, pueden producir diversos síntomas y desencadenar una cardiomiopatía inducida por las extrasístoles, que lleva a un deterioro de la función sistólica ventricular izquierda y en última instancia, insuficiencia cardiaca. Cuando el tratamiento farmacológico de las EEVV fracasa, nos queda la ablación de arritmias por catéter de radiofrecuencia como única alternativa eficaz. Este es el caso de la paciente de sexo femenino de 62 años, hipertensa, dislipidémica y tabaquista, que demuestra la seguridad de la ablación por catéter de radiofrecuencia, la eficacia terapéutica, y el restablecimiento integro de la función ventricular del corazón.

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

Cited by 2 publications

References 15 publications

Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data

Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data

Ablación por cateter de radiofrecuencia de extrasístoles ventriculares con origen en el seno coronario izquierdo

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