Genetic testing in children with Brugada syndrome: results from a large prospective registry

Pannone, Luigi; Bisignani, Antonio; Osei, Randy; Gauthey, Anaïs; Sorgente, Antonio; Vergara, Pasquale; Monaco, Cinzia; Rocca, Domenico G. Della; Monte, Alvise Del; Strazdas, Antanas; Mojica, Joerelle; Housari, Maysam Al; Miraglia, Vincenzo; Mouram, Sahar; Paparella, Gaetano; Ramak, Robbert; Overeinder, Ingrid; Bala, Gezim; Almorad, Alexandre; Ströker, Erwin; Pappaert, Gudrun; Sieira, Juan; Ravel, Thomy de; Meir, Mark La; Brugada, Pedro; Chierchia, Gian Battista; Dooren, Sonia Van; Asmundis, Carlo de

doi:10.1093/europace/euad079

Cited by 12 publications

(4 citation statements)

References 39 publications

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“… 16–19 However, the ideal age for the drug test is controversial. 17 In these cases, an ECG obtained during a febrile episode is useful to rule out a Brugada type 1 ECG appearance, still it cannot be considered a substitute of the sodium channel blocker test. Furthermore, the drug test should be performed in a safe environment (i.e.…”

Section: Discussionmentioning

confidence: 99%

Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach

Zanchi,

Faraci,

Gharaviri

et al. 2023

Europace

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Background and Aims Brugada syndrome (BrS) is an inherited disease associated with an increased risk of ventricular arrhythmias. Recent studies have reported the presence of an altered atrial phenotype characterized by abnormal P-wave parameters. The aim of this study was to identify BrS based exclusively on P waves features through an artificial intelligence (AI)-based model. Methods Continuous 5-minute 12-lead ECG recordings were obtained in sinus rhythm from a) patients with spontaneous or ajmaline-induced BrS and no history of AF and b) subjects with suspected BrS and negative ajmaline challenge. The recorded ECG signals were processed and divided into epochs of 15 seconds each. Within these epochs, P-waves were first identified and then averaged. From the averaged P-waves, a total of 67 different features considered relevant to the classification task were extracted. These features were then used to train nine different artificial intelligence (AI)-based supervised classifiers. Results A total of 2228 averaged P-wave observations, resulting from the analysis of 33420 P-waves were obtained from 123 patients (79 BrS + and 44 BrS-). Averaged P-waves were divided using a patient-wise split, allocating 80% for training and 20% for testing, ensuring data integrity and reducing biases in AI-based models training. BrS + patients presented with longer P-wave duration (136 ms vs. 124 ms, p < 0.001) and higher terminal force in lead V1 (2.5 a.u. vs. 1.7 a.u., p < 0.01) compared with BrS- subjects. Among classifiers, AdaBoost model had the highest values of performance for all the considered metrics, reaching an accuracy of over 81% (sensitivity 86%, specificity 73%). Conclusion An AI machine learning model is able to identify patients with BrS based only on P-wave characteristics. These findings confirm the presence of an atrial hallmark and open new horizons for AI-guided BrS diagnosis.

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Section: Discussionmentioning

confidence: 99%

Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach

Zanchi,

Faraci,

Gharaviri

et al. 2023

Europace

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“…The predictive value of having an SCN5A mutation was recently evaluated by Pannone et al [190]. In a single center court of 63 patients, 29 had an SCN5A mutation and 34 did not, and this court was followed for 125.9 ± 176.4 months.…”

Section: Geneticsmentioning

confidence: 99%

“…BrS clinical presentation in children may be heterogeneous, including palpitation, syncope, AF, VT, VF, SCD, and SID [196]. The most frequent first evidence of BrS is a positive family history (47%), casual ECG findings (25%), syncope (14%), and arrhythmias (13%), such as AF (10%)-aborted SD (1%) [189][190][191][192][193][194][195][196][197]. The majority of syncopal episodes happen at rest and can be precipitated by fever, as well as vaccination-related fever [169,177].…”

Section: Clinical Manifestation In Childrenmentioning

confidence: 99%

Inherited Arrhythmias in the Pediatric Population: An Updated Overview

Mariani,

Pierucci,

Fanisio

et al. 2024

Medicina

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Pediatric cardiomyopathies (CMs) and electrical diseases constitute a heterogeneous spectrum of disorders distinguished by structural and electrical abnormalities in the heart muscle, attributed to a genetic variant. They rank among the main causes of morbidity and mortality in the pediatric population, with an annual incidence of 1.1–1.5 per 100,000 in children under the age of 18. The most common conditions are dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM). Despite great enthusiasm for research in this field, studies in this population are still limited, and the management and treatment often follow adult recommendations, which have significantly more data on treatment benefits. Although adult and pediatric cardiac diseases share similar morphological and clinical manifestations, their outcomes significantly differ. This review summarizes the latest evidence on genetics, clinical characteristics, management, and updated outcomes of primary pediatric CMs and electrical diseases, including DCM, HCM, arrhythmogenic right ventricular cardiomyopathy (ARVC), Brugada syndrome (BrS), catecholaminergic polymorphic ventricular tachycardia (CPVT), long QT syndrome (LQTS), and short QT syndrome (SQTS).

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“…Of note, despite several studies showing a higher prevalence of SCN5A mutation in symptomatic patients than in asymptomatic ones, to date, there has been no strong evidence that the presence of a mutation is associated with increased arrhythmic risk [ 7 , 11 , 22 ].…”

Section: Risk Stratificationmentioning

confidence: 99%

Brugada Syndrome: Focus for the General Pediatrician

Speranzon,

Chicco,

Bonazza

et al. 2024

Children

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Brugada Syndrome is an “inherited” channelopathy characterized by a predisposition to syncope and sudden death. It typically presents in young adults but is also known to affect the pediatric population, even if the prevalence is low compared to the adult population. The diagnostic ECG pattern shows coved-type ST-segment elevation in the right precordial leads, occurring spontaneously or after provocative drug tests with IV administration of Class I antiarrhythmic drugs. However, the electrocardiographic findings may vary, and transient or concealed forms of the syndrome further complicate diagnosis, necessitating thorough evaluation and close clinical follow-up. The clinical presentation of Brugada Syndrome may range from asymptomatic individuals to patients who have experienced syncope or sudden cardiac arrest. The syndrome remains underdiagnosed due to its elusive symptoms and the absence of abnormal findings between episodes. Additionally, specific triggers such as fever, certain medications and alcohol consumption may unmask the electrocardiographic changes and provoke arrhythmias in susceptible individuals. Given its elusive nature, early diagnosis and risk stratification are crucial in identifying individuals who may benefit from an implantable cardioverter defibrillator, the mainstay of treatment for high-risk patients, or pharmacological interventions.

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Genetic testing in children with Brugada syndrome: results from a large prospective registry

Cited by 12 publications

References 39 publications

Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach

Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach

Inherited Arrhythmias in the Pediatric Population: An Updated Overview

Brugada Syndrome: Focus for the General Pediatrician

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