Sudden cardiac death (SCD) is one of the leading causes of death in developed countries. These deaths (more than 5,000 per year in France) are due to hereditary arrhythmias or cardiomyopathies. Early diagnosis of SCD is often achieved through family screening, but the main challenge is to stratify the risk of SCD in these patients. Indeed, prevention of SCD relies mainly on the implantation of an automatic defibrillator. The challenge is to identify patients who will develop SCD and avoid implanting an implantable cardioverter defibrillator (ICD) in patients who will never develop arrhythmias but who will face complications related to the ICD (inappropriate shocks, infection, lead failure), leading to a reduced quality of life and significant costs for the healthcare system. However, there is a lack of relevant clinical and biological markers for risk stratification, which rules out any possibility of preventive screening. Most of the clinical and ECG (electrocardiogram) parameters identifying an increased risk of SCD have not been reproduced in replication studies. In this project, the investigator will develop a data processing and analysis pipeline using artificial intelligence methods to assess the individual risk of serious arrhythmic events or heart failure in patients with hereditary arrhythmic diseases or cardiomyopathies through the automated processing of multimodal data (clinical data, electrocardiogram (ECG), imaging (echocardiography, MRI magnetic resonance imaging), genetic data, biomarkers).
The eligibility of patients (index cases and related cases) for the study will be determined during a cardiology consultation or day hospitalisation, carried out as part of routine care. The investigator undertakes to obtain the person's free, informed and express consent, collected in writing, after providing them with oral and written information on the protocol and allowing them sufficient time to reflect. In the case of a minor patient, the investigator undertakes to inform the minor patient and their legal representatives orally and in writing and to obtain the minor's assent, i.e. their oral or written agreement depending on their age, and the written consent of their legal representatives. Specific acts for research: * Collection of two additional EDTA tubes (2 x 5 ml) during a blood test carried out as part of routine care, except for minors under the age of 4, for whom a saliva sample will be offered instead. * Collection of one dry tube (5 ml) during a blood test carried out as part of routine care for biomarker analysis and only for adult patients. Clinical data will be collected in a parameterised and secure eCRF (electronic Case Report Form). Patients will be followed in the routine care for maximum 10 years. Arrhythmias, heart failure and sudden cardiac death will be collected during follow-up. A data processing and analysis pipeline using artificial intelligence methods to assess the individual risk of serious arrhythmic events or heart failure in patients with hereditary arrhythmic diseases or cardiomyopathies through the automated processing of multimodal data (clinical data, electrocardiogram (ECG), imaging (echocardiography, MRI magnetic resonance imaging), genetic data, biomarkers) will be developped.
Study Type
OBSERVATIONAL
Enrollment
1,000
CHU de Bordeaux
Bordeaux, France
CHU de Brest
Brest, France
CHU de Clermont-Ferrand
Clermont-Ferrand, France
CHU de Dijon
Dijon, France
CHU de La Rochelle
La Rochelle, France
CHU de Limoges
Limoges, France
CHU de Montpellier
Montpellier, France
CHU de Nantes
Nantes, France
CHU de Poitiers
Poitiers, France
CHU de Rennes
Rennes, France
...and 5 more locations
Arrhythmic and heart failure risk stratification
to assess the arrhythmic risk and/or risk of heart failure in patients with hereditary heart disease at 5, 8 and 10 years, using a model combining clinical, electrocardiographic, imaging, genetic and biomarker data.
Time frame: 5, 8 and 10 years
Demographics data
Determine the risk of arrhythmia and/or heart failure based on demographics data
Time frame: 5, 8 and 10 years
Diagnosis of Brugada syndrome
Evaluate the diagnostic performance of the model combining clinical, electrocardiographic, genetic and biomarker data for the identification of Brugada syndrome.
Time frame: 5 years
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.