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Life sciences · Observational Study
ClinicalTrials.gov · October 27, 2026
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Observational Study.
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Registry record from ClinicalTrials.gov (NCT07257289). This is a study registration, not published results. Lead sponsor: Nantes University Hospital. Recruitment status: RECRUITING. Study type: OBSERVATIONAL. Enrollment: 1000 participants (ESTIMATED). Conditions: Hereditary Heart Diseases. Interventions: BIOLOGICAL: Blood sample. Primary outcome measures: Arrhythmic and heart failure risk stratification , 5, 8 and 10 years. Brief summary: 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).