Machine Learning Approach Based on Echocardiographic Data to Improve Prediction of Cardiovascular Events in Hypertrophic Cardiomyopathy

Summary

Hypertrophic cardiomyopathy is a pathology with a highly variable course, ranging from patients who are asymptomatic throughout their lives to those who experience sudden death and/or terminal heart failure. The main objective is to develop and validate an algorithm (constructed through supervised learning) using cardiac imaging data to predict the risk of cardiovascular events in sarcomeric hypertrophic cardiomyopathy.

Eligibility

Inclusion Criteria:

* Age \>18
* Patients with confirmed sarcomeric hypertrophic cardiomyopathy

Exclusion Criteria:

* Echocardiographic data not allowing deep analysis (technical default, bad echogenicity of the patient)
* Other causes of left ventricular hypertrophy that may hamper the diagnosis (p.e. aortic or sub-aortic stenosis, severe renal insufficiency, hypertension).
* History of ischemic heart disease or associated myocarditis
* Opposition of the patient to the use of his/her data

Conditions2

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