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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

870 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-05-06

Study Completion Date

2024-05-06

Brief Summary

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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.

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Detailed Description

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Conditions

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Hypertrophic Cardiomyopathy

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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Patients with sarcomeric hypertrophic cardiomyopathy and cardiovascular events

Patients with confirmed sarcomeric hypertrophic cardiomyopathy who experienced cardiovascular events.

No interventions assigned to this group

Patients with sarcomeric hypertrophic cardiomyopathy free of cardiovascular events

Patients with confirmed sarcomeric hypertrophic cardiomyopathy free of cardiovascular events.

No interventions assigned to this group

Eligibility Criteria

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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

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No