Trials / Unknown
UnknownNCT06256913
Machine Learning Approach Based on Echocardiographic Data to Improve Prediction of Cardiovascular Events in Hypertrophic Cardiomyopathy
Phénogroupage basé Sur l'Apprentissage Automatique Dans la Cardiomyopathie Hypertrophique Pour Identifier Les Facteurs prédictifs de la Fibrose Myocardique et Les événements Cardiovasculaires
- Status
- Unknown
- Phase
- —
- Study type
- Observational
- Enrollment
- 870 (estimated)
- Sponsor
- Pr. Nicolas GIRERD · Academic / Other
- Sex
- All
- Age
- 18 Years
- Healthy volunteers
- Not accepted
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.
Conditions
Timeline
- Start date
- 2023-05-06
- Primary completion
- 2024-05-06
- Completion
- 2024-05-06
- First posted
- 2024-02-13
- Last updated
- 2024-02-13
Locations
2 sites across 1 country: France
Source: ClinicalTrials.gov record NCT06256913. Inclusion in this directory is not an endorsement.