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

NCT06256913 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 870

Last updated 2024-02-13

No results posted yet for this study

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

Sponsors & Collaborators

  • Pr. Nicolas GIRERD

    lead OTHER

Principal Investigators

  • Nicolas Girerd, MD · CHRU de Nancy

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-05-06
Primary Completion
2024-05-06
Completion
2024-05-06

Countries

  • France

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06256913 on ClinicalTrials.gov