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NCT06256913: 2022PI172

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

Status unknown Last updated 13 February 2024
What this trial tests

trial in Hypertrophic Cardiomyopathy in 870 participants. Status unknown.

Timeline
6 May 2023
Primary endpoint
6 May 2024
6 May 2024

Quick facts

Lead sponsorPr. Nicolas GIRERD
StatusStatus unknown
Study typeOBSERVATIONAL
Enrollment870
Start date6 May 2023
Primary completion6 May 2024
Estimated completion6 May 2024
Sites2 locations across France

Conditions studied

Sponsor

Pr. Nicolas GIRERD — full company profile →

Who can join

18 and older, any sex, with Hypertrophic Cardiomyopathy. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.

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Other recruiting trials for Hypertrophic Cardiomyopathy

Currently open trials in the same condition.

Other Pr. Nicolas GIRERD trials

Trials by the same sponsor.

Verify against primary sources

Data sources for this page

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT06256913.

Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing