Hypertrophic Cardiomyopathy Federated Learning Implementation Platform

Hypertrophic Cardiomyopathy Clinical Trial

— HCM FLIP  

Official title:

Detection of Hypertrophic Cardiomyopathy Using Electrocardiograms and Echocardiograms Through Federated Learning

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06461468
• Other study ID #: HCM FLIP
• Status: Not yet recruiting
• Start date: June 2024
• Est. completion date: January 2026

Study information

• Verified date: June 2024
• Source: American Heart Association
• Contact: Shaina Costello, MPH
• Phone: 3146925607
• Email: ClinicalStudies[@]heart.org
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

HCM FLIP study is a two-phase protocol focusing on the detection of Hypertrophic Cardiomyopathy using Electrocardiograms and Echocardiograms through Federated Learning.

Description:

HCM FLIP (Hypertrophic Cardiomyopathy Federated Learning Implementation Platform) aim to build and test a model's system impact to detect hypertrophic cardiomyopathy (HCM) by training a machine learning (ML) model with electrocardiograms (ECGs) and echocardiograms (ECHOs). Approximately 10-1000 HCM cases and 30-10,000 age/sex-matched controls per institution, depending on size, will be included in the study. We hypothesize that a federated ML model will discriminate cases of HCM from those without HCM in a real-world setting.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 1000
• Est. completion date: January 2026
• Est. primary completion date: January 2026
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

HCM-Labeled Case Inclusion Criteria:

• Patients with maximum left ventricular wall thickness exceeding 15 mm (including the right ventricular component of the septum) without any other explanation for ventricular hypertrophy (e.g., severe hypertension, cardiac amyloidosis, severe AS, as determined by local investigators). The measurement could be made in an ECHO or on magnetic resonance imaging (MRI).

• Patients must have > one (1) ECG and/or > one (1) ECHO available that meet minimum compatibility requirements. If multiple ECGs and ECHOs are available per patient, then all available data meeting compatibility requirements will be used for model training purposes.

HCM-Labeled Case Exclusion Criteria:

• Any sign of infiltration found in cardiac MRI, if performed.

Control Case (Non-HCM) Inclusion Criteria:

• No diagnosis of HCM

• Age/sex are matched to HCM cases (+/- 5 years, if possible; +/- 10 years if numbers do not permit).

• Patient must have > one (1) ECG and/or > one (1) ECHO available that meet minimum compatibility requirements. If multiple ECGs and ECHOs are available per patient, then all available data meeting compatibility requirements will be used for model training purposes.

Control Case (Non-HCM) Exclusion Criteria

• Suggestion of HCM in a clinically obtained ECHO or cardiac MRI report unless subsequently confirmed no diagnosis of HCM. Any new clinical information discovered during the study will be left to the discretion of the local investigator.

Related Conditions & MeSH terms

• Cardiomyopathies
• Cardiomyopathy, Hypertrophic
• Hypertrophic Cardiomyopathy
• Hypertrophy

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
American Heart Association

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnosis of HCM	The number/instances of HCM diagnoses as identified by the ML model as compared to clinical diagnosis confirmation. Due to model training and efficacy goals, HCM diagnosis determined clinically via EKG/ECHO reading will be compared to the ML model's capacity to identify HCM correctly and efficiently.	Through study completion, an average of 2 years
Secondary	Diagnosis of different types of HCM	Diagnosis of different types of HCM (i.e., apical, obstructive), HCM without hypertrophy, genetic positive/negative indicators, among others, as identified by the ML model as compared to clinical diagnosis confirmation. Due to model training and efficacy goals, HCM diagnosis determined clinically via EKG/ECHO reading will be compared to the ML model's capacity to identify HCM correctly and efficiently.	Through study completion, an average of 2 years