Hypertrophic Cardiomyopathy Federated Learning Implementation Platform

N/AEnrolling By InvitationNCT06461468

American Heart Association1,000 enrolled

Overview

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

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.

Study Type

OBSERVATIONAL

Enrollment

1,000

Conditions

Hypertrophic Cardiomyopathy

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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.

Locations (6)

Thomas Hospital

Fairhope, Alabama, United States

Riverside Medical Center

Kankakee, Illinois, United States

Johns Hopkins University School of Medicine

Baltimore, Maryland, United States

University of Michigan Medical Center

Ann Arbor, Michigan, United States

Outcomes

Primary Outcomes

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.

Time frame: Through study completion, an average of 2 years

Secondary Outcomes

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.

Time frame: Through study completion, an average of 2 years

Data from ClinicalTrials.gov

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