Hypertrophic Cardiomyopathy Federated Learning Implementation Platform

Detection of Hypertrophic Cardiomyopathy Using Electrocardiograms and Echocardiograms Through Federated Learning

Status

Enrolling by invitation

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06461468

Acronym

HCM FLIP

Enrollment

1000

Registered

2024-06-17

Start date

2024-06-05

Completion date

2026-01-31

Last updated

2024-08-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Hypertrophic Cardiomyopathy

Keywords

HCM, Hypertrophic Cardiomyopathy, Federated Learning, Federated Learning Platform, FLP

Brief summary

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

Detailed 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.

Interventions

None listed

Study design

Observational model

CASE_CONTROL

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

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)

Design outcomes

Primary

Measure	Time frame	Description
Diagnosis of HCM	Through study completion, an average of 2 years	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.

Secondary

Measure	Time frame	Description
Diagnosis of different types of HCM	Through study completion, an average of 2 years	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.

Countries

United States

Outcome results