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.

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.

Enrollment Target:

Phase 1: Approximately 10-100 HCM cases and 30-10,000 age/sex-matched controls per institution, depending on size, will be included in the study. Since a larger sample size benefits training ML models, additional cases could be included based on availability within each institution.

Phase 2: Institution-specific cohort of patients (see Section 7.4.2) to simulate implementation of the algorithm; natural case control ratio and consecutive patients for identified period of time. The numbers of patients will be dependent on the volume of each institution and should reflect the local practice.

No participant data is identifiable nor will be shared across participating institutions.