A Study of Artificial Intelligence ECG With ECG Devices to Detect Hypertrophic Cardiomyopathy Distinct From Athlete's

Prospective Evaluation of Artificial Intelligence ECG With Consumer-Facing ECG Devices for Detection of Hypertrophic Cardiomyopathy and Distinction From Athlete's

The purpose of this study is to evaluate the AI-ECG algorithm for HCM in detecting HCM and in differentiating it from athlete's using not only the standard 12-lead ECG, but also ECGs obtained with the Apple Watch and Alivecor KardiaMobile devices.

Study Overview

• Status: Active, not recruiting
• Conditions: Hypertrophic Cardiomyopathy
• Intervention / Treatment: Diagnostic test: 12-Lead ECG; Diagnostic test: Apple Smart Watch Single Lead ECG; Diagnostic test: AliveCor KardiaMobile 6-Lead ECG

• Study Type: Observational
• Enrollment (Estimated): 183

Contacts and Locations

Study Locations

• United States
  • Minnesota
    • Rochester, Minnesota, United States, 55905
      • Mayo Clinic in Rochester

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Outpatients scheduled for appointments in the sports cardiology or HCM clinic at Mayo Clinic in Rochester, MN will be approached to participate in the study.

Description

Inclusion Criteria:

• Patients with clinically validated diagnoses of HCM (n=150) and athlete's (n=150) will be identified by pre-screening of the clinic appointments for each of the specialty HCM and Sports Cardiology clinics or in the CV fellows' clinic (in patients with an established diagnosis and no pending testing). All diagnoses will need to be supported by unequivocal imaging and other ancillary data per our standard of care and at the determination of clinic experts.

Exclusion Criteria:

• Any exception to the above criteria.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Hypertrophic Cardiomyopathy (HCM); Subjects with clinically validated diagnoses of HCM will be enrolled and have a clinically indicated 12-Lead ECG obtained as well as ECG tracings collected using an Apple Smart Watch (single-lead) and AliveCor KardiaMobile (6-Lead).	Diagnostic test: 12-Lead ECG; A clinically performed 12-lead ECG tracing within 30 days of the appointment will be obtained from the subject medical record and will be used for AI-ECG analyses.; Diagnostic test: Apple Smart Watch Single Lead ECG; A single lead ECG tracing will be collected using an Apple Smart Watch and tracing will be used for AI-ECG analyses.; Diagnostic test: AliveCor KardiaMobile 6-Lead ECG; A 6-lead ECG tracing will be collected using an AliveCor KardiaMobile device and tracing will be used for AI-ECG analyses.
Athlete's; Athlete's will be enrolled and have a clinically indicated 12-Lead ECG obtained as well as ECG tracings collected using an Apple Smart Watch (single-lead) and AliveCor KardiaMobile (6-Lead).	Diagnostic test: 12-Lead ECG; A clinically performed 12-lead ECG tracing within 30 days of the appointment will be obtained from the subject medical record and will be used for AI-ECG analyses.; Diagnostic test: Apple Smart Watch Single Lead ECG; A single lead ECG tracing will be collected using an Apple Smart Watch and tracing will be used for AI-ECG analyses.; Diagnostic test: AliveCor KardiaMobile 6-Lead ECG; A 6-lead ECG tracing will be collected using an AliveCor KardiaMobile device and tracing will be used for AI-ECG analyses.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Distribution of AI-ECG probabilities in HCM	Artificial Intelligence (AI) scores will be measured using the AI Algorithm on ECG tracings obtained from clinically indicated 12-Lead ECG, Apple Smart Watch (single-lead), and AliveCor KardiaMobile (6-Lead) in subjects with HCM. The AI scores will be utilized to generate the AI-ECG probability of accurately diagnosing HCM (labelled as true positive, true negative, false positive, false negative) and the distribution of AI-ECG probabilities will be evaluated. A higher distribution of AI-ECG probabilities (more true positives) will reflect better diagnostic performance of the AI-ECG Algorithm.	Baseline
Comparative diagnostic performance between tracings obtained from different devices	Artificial Intelligence (AI) scores will be measured using the AI Algorithm on ECG tracings obtained from clinically indicated 12-Lead ECG, Apple Smart Watch (single-lead), and AliveCor KardiaMobile (6-Lead). Diagnostic performance of AI Algorithm (labelled as true positive, true negative, false positive, false negative) based on tracing from each ECG form factor (12-lead, single-lead, 6-lead) will be evaluated and compared.	Baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Correlation with false negative AI ECG result	Artificial Intelligence (AI) scores will be measured using the AI Algorithm on ECG tracings obtained from clinically indicated 12-Lead ECG, Apple Smart Watch (single-lead), and AliveCor KardiaMobile (6-Lead). Diagnostic performance of AI Algorithm (labelled as true positive, true negative, false positive, false negative) based on tracing from each ECG form factor (12-lead, single-lead, 6-lead) will be evaluated and the correlation of the form factor to a false negative AI ECG result will be determined.	Baseline
Distribution of AI-ECG probabilities in Athlete's	Artificial Intelligence (AI) scores will be measured using the AI Algorithm on ECG tracings obtained from clinically indicated 12-Lead ECG, Apple Smart Watch (single-lead), and AliveCor KardiaMobile (6-Lead) in subjects with Athlete's. The AI scores will be utilized to generate the AI-ECG probability of accurately diagnosing HCM (true positive, true negative, false positive, false negative) and the distribution of AI-ECG probabilities will be evaluated. A higher distribution of AI-ECG probabilities (more true positives) will reflect better diagnostic performance of the AI-ECG Algorithm.	Baseline

Collaborators and Investigators

• Sponsor: Mayo Clinic
• Investigators: Principal Investigator: Konstantinos Siontis, MD, Mayo Clinic

Study record dates

Study Major Dates

• Study Start (Actual): May 7, 2024
• Primary Completion (Actual): June 25, 2025
• Study Completion (Estimated): December 1, 2026

Study Registration Dates

• First Submitted: February 26, 2024
• First Submitted That Met QC Criteria: February 26, 2024
• First Posted (Actual): March 4, 2024

Study Record Updates

• Last Update Posted (Actual): March 19, 2026
• Last Update Submitted That Met QC Criteria: March 17, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Aortic Valve Disease; Cardiovascular Diseases; Heart Diseases; Heart Valve Diseases; Cardiomyopathies; Aortic Stenosis, Subvalvular; Aortic Valve Stenosis; Cardiomyopathy, Hypertrophic; Diagnostic Techniques and Procedures; Diagnosis; Diagnostic Techniques, Cardiovascular; Heart Function Tests; Electrodiagnosis; Electrocardiography
• Other Study ID Numbers: 23-007685

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No