Hypertrophic Cardiomyopathy Clinical Trial
Official title:
Prospective Evaluation of Artificial Intelligence ECG With Consumer-Facing ECG Devices for Detection of Hypertrophic Cardiomyopathy and Distinction From Athlete's Heart
NCT number | NCT06290570 |
Other study ID # | 23-007685 |
Secondary ID | |
Status | Recruiting |
Phase | |
First received | |
Last updated | |
Start date | May 7, 2024 |
Est. completion date | September 2025 |
Verified date | May 2024 |
Source | Mayo Clinic |
Contact | Heart Rhythm Services Research Team |
Phone | (507) 255-0774 |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
The purpose of this study is to evaluate the AI-ECG algorithm for HCM in detecting HCM and in differentiating it from athlete's heart using not only the standard 12-lead ECG, but also ECGs obtained with the Apple Watch and Alivecor KardiaMobile devices.
Status | Recruiting |
Enrollment | 300 |
Est. completion date | September 2025 |
Est. primary completion date | September 2025 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - Patients with clinically validated diagnoses of HCM (n=150) and athlete's heart (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. |
Country | Name | City | State |
---|---|---|---|
United States | Mayo Clinic in Rochester | Rochester | Minnesota |
Lead Sponsor | Collaborator |
---|---|
Mayo Clinic |
United States,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | 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 | |
Primary | 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 | Distribution of AI-ECG probabilities in Athlete's Heart | 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 Heart. 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 | |
Secondary | 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 |
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