Hypertrophic Cardiomyopathy
Conditions
Brief summary
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.
Interventions
DIAGNOSTIC_TEST12-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_TESTApple 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_TESTAliveCor 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.
Sponsors
Mayo Clinic
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Distribution of AI-ECG probabilities in HCM | Baseline | 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. |
| Comparative diagnostic performance between tracings obtained from different devices | Baseline | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Distribution of AI-ECG probabilities in Athlete's | Baseline | 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. |
| Correlation with false negative AI ECG result | Baseline | 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. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORKonstantinos Siontis, MD
Mayo Clinic
Outcome results
None listed