- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06290570
A Study of Artificial Intelligence ECG With ECG Devices to Detect Hypertrophic Cardiomyopathy Distinct From Athlete's Heart
April 2, 2024 updated by: Konstantinos Siontis, Mayo Clinic
Prospective Evaluation of Artificial Intelligence ECG With Consumer-Facing ECG Devices for Detection of Hypertrophic Cardiomyopathy and Distinction From Athlete's Heart
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.
Study Overview
Status
Not yet recruiting
Conditions
Study Type
Observational
Enrollment (Estimated)
300
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Contact
- Name: Heart Rhythm Services Research Team
- Phone Number: (507) 255-0774
Study Contact Backup
- Name: Konstantinos Siontis, MD
- Phone Number: (507) 255-1051
- Email: Siontis.Konstantinos@mayo.edu
Study Locations
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Minnesota
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Rochester, Minnesota, United States, 55905
- Mayo Clinic in Rochester
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Contact:
- Heart Rhythm Services Research Team
- Phone Number: 507-255-0774
-
Contact:
- Konstantinos Siontis, MD
- Phone Number: (507) 255-1051
- Email: Siontis.Konstantinos@mayo.edu
-
Principal Investigator:
- Konstantinos Siontis, MD
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-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
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 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.
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
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).
|
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.
A single lead ECG tracing will be collected using an Apple Smart Watch and tracing will be used for AI-ECG analyses.
A 6-lead ECG tracing will be collected using an AliveCor KardiaMobile device and tracing will be used for AI-ECG analyses.
|
Athlete's Heart
Subjects with clinically validated diagnoses of Athlete's Heart 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).
|
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.
A single lead ECG tracing will be collected using an Apple Smart Watch and tracing will be used for AI-ECG analyses.
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
Time Frame: 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.
|
Baseline
|
Comparative diagnostic performance between tracings obtained from different devices
Time Frame: 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.
|
Baseline
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Correlation with false negative AI ECG result
Time Frame: 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.
|
Baseline
|
Distribution of AI-ECG probabilities in Athlete's Heart
Time Frame: 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 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
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Investigators
- Principal Investigator: Konstantinos Siontis, MD, Mayo Clinic
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Estimated)
April 1, 2024
Primary Completion (Estimated)
September 1, 2025
Study Completion (Estimated)
September 1, 2025
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)
April 3, 2024
Last Update Submitted That Met QC Criteria
April 2, 2024
Last Verified
April 1, 2024
More Information
Terms related to this study
Additional Relevant MeSH Terms
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
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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