AI-Enhanced ECG Screening for Cardiomyopathy

Overseen By Brendan Mark

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Mayo Clinic

Disqualifiers: Ischemic heart disease, Valvular abnormality, Congenital heart disease, Hypertension, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 1 Jurisdiction This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 1

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new method for screening heart problems in families affected by dilated cardiomyopathy (DCM), a condition that reduces the heart's ability to pump blood. The goal is to determine if a mobile ECG device, combined with AI analysis, can detect early signs of heart issues in family members of those already diagnosed. The trial includes two groups: individuals with DCM and their first-degree relatives. Suitable participants are those with DCM or their close family members who have a smartphone or tablet with internet access. As an unphased trial, this study offers participants the opportunity to contribute to groundbreaking research that could enhance early detection of heart issues.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What prior data suggests that this protocol is safe for screening cardiomyopathy?

Research has shown that the KardiaRx ECG Screening device is safe for use. The device in this trial, the AliveCor KardiaMobile 6L, received FDA approval, confirming its safety for its intended purpose.

Studies have demonstrated that this device can accurately record heart rhythms in just 30 seconds to 5 minutes. It functions like a small, handheld heart monitor. No major reports of negative side effects have emerged from using this device to check for heart issues.

For those considering joining this trial, it is reassuring that the device has already been tested and proven safe in similar situations. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the AI-Enhanced ECG Screening for cardiomyopathy because it promises a new way to identify heart issues early, using advanced technology. Unlike traditional ECG screenings, which rely on a clinician's interpretation, this method uses artificial intelligence to detect subtle patterns that might indicate dilated cardiomyopathy (DCM). This could lead to earlier diagnosis and intervention, especially for first-degree relatives of patients with DCM, who are at higher risk. By enhancing the accuracy of ECG screenings, this approach could revolutionize how cardiomyopathy is detected, potentially improving outcomes for patients.

What evidence suggests that this protocol is effective for detecting reduced LVEF in cardiomyopathy?

Research has shown that AI-enhanced ECG (electrocardiogram) screenings, such as the KardiaRx ECG Screening used in this trial, are promising tools for detecting heart issues. This screening excels at identifying reduced left ventricular ejection fraction (LVEF), an early sign of dilated cardiomyopathy. This condition occurs when the heart enlarges and struggles to pump blood effectively. Studies on similar AI-driven ECG devices indicate they can identify heart problems with high accuracy. The KardiaMobile 6L device, used in this screening, has already received FDA approval for recording heart activity. This suggests that AI analysis of ECGs could significantly aid in early detection of heart conditions. ^{1 2 6 7 8}

Who Is on the Research Team?

Naveen Pereira, M.D.

Principal Investigator

Mayo Clinic

Are You a Good Fit for This Trial?

This trial is for first-degree relatives (FDR) of individuals with dilated cardiomyopathy (DCM), aiming to detect early heart function issues using a mobile ECG device. Participants should be willing to use the device and transmit data for analysis.

Inclusion Criteria

FDR: Proband has provided informed consent

FDR: Able to provide informed consent

I have been diagnosed with dilated cardiomyopathy and my heart's pumping efficiency is 45% or less.

Exclusion Criteria

My heart condition is not caused by other known heart issues.

My heart muscle disease was caused by a sudden or reversible condition.

I have told my close relatives to get heart screenings.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

AI-ECG Screening

Participants undergo AI-enhanced ECG screening to detect reduced left ventricular ejection fraction

Baseline

1 visit (virtual)

Follow-up

Participants are monitored for safety and effectiveness after AI-ECG screening

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• KardiaRx ECG Screening

Trial Overview The study tests the KardiaRx ECG Screening's effectiveness in identifying reduced left ventricular ejection fraction, an indicator of potential heart problems, compared to standard care which includes screening with an echocardiogram.

How Is the Trial Designed?

Group I: First degree relatives Experimental Treatment 1 Intervention

Subjects who are first-degree relatives of patients with DCM

Group II: DCM (Dilated Cardiomyopathy) Patients Experimental Treatment 1 Intervention

Subjects who are diagnosed with DCM (dilated cardiomyopathy).

Who Is Running the Clinical Trial?

Mayo Clinic

Lead Sponsor

Trials

3,427

Recruited

3,221,000+

Published Research Related to This Trial

A study involving 221,846 ECGs from four institutions aimed to develop AI models for detecting left ventricular systolic dysfunction (LVSD) with an ejection fraction (EF) <40%, showing promising internal accuracy but variable external validation results.

The performance of AI models varied significantly between institutions, emphasizing the need for external validation and careful consideration of patient characteristics and ECG abnormalities when using AI for LVSD detection.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Importance of external validation and subgroup analysis of artificial intelligence in the detection of low ejection fraction from electrocardiograms. Yagi, R., Goto, S., Katsumata, Y., et al.[2023]

The Alivecor Kardia Mobile device produces accurate single-lead ECG tracings in pediatric patients, showing strong agreement with standard 12-lead ECGs, making it a reliable tool for monitoring heart rhythms in children.

In a study of 30 pediatric patients, the device demonstrated a specificity of 87% for detecting atrial fibrillation, indicating it can effectively identify abnormal rhythms, although it had some false positives related to other rhythm abnormalities.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Can smartphone wireless ECGs be used to accurately assess ECG intervals in pediatrics? A comparison of mobile health monitoring to standard 12-lead ECG. Gropler, MRF., Dalal, AS., Van Hare, GF., et al.[2019]

In a study of 156 healthy volunteers undergoing 24-hour ambulatory cardiac monitoring, only 13% showed normal sinus rhythm, highlighting a significant prevalence of cardiac abnormalities in this population.

The most common finding was supraventricular ectopics (83%), with other issues like ventricular ectopics and sinus pauses also noted, suggesting the need for clear guidelines to evaluate cardiac health before trials of new drugs.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Use of 24 h ambulatory ECG recordings in the assessment of new chemical entities in healthy volunteers. Stinson, JC., Pears, JS., Williams, AJ., et al.[2020]

In a study of 14 apparently healthy patients with high amplification ECG abnormalities, minor conduction disorders, particularly in the right bundle branch (RBB), were identified as the likely cause, despite normal heart function and no significant cardiac events during follow-up.

The most common ECG finding in these patients was a deep S wave in leads V5-V6, which can help in diagnosing these minor conduction issues after ruling out other heart conditions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Possible mechanisms of false positive results of averaged ECG]. Brembilla-Perrot, B., Beurrier, D., Jacquemin, L., et al.[2014]

Artificial intelligence-enabled electrocardiography (AIeECG) shows high diagnostic accuracy for detecting left ventricular systolic dysfunction (LVSD), with a median area under the curve (AUC) of 0.90, sensitivity of 83.3%, and specificity of 87% across various populations.

AIeECG can be particularly beneficial in non-cardiology settings and when used alongside natriuretic peptide testing, but further prospective randomized trials are needed to assess its impact on treatment outcomes and cost-effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review. Bjerkén, LV., Rønborg, SN., Jensen, MT., et al.[2023]

Computer-assisted interpretation of ECGs and VCGs significantly enhances diagnostic accuracy compared to traditional visual analysis, addressing limitations of conventional methods.

The optimization of ECG data processing functions, along with ongoing improvements in arrhythmia and pediatric VCG programs, suggests that computerized electrocardiography will lead to more standardized and accurate diagnoses in the future.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-assisted interpretation of electro- and vectorcardiograms. Chapter IV. Achievements in the field of computer-assisted interpretation of electrocardiograms. Brohet, C.[2004]

An artificial intelligence model (AI-ECG) demonstrated high accuracy in detecting hypertrophic cardiomyopathy (HCM) in pediatric patients, achieving a sensitivity of 92% and specificity of 95% based on a study of 300 children and adolescents.

The AI-ECG model showed excellent performance across different age groups, with an area under the curve (AUC) of 0.98, indicating its potential as a reliable screening tool for HCM using standard 12-lead ECGs.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents. Siontis, KC., Liu, K., Bos, JM., et al.[2021]

The AI-ECG algorithm demonstrated strong performance in detecting left ventricular systolic dysfunction (LVSD) in an external population of 4277 adults, achieving an area under the receiver operating curve of 0.82, indicating good accuracy.

While the AI-ECG showed high specificity (97.4%) and accuracy (97.0%), its sensitivity was lower at 26.9%, suggesting that population-specific cut-offs may be needed for optimal clinical use, especially given the differences in patient characteristics compared to the original study.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. Attia, IZ., Tseng, AS., Benavente, ED., et al.[2021]

An artificial intelligence-augmented ECG (AI-ECG) model can accurately predict left ventricular systolic dysfunction (LVSD) in critical care patients, providing a non-invasive and cost-effective alternative to traditional echocardiography.

The AI-ECG algorithm performs well regardless of the presence of atrial fibrillation (AF), making it a versatile tool for early detection of LVSD, which can improve clinical decision-making in acute care settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The effect of cardiac rhythm on artificial intelligence-enabled ECG evaluation of left ventricular ejection fraction prediction in cardiac intensive care unit patients. Kashou, AH., Noseworthy, PA., Lopez-Jimenez, F., et al.[2021]

In a study involving 583 patients across 27 cardiology practices, the ZOLL Arrhythmia Monitoring System, which combines ECG data with additional biometric information, led to treatment plan changes in 96% of cases, demonstrating its significant impact on patient management.

The integration of biometric data allowed for modifications in medications, follow-up, and lifestyle changes for 64% of patients, suggesting that this next-generation monitoring system enhances the ability of physicians to assess cardiovascular risks and improve overall patient care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Next-generation Mobile Cardiac Telemetry: Clinical Value of Combining Electrocardiographic and Physiologic Parameters. Yoo, D., Bhalla, K., Manyam, H., et al.[2022]

AI-enhanced electrocardiograms (AI-ECG) can effectively track changes in cardiac structure and function in patients with obstructive hypertrophic cardiomyopathy (HCM) undergoing treatment with mavacamten, as shown in a study involving 13 patients and 216 ECGs.

Both AI-ECG algorithms demonstrated significant reductions in HCM scores during treatment, correlating well with echocardiographic measures and laboratory markers, suggesting that AI-ECG could be a valuable tool for monitoring therapeutic responses in HCM.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient-Level Artificial Intelligence-Enhanced Electrocardiography in Hypertrophic Cardiomyopathy: Longitudinal Treatment and Clinical Biomarker Correlations. Siontis, KC., Abreau, S., Attia, ZI., et al.[2023]

A study involving 44,959 patients demonstrated that an AI model applied to ECG data can effectively identify asymptomatic left ventricular dysfunction (ALVD), achieving high accuracy (85.7%) and sensitivity (86.3%).

Patients who screened positive for ALVD using the AI model were found to be four times more likely to develop future ventricular dysfunction, highlighting the potential of AI-enhanced ECG as a proactive screening tool for early intervention.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Attia, ZI., Kapa, S., Lopez-Jimenez, F., et al.[2022]

Citations

1. clinicaltrials.gov clinicaltrials.gov/study/NCT06688396

Dilated Cardiomyopathy Detection Using AI and Screening ... This study's primary endpoint is to assess the feasibility of performing AI-EKG test and the subsequent uptake of cardiac screening in FDR of DCM patients. The ...

2. trialx.com trialx.com/clinical-trials/listings/311101/dilated-cardiomyopathy-detection-using-ai-and-screening-with-mobile-technology-dcm-detect/

Dilated Cardiomyopathy Detection Using AI and Screening ... The AliveCor KardiaMobile 6L device is an FDA approved handheld smart device that records an EKG in 30 seconds to 5-minute increments. The ...

3. withpower.com withpower.com/trial/phase-cardiomyopathies-9-2024-bf6d9

AI-Enhanced ECG Screening for Cardiomyopathy Trial Overview The study tests the KardiaRx ECG Screening's effectiveness in identifying reduced left ventricular ejection fraction, an indicator of potential ...

4. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC9907988/

ECG Screening for Dilated Cardiomyopathy Our study developed a simple screening model for DCM patients only based on ECG parameters which demonstrated satisfactory performance, and our ...

5. heartandlung.org heartandlung.org/article/S0147-9563(25)00192-X/fulltext

AI-driven ECG diagnostics: A game-changer for ... This meta-analysis pools data from 21 studies and reveals that Machine Learning algorithms achieve exceptional HCM diagnostic performance, with a pooled area ...

6. alivecor.com alivecor.com/press/press_release/new-study-shows-alivecors-kardia-12l-ecg-system-performs-equivalently-to-a-standard-12-lead-ecg

New Study Shows AliveCor's Kardia 12L ECG System ... Overall, the results indicate the Kardia 12L ECG System's ability to maintain robust diagnostic accuracy for most major morphology classes. "The ...

7. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC11194732/

Evaluating the use of the mobile electrocardiogram ... Respondents in this survey generally hold positive attitudes to the use of KardiaMobile™ ECG technology for AF detection but indicate a desire ...

8. academic.oup.com academic.oup.com/ehjdh/article/6/3/476/8093453

Artificial intelligence-enhanced six-lead portable ... In this prospective, single-centre study, we assessed the diagnostic performance of AI-ECG for detecting LVSD using a six-lead hand-held portable device.