AI-Driven EKG Screening for Dilated Cardiomyopathy

(DCM-DETECT Trial)

Overseen By Jonathan Derr, MBA

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Lancaster General Hospital

Disqualifiers: Ischemic heart disease, Valvular abnormality, Congenital heart disease, Severe hypertension, others

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

Compensation: Varies

Number of Visits: Unknown

150 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to determine if an AI-enhanced mobile EKG can help detect heart issues early in individuals related to someone with dilated cardiomyopathy (DCM), a condition where the heart enlarges and struggles to pump blood. Participants diagnosed with DCM will invite close family members to use a smartphone or tablet for heart screening. Individuals with DCM who have an adult family member not yet screened for heart issues might be suitable candidates. As an unphased trial, this study offers families a unique opportunity to contribute to innovative heart health research and potentially benefit from early detection.

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 AI-driven EKG screening is safe for dilated cardiomyopathy?

Research has shown that the Mobile 6L AI-EKG device is FDA-approved for recording electrocardiograms (EKGs), confirming its safety and effectiveness. The device captures heart data quickly, in just 30 seconds to 5 minutes.

Artificial intelligence aids in analyzing the EKGs. Studies with similar AI-enhanced devices have shown they can accurately predict heart conditions, suggesting this device provides reliable readings.

Since the device is approved for use, it has been deemed safe for recording heart activity. No major reports of negative effects have emerged, indicating it is well-tolerated for its intended purpose. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about this trial because it harnesses the power of AI to enhance electrocardiogram (EKG) technology, making screening for dilated cardiomyopathy (DCM) more accessible and potentially more accurate. Unlike traditional EKGs, which are often conducted in clinical settings and require specialized equipment, this trial uses a mobile 6-lead EKG device that can be used at home. The AI component aims to improve the early detection of DCM, especially among first-degree relatives of those already diagnosed, by analyzing complex heart patterns that might be missed by the human eye. This approach could lead to earlier intervention and better outcomes for those at risk.

What evidence suggests that this AI-driven EKG screening is effective for dilated cardiomyopathy?

Research has shown that a new AI-enhanced mobile heart monitor, which trial participants will use, can effectively identify heart issues related to dilated cardiomyopathy. In studies, this AI-powered device accurately detected left ventricular systolic dysfunction, a specific heart problem. By using smart algorithms, it can identify heart issues similar to those in dilated cardiomyopathy. The AI system predicts important heart measurements as well as traditional methods, offering promise for early detection. Overall, early evidence suggests this tool could be valuable for screening family members of individuals with dilated cardiomyopathy. ^{1 2 4 6 7}

Who Is on the Research Team?

Roy Small, MD

Principal Investigator

Penn Medicine / Lancaster General Hospital

Are You a Good Fit for This Trial?

This trial is for people with dilated cardiomyopathy who will invite their close family members to participate in mobile heart screening using an AI-enhanced electrocardiogram (EKG).

Inclusion Criteria

Proband: Able to provide informed consent

FDR: Proband has provided informed consent

FDR: Able to provide informed verbal consent

Exclusion Criteria

I was born with a heart defect.

My heart condition is due to a sudden or treatable cause.

Proband: Home address outside of traveling range (CPC Participants only)

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

DCM Screening

Participants undergo AI-enhanced 6Lead mobile electrocardiogram (EKG) screening to detect impaired left ventricular function

6 months

Remote and on-site visits using mobile technology

Follow-up

Participants are monitored for safety and effectiveness after screening, with a focus on cardiac follow-up

3-6 months

What Are the Treatments Tested in This Trial?

Interventions

• Mobile 6L AI-EKG Screening

Trial Overview The study is testing a new way to detect heart conditions using a portable device that records the electrical activity of the heart, enhanced by artificial intelligence.

How Is the Trial Designed?

Group I: DCM Screening Experimental Treatment 1 Intervention

Participants (probands) with dilated cardiomyopathy (DCM) will invite their first-degree relatives (FDR) to participate in DCM screening to examine the impact of an "Artificial Intelligent" (AI) enhanced 6lead (6L) mobile electrocardiogram (EKG) in encouraging FDR of probands with a DCM to obtain appropriate cardiac screening.

Who Is Running the Clinical Trial?

Lancaster General Hospital

Lead Sponsor

Trials

25

Recruited

4,100+

Mayo Clinic

Collaborator

Trials

3,427

Recruited

3,221,000+

Published Research Related to This Trial

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]

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]

In a study of 414 patients with dilated cardiomyopathy (DCM) followed for a median of 125 months, specific ECG features such as left ventricular hypertrophy and heart rate were found to be significant predictors of death or heart transplant (D/HT).

The study demonstrated that incorporating ECG models into clinical evaluations improved the ability to predict outcomes like sudden death or malignant ventricular arrhythmias (SD/MVA), enhancing prognostic accuracy for DCM patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

ECG in dilated cardiomyopathy: specific findings and long-term prognostic significance. Merlo, M., Zaffalon, D., Stolfo, D., et al.[2019]

Recent advancements in automated patch clamp technology allow for medium-to-high throughput screening of individual ion channels, improving the efficiency and predictive accuracy of cardiac safety assessments for new drugs.

The study emphasizes the importance of screening multiple cardiac ion channels beyond just the hERG channel to better identify potential risks of acquired long QT syndrome, which can lead to drug withdrawals and development failures.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

In vitro profiling against ion channels beyond hERG as an early indicator of cardiac risk. Chen, MX., Helliwell, RM., Clare, JJ.[2016]

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]

Dilated Cardiomyopathy (DCM) patients often face significant risks despite treatment, indicating that traditional measures like left ventricular ejection fraction are insufficient for predicting outcomes, highlighting the need for better disease characterization.

Cardiac MRI (CMR) combined with advanced artificial intelligence techniques, particularly deep learning, can enhance the assessment of DCM by providing detailed functional and tissue characterization, which may improve diagnosis and risk stratification for patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Role of AI in Characterizing the DCM Phenotype. Asher, C., Puyol-Antón, E., Rizvi, M., et al.[2023]

The QT-Screen system allows for high-throughput screening of drug-induced QT interval prolongation using primary cardiac myocytes, enabling the assessment of over 100 compounds daily with detailed dose-response data.

This system can significantly reduce the financial risk in drug development by identifying potential cardiac safety issues earlier in the process, with a cost-effective operation at approximately 0.20 US pennies per data point.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

QT-screen: high-throughput cardiac safety pharmacology by extracellular electrophysiology on primary cardiac myocytes. Meyer, T., Leisgen, C., Gonser, B., et al.[2013]

A comprehensive preclinical evaluation of new drugs is essential to assess their potential cardiac toxicity, including effects on heart structure and function, using various animal models and in vitro assays.

Key tests include assessing the proarrhythmic risk through in vitro cardiac ion channel studies and in vivo QT interval prolongation in telemetered dogs, with additional evaluations of cardiac contractility and electrophysiological function to ensure safety before human trials.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Preclinical cardiac safety assessment of drugs. Hanton, G.[2018]

The study found that the radioligand-binding assay at the L-type calcium channel was the most predictive of contractility effects in cardiac myocytes, with a sensitivity of 73% and specificity of 83%, making it a valuable tool in early drug discovery.

Automated electrophysiology was less effective in predicting contractility effects, highlighting the importance of using radioligand-binding assays to assess potential cardiovascular risks of new drugs efficiently and cost-effectively.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays. Morton, MJ., Armstrong, D., Abi Gerges, N., et al.[2014]

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]

Artificial intelligence-enabled electrocardiography (AI-ECG) showed high sensitivity (98.8%) and excellent negative predictive value (100% at 1% prevalence) for detecting dilated cardiomyopathy (DC), indicating it could effectively rule out the disease in patients.

With an area under the curve (AUC) of 0.955 for detecting reduced left ventricular ejection fraction (LVEF), AI-ECG presents a promising, cost-effective alternative to traditional echocardiography for screening, especially for first-degree relatives of DC patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial Intelligence-Enabled Electrocardiography to Screen Patients with Dilated Cardiomyopathy. Shrivastava, S., Cohen-Shelly, M., Attia, ZI., et al.[2021]

In a study involving 31 patients with idiopathic dilated cardiomyopathy, those who responded well to long-term beta blocker treatment had shorter QRS durations and higher RMS40 values compared to poor responders, indicating that specific ECG measurements can predict treatment outcomes.

The signal averaged ECG criteria demonstrated high sensitivity (86%) and specificity (89%) in a prospective study, suggesting it could be a valuable tool for determining which patients are likely to benefit from beta blocker therapy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Prediction of the effectiveness of long-term beta blocker treatment for dilated cardiomyopathy by signal averaged electrocardiography. Yamada, T., Fukunami, M., Shimonagata, T., et al.[2019]

Current in vitro and in vivo assays for assessing drug-induced cardiac safety, such as IKr and APD tests, are not fully reliable for predicting serious arrhythmias like Torsades de Pointes (TdP) and other cardiac dysfunctions.

The Consortium for Safety Assessment using Human iPS cells (CSAHi) is developing advanced screening strategies using human iPS cell-derived cardiomyocytes and innovative platforms like multi-electrode arrays and motion field imaging, which show more relevant pharmacological responses and could better predict a range of cardiotoxicities compared to traditional methods.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Comprehensive in vitro cardiac safety assessment using human stem cell technology: Overview of CSAHi HEART initiative. Takasuna, K., Asakura, K., Araki, S., et al.[2017]

Citations

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

Dilated Cardiomyopathy Detection Using AI and Screening ... The purpose of this study is to examine the impact of an "Artificial Intelligent" (AI) enhanced 6Lead (6L) mobile electrocardiogram (EKG) in encouraging ...

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 purpose of this study is to examine the impact of an "Artificial Intelligent" (AI) enhanced 6Lead (6L) mobile electrocardiogram (EKG) in ...

3. pubmed.ncbi.nlm.nih.gov pubmed.ncbi.nlm.nih.gov/40395421/

Artificial intelligence-enhanced six-lead portable ... The AI-ECG model constructed with the six-lead hand-held portable ECG device effectively identifies LVSD, demonstrating comparable accuracy ...

4. heartrhythmjournal.com heartrhythmjournal.com/article/S1547-5271(21)00762-1/pdf

B-PO01-080 ARTIFICIAL INTELLIGENCE-ENABLED ... Conclusion: The AI/DNN accurately predicted the QTc of a standard 12-lead ECG or that derived from a smartphone enabled mobile device. This AI/DNN-based QTc ...

5. centerwatch.com centerwatch.com/clinical-trials/listings/condition/382/cardiomyopathy

Cardiomyopathy Clinical Research Trials The purpose of this study is to examine the impact of an "Artificial Intelligent" (AI) enhanced 6Lead (6L) mobile electrocardiogram (EKG) in encouraging first ...

6. smartpatients.com smartpatients.com/trials/NCT06688396

Dilated Cardiomyopathy Detection Using AI and Screening ... This is a prospective, single-arm clinical trial in which participants with dilated cardiomyopathy will invite their first degree relatives to undergo mobile ...

7. ahajournals.org ahajournals.org/doi/10.1161/CIRCULATIONAHA.120.050231

Artificial Intelligence–Enabled Assessment of the ... Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG.