AI-Driven EKG Screening for Dilated Cardiomyopathy

(DCM-DETECT Trial)

Recruiting in Palo Alto (17 mi)

Overseen by Roy Small, MD

Age: 18+

Sex: Any

Travel: May Be Covered

Time Reimbursement: Varies

Trial Phase: Academic

Recruiting

Sponsor: Lancaster General Hospital

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

No Placebo Group

Trial Summary

What is the purpose of this trial?

This is a prospective, single-arm clinical trial in which participants with dilated cardiomyopathy will invite their first degree relatives to undergo mobile cardiac, electrocardiogram screening.

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 data supports the effectiveness of the treatment Mobile 6L AI-EKG Screening for detecting dilated cardiomyopathy?

Research shows that AI-enhanced electrocardiograms (ECGs) can effectively detect dilated cardiomyopathy (DC) with high sensitivity, meaning it can accurately identify those with the condition. This AI-ECG method is a simple and cost-effective screening tool, especially useful for identifying DC in patients and their close relatives. ^{1 2 3 4 5}

Is AI-Driven EKG Screening for Dilated Cardiomyopathy safe for humans?

The research articles discuss various methods for assessing cardiac safety, focusing on potential heart-related risks of new drugs. These studies highlight the importance of early screening for heart risks, such as changes in heart rhythm or contractility, using advanced technologies. However, they do not provide specific safety data for AI-Driven EKG Screening for Dilated Cardiomyopathy or similar technologies. ^{6 7 8 9 10}

How does the Mobile 6L AI-EKG Screening treatment differ from other treatments for dilated cardiomyopathy?

The Mobile 6L AI-EKG Screening treatment is unique because it uses artificial intelligence to analyze electrocardiograms (ECGs) for early detection of dilated cardiomyopathy, offering a simple and cost-effective alternative to traditional methods like echocardiography, which are more expensive and labor-intensive. ^{1 2 11 12 13}

Research Team

Roy Small, MD

Principal Investigator

Penn Medicine / Lancaster General Hospital

Eligibility Criteria

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)

Trial Timeline

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

Treatment Details

Interventions

• Mobile 6L AI-EKG Screening (AI 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.

Participant Groups

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.

Find a Clinic Near You

Research Locations Nearby Select from list below to view details:

Lancaster General Hospital Lancaster, PA

Who Is Running the Clinical Trial?

Lancaster General Hospital

Lead Sponsor

Trials

25

Patients Recruited

4,100+

Mayo Clinic

Collaborator

Trials

3427

Patients Recruited

3,221,000+

Findings from Research

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 (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]

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]

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]

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]

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]

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]

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]

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

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

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]

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]

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]

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]

References

1. United States pubmed.ncbi.nlm.nih.gov

Patient-Level Artificial Intelligence-Enhanced Electrocardiography in Hypertrophic Cardiomyopathy: Longitudinal Treatment and Clinical Biomarker Correlations. [2023]

2. United States pubmed.ncbi.nlm.nih.gov

Artificial Intelligence-Enabled Electrocardiography to Screen Patients with Dilated Cardiomyopathy. [2021]

3. United States pubmed.ncbi.nlm.nih.gov

Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. [2022]

4. United States pubmed.ncbi.nlm.nih.gov

Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review. [2023]

5. Netherlands pubmed.ncbi.nlm.nih.gov

External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction. [2021]

6. England pubmed.ncbi.nlm.nih.gov

In vitro profiling against ion channels beyond hERG as an early indicator of cardiac risk. [2016]

7. United States pubmed.ncbi.nlm.nih.gov

Comprehensive in vitro cardiac safety assessment using human stem cell technology: Overview of CSAHi HEART initiative. [2017]

8. United States pubmed.ncbi.nlm.nih.gov

QT-screen: high-throughput cardiac safety pharmacology by extracellular electrophysiology on primary cardiac myocytes. [2013]

9. New Zealand pubmed.ncbi.nlm.nih.gov

Preclinical cardiac safety assessment of drugs. [2018]

10. United States pubmed.ncbi.nlm.nih.gov

Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays. [2014]

11. England pubmed.ncbi.nlm.nih.gov

Prediction of the effectiveness of long-term beta blocker treatment for dilated cardiomyopathy by signal averaged electrocardiography. [2019]

12. United States pubmed.ncbi.nlm.nih.gov

ECG in dilated cardiomyopathy: specific findings and long-term prognostic significance. [2019]

13. Switzerland pubmed.ncbi.nlm.nih.gov

The Role of AI in Characterizing the DCM Phenotype. [2023]