Patients identified with reduced ejection fraction using Eko Health AI were twice as likely to experience heart attacks, heart failure, hospitalization, and all-cause mortality over two years.
Summary: New research from Imperial College London, utilizing Eko Health’s AI for low ejection fraction, demonstrated that patients identified by the AI were at significantly higher risk of major adverse cardiac events, such as heart attacks, heart failure, hospitalization, and mortality. Eko’s AI, FDA-cleared and UKCA-marked, was shown to double the risk of major adverse cardiac events in patients and increase mortality rates by 65%, even when traditional markers showed normal results. The study highlights the potential of AI for early detection, remote patient monitoring, and scalability for integration into healthcare settings.
Key Takeaways:
- AI Risk Prediction: Eko Health’s AI flagged patients with reduced ejection fraction, who were twice as likely to experience major cardiac events and had a 65% higher mortality risk compared to traditional diagnostic tools.
- Potential for Remote Monitoring: Eko’s AI demonstrated accuracy in remotely monitoring heart failure patients by predicting changes in left ventricular ejection fraction.
- Scalability and Integration: The AI was successfully integrated into 71 primary care sites in the UK over 12 weeks, showing its practicality and scalability for broad adoption in clinical settings without burdening healthcare systems.
Eko Health, a pioneer in applying artificial intelligence (AI) for early detection of heart and lung diseases, announced a new independent study from researchers at Imperial College London that demonstrated how AI can identify patients with significantly higher risk of experiencing major adverse cardiac events, including heart attacks and heart failure.
Researchers used Eko Health’s US Food and Drug Administration (FDA)-cleared and UKCA-marked Low Ejection Fraction AI to conduct the study, which reinforced the power of Eko’s AI for early detection while also showing its potential to improve cardiovascular care in both clinical and remote settings.
Patients identified with reduced ejection fraction using Eko Health AI were twice as likely to experience heart attacks, heart failure, hospitalization, and all-cause mortality over two years.
[RELATED: Eko Raises $41M to Scale AI-Powered Heart and Lung Disease Detection Tool]
Imperial researchers unveiled three significant studies at the European Society of Cardiology (ESC) Congress 2024, demonstrating:
- Eko AI Predictive of Major Adverse Cardiac Events and Mortality
- In a pivotal study involving over 1,000 patients, Eko’s AI was shown to predict major adverse cardiac events—including heart attacks, heart failure, and hospitalization—as well as all-cause mortality. Patients flagged by the AI for low ejection fraction were twice as likely to experience major adverse cardiac events compared to those without a positive AI result. These patients also faced a 65% higher mortality rate, even after adjusting for traditional risk factors.
- “These findings underscore the power of AI-ECG in identifying patients at a significantly higher risk of major adverse cardiac events and mortality, even when traditional markers like left ventricular ejection fraction appear normal. In our study, patients with a positive AI-ECG result had more than double the risk of major adverse cardiac events and a 65% higher risk of mortality. This technology represents a critical advancement in early cardiac risk stratification, offering the potential for more targeted interventions through the simple addition of a single-lead ECG,” says Patrik Bächtiger, PhD, one of the co-leads of this research at Imperial.
- “Notably, the AI identified at-risk patients who had unremarkable results from traditional diagnostic tools, such as echocardiograms. This highlights the technology’s ability to detect hidden risk factors, offering healthcare providers a powerful tool for early intervention and improved patient outcomes,” says Prof. Nicholas S. Peters, director of the Health Impact Lab at Imperial.
- Eko AI Expands Remote Care Capabilities for Heart Failure Patients
- In another Imperial study presented at ESC 2024, Eko’s AI technology demonstrated its potential for remote patient monitoring, particularly for individuals with heart failure. The study found that Eko’s AI could accurately predict changes in left ventricular ejection fraction (LVEF), which is a critical indicator of heart function in heart failure patients. By being able to monitor changes in LVEF from home, patients could benefit from earlier interventions and personalized adjustments to their treatment plans, reducing the need for frequent hospital visits and providing peace of mind.
- Eko AI Demonstrates Scalability for Widespread Clinical Integration
- Over a 12-week period, the Imperial team successfully integrated Eko’s AI across 71 primary care sites in the UK. The study highlighted both the consistent and seamless adoption of the technology by healthcare providers, in addition to its ability to enhance patient care without straining existing healthcare systems. “This demonstrates the technology’s practicality and scalability for widespread clinical use, paving the way for broader implementation in routine medical practice,” said Dr. Mihir Kelshiker, one of the co-leads of this program at Imperial.
Together, these findings further solidify Eko’s Low Ejection Fraction AI technology as an important innovation for the early detection and management of cardiovascular disease, according to a release from Eko Health.
“This important research from Imperial College London highlights the transformative potential of Eko’s AI technology in the fight against heart disease,” says Connor Landgraf, co-founder and CEO of Eko Health. “By identifying patients at elevated risk for major cardiac events with a simple, non-invasive test, we are empowering clinicians worldwide to take action earlier, ultimately saving lives and improving care outcomes on a global scale.”
Photo credit: Eko Health
