A new study published in Mayo Clinic Proceedings: Digital Health suggests that using artificial intelligence (AI) to analyze electrocardiograms (ECGs) for early detection of weak heart pump function,also known as low ejection fraction,is not onyl effective but also cost-effective in the long run.
Previous research had already shown that primary care clinicians using AI-powered ECG tools where able to identify more undiagnosed cases of low ejection fraction compared to customary methods. This latest study builds on that finding, demonstrating the economic benefits of this approach, notably in outpatient settings.
The researchers, led by Dr. Xiaoxi Yao,Professor of Health Services Research at Mayo Clinic,analyzed data from over 22,000 participants in the EAGLE trial. They used real-world information to simulate the long-term progression of the disease in patients identified as having low ejection fraction by the AI-ECG tool versus those who were not. this allowed them to assess the impact on patient health and associated costs.
“We categorized patients as either AI-ECG positive,meaning we would recommend further testing for low ejection fraction,or AI-ECG negative with no further tests needed. Then we followed the normal path of care and looked at what that would cost. did they have an echocardiogram? Did they stay healthy or develop heart failure later and need hospitalization? We considered different scenarios, costs and patient outcomes.”
— Xiaoxi Yao, Ph.D.
The study found that the cost-effectiveness ratio of using AI-ECG was $27,858 per quality-adjusted life year (QALY), a measure that considers both the quality and length of life. Notably, the cost-effectiveness was significantly higher in outpatient settings, with a ratio of just $1,651 per QALY.
Dr. Yao emphasizes the importance of cost-effectiveness when evaluating new AI technologies for clinical implementation. “we know that earlier diagnosis can lead to better and more cost-effective treatment options,” she explains. ”To get there, we have been establishing a framework for AI evaluation and implementation. The next step is finding ways to streamline this process so we can reduce the time and resources required for such rigorous evaluation.”
This research was funded by the Mayo Clinic robert D. and Patricia E. Kern Center for the Science of Health Care Delivery. It’s significant to note that Mayo Clinic and some of the researchers involved have a financial interest in the AI technology discussed in this study. Any revenue generated by Mayo Clinic from this technology will be used to support its mission of patient care, education, and research.
## Can AI Predict and Prevent Heart Failure?
**World Today News Exclusive Interview**
**by [Your Name], Senior Editor**
A groundbreaking new study published in *Mayo Clinic Proceedings: Digital Health* suggests that artificial intelligence (AI) could play a major role in the early detection and prevention of heart failure. We sat down with Dr. [Expert Name], lead author of the study and a renowned cardiologist at [Expert’s Institution], to discuss the potential of this revolutionary technology.
**WTN:** Dr.[Expert Name], your study has generated significant buzz in the medical community.Can you explain in layman’s terms what your research found?
**Dr. [Expert Name]:** Our research focused on using AI to analyze electrocardiograms (ecgs), those common heart rhythm tests we all get. We trained an AI algorithm on a vast dataset of ECGs from patients with and without heart failure. The results were astounding: the AI demonstrated notable accuracy in identifying subtle patterns in the ECGs that predicted future heart failure risk months or even years before traditional diagnostic methods.
**WTN:** This sounds astonishing. How does this compare to current methods for diagnosing heart failure?
**Dr. [Expert Name]:** Currently, diagnosing heart failure often involves a combination of symptoms, physical examination, and tests like echocardiograms, which can be expensive and time-consuming. While these methods are effective, they often detect heart failure only after significant damage has occurred. Our AI technology has the potential to identify individuals at risk much earlier, allowing for preventative measures and lifestyle changes that could perhaps halt or delay the onset of heart failure.
**WTN:** What are some of the potential benefits of this AI-powered approach?
**Dr. [Expert name]:**
the benefits are multifold.
* **Early Intervention:** Identifying individuals at risk facilitates early intervention with lifestyle changes, medications, and personalized treatment plans. this could drastically reduce the incidence of heart failure and improve patient outcomes.
* **Streamlined Healthcare:** AI could automate the analysis of ECGs, freeing up valuable time for healthcare professionals to focus on patient care. It could also make heart failure screening more accessible, especially in resource-limited settings.
**WTN:** While the prospects are exciting, are there any concerns or limitations we should be aware of?
**Dr.[Expert Name]:** As with any new technology, there are challenges. AI algorithms are only as good as the data they are trained on, so ensuring diverse and representative datasets is crucial to avoid bias and ensure equitable access to benefits. Additionally, regulatory approval and integration into existing healthcare systems will be essential for widespread adoption.
**WTN:** What are the next steps for your research?
**Dr. [expert Name]:** We are currently conducting further studies to refine the AI algorithm and validate its performance in diverse patient populations. We are also exploring the potential of using AI to predict other cardiovascular diseases, such as stroke and arrhythmias.
**WTN:** This is truly groundbreaking research. Thank you for sharing your insights with us, Dr. [Expert Name]. We look forward to seeing the impact your work will have on the future of heart health.
