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Heart Failure is Detectable at Point of Care Using ECG-Enabled Stethoscope, Mayo Clinic and Eko Finds

by Fred Pennic 11/18/2019 Leave a Comment

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Eko Duo Receives FDA Clearance to Market Combined Digital Stethoscope/ECG Smart Device

– Eko and Mayo Clinic announced they have proven that heart failure is detectable during routine physical exams, further validating the Eko DUO digital stethoscopes as a heart failure screening tool.

– Today’s news marks the first time that a point of care device with a single lead ECG combined with an AI algorithm identified low ejection fraction in patients.

– The results of these findings, which are comparable to research published earlier this year in NatureMedicine, were presented over the weekend at the AHA event.

Eko, a digital health company applying artificial intelligence (AI) and machine learning in the fight against heart disease, presented results in collaboration with Mayo Clinic demonstrating the use of the DUO digital stethoscope as a heart failure screening tool at the American Heart Association Scientific Sessions 2019.

Eko DUO Point of Care Device

The DUO, which patients can obtain with a prescription, can seamlessly stream cardiac data (stethoscope + ECG) to specialists, providing the most holistic picture of heart function. It allows patients to accurately and conveniently navigate dangerous heart health conditions, with the stress-reducing knowledge that the cardiologist is a push of a button away.

The device also offers clinicians a high-fidelity heart diagnostic tool for in-clinic use to quickly spot heart abnormalities including arrhythmias, heart murmurs, and valvular heart diseases. DUO pairs wirelessly with the Eko app on an iPhone, iPad, Windows PC or Android device enabling quick diagnosis and monitoring anywhere. All the data on the app is stored in a HIPAA compliant manner and can be connected to the patient’s medical record.

Eko-Mayo Clinic Research Background & Protocols

Ejection fraction is an important method of mortality prediction among cardiac patients and a low ejection fraction number suggests problems with the heart’s pumping function, and may be associated with heart failure. An estimated 6.2 million Americans suffer from heart failure, according to federal statistics. The American Heart Association predicts that more than eight million will have the condition by 2030.

When tested on 100 patients, the Eko DUO device combined with an AI model was able to detect ejection fraction < 35% with an area under the curve (AUC) of 0.90, which is comparable to previously published research in Nature Medicine. These findings could help identify patients with a low ejection fraction during routine physical examinations, facilitating rapid clinical recognition of those requiring further testing. This marks the first time that a point of care device with a single lead ECG combined with an AI algorithm identified low ejection fraction in patients.

Impact of Initial Findings

“With treatment many people live well with heart failure, but detection is critical,” said Dr. Steve Pham, MD and vice president of clinical research and affairs at Eko, and co-author of the study. “Eko is working to change how auscultation is done, using cutting-edge machine learning and data science to predict, and prevent, the progression of chronic cardiovascular diseases. The results presented today validate Eko’s reinvention of the 200-year-old stethoscope and further cement its place in modern healthcare practices as a tool for detection and improved treatment.” 

Next Steps

Eko will continue to collaborate with the Mayo Clinic to test the low ejection fraction screening algorithm, and intends to eventually submit for regulatory clearance

RELATED:   Rise of Digital Voice Assistants & The Hospital Room of the (Near) Future

Tagged With: AI, American Heart Association, Artificial Intelligence, Cardiac Monitoring, Cardiologist, clinical research, digital health, digital stethoscope, ecg, Eko Core, Eko Devices, Eko DUO, FDA Clearance, FDA Cleared Devices, Heart, heart disease, HIPAA, Machine Learning, Mayo Clinic, MD, model, point of care

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