In a new study, researchers at Mayo Clinic investigate whether an Apple Watch and its ECG feature can help early detect heart abnormalities, mainly left ventricular dysfunction.
Left Ventricular dysfunction of the heart is usually followed by congestive heart failure that can lead to a multitude of cardiac disorders.
The left ventricle is primarily responsible for pumping oxygen to your body’s vital organs. It is, therefore, important to diagnose any problems with the left ventricle as early as possible.
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Mayo Clinic looks to the Apple Watch to help with heart health
According to the Mayo Clinic, one of the problems is that patients with cardiac dysfunction are often undiagnosed because people often don’t show symptoms (asymptomatic.)
So, researchers at Mayo Clinic evaluated the potential of using the Apple Watch ECG to help diagnose left ventricular dysfunction.
Heart failure is a growing global health problem
Heart failure (HF) is a global pandemic affecting at least 26 million people worldwide and is increasing in prevalence.
HF health expenditures are considerable and increase dramatically with as the population ages.
Despite the significant advances in therapies and prevention, mortality and morbidity are still high, and quality of life is poor.
That is one of the reasons technology companies are working hand in hand with biomedical firms to evaluate new ways to detect heart abnormalities easily.
Although artificial intelligence (AI) algorithms are capable of identifying cardiac dysfunction, defined as ejection fraction (EF) ≤ 40%, from 12-lead electrocardiograms (ECGs), identification of cardiac dysfunction using the single-lead ECG of a smartwatch has yet to be tested.
About this new study, “Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction.”
This is a pioneering study as the researchers used the single-lead ECG capability of the Apple Watch to collect the data and process it to identify left-ventricular dysfunction.
The researchers digitally enrolled 2,454 patients from 46 US states and 11 countries, who sent 125,610 ECGs to the data platform between August 2021 and February 2022 using their Apple Watch.
The ECG data from the Single lead capable Apple Watches were scrubbed and processed via a proprietary AI algorithm developed by the researchers.
The AI algorithm detected patients with low EF(ejection fraction) with an area under the curve of 0.885 (95% confidence interval 0.823–0.946) and 0.881 (0.815–0.947), using the mean prediction within a 30-d window or the closest ECG relative to the echocardiogram that determined the EF, respectively.
These findings indicate that consumer-watch ECGs acquired in nonclinical environments can identify patients with cardiac dysfunction, a potentially life-threatening and often asymptomatic condition. Another inference is that the potential of smartwatches to assist with conducting remote digital health studies is just in the beginning phases.
The results and findings from this groundbreaking clinical study were published in Nature Medicine this week.
The code behind the AI algorithms itself cannot be shared because it contains and is proprietary intellectual property (patent pending) that has been licensed and is under FDA review.
The researchers have previously published the AI algorithm architecture in a separate article.