In a study published in Communications Medicine, David Ouyang, MD, assistant professor of Cardiology and Medicine at Cedars-Sinai, along with Chugh and fellow investigators trained a deep learning algorithm to study patterns in electrocardiograms, also known as ECGs, which are recordings of the heart’s electrical activity.
The model studied electrocardiograms from people who experienced sudden cardiac arrest and people who did not. The study included 1,827 pre-cardiac arrest electrocardiograms from 1,796 people who later experienced sudden cardiac arrest. It also included 1,342 electrocardiograms taken from 1,325 people who did not experience sudden cardiac arrest.
The investigators found the Cedars-Sinai-developed AI model more accurately predicted who would experience out-of-hospital sudden cardiac arrest than did the more conventional method, called the ECG risk score. This is a way for doctors to calculate a person’s risk for sudden cardiac arrest that incorporates information from electrocardiogram readings.
“The entire digital electrocardiogram signal performed significantly better than a few of its components,” said Chugh, who is also the Pauline and Harold Price Chair in Cardiac Electrophysiology Research and associate director in the Smidt Heart Institute. “We plan to continue to study this AI method to learn how it could be used in a clinical setting.”
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