In a groundbreaking advancement poised to transform cardiovascular medicine, researchers have engineered sophisticated artificial intelligence (AI) models capable of parsing extensive electronic health records (EHR) and electrocardiograms (EKGs) to identify individuals at high risk of sudden cardiac arrest (SCA).
Published in the esteemed journal JACC: Advances, the research employed a vast dataset encompassing nearly 1.7 million patient records from a large integrated healthcare system in the U.S., encompassing both EHR data and 12-lead EKGs.
The team's approach leverages three distinct AI models: one informed solely by EKG waveforms, another utilizing structured EHR inputs comprising more than 150 clinical variables, and a third hybrid model integrating both data sources.
Remarkably, the integrated EHR-EKG AI model correctly identified 153 of the 228 patients who experienced cardiac arrest as high-risk, exhibiting an enrichment in risk prediction that elevated from a baseline of 1 in 1,000 to 1 in 100.
The newly developed AI tools mark a paradigm shift, offering the first tangible method to forecast this often-unheralded event with meaningful accuracy.
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