AI Model Far Outperforms Doctors in Predicting Sudden Cardiac Death Risk: Study Finds

Published On 2025-07-07 02:30 GMT   |   Update On 2025-07-07 02:30 GMT
Advertisement

A study published in Nature Cardiovascular Research reveals that a new artificial intelligence model developed by Johns Hopkins University significantly outperforms current clinical guidelines in predicting sudden cardiac death among patients with hypertrophic cardiomyopathy. The model, named Multimodal AI for Ventricular Arrhythmia Risk Stratification (MAARS), offers up to 93% accuracy in identifying high-risk patients, far surpassing the approximately 50% accuracy of existing methods.

Advertisement

Hypertrophic cardiomyopathy, one of the most common inherited heart conditions, affects 1 in every 200 to 500 people worldwide and is a leading cause of sudden cardiac death, particularly among young individuals and athletes.

The AI model changes that by harnessing a wide range of patient data, including long-overlooked contrast-enhanced MRI images and full-spectrum electronic health records. These images, while difficult for human doctors to interpret in detail, contain critical patterns of fibrosis—or heart scarring—that are strongly associated with cardiac arrest risk.

Tested against real-world patient data from Johns Hopkins Hospital and the Sanger Heart & Vascular Institute in North Carolina, MAARS achieved 89% overall accuracy and 93% accuracy in patients aged 40 to 60—those most vulnerable to sudden cardiac death. Importantly, the AI model also explains why a patient is high risk, enabling doctors to tailor treatment more precisely.

“Currently we have patients dying in the prime of their life because they aren't protected and others who are putting up with defibrillators for the rest of their lives with no benefit,” said senior author Natalia Trayanova. “We have the ability to predict with very high accuracy whether a patient is at very high risk for sudden cardiac death or not.”

Reference: Changxin Lai, Minglang Yin, Eugene G. Kholmovski, Dan M. Popescu, Dai-Yin Lu, Erica Scherer, Edem Binka, Stefan L. Zimmerman, Jonathan Chrispin, Allison G. Hays, Dermot M. Phelan, M. Roselle Abraham, Natalia A. Trayanova. Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy. Nature Cardiovascular Research, 2025; DOI: 10.1038/s44161-025-00679-1

Full View
Tags:    
Article Source : Nature Cardiovascular Research

Disclaimer: This website is primarily for healthcare professionals. The content here does not replace medical advice and should not be used as medical, diagnostic, endorsement, treatment, or prescription advice. Medical science evolves rapidly, and we strive to keep our information current. If you find any discrepancies, please contact us at corrections@medicaldialogues.in. Read our Correction Policy here. Nothing here should be used as a substitute for medical advice, diagnosis, or treatment. We do not endorse any healthcare advice that contradicts a physician's guidance. Use of this site is subject to our Terms of Use, Privacy Policy, and Advertisement Policy. For more details, read our Full Disclaimer here.

NOTE: Join us in combating medical misinformation. If you encounter a questionable health, medical, or medical education claim, email us at factcheck@medicaldialogues.in for evaluation.

Our comments section is governed by our Comments Policy . By posting comments at Medical Dialogues you automatically agree with our Comments Policy , Terms And Conditions and Privacy Policy .

Similar News