Curated News
By: NewsRamp Editorial Staff
October 29, 2026
AI Early Warning System Targets Out-of-Hospital Cardiac Arrest
TLDR
- The American Heart Association funded Cedars-Sinai to build an AI early warning system for cardiac arrest, potentially giving adopters a lifesaving edge.
- The system combines symptoms, Fitbit data, medical history, blood tests, and electrocardiogram data with artificial intelligence to update risk in real time.
- This American Heart Association project aims to improve cardiac arrest survival by enabling earlier, personalized warnings that could save lives.
- Over 600,000 cardiac arrests occur yearly in the U.S., and this project uses Fitbit data and AI to predict them hours or days in advance.
Impact - Why it Matters
This research matters because out-of-hospital cardiac arrest remains a leading cause of death with survival rates that have stayed stubbornly low for decades. By leveraging AI and everyday data from wearable devices and electronic health records, the project could shift cardiac arrest care from reactive to proactive—potentially giving individuals and their doctors crucial hours or days of warning to intervene. If successful, the system could be scaled to millions of people, transforming how we predict and prevent sudden cardiac death and ultimately saving countless lives.
Summary
The American Heart Association has awarded a new research grant to a team from Cedars-Sinai Medical Center in Los Angeles to develop an early warning system for out-of-hospital cardiac arrest, a condition that strikes more than 600,000 Americans each year with persistently low survival rates. The project, titled A Personalized and Scalable Early Warning System for Prediction of Imminent Out-of-Hospital Cardiac Arrest, is the latest initiative funded through the Cardiac Arrest Research Team (CART) Network, a collaboration between the American Heart Association and Heart & Stroke™, the Heart and Stroke Foundation of Canada. Led by principal investigator Sumeet Chugh, M.D., vice-dean and chief AI health research officer at Cedars-Sinai, and co-principal investigator Kyndaron Reinier, M.P.H., Ph.D., the team will combine multiple types of health data—including symptoms like chest pain or trouble breathing, Fitbit activity tracker data, medical history, blood tests, and electrocardiogram data—using artificial intelligence to update a person’s risk level in real time. The research team will be known as The K. Scott Gordon Memorial Research Team for Cardiac Arrest Prediction and Prevention, made possible through a generous donor gift.
The CART Network was originally launched in June 2026 to advance the prediction, detection, treatment, and recovery from cardiac arrest, bringing together teams from the U.S. and Canada to share expertise and accelerate scientific discoveries. Manesh R. Patel, M.D., FAHA, the American Heart Association's 2026-2027 volunteer president, emphasized the importance of the project: “This new project is intended to support a more personalized and scalable approach to identifying people whose risk of cardiac arrest may be increasing. Cardiac arrest is a profound and tragic occurrence, and we know seconds matter in making sure people get the right lifesaving treatment at the right time.” The American Heart Association has now funded more than $6.3 billion in cardiovascular, cerebrovascular and brain health research since 1949, making it the single largest non-profit, non-government supporter of heart and brain health research in the U.S.
For more information about cardiac arrest, visit heart.org.
Source Statement
This curated news summary relied on content distributed by NewMediaWire. Read the original source here, AI Early Warning System Targets Out-of-Hospital Cardiac Arrest
