New AI Tool from UC Berkeley Predicts Sudden Cardiac Arrest More Accurately

Updated: CaliforniaToday Alameda County

The study was led by Ziad Obermeyer with colleagues Alexander Schubert, James Ross, Sendhil Mullainathan, and Markus Lingman. The paper is titled "An ECG biomarker for sudden cardiac death discovered with deep learning." Sudden cardiac death is the greatest cause of natural death in the U.S., most frequently impacting older adults (aged 65+) with rates peaking between 75 and 85 years, and men are significantly more prone at a ratio of 2 to 1.

It also kills young athletes with no history of heart issues; for those under 35, it is heavily linked to undetected genetic heart abnormalities such as arrhythmias or cardiomyopathies. According to a 2023 scientific session from the American Heart Association, SCD was the leading medical cause of death among NCAA athletes, comprising 13% of 1,102 total deaths, with male, Black, and Division 1 basketball players facing the highest risk.

Sudden cardiac death causes 17 million deaths worldwide every year. The model's high-risk group comprises 2.2% of the sample, and 86.1% of the model’s high-risk patients were not flagged by standard tests.

Source: northcoastjournal.com

A groundbreaking artificial intelligence system developed at UC Berkeley has demonstrated a remarkable ability to predict sudden cardiac arrest, a condition that kills over 300,000 people in the United States each year. By analyzing electrocardiograms (EKGs), the AI can identify patients at high risk of sudden cardiac death with greater accuracy than current clinical methods, potentially saving thousands of lives annually.

The study, published on June 24 in the journal Nature, used more than 440,000 EKGs from Sweden, paired with death certificate data, to train the AI model. Researchers fed the system scans from healthy individuals, at-risk patients, and those who later died from sudden cardiac arrest.

Over several years, the model was validated on thousands of additional patient records from hospitals in San Diego and Taipei. The results showed that the AI could isolate a high-risk group with a 7% annual rate of sudden cardiac death, outperforming the standard test—which measures the heart's ejection fraction—that identifies a group with only a 4.6% annual rate.

"Medical decisions are really hard, and I think that's why AI is so exciting for me," said Ziad Obermeyer, an associate professor at UC Berkeley's School of Public Health and the study's lead author. "We can not only make better decisions, but also start to understand what's actually going on with these patients before their heart stops."

Sudden cardiac arrest occurs when the heart's electrical system malfunctions without warning, leading to a sudden stop in heartbeat. Unlike a heart attack, which is caused by restricted blood flow, cardiac arrest is an electrical failure.

While implantable defibrillators can save lives by shocking the heart back into rhythm, doctors have struggled to identify who truly needs one. Current guidelines rely on measuring the heart's ejection fraction—a test that requires a more involved medical evaluation and often misses at-risk individuals.

In fact, two-thirds of patients who receive an implantable defibrillator never need it, while thousands of others die suddenly without ever knowing they were at risk.

The AI model uncovered a previously unrecognized signal in EKGs that appears to correlate with the risk of sudden cardiac death. This discovery opens new avenues for understanding the physiological mechanisms behind the condition.

"There is also going to be a new way of doing science that comes out of these tools," Obermeyer said, emphasizing the potential for AI to advance medical knowledge.

Obermeyer and his team used three distinct data sources to build and test the model. Training data came from six years of Swedish health records, while validation used two years of deidentified EKGs from San Diego and a separate dataset from Taipei.

Compiling the data took about a decade and involved two groups Obermeyer co-founded: Dandelion Health and Nightingale Open Science. The next phase will deploy the algorithm in hospital EKG databases in Sweden, Taiwan, and the U.S.

For scans flagged as high-risk, doctors will notify patients and offer continuous heart monitoring to better understand the underlying signals.

A website has also been launched where individuals can submit basic information and an email address to be contacted for EKG analysis once the tool is more widely available. Obermeyer expressed optimism about the role of AI in transforming medicine: "It's one of the tremendous luxuries of being at Berkeley—you can pursue these projects that just are a slow burn and then have—hopefully—a big return when they get over the finish line."

Source: northcoastjournal.com

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