“The majority of people who have a sudden cardiac arrest outside of a hospital will die, so the best protection is being aware of risk,” said Kyndaron Reinier, PhD, MPH, associate director of Epidemiology in the Center for Cardiac Arrest Prevention in the Smidt Heart Institute at Cedars-Sinai and an author of both studies. 

Sudden cardiac arrest happens when a problem with the heart’s electrical system causes the heart to stop beating. It is different from a heart attack, which is caused by a lack of blood flow to the heart. More than 350,000 people experience sudden cardiac arrest outside of a hospital in the U.S. each year, and only about 10% survive. 

Experts know that people with low left ventricular ejection fraction, when the heart’s main pumping chamber is weak and pumps less than it should, are at higher risk for sudden cardiac arrest. But this marker has become less effective and other indicators are needed to capture more people at risk.

“More than two-thirds of people who have cardiac arrest don’t have low left ventricular ejection fraction, so using this marker alone misses too many people,” Reinier said. 

A study published in the journal Circulation: Arrhythmia and Electrophysiology reports that warning symptoms combined with clinical history could predict imminent sudden cardiac arrest. A second study, published in the Journal of the American Heart Association (JAHA), reports that having more than one cardiac event over time could signal rising risk. 

Reading Warning Symptoms

In the Circulation: Arrhythmia and Electrophysiology study, investigators used machine learning to identify combinations of symptoms and medical history that best predicted sudden cardiac arrest in the near future. 

The study included people enrolled in two separate, longstanding studies in Oregon and Ventura County, California, that were founded by Sumeet Chugh, MD, director of the Center for Cardiac Arrest Prevention in the Smidt Heart Institute. The investigators compared 364 people who called 911 when experiencing symptoms such as chest pain and survived sudden cardiac arrest, with 313 people who called 911 for similar symptoms but did not experience sudden cardiac arrest. 

The analysis found that people with sudden cardiac arrest were more likely to have a combination of shortness of breath and diagnosed coronary artery disease or heart failure than people who did not experience sudden cardiac arrest. Seizure-like symptoms without chest pain or shortness of breath were also more common in people who experienced sudden cardiac arrest.

The investigators also found that chest pain combined with coronary artery disease predicted imminent arrest in women, while chest pain combined with heart failure predicted it in men. 

Warning symptoms most often occurred at least 15 minutes prior to the sudden cardiac arrest, a time frame that would make it possible to call 911. In earlier research, the investigators found that 81% of people delayed their 911 call, reducing their chances of successful revival by ambulance paramedics. 

These findings could be used to create risk-predicting algorithms for use by urgent care and emergency medicine providers, the investigators said. “While more research is needed, such risk prediction algorithms have the potential to avoid 911 call delays following warning symptoms of sudden cardiac arrest,” said Chugh, vice dean and chief AI health research officer at Cedars-Sinai.

Tracking Risk Over Time

The JAHA study was carried out in the Observational Study of Cardiac Arrest Risk (O.S.C.A.R.) cohort at Cedars-Sinai, established by Chugh, that has been tracking the health of approximately 400,000 residents of Los Angeles County from 2017 onward. Of these, the investigators followed more than 6,700 people hospitalized at Cedars-Sinai for heart failure and more than 2,900 people hospitalized in the health system for acute coronary syndrome, when an artery blockage reduces blood flow to the heart. 

Investigators found that patients in both groups who experienced a recurrent cardiovascular event faced a higher risk of sudden cardiac arrest. 

Patients who had a second coronary artery blockage were more than three times as likely to experience sudden cardiac arrest as those without a recurrence. Patients who were hospitalized a second time for heart failure were nearly twice as likely to experience sudden cardiac arrest. Risk climbed with each additional heart failure hospitalization. The investigators compared the findings to what was found in participants of the Framingham Heart Study, which enrolled participants about 50 years earlier. The results were similar, however the heart failure result in that study did not reach statistical significance.

Reinier said that physicians should consider sudden cardiac arrest as being more likely when a patient is hospitalized for a heart issue a second time.

“This may mean running additional tests,” Reinier said. “It may mean educating the patient about what cardiac arrest is and the importance of having a family member who knows to call 911 and start CPR immediately if their loved one collapses.”

Moving Beyond Dead Ends

Chugh, senior author of both studies, said the combined findings point toward a more flexible approach to prediction.

“Near-term and long-term predictions are parallel approaches that can help us move past roadblocks we face in preventing death from this lethal heart event,” Chugh said.

Additional research, including studies in different populations, are needed to confirm whether the factors the investigators studied could be used in prediction tools, study authors said. 

Cedars-Sinai investigators continue to study predictors of sudden cardiac arrest, and their work includes using AI to study patterns in heart tests called electrocardiograms and looking for genetic causes. 

Additional Cedars-Sinai authors in the Circulation: Arrhythmia and Electrophysiologystudy include Harpriya Chugh, BS; Vishnu Kadiyala, MD; Arayik Sargsyan, MPH; Audrey Uy-Evanado, MD; Kotoka Nakamura, PhD; Elizabeth Heckard, MS; Marco Mathias, BS.

Other authors include Tristan Grogan, MS; David Elashoff, PhD; Angelo Salvucci, MD; and Jonathan Jui, MD, MPH.

Funding: Dr. Chugh was funded by the National Heart Lung and Blood Institute, National Institutes of Health.

Additional Cedars-Sinai authors in the Journal of the American Heart Association study include Marita Knudsen Pope, MD, PhD; Harpriya Chugh, BS, MSHS; Thien Tan Tri Tai Truyen, MD; Marco Mathias, BS; and Audrey Uy-Evanado, MD.

Other authors include Honghuang Lin, PhD; Dan Atar, MD, Nichole Bosson, MD, MPH, and Emelia J. Benjamin, MD, ScM

Funding: The Framingham Heart Study is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health. Dr. Benjamin is partially funded by The National Heart, Lung, and Blood Institute, National Institutes of Health.

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

Younger people who experience sudden cardiac arrest are more likely to have a genetic cause than older people who experience it, according to new research from the

“If you have a family member who suffered sudden cardiac arrest, it is important to undergo genetic testing to determine if you harbor a genetic variant that increases your risk of sudden cardiac arrest or other heart conditions,” said Evan Kransdorf, MD, PhD, assistant professor of Cardiology in the Smidt Heart Institute and first author of the study. “For people who carry a variant linked to sudden cardiac arrest, a cardiologist can prescribe medications or lifestyle changes that can decrease the chances of experiencing this dangerous event.”

Sudden cardiac arrest, an electrical malfunction that causes the heart to beat very rapidly, is fatal in 90% of cases, according to the American Heart Association. The Cedars-Sinai Health Sciences University investigators found that 10% of people 29 and younger who have sudden cardiac arrest carry genetic variants linked with the condition.

The team analyzed blood samples from more than 3,000 people who experienced sudden cardiac arrest in Portland, Oregon, and Ventura County, California. The samples came from the ongoing Oregon Sudden Unexpected Death Study and the Ventura Prediction of Sudden Death in Multi-Ethnic Communities study—both created by Sumeet Chugh, MD, director of the Center for Cardiac Arrest Prevention in the Smidt Heart Institute, to improve understanding of the condition.

Investigators performed whole genome sequencing, which maps a person’s entire genetic code. They identified 15 genes in which damaging genetic variants can occur and disrupt the gene’s function and increase risk of sudden cardiac arrest. They found the prevalence of damaging genetic variants decreased with age:

• 10% of people age 29 and younger harbored a damaging genetic variant.
• 7% of people age 30-49 harbored a damaging genetic variant.
• 4% of people age 50-69 harbored a damaging genetic variant.
• 3% of people age 70 and older harbored a damaging genetic variant.

In older people, sudden cardiac arrest is more likely to be caused by a narrowed or blocked heart blood vessel rather than a heart condition caused by a damaging genetic variant, according to the investigators.

The investigators said more research will help uncover other genes linked to sudden cardiac arrest.

“This study is more representative of the U.S. population than other studies because it includes data from two communities rather than data from people already being seen at a medical center,” said Chugh, who is also vice dean and chief artificial intelligence health research officer at Cedars-Sinai and senior author of the study.

Additional Cedars-Sinai authors include Marco Mathias, BS; Kotoka Nakamura, PhD; Harpriya Chugh, BE, MSHS; David Nguyen, BS; Paul D. Pharoah, MD, PhD; and Kyndaron Reinier, MPH, PhD.

Other authors include Jonathan Tyrer, PhD; Zeynep Akdemir, PhD; Eric Boerwinkle, PhD; and Bing Yu, PhD. 

Funding: The study was funded by The National Institute of Health, NHLBI Grants R01HL145675 and R01HL147358; NIH DHHS Contracts HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I, HHSN268201700005I.

Read more from Cedars-Sinai Stories and Insights: Heart Attack, Cardiac Arrest, Heart Failure—What’s the Difference?

, whose research into sudden cardiac arrest has led to novel methods of predicting the usually fatal condition, has been promoted to vice dean and chief artificial intelligence health research officer at Cedars-Sinai.

Chugh was also honored recently with the Heart Rhythm Society’s 2025 Distinguished Scientist Award for clinical science. The award was presented April 26 during Heart Rhythm 2025 in San Diego. 

“This award recognizes Dr. Chugh’s dedication to understanding sudden cardiac arrest and preventing this deadly emergency,” said Eduardo Marbán, MD, PhD, executive director of the Smidt Heart Institute at Cedars-Sinai.

The Distinguished Scientist Award is given to an investigator who has made a lasting impact on patient care and the field of heart rhythm research. Chugh has dedicated his career to improving prediction and prevention of sudden cardiac arrest, a heart rhythm disorder that causes the heart to stop and often leads to instant death. He has published more than 275 scientific papers.

“I’m grateful to the Heart Rhythm Society for this recognition of our work and accept it on behalf of my colleagues and mentees at Cedars-Sinai,” said Chugh, the Pauline and Harold Price Chair in Cardiac Electrophysiology Research. “Our team is motivated to make a real impact on this deadly condition.”

According to the American Heart Association, more than 356,000 out-of-hospital cardiac arrests occur each year in the U.S. Nearly 90% of them are fatal.

Chugh’s team discovered a new method for identifying the best candidates for the implantable defibrillator, a lifesaving intervention. They also combined data from emergency responders, medical records and biological samples to predict imminent sudden cardiac arrest within hours to days of warning symptoms. Their work has improved identification of people at risk and led to the term “near-term prevention” of sudden cardiac arrest.

Chugh is also a leader in artificial intelligence research, which he has harnessed to improve prediction of sudden cardiac arrest.

In his new role as vice dean and chief artificial intelligence health research officer, Chugh will oversee the translation of AI research into clinical trials and patient care. He will lead the new Artificial Intelligence in Medicine Research Center (AIMRC), which will help departments and institutes incorporate AI into their research.

“Dr. Chugh is a foremost investigator in both heart rhythm and AI research,” said Jeffrey Golden, MD, executive vice dean for Research and Education at Cedars-Sinai. “His use of AI to assess the risk for sudden cardiac arrest has the potential to advance the field and save lives. We are eager for him to broaden his AI research experience to all areas of clinical care at Cedars-Sinai.”

In 2024, Chugh received the Distinguished Scientist Award for clinical science from the American College of Cardiology. He is a member of the American Society for Clinical Investigation and the Association of American Physicians, and is past president of the Association of University Cardiologists and the Cardiac Electrophysiology Society.

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

The work builds upon research published last year showing that an AI program can detect disease in the heart’s mitral valve by analyzing ultrasound images of the heart. For this new study, published in JAMA Cardiology, investigators applied AI to identify tricuspid regurgitation, a condition in which the heart’s tricuspid valve doesn’t close fully when the heart contracts, causing blood to flow backward, which can result in heart failure.  

“This AI program can augment cardiologists’ evaluation of echocardiograms, images from a screening and diagnostic test that many patients with heart disease symptoms would already be getting,” said David Ouyang, MD, a research scientist in the Smidt Heart Institute, an investigator in the Division of Artificial Intelligence in Medicine and senior author of the study. “By applying AI to echocardiograms, we can help clinicians more easily detect the signs of heart valve disease so that patients get the care they need as soon as possible.”

Investigators trained a deep-learning program to flag patterns of tricuspid regurgitation in 47,312 echocardiograms done at Cedars-Sinai between 2011 and 2021.

The program detected tricuspid regurgitation in patients and categorized cases as mild, moderate or severe. They then tested the program on echocardiograms that the AI program never saw before from additional patients who underwent echocardiography at Cedars-Sinai in 2022 and patients from Stanford Healthcare. The program predicted severity of tricuspid regurgitation with similar accuracy as cardiologists who evaluated echocardiograms and when compared with results from MRI images.

“Future studies will focus on obtaining even more specific information about valve disease, such as the volume of blood flowing backward through a valve, and predicting outcomes if patients undergo treatment for heart valve disease,” said first author Amey Vrudhula, MD, a research fellow at Cedars-Sinai.

Investigators in the Smidt Heart Institute are applying AI to a variety of cardiac imaging tests. 

“A major advantage of AI algorithms is that they never get fatigued and have the capacity to identify valve abnormalities from large populations of patients, taking personalized cardiology to a whole different level,” said Sumeet Chugh, MD, director of the Division of Artificial Intelligence in Medicine and the Pauline and Harold Price Chair in Cardiac Electrophysiology Research. 

Other Cedars-Sinai authors involved in the study include Amey Vrudhula, MD; Milos Vukadinovic, BS; Alan C. Kwan, MD; Daniel Berman, MD; Robert Siegel, MD; Susan Cheng, MD, MMSc, MPH.

Other authors include Christiane Haeffele, MD, and David Liang, MD, PhD.

The work was supported by the Sarnoff Cardiovascular Research Award, research grants R00 HL157421 and R01HL173526 and support from AstraZeneca Alexion, as well as consulting from EchoIQ, Ultromics, Pfizer and InVision.

Cedars-Sinai Health Sciences University is advancing groundbreaking research and educating future leaders in medicine, biomedical sciences and allied health sciences. Learn more about the university.

The program’s findings, published in Circulation, may help clinicians identify patients whose mitral valve regurgitation is manageable with medication as well as patients with more severe cases who would benefit from a minimally invasive valve repair procedure or surgery.

“Mitral regurgitation is a common but often missed valvular heart disease. It can be challenging to precisely assess the disease severity, which is critical to know which patients can take a watch-and-wait approach and which should proceed to an intervention,” said David Ouyang, MD, a cardiologist in the Department of Cardiology in the Smidt Heart Institute, an investigator in the Division of Artificial Intelligence in Medicine, and corresponding author of the study. “The program we developed may one day be used by doctors when considering the best treatment approach for individual patients.”

The AI program further cements the Smidt Heart Institute’s longstanding leadership in heart valve care. Interventionalists with the Smidt Heart Institute are believed to have performed more mitral valve repairs than any other center in the U.S., with outcomes that place Cedars-Sinai among the top-performing programs nationally. The Smidt Heart Institute team has also completed more than 1,500 robotic mitral valve repairs with a near 100% success rates.

“This could improve how we identify patients with mitral regurgitation, which is becoming more prevalent in our aging population, and to personalize treatment even more so than we already do,” said Raj Makkar, MD, associate director of the Smidt Heart Institute, vice president of Cardiovascular Innovation and Intervention for Cedars-Sinai and a leader in treating mitral valve disease.

The heart has four valves that open and close to move blood throughout the body. In some people the mitral valve, located on the left side of the heart, does not close properly, which allows blood to flow backwards, a condition called mitral valve regurgitation. The condition prevents enough blood from circulating throughout the body and, over time, can lead to shortness of breath, arrhythmia and heart failure.

“At Cedars-Sinai we are pursuing the use of AI as a complementary tool in diagnosing and treating conditions such as mitral valve regurgitation,” said Sumeet Chugh, MD, director of the Division of Artificial Intelligence in Medicine and the Pauline and Harold Price Chair in Cardiac Electrophysiology Research.

In developing the new program, investigators used more than 58,000 transthoracic echocardiograms from Cedars-Sinai. Echocardiograms are video images of patients’ hearts taken by ultrasound and is the most common way to assess mitral regurgitation. The investigators tested the program on echocardiograms from 1,800 patients at Cedars-Sinai as well on echocardiograms from 915 patients from Stanford Healthcare in Northern California.

The model was able to automatically identify moderate and severe mitral valve regurgitation with high precision.

“Our deep learning model analyzed videos from more than 50,000 echocardiogram studies and can pinpoint the most relevant and important videos to assess mitral regurgitation severity,” said first author Amey Vrudhula, a fellow at Cedars-Sinai.

To treat severe mitral valve regurgitation, experts at the Smidt Heart Institute at Cedars-Sinai rely on either the minimally invasive TEER procedure or minimally invasive surgery. All patients meet with an interventional cardiologist as well as a cardiac surgeon before making their treatment decision.

Other Cedars-Sinai authors involved in the study include Grant Duffy B.S., Milos Vukadinovic B.S^., Susan Cheng, M.D., M.M.Sc., M.P.H.

This work was supported in part by the Sarnoff Cardiovascular Research Award, research grants NIH R01-HL131532, NIH R01-HL142983, R00 HL157421, and R01HL173526.

Read more from the Cedars-Sinai Blog: Treatment Options for Heart Valve Disease

“Sudden cardiac arrest is a mostly lethal condition, and prevention will make the biggest impact, but we need to find novel clinical tools to make that possible,” said Sumeet Chugh, MD, director of the Division of Artificial Intelligence in Medicine at Cedars-Sinai and senior author of both studies. “Using AI algorithms to improve prediction of sudden cardiac arrest could help doctors identify which patients might be at higher risk of experiencing this devastating condition.”

More than 350,000 people have an out-of-hospital sudden cardiac arrest in the United States every year, according to the Centers for Disease Control and Prevention.

During sudden cardiac arrest, a change in the heart’s electrical activity causes it to suddenly stop beating. Having a heart condition can make a person more likely to experience sudden cardiac arrest, but it also can occur in people with no known heart condition.  

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.”

In another study, published in Circulation: Arrhythmia and Electrophysiology, Chugh along with fellow investigator Piotr Slomka, PhD, director of Innovation in Imaging at Cedars-Sinai and a research scientist in the Division of Artificial Intelligence in Medicine and the Smidt Heart Institute; and other colleagues trained an AI model to differentiate between two underlying causes of sudden cardiac arrest: pulseless electrical activity and ventricular fibrillation.

Pulseless electrical activity means that the heart’s electrical signals are too weak to produce a heartbeat. It cannot be treated with a defibrillator and often leads to death. Ventricular fibrillation is a type of irregular heartbeat that can cause the heart to stop beating, but an electric shock from a defibrillator can trigger the beating again. 

After the AI model reviewed patterns in electrocardiogram readings as well as patient characteristics, investigators were able to determine risk factors for both types of sudden cardiac arrest.

People who had pulseless electrical activity sudden cardiac arrest, for example, were more likely to have been older, been overweight, have had anemia, or experienced shortness of breath as a warning symptom. Those who had ventricular fibrillation were more likely to be younger, have had coronary artery disease or experienced chest pain as a warning symptom.

“We have ways of preventing sudden cardiac arrest through technologies like a defibrillator, but the challenge is knowing who is most likely to benefit from this intervention,” said Lauri Holmstrom, MD, PhD, a visiting postdoctoral scientist at Cedars-Sinai and first author of both studies. “These findings could help cardiologists identify which patients are likely to have a pulseless electrical activity sudden cardiac arrest or ventricular fibrillation sudden cardiac arrest, and help them prevent these events from occurring.”

The AI models used in both studies were trained, tested, and validated using data from two ongoing studies of sudden cardiac arrest founded and led by Chugh: the Oregon Sudden Unexpected Death Study and the Ventura Prediction of Sudden Death in Multi-Ethnic Communities (PRESTO) study.

“These studies exemplify the potential for AI to detect patterns in the body that the human eye and standard medical tests cannot,” said Paul Noble, MD, the Vera and Paul Guerin Family Distinguished Chair in Pulmonary Medicine and chair of the Department of Medicine at Cedars-Sinai, who was not involved in the studies. “We are getting closer to being able to use AI to prevent dangerous events such as sudden cardiac arrest.”

Other Cedars-Sinai investigators who worked on the Communications Medicine study include Harpriya Chugh; Kotoka Nakamura, PhD; Ziana Bhanji; Madison Seifer; Audrey Uy-Evanado, MD; and Kyndaron Reinier, PhD.

Other Cedars-Sinai investigators who worked on the Circulation: Arrhythmia and Electrophysiology study include Bryan Bednarski; Harpriya Chugh; Habiba Aziz; Hoang Nhat Pham, MD; Arayik Sargsyan, MD; Audrey Uy-Evanado, MD; Damini Dey, PhD; and Kyndaron Reinier, PhD.

Funding: Both studies were funded, in part, by the National Heart, Lung, and Blood Institute.

Read more on the Cedars-Sinai Blog: Heart Attack, Cardiac Arrest, Heart Failure—What's the Difference?

The study was the first to explore risk factors for sudden cardiac arrest—when the heart unexpectedly stops beating—among Latino people in the U.S.

Reinier, the study’s lead author, told STAT News that Latino people have not historically been well-represented in cardiovascular research.

“Sudden cardiac arrest is a major cause of death, yet little is known about the risk factors … among Hispanic and Latino individuals, who make up about 19% of the U.S. population,” Reinier said.

Chugh, a professor of Cardiology and one of the study’s investigators, told STAT News that it is not yet clear why patients with chronic kidney disease have an increased risk of sudden cardiac arrest.

“While more research needs to be done, it is possible that dialysis treatment, which is used in severe or end-stage [chronic kidney disease], could be associated with increased risk of lethal arrhythmias resulting in [sudden cardiac arrest],” Chugh said. “It turns out that even moderate [sudden cardiac arrest] could increase … risk, but the mechanisms by which this happens have not been determined yet.”

Chugh added that inadequate access to healthcare among Latino patients also could contribute to the disparity. 

Click here to read the complete article from STAT News. 

The algorithm, which identified hidden signals in common medical diagnostic testing, may help doctors better prevent strokes and other cardiovascular complications in people with atrial fibrillation—the most common type of heart rhythm disorder.

Previously developed algorithms have been primarily used in white populations. This algorithm works in diverse settings and patient populations, including U.S. veterans and underserved populations. The findings were published today in the peer-reviewed journal JAMA Cardiology.

“This research allows for better identification of a hidden heart condition and informs the best way to develop algorithms that are equitable and generalizable to all patients,” said David Ouyang, MD, a cardiologist in the Department of Cardiology in the Smidt Heart Institute at Cedars-Sinai, a researcher in the Division of Artificial Intelligence in Medicine, and senior author of the study.

Experts estimate that about 1 in 3 people with atrial fibrillation do not know they have the condition.

In atrial fibrillation, the electrical signals in the heart that regulate the pumping of blood from the upper chambers to the lower chambers are chaotic. This can cause blood in the upper chambers to pool and form blood clots that can travel to the brain and trigger an ischemic stroke.

To create the algorithm, investigators programmed an artificial intelligence tool to study patterns found in electrocardiogram readings. An electrocardiogram is a test that monitors electrical signals from the heart. People who undergo this test have electrodes placed on their body that detect the heart’s electrical activity.

The program was trained to analyze electrocardiogram readings taken between Jan. 1, 1987, and Dec. 31, 2022, from patients seen at two Veterans Affairs health networks. The algorithm was trained on almost a million electrocardiograms and it accurately predicted patients would have atrial fibrillation within 31 days.

The AI model was also applied to medical records from patients at Cedars-Sinai and it similarly—and accurately—predicted cases of atrial fibrillation within 31 days.

“This study of veterans was geographically and ethnically diverse, indicating that the application of this algorithm could benefit the general population in the U.S.,” said Sumeet Chugh, MD, director of the Division of Artificial Intelligence in Medicine in the Department of Medicine and medical director of the Heart Rhythm Center in the Department of Cardiology. “This research exemplifies one of the many ways that investigators in the Smidt Heart Institute and the Division of Artificial Intelligence in Medicine are using AI to address preemptive management of complex and challenging cardiac conditions.”  

The study was a collaborative effort between physicians and investigators at Cedars-Sinai and the San Francisco and Palo Alto Veterans Affairs hospitals. First author Neal Yuan, MD, is an investigator with the Smidt Heart Institute at Cedars-Sinai. Cedars-Sinai investigators Grant Duffy and John Theurer also worked on the study.

The investigators plan to continue to study the algorithm as part of prospective clinical trials to learn if it helps identify those at risk for heart attack and stroke. They also plan to develop more AI algorithms.

Funding: The study was funded by the National Institutes of Health and the U.S. Department of Veterans Affairs.

Read more on the Cedars-Sinai Blog: Treating Atrial Fibrillation: Risks and Complications

During sudden cardiac arrest, the heart unexpectedly stops beating.

“Because people who experience sudden cardiac arrest have a survival rate of less than 10%, prevention is extremely important,” said Kyndaron Reinier, PhD, associate director of Epidemiology in the Center for Cardiac Arrest Prevention at the Smidt Heart Institute and lead author of the study published in the Journal of the American Heart Association.

“This study highlights the importance for Hispanic and Latino individuals with chronic kidney disease to understand their risk of sudden cardiac arrest, and to closely monitor and manage their renal disease with their medical care team.”

The study also reports that Hispanics and Latinos with cardiovascular disease have an increased risk of sudden cardiac arrest.

The research was conducted as part of an ongoing study called the Prediction of Sudden Death in Multi-Ethnic Communities (PRESTO) that is led by Sumeet Chugh, MD, director of the Center for Cardiac Arrest Prevention in the Smidt Heart Institute. Investigators involved are gathering data on people in Ventura County, California, who experience sudden cardiac arrest. The goal is to learn about possible causes for this often-fatal event.

In this study, investigators reviewed information from 1,468 adults in Ventura County who had sudden cardiac arrest between 2015 and 2021. From this cohort, they extracted data from 295 people who were Hispanic or Latino.

To create a comparison group, investigators looked at data from 3,033 Hispanic and Latino adults who participated in the San Diego site of a study called the Hispanic Community Health Survey/Study of Latinos. Participants in the Hispanic Community Health Survey study completed one medical examination between 2008 and 2011 and another between 2014 and 2017. Investigators selected 590 people from this cohort to compare with the group that had experienced sudden cardiac arrest.

The investigators took into account age, sex, and other variables when comparing people who suffered sudden cardiac arrest with those who did not.

They found that people who suffered sudden cardiac arrest were more likely than people who did not suffer sudden cardiac arrest to have had chronic kidney disease, a stroke, atrial fibrillation, coronary artery disease, heart failure, diabetes or to have been heavy drinkers.

Of the people who had sudden cardiac arrest, 51% had a prior diagnosis of chronic kidney disease, and 20% were on dialysis at the time of the event.

“This study is the first we know of to analyze risk factors for sudden cardiac arrest among U.S. Hispanic and Latino individuals,” said Chugh, the Pauline and Harold Price Chair in Cardiac Electrophysiology Research and senior author of the study.

Reinier said she hopes the findings spur research into the connection between chronic kidney disease and sudden cardiac arrest.

Cedars-Sinai investigators Harpriya Chugh; Arayik Sargsyan, MD; Kotoka Nakamura, PhD; Faye Norby, PhD; and Audrey Uy-Evanado, MD, also worked on the study.

Funding: The PRESTO study was funded, in part, by the National Heart, Lung, and Blood Institute.

The Hispanic Community Health Survey/Study of Latinos study was supported by the National Heart, Lung, and Blood Institute; National Institute on Minority Health and Health Disparities; National Institute on Deafness and Other Communication Disorders; National Institute of Dental and Craniofacial Research; National Institute of Diabetes and Digestive and Kidney Disease; National Institute of Neurological Disorders and Stroke; and the Office of Dietary Supplements.

Read more on the Cedars-Sinai Blog: New Research on Sudden Cardiac Arrest Shows Racial Gap

In the study, which was published in the peer-reviewed journal The Lancet Digital Health, Chugh and his co-authors analyzed data from two ongoing studies: the Prediction of Sudden Death in Multi-Ethnic Communities (PRESTO) Study in Ventura County, California, and the Oregon Sudden Unexpected Death Study (SUDS) based in Portland, Oregon.

Investigators then assessed the symptoms of individuals prior to experiencing a cardiac arrest and compared the data with control groups that sought medical attention for similar reasons.

The findings showed that people experience warning symptoms up to 24 hours before a sudden cardiac arrest, but these symptoms vary by sex. Chugh explained that men typically experienced chest pain before a cardiac arrest, whereas women typically reported shortness of breath.

Symptoms for the subgroup of people who didn’t have a cardiac arrest included dizziness, abdominal pain or discomfort, weakness, and nausea or vomiting.

“Now that we have demonstrated which warning symptoms are more important, we are hoping that more individuals at risk of imminent sudden death will pay attention to their symptoms and call 911 early, thereby increasing their likelihood of survival from this mostly lethal condition,” Chugh told Healthline.

He and his co-authors plan to conduct further research to improve the prediction of sudden cardiac arrest and increase the chances of survival for people who are at risk for this condition.

Click here to read the complete story on Healthline

The study, led by sudden cardiac arrest expert Sumeet Chugh, MD, found that 50% of individuals who experienced a sudden cardiac arrest also experienced a telling symptom 24 hours before their loss of heart function.

Smidt Heart Institute investigators also learned that this warning symptom was different for women than it was for men. For women, the most prominent symptom of an impending sudden cardiac arrest was shortness of breath, whereas men experienced chest pain.

Smaller subgroups of both genders experienced abnormal sweating and seizure-like activity.

Out-of-hospital sudden cardiac arrest claims the lives of 90% of people who experience it, marking an urgent need to better predict—and prevent—the condition.

“Harnessing warning symptoms to perform effective triage for those who need to make a 911 call could lead to early intervention and prevention of imminent death,” said Chugh, director of the Center for Cardiac Arrest Prevention in the Smidt Heart Institute and senior author of the study. “Our findings could lead to a new paradigm for prevention of sudden cardiac death.”

For this study, investigators used two established and ongoing community-based studies, each developed by Chugh: the ongoing Prediction of Sudden Death in Multi-Ethnic Communities (PRESTO) Study in Ventura County, California, and the Oregon Sudden Unexpected Death Study (SUDS), based in Portland, Oregon.

Both studies provide Cedars-Sinai investigators with unique, community-based data to establish how to best predict sudden cardiac arrest.

“It takes a village to do this work,” said Chugh, the Pauline and Harold Price Chair in Cardiac Electrophysiology Research, medical director of the Heart Rhythm Center in the Department of Cardiology, and director of the Division of Artificial Intelligence in Medicine in the Department of Medicine. “We initiated the SUDS study 22 years ago and the PRESTO study eight years ago. These cohorts have provided invaluable lessons along the way. Importantly, none of this work would have been possible without the partnership and support of first responders, medical examiners and the hospital systems that deliver care within these communities.”  

In both the Ventura and Oregon studies, Smidt Heart Institute investigators evaluated the prevalence of individual symptoms and sets of symptoms prior to sudden cardiac arrest, then compared these findings to control groups that also sought emergency medical care.

The Ventura-based study showed that 50% of the 823 people who had a sudden cardiac arrest witnessed by a bystander or emergency medicine professional, such as an emergency medicine service (EMS) responder, experienced at least one telltale symptom before their deadly event. The Oregon-based study showed similar results.

“This is the first community-based study to evaluate the association of warning symptoms—or sets of symptoms—with imminent sudden cardiac arrest using a comparison group with EMS-documented symptoms recorded as part of routine emergency care,” said Eduardo Marbán, MD, PhD, executive director of the Smidt Heart Institute and the Mark Siegel Family Foundation Distinguished Professor.

Such a study, Marbán says, paves the way for additional prospective studies that will combine all symptoms with other features to enhance prediction of imminent sudden cardiac arrest.

“Next we will supplement these key sex-specific warning symptoms with additional features—such as clinical profiles and biometric measures—for improved prediction of sudden cardiac arrest,” said Chugh.

DOI: https://doi.org/10.1016/S2589-7500(23)00147-4

Funding: This work was funded, in part, by National Institutes of Health, National Heart Lung and Blood Institute Grants R01HL145675 and R01HL147358 to SSC.

Read more from the Cedars-Sinai Blog: A Better Model of Heart Disease Prediction

Sudden cardiac arrest occurs when the heart stops beating. It causes at least 300,000 deaths in the U.S. each year. Most people die within 10 minutes of cardiac arrest. Some people survive if they can get CPR chest compressions or shocks with a defibrillator right away.

“If a person has a family member who has suffered sudden cardiac arrest, it may be helpful to undergo genetic testing to help understand one’s own risk for experiencing this dangerous condition,” said Evan Kransdorf, MD, PhD, assistant professor of Cardiology in the Smidt Heart Institute at Cedars-Sinai and senior author of the study.

Scientists are finding that some forms of sudden cardiac arrest can be prevented with an implantable defibrillator, whereas other forms of the condition aren’t responsive to interventions like defibrillators and shocks.

The condition is rare in people under the age of 35. Sudden cardiac arrest is, however, the top cause of death among young athletes.

Previous studies have found that between 12%-30% of children and young adults who experienced sudden cardiac arrest carry certain genetic variants that may have put them at higher risk for the condition.

But Kransdorf said a true figure is difficult to determine because most studies have relied on studying blood samples collected from survivors or their family members by specialized referral clinics. These samples, he said, aren’t representative of the U.S. population.

For this study, investigators analyzed blood samples collected through two ongoing community-based studies of sudden cardiac arrest: the Oregon SUDS and Ventura PRESTO studies. These are longstanding community-based programs established by Sumeet Chugh, MD, director of the Center for Cardiac Arrest Prevention in the Smidt Heart Institute and the Pauline and Harold Price Chair in Cardiac Electrophysiology Research.

As part of these studies, first responders collect blood samples from people who experience sudden cardiac arrest in Portland, Oregon, and Ventura County, California.

Investigators performed whole-genome sequencing on blood samples collected from 52 people age 21 and under who experienced sudden cardiac arrest. They found that two of the 52 young people carried genetic variants that previous studies have found to be highly associated with the condition. Four of the young people carried variants that might play a role in increasing risk for the condition, but the significance of these variants is still unknown.  

The rate of genetic variants in this population was lower than those found in other studies.  

“Additional community-based studies are going to be important to help us understand how the genetics of a diverse population compare with what has been previously established in the medical literature through studying more referral-based populations,” said Chugh.

Kransdorf, Chugh and colleagues at the Center for Cardiac Arrest Prevention are currently analyzing data from a sample of more than 3,000 people who experienced sudden cardiac arrest. They seek to better understand the extent to which genetic variants contribute to the condition.

Other Cedars-Sinai investigators who worked on the study include Lauri Holmstrom, MD, PhD, a visiting postdoctoral scientist; Kotoka Nakamura, PhD, a project scientist and team leader with the Center for Cardiac Arrest Prevention; Harpriya Chugh, a clinical research data specialist; Audrey Uy-Evanado, MD, a project scientist and team leader with the Phenotyping Core; and Faye Norby, PhD, a research assistant professor in the Department of Cardiology.

Funding: The study was funded by the National Institutes of Health (award number R01HL145675); National Heart, Lung and Blood Institute grants (R01HL147358); the Sigrid Jusélius Foundation; the Finnish Cultural Foundation; the Instrumentarium Science Foundation; the Orion Research Foundation; and the Paavo Nurmi Foundation. 

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