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Investigators Closer to Predicting Two Common Heart Conditions

Sumeet Chugh, MD, right, has focused his career on developing ways to predict sudden cardiac arrest before it strikes. Photo by Cedars-Sinai.
Sumeet Chugh, MD, right, has focused his career on developing ways to predict sudden cardiac arrest before it strikes. Photo by Cedars-Sinai.
Sumeet Chugh, MD, right, has focused his career on developing ways to predict sudden cardiac arrest before it strikes. Photo by Cedars-Sinai.

Novel Research Studies From Cedars-Sinai Move the Needle on Predicting Sudden Cardiac Arrest and Coronary Artery Disease

Two novel research studies from Cedars-Sinai move the needle on predicting two important heart conditions: sudden cardiac arrest and increased coronary artery calcium.

The studies were led by investigators in the Smidt Heart Institute at Cedars-Sinai and the medical center’s Division of Artificial Intelligence in Medicine (AIM).

Investigators identified a panel of novel blood biomarkers specifically associated with sudden cardiac arrest. These biomarkers have the potential to enhance the clinical prediction of sudden cardiac arrest—a life-threatening condition making headlines after NFL player Damar Hamlin experienced cardiac arrest midgame in January. Findings were published in Heart Rhythm.

“The dismal survival rates for sudden cardiac arrest exemplify the need for risk determination, early prediction and improved primary prevention,” said Sumeet Chugh, MD, senior author of the study and professor of Cardiology, medical director of the Heart Rhythm Center and director of the Division of AIM at Cedars-Sinai.

Chugh, who is also the Pauline and Harold Price Chair in Cardiac Electrophysiology Research and director of the Center for Cardiac Arrest Prevention in the Smidt Heart Institute, has devoted his career to studying sudden cardiac arrest. His research has led to novel methods of predicting sudden cardiac arrest that are currently being evaluated with the goal of deploying these in clinical care.  

In this study—a collaborative effort with the Van Eyk Laboratory at Cedars-Sinai—researchers analyzed a large number of blood biomarkers obtained from survivors of sudden cardiac arrest, comparing results from one cohort of people without coronary artery disease and one cohort with the disease.

“We identified a total of 26 protein biomarkers associated with sudden cardiac arrest when cases were compared to controls, of which 20 differentiated sudden cardiac arrest from coronary artery disease,” said Faye Norby, PhD, a research scientist at the Center for Cardiac Arrest Prevention and first author of the study. “While these biomarkers have the potential to enhance prediction of sudden cardiac arrest, future studies are needed to replicate these findings in a larger group of patients.”

The coronary artery calcium study, published in the Journal of the American Society of Echocardiographyshows for the first time that an ultrasound image of the heart can be “read” by an AI algorithm to accurately identify whether a patient has a large amount of calcium buildup in their coronary arteries. 

Traditionally, coronary artery calcium buildup is diagnosed using CT scans, which are not available at every center, expose patients to radiation and are costly. On the other hand, heart ultrasounds—also called echocardiograms—can be done in a clinic or doctor’s office, do not produce radiation and tend to be much less expensive.

“We show that echocardiograms, when interpreted with our AI software, can predict coronary artery calcium and predict heart attack risk nearly as well as CT scans,” said senior author David Ouyang, MD, a cardiologist in the Department of Cardiology in the Smidt Heart Institute and a researcher in the Division of AIM. “This proved true even in cases where the naked eye of an expert reader sees the ultrasound image of the heart as appearing fairly normal.”

Using a dataset of 2,881 echocardiogram images, investigators trained a video-based AI tool to predict coronary artery calcium scores. Scores range from zero, which represents a “perfect” score with no indication of coronary artery calcium buildup, to more than 2,000, which indicates a poor prognosis for an individual and represents a high risk of heart attack and coronary artery disease.

The video-based deep-learning model successfully predicted scores of zero in patients with good health as well as high coronary calcium scores, likely foreshadowing a worse future prognosis.

Ouyang and his team hope this efficient technology—inclusive of a coronary artery calcium score for each patient—may be used in all echocardiogram laboratories. This type of resource, he said, “will allow for faster, potentially more frequent, and generally more cost-effective imaging that provides clinically valuable, predictive information.”

Read more from the Cedars-Sinai Blog: Can a Smartwatch Save Your Life?