Saturday, December 10, 2022

Predict sudden cardiac arrest: Distinguish between treatable and untreatable sudden cardiac arrest

Clinical scientists at the Smidt Heart Institute at Cedars-Sinai have developed a clinical algorithm that distinguishes for the first time between treatable sudden cardiac arrest and untreatable forms of the condition.

The findings, published today in the peer-reviewed Journal of the American College of Cardiology: Clinical Electrophysiologyhas the potential to improve prevention of sudden cardiac arrest – unexpected loss of cardiac function – based on key risk factors identified in this study.

“All sudden cardiac arrest is not the same,” explains Sumeet Chugh, MD, director of the Center for Cardiac Arrest Prevention and lead author of the study. “To date, no previous research has distinguished between potentially treatable sudden cardiac arrest versus incurable forms that cause death in almost all cases.”

Sudden cardiac arrest outside the hospital claims at least 300,000 American lives annually. For those affected, 90% will die within 10 minutes after cardiac arrest.

For this largely fatal condition, prevention will have a major impact. However, the biggest challenge lies in distinguishing between those who will benefit most from an implantable cardioverter-defibrillator – and those who will not benefit from the electric shock.

“Defibrillators are expensive and unnecessary for individuals with the type of sudden cardiac arrest that will not respond to an electric shock,” Chugh said. “For patients with treatable or ‘shockable’ forms of the disease, however, a defibrillator is life-saving.”

Chugh, also a professor and the Pauline and Harold Price Chair in Cardiac Electrophysiology Research, says this new research provides a clinical risk assessment algorithm that can better identify patients with the highest risk of treatable sudden cardiac arrest – and thus a better understanding of the patients who would benefit from a defibrillator.

The risk assessment algorithm consists of 13 clinical, electrocardiogram, and echocardiographic variables that may place a patient at greater risk for treatable sudden cardiac arrest.

The risk factors include diabetes, myocardial infarction, atrial fibrillation, stroke, heart failure, chronic obstructive pulmonary disease, seizure disorders, syncope – a temporary loss of consciousness caused by a drop in blood pressure – and four separate indicators found with a test gram, electrocardiogram including heart rate.

“This first of its kind algorithm has the potential to improve the way we currently predict sudden cardiac arrest,” says Eduardo Marbán, MD, PhD, executive director of the Smidt Heart Institute and the Mark S. Siegel Family Foundation Distinguished Professor. “If validated in clinical trials, we will be able to better identify high-risk patients and thus save lives.”

The research study used data from two continuous multi-year studies founded by Chugh. The Oregon Sudden Unexpected Death Study is a comprehensive evaluation of sudden cardiac arrest among the 1 million residents of the Portland, Oregon, metropolitan area.

The Ventura Prediction of Sudden Death in Multi-ethnic Communities (PRESTO) study is based in Ventura, California, with approximately 850,000 residents. Both studies are unique community partnerships with residents of the area, as well as first-response staff, medical investigators, and hospital systems that provide care within the two communities.

Both led by Chugh, the projects – now running in Oregon for nearly 20 years, and more recently in Ventura – provide researchers with unique, community-based information to help determine how to best predict sudden cardiac arrest.

As a next step, Chugh plans to test their risk assessment algorithm, funded by the National Heart, Lung and Blood Institute (R01HL126938 and R01HL145675), in separate prospective studies, as well as randomized clinical trials.

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