predict cardiac arrest

Clinical scientists at Cedars-Sinai's Smit Heart Institute have developed, for the first time, a clinical algorithm that can differentiate between treatable cardiac arrest and non-treatable cardiac arrest

Research published today in the peer-reviewed Journal of the American College of Cardiology: Clinical Electrophysiology has the potential to improve prevention of sudden cardiac arrest -- the unintended loss of heart function -- based on the key risk factors identified in this study

"All cardiac arrests are different," explained Sumeet Chugh, MD, director of the Cardiac Arrest Prevention Center and lead author of the study

Out-of-hospital cardiac arrest kills at least 300,000 Americans each year

For this deadly disease, prevention will have a profound impact

"Defibrillators are expensive and unnecessary for cardiac arrest patients who do not respond to shocks," Chugh said

Chug, also with Pauline Professor Harold Price's Chair in Cardiac Electrophysiology Research, said the new study provides a clinical risk assessment algorithm that could better identify patients with the highest risk of arrest for treatment of sudden cardiac death And, therefore, a better understanding of the patients who would benefit from a defibrillator

The risk-assessment algorithm included 13 clinical, electrocardiographic and echocardiographic variables that may put patients at higher risk for treatable cardiac arrest

These risk factors included diabetes, myocardial infarction, atrial fibrillation, stroke, heart failure, chronic obstructive pulmonary disease, epilepsy, syncope (a temporary loss of consciousness caused by a drop in blood pressure), and four independent measures detected by electrocardiogram testing, including heart rate

"This first-of-its-kind algorithm has the potential to improve our current methods of predicting cardiac arrest," said Eduardo Marbán, MD, executive director of the Smidt Heart Institute and Mark S.

The study used data from two studies that were created by Chug over many years

The Prediction of Sudden Death in Multiracial Communities in Ventura (PRESTO) study is based in Ventura, California, approximately 850,000 residents

Both projects, led by Chugh, are now in Oregon for nearly 20 years, and the most recent project in Ventura provides researchers with unique, community-based information to help determine how best to predict Cardiac arrest

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