Predicting sudden cardiac arrest
Distinguishing between treatable and untreatable sudden cardiac arrest


Date:
March 30, 2022
Source:
Cedars-Sinai Medical Center
Summary:
Clinician-scientists have developed a clinical algorithm that,
for the first time, distinguishes between treatable sudden cardiac
arrest and untreatable forms of the condition.



FULL STORY ========================================================================== Clinician-scientists in the Smidt Heart Institute at Cedars-Sinai
developed a clinical algorithm that, for the first time, distinguishes
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 Electrophysiology, have the potential
to enhance prevention of sudden cardiac arrest -- unexpected loss of
heart function -- based on key risk factors identified in this study.

"All sudden cardiac arrest is not the same," explained Sumeet Chugh, MD, director of the Center for Cardiac Arrest Prevention and lead author
of the study. "Until now, no prior research has distinguished between potentially treatable sudden cardiac arrest versus untreatable forms that
cause death in almost all instances." Out-of-hospital sudden cardiac
arrest claims at least 300,000 U.S. lives annually. For those affected,
90% will die within 10 minutes of cardiac arrest.

For this largely fatal condition, prevention would have a profound
impact. The biggest challenge, however, lies in distinguishing between
those who stand to benefit the most from an implantable cardioverter defibrillator -- and those who would not benefit from the electric jolt.

"Defibrillators are expensive and unnecessary for individuals
with the type of sudden cardiac arrest that will not respond to an
electrical shock," said Chugh. "However, for patients with treatable,
or 'shockable,' forms of the disease, a defibrillator is lifesaving."
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 at
highest risk of treatable sudden cardiac arrest -- and thus, a better understanding of those patients who would benefit from a defibrillator.



==========================================================================
The risk assessment algorithm consists of 13 clinical, electrocardiogram,
and echocardiographic variables that could put a patient at higher risk
of 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 fall in blood pressure -- and four separate indicators found
with an electrocardiogram test, including heart rate.

"This first-of-its-kind algorithm has the potential to improve the
way we currently predict sudden cardiac arrest," said Eduardo Marba'n,
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 therefore, save lives." The research study utilized data from two
ongoing multiyear studies founded by Chugh. The Oregon Sudden Unexpected
Death Study is a comprehensive assessment of sudden cardiac arrests
among the 1 million residents of the Portland, Oregon, metropolitan area.

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

Both studies are unique community partnerships with area residents, as
well as first responders, medical examiners and hospital systems that
deliver care within the two communities.

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

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


========================================================================== Story Source: Materials provided by Cedars-Sinai_Medical_Center. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Sumeet S. Chugh, Kyndaron Reinier, Audrey Uy-Evanado, Harpriya
S. Chugh,
David Elashoff, Christopher Young, Angelo Salvucci, Jonathan Jui.

Prediction of Sudden Cardiac Death Manifesting With Documented
Ventricular Fibrillation or Pulseless Ventricular Tachycardia. JACC:
Clinical Electrophysiology, 2022; DOI: 10.1016/j.jacep.2022.02.004 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220330141437.htm

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