Detecting and Predicting Abrupt Cardiac Arrest

Detecting and Predicting Abrupt Cardiac Arrest

Since the identification of cardiac arrest as the primary cause of sudden cardiac death (SCD), medical researchers and doctors have sought techniques to predict and prevent these episodes. During the last decade, significant progress has been made in the prediction and prevention of life-threatening arrhythmias. The findings of four newly published randomized studies show that implanted cardioverter defibrillators (ICDs) provide a mortality benefit over traditional medication therapy in extremely narrow subsets of patients.

Clinician-scientists at Cedars-Smidt Sinai’s Heart Institute created a clinical algorithm that, for the first time, distinguishes between curable and untreatable forms of sudden cardiac arrest. Based on significant risk variables discovered in this study, the findings, published today in the peer-reviewed Journal of the American College of Cardiology: Clinical Electrophysiology, offer the potential to improve the prevention of sudden cardiac arrest (unexpected loss of heart function).

“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.”

This first-of-its-kind algorithm has the potential to revolutionize the way we now anticipate sudden cardiac arrest. If validated in clinical trials, we will be able to better detect high-risk patients, saving lives.

Professor Eduardo Marbán

Every year, at least 300,000 people in the United States die as a result of sudden cardiac arrest. 90% of those affected will die within 10 minutes of going into cardiac arrest. Prevention would have a significant impact on this largely fatal disorder. The most difficult task, however, is differentiating between individuals who will gain the most from an implantable cardioverter-defibrillator and those who will not benefit from the electric shock.

“Defibrillators are pricey and unnecessary for those who have a sort of abrupt cardiac arrest that does not respond to an electrical shock,” Chugh added. “However, for patients with curable, or ‘shockable,’ types of the disease, a defibrillator can save their lives.”

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

Predicting sudden cardiac arrest

The risk assessment method is made up of 13 clinical, ECG, and echocardiographic characteristics that can place a patient at risk of treated sudden cardiac arrest. 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 electrocardiogram indicators, including heart rate, are among the risk factors.

Sudden cardiac arrest is a significant public health challenge. Successful preventative efforts in some Western nations may be undermined when the frequency of heart disease rises in other parts of the world. Efforts to prevent, diagnose, and treat coronary artery disease and congestive heart failure continue to be a cornerstone of SCA preventive medicine.

“This first-of-its-kind algorithm has the potential to revolutionize the way we now anticipate sudden cardiac arrest,” said Eduardo Marbán, MD, PhD, Smidt Heart Institute executive director and Mark S. Siegel Family Foundation Distinguished Professor. “If validated in clinical trials, we will be able to better detect high-risk patients, saving lives.”

The data for the study came from two ongoing multi-year investigations founded by Chugh. The Oregon Abrupt Unexpected Death Study is a systematic examination of sudden cardiac arrests among the 1 million people of Portland, Oregon.

Background in the United States, around 500 000 sudden cardiac arrests (SCAs) occur each year. Clinical prediction models (CPMs) may be useful tools for distinguishing between patients who are likely to survive or recover well from those who are not. However, it is unknown whether CPMs are most reliable for differentiating between outcomes in SCA.

The Ventura Prediction of Sudden Death in Multiethnic Communities (PRESTO) project is headquartered in Ventura, California, which has a population of around 850,000 people. Both projects are one-of-a-kind community collaborations with local citizens, as well as first responders, medical examiners, and hospital systems that provide care in the two cities.

Both initiatives, directed by Chugh, have been running in Oregon for almost 20 years, and more recently in Ventura, providing researchers with unique, community-based data to assist establish how to best anticipate sudden cardiac arrest.