New Method of Analyzing ECG Test Helps Clinicians Predict Sudden Cardiac Arrest

This medical emergency results in a 90% fatality rate. Identifying high-risk patients for sudden cardiac arrest allows for proactive treatments, such as medication or the surgical implantation of a defibrillator to revive a halted heart. However, over the last two decades, it has become increasingly difficult to accurately identify ideal candidates for defibrillator implantation. Now, a novel approach discovered by researchers utilizes a common cardiovascular test to predict this heart malfunction.

Investigators in the Smidt Heart Institute at Cedars-Sinai (Los Angeles, CA, USA) examined the progression of electrocardiogram (ECG) changes in individuals who later experienced cardiac arrest against those who did not. The study participants were selected from ongoing community-based research projects in Oregon and California, focusing on individuals with at least two ECGs. The team measured variability using six established indicators of electrical risk, including heart rate, left ventricular hypertrophy (heart wall thickening), and four indicators related to the heart muscle's electrical activation and recovery.

The study was conducted in two phases. Initially, the team compared ECGs from Oregon participants who suffered sudden cardiac arrest with those who did not experience such an event. Subsequently, this comparison was replicated with a similar cohort in California. In both cases, those who had sudden cardiac arrest showed a significant increase in electrical risk as indicated by their ECGs, unlike the control groups.

Upon deeper analysis, it was found that the elevated electrical risk was consistently present in the five years preceding the sudden cardiac arrest. After matching the participants based on gender, age, and the interval between their two ECGs, the researchers found that when considering all clinical conditions, baseline ECG readings, and the dynamic change in electrical risk, the latter further enhanced the prediction of risk. This new approach requires further validation through a study designed to monitor how effectively these dynamic changes can predict sudden cardiac arrest in patients over time.

“Based on these findings, we now know that electrocardiogram abnormalities are escalated over time in people who have sudden cardiac arrest compared to people who are not destined to experience the condition,” said Sumeet Chugh, MD, senior author of the study. “We can potentially leverage this dynamic change to improve how we identify candidates who will benefit the most from an implantable defibrillator.”

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Smidt Heart Institute at Cedars-Sinai