Specific Heart Contractions Could Predict Atrial Fibrillation

A commonly used heart monitor may be a simple tool for predicting the risk of atrial fibrillation, the most frequently diagnosed type of irregular heart rhythm, according to researchers at UC San Francisco.

In a study to be published in the Dec. 3 edition of the Annals of Internal Medicine, researchers discovered that patients who have more premature atrial contractions (PACs) detected by a routine 24-hour Holter monitor have a substantially higher risk for atrial fibrillation. PACs are premature heartbeats which originate in the atria, or the two upper chambers of the heart. A Holter monitor is a portable electronic device used to continuously monitor the electrical activity of a person’s heart.

“We sought to determine how well PACs predict atrial fibrillation compared to an established but substantially more complex prediction model derived from the Framingham Heart Study,” said senior author Gregory Marcus, MD, MAS, an associate professor of medicine who specializes in electrophysiology in the UCSF Division of Cardiology. “Because PACs may themselves have a causal relationship with atrial fibrillation, it is theoretically possible that their eradication, such as through drugs or a catheter ablation procedure, could actually modify atrial fibrillation risk.”

People who have atrial fibrillation may not show any symptoms, but the condition can increase one’s risk of heart failure or stroke. Atrial fibrillation occurs when rapid, random electrical signals cause the atria to contract irregularly and quickly.

Marcus and his colleagues studied a random sample of individuals 65 years and older who underwent 24-hour Holter monitoring as part of the national Cardiovascular Health Study from 1989 and 1990. In the subset of 1,260 participants without previously diagnosed atrial fibrillation, those who had a higher PAC count – or more contractions  – had an 18 percent increased risk for developing atrial fibrillation.

They then compared their results with the Framingham Heart Study model, which uses information including body mass index (derived from height and weight), demographic information, past medical history, and data from electrocardiograms to calculate risk prediction.

“We found that the PAC count by itself was as good as or better than the Framingham model in discriminating those who would, versus would not, ultimately develop atrial fibrillation,” Marcus said.

“While this study holds promise regarding both a relatively simple and powerful measure to predict atrial fibrillation and may provide some clues regarding specific strategies that might actually work to prevent the disease, it is important to emphasize that this study was not designed to prove a causal link between PACs and new-onset atrial fibrillation,” Marcus said.

The first author of the paper is Thomas Dewland, MD from UCSF’s Division of Cardiology, Electrophysiology Section. Co-authors include Eric Vittinghoff, PhD, MPH, from UCSF’s Department of Epidemiology and Biostatistics; Mala C. Mandyam, BS, from UCSF’s Division of Cardiology, Electrophysiology Section; Susan R. Heckbert, MD, PhD, from the Group Health Research Institute and University of Washington’s Cardiovascular Health Research Unit and Department of Epidemiology; David S. Siscovick, MD, MPH, from University of Washington’s Cardiovascular Health Research Unit, Department of Epidemiology, and Department of Medicine; Phyllis K. Stein, PhD, from Washington University School of Medicine’s Division of Cardiology; Bruce M. Psaty, MD, PhD, from the Group Health Research Institute and University of Washington’s Cardiovascular Health Research Unit, Department of Epidemiology, Department of Medicine, and Department of Health Services; Nona Sotoodehnia, MD, from University of Washington’s Division of Cardiology; and John S. Gottdiener, MD, from University of Maryland Hospital’s Department of Medicine.

This study was supported by the American Heart Association, the Joseph Drown Foundation, and the National Institutes of Health.

The authors have reported that they have no conflicts of interest relevant to the contents of this paper to disclose.

UCSF is a leading university dedicated to promoting health worldwide through advanced biomedical research, graduate-level education in the life sciences and health professions, and excellence in patient care. It includes top-ranked graduate schools of dentistry, medicine, nursing and pharmacy, a graduate division with nationally renowned programs in basic biomedical, translational and population sciences, as well as a preeminent biomedical research enterprise and two top-ranked hospitals, UCSF Medical Center and UCSF Benioff Children’s Hospital.