Johns Hopkins University scientists have developed a new tool for predicting which patients suffering from a complex inflammatory heart disease are at risk of sudden cardiac arrest. Published in Science Advances, their method is the first to combine models of patients’ hearts built from multiple images with the power of machine learning.
A team of researchers including Ira S. Cohen, MD, PhD, of the Renaissance School of Medicine at Stony Brook University, has identified a compound that prevents the lengthening of the heart’s electrical event which can cause a lengthening of the EKG’s Q-T interval and a sometimes deadly arrhythmia.
Stephen Chelko, an assistant professor of biomedical sciences at the Florida State University College of Medicine, has developed a better understanding of the pathological characteristics behind arrhythmogenic cardiomyopathy, as well as promising avenues for prevention.
Heart cells have tiny pores that generate electrical signals to initiate each heart beat. Structural studies of these channels are providing details how they work, how they malfunction due to different inherited mutations, and how they respond to drugs.
The Ohio State University Wexner Medical Center’s Heart and Vascular Center has named Dr. Dan Roden, senior vice president for Personalized Medicine at Vanderbilt University Medical Center, as recipient of the 2019 Jay and Jeanie Schottenstein Prize in Cardiovascular Sciences.
ANN ARBOR, Mich. – For the more than 350,000 Americans that experience an out-of-hospital cardiac arrest each year, less than 1 in 10 of those treated will survive with good neurologic function. “Survival for these patients decreases with every minute there is a delay…