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.
Cardiovascular physicians with UC San Diego Health have joined an international clinical trial utilizing a new Extravascular Implantable Cardioverter-Defibrillator (EV ICD) system to help treat sudden cardiac arrest.
Receiving the diagnosis of a genetic heart disease such as long QT syndrome, which can cause sudden cardiac death, has long been a game-ender for young athletes. But a 20-year study at Mayo Clinic following such athletes who were allowed to return to play suggests that the risks can be managed through a shared decision-making process. The retrospective study findings will be presented at the annual meeting of the Heart Rhythm Society on Tuesday, July 27, and simultaneously published in the Journal of the American College of Cardiology.
New research from the Center for Cardiac Arrest Prevention in the Smidt Heart Institute at Cedars-Sinai has found for the first time that during nighttime hours, women are more likely than men to suffer sudden death due to cardiac arrest. Findings were published in the journal Heart Rhythm.
Research published today indicates that screenings that incorporate an ECG are more effective at detecting cardiac conditions that put athletes at risk, and more efficient in terms of cost-per-diagnosis of at-risk players, than screenings involving only a physical exam and patient history.
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…