A real-world study has potentially validated a risk model put forward by the European Society of Cardiology for management of acute coronary syndromes without persistent ST-segment elevation.
Researchers discuss a novel clinical decision support system based on machine learning models to predict an individual’s probability of myocardial infarction.
New mechanisms underlying this condition, often dubbed "broken heart syndrome," have been suggested from hemodynamic studies, which could lead to new treatment strategies.
Women age 55 and younger have more adverse outcomes 1 year after myocardial infarction and are more likely to be rehospitalized than men of the same age.