A Proteomics Model for Predicting Recurrent ASCVD Risk Outperforms Clinical Risk Model
Researchers evaluated the ability to improve risk stratification of patients with ASCVD via a novel machine-learning model.
Researchers evaluated the ability to improve risk stratification of patients with ASCVD via a novel machine-learning model.
A study was conducted to determine clinical outcomes for patients who received CYP2C19-guided antiplatelet therapy after percutaneous coronary intervention.
In a meta-analysis, researchers evaluated the association of more intensive vs less intensive LDL-C-lowering statin-based therapies with outcomes for patients with ischemic stroke.
Researchers evaluated AI analysis of coronary CT angiography and its ability to decrease overestimation of coronary artery disease stenosis severity.
Randomized trials were conducted to evaluate the relative cardiovascular effectiveness of SGLT2 inhibitors and GLP-1 receptor agonists in patients with CVD and HF.
Researchers assessed potential discordance among the Friedewald, Sampson, and Martin/Hopkins equations used for estimating LDL-cholesterol.
Investigators sought to determine if there is a relationship between pulse wave arterial stiffness index and risk for development of type 2 diabetes.
Researchers sought to determine how epigenetic and transcriptional mechanisms that mediate cell state change are associated with risk for coronary artery disease.
Using data from adults with premature atherosclerotic cardiovascular disease, researchers analyzed the relationship between sex and physical and mental health, as well as health care access.
Radmila Lyubarova, MD, and Michael G. Nanna, MD, discuss the lack of awareness regarding risk for ASCVD in women and disparities in treatment that they face.