TOPLINE:
A universal cardiovascular disease (CVD) prediction tool performs well in patients with and without atherosclerotic CVD (ASCVD), a new study showed, suggesting this model could facilitate transition from primary to secondary prevention by streamlining risk classification.
METHODOLOGY:
- Researchers used different models to evaluate whether established CVD predictors, including age, sex, race, diabetes, systolic blood pressure, or smoking, are associated with major adverse cardiovascular events (MACEs), including myocardial infarction (MI), stroke, and heart failure (HF), among 9138 patients, mean age 63.8 years, in the Atherosclerosis Risk in Communities (ARIC) study.
- Of these, 609 had ASCVD (history of MI, ischemic stroke, or symptomatic peripheral artery disease) and 8529 did not.
- They extended their exploration to other predictors available in clinical practice, including family history of premature ASCVD, high-sensitivity C-reactive protein, lipoprotein(a), triglycerides, and apolipoprotein B, as well as predictors of HF such as body mass index and heart rate and blood-based cardiac biomarkers.
- An external validation analysis included 5322 participants in the Multi-Ethnic Study of…
