The understanding of cardiovascular (CV) risk continues to evolve, as does the ongoing search for novel biomarkers to improve risk prediction. The use of new markers for lipid metabolism and systemic inflammation has yet to be fully integrated into CV risk assessment algorithms, but emerging data document the potential utility of novel biomarkers in refining CV risk estimates. For now, clinical CV assessment continues to be based on well-established cardiac risk factors derived from cohort studies, such as the community-based Framingham Heart Study (FHS), the longitudinal nature of which have provided robust means to assess the relative impact of clinical factors on cardiac morbidity and mortality. The additive CV effects of cigarette smoking, hypertension, total cholesterol, and high density lipoprotein (HDL) cholesterol have been found to predict the risk of major cardiac events (angina, myocardial infarction [MI], and sudden cardiac death) over a 10 year period, and can be calculated with empirically derived CV risk algorithms. The FHS derived formula may overestimate CV risk in Hispanic men and Native Americans compared to Whites and African-Americans, but is considered a standard and important method for clinical CV risk calculations.Although the standard FHS algorithm and the gender-specific charts from the National Cholesterol Education Program (NCEP) are based on extensive clinical data, there are limitations to these predictive models.
|Original language||English (US)|
|Title of host publication||Antipsychotic Trials in Schizophrenia|
|Subtitle of host publication||The CATIE Project|
|Publisher||Cambridge University Press|
|Number of pages||16|
|State||Published - Jan 1 2010|
ASJC Scopus subject areas