CV risk prediction tools: Imperfect, Yes, but are they serviceable?
CVD risk prediction tools have largely been used to determine the advisability of statin therapy. They might be better used to counsel patients about across-the-board risk factor modification.
PRACTICE RECOMMENDATIONS
› Avoid the inclination to think that there is 1 best tool for accurately estimating an asymptomatic patient’s risk of cardiovascular disease (CVD). C
› Be mindful that 2013 ACC/AHA Pooled Cohort Risk equations can overestimate CVD risk depending on multiple factors, including the population being evaluated (even though the equations might be the most generalizable of available CVD risk calculators). C
› Consider using one of the newer CVD risk markers to further inform treatment recommendations when quantitative risk assessment does not offer information for making a clear treatment decision. C
Strength of recommendation (SOR)
A Good-quality patient-oriented evidence
B Inconsistent or limited-quality patient-oriented evidence
C Consensus, usual practice, opinion, disease-oriented evidence, case series
Of the 4 risk markers, coronary artery calcium provides the most significant increase in discrimination compared to the FRS alone; comparative data using PCR equations is unavailable.20 ACC/AHA guidelines specifically recommend against routine measurement of carotid intima-media thickness for assessment of risk of a first atherosclerotic event.12
Second, a revised set of PCR equations offers improved discrimination and calibration compared to the 2013 PCR equations. A National Institutes of Health (NIH)-sponsored group updated the equations’ cohort by 1) eliminating the original Framingham Heart Study (FHS) data, which was first collected in 1948, and 2) adding data from the Jackson Heart Study and the Multi-Ethnic Study of Atherosclerosis (MESA). Both new cohorts include patient data from 2000 to 2012. Additionally, the NIH group modified the statistical methods used to derive PCR equations. Although these revised PCR equations offer a substantially more accurate estimate of CVD risk, they have not yet been validated for routine clinical use.21
Bottom line: In prediction there persists imperfection
It is widely held that CVD risk prediction, with subsequent treatment to reduce identified risk, is an important component of an overall strategy to reduce the burden of CVD. Cardiovascular risk factors, such as BP and lipid values, do show limited improvement among populations in which systematic screening is practiced, but the true impact of systematic CVD risk assessment alone for healthy people has yet to be demonstrated in terms of hard clinical outcomes.22
CVD risk prediction is most widely used to inform recommendations for statin treatment. However, ACC/AHA PCR equations might substantially overestimate CVD risk and lead to expanded use of statins in patient populations for which such treatment has less potential benefit. Nonetheless, PCR equations do offer good discrimination between higher-risk and lower-risk people.
CVD risk prediction remains an imperfect science—science that is best used as an adjunct to discussion of comprehensive CVD risk factor modification with the individual patient.
CORRESPONDENCE
Jonathon M. Firnhaber, MD, Brody School of Medicine, East Carolina University, 101 Heart Drive, Greenville, NC 27834; firnhaberj@ecu.edu.
