CV risk prediction tools: Imperfect, Yes, but are they serviceable?

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Applied Evidence

CV risk prediction tools: Imperfect, Yes, but are they serviceable?

J Fam Pract. 2018 September;67(9):E3-E8

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References

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Pooled cohort risk equations might overestimate CVD risk

In contrast, a more recent study followed a large, integrated US health-care delivery system population over 5 years, starting in 2008.¹⁷ In this group of adults without diabetes, PCR equations substantially overestimated actual 5-year risk of CVD in both sexes and across multiple socioeconomic strata. Similar overestimation of CVD risk was demonstrated in non-Hispanic white, non-Hispanic black, Asian/Pacific Islander, and Hispanic subjects. The latter 2 ethnic groups are considered “white or other” in the atherosclerotic CVD risk equation, raising additional concern that PCR equations may not be accurate for broad, multiethnic application.¹⁷ The ACC/AHA Cardiovascular Risk Assessment guideline recognizes this concern, as well, noting that PCR equations may overestimate risk for Hispanic and Asian Americans.¹²

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.

Predicted 10-year CVD risk using PCR equations was compared with observed event rates in 3 large-scale primary prevention cohorts: the Women’s Health Study, the Physicians’ Health Study, and the Women’s Health Initiative Observational Study.¹⁸ In each cohort, the ACC/AHA risk prediction algorithm overestimated observed risk by 75% to 150%. The authors concluded that 40% to 50% of the 33 million middle-aged Americans deemed statin-eligible by ACC/AHA guidelines may not have actual CVD risk that exceeds the 7.5% threshold recommended for statin treatment.¹⁸

Therefore, the discrimination of PCR equations—their ability to differentiate between individuals who are more or less likely to develop clinical CVD—is good. The calibration of the equations—the difference between predicted and observed risk—is not as good, however: PCR equations appear to overestimate actual risk in many groups.¹⁵

Additional limitations to pooled cohort risk equations

The predictive value of PCR equations is hampered by several factors:

Despite expansion of the studied cohorts beyond the original Framingham population, the groups still include people screened for study participation or enrolled in clinical trials. The generalizability of this study population to the diverse population treated in a typical clinical practice is, potentially, limited.
Use of strategies for primary prevention of CVD (eg, statin therapy, antiplatelet therapy, BP control, blood glucose control) continues to increase. Lowering the risk of CVD in the general population with a broad primary prevention approach effectively widens the gap between observed and equation-predicted CVD risk—and thus strengthens the impression of overestimation of risk by PCR equations.
Lack of comprehensive surveillance in some studies may result in underassessment of CVD events. In this case, PCR equations would, again, appear to overestimate risk.¹⁹

Novel tools are available; their use is qualified

First, newer risk markers offer additional options for improving risk prediction offered by the ACC/AHA PCR equations: Coronary artery calcium, ankle-brachial index, high-sensitivity C-reactive protein, and a family history of CAD are all independently associated with incident CAD. ACC/AHA guidelines suggest that assessment of 1 or more of these variables might be considered an adjunct when risk assessment using PCR equations alone does not offer information for making a clear treatment decision.¹²

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