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New method improves CVD risk prediction

Updating past pooled cohort equations improved the accuracy of CVD risk estimates and indicated a reduction in individuals labeled as high-risk, according to findings published in Annals of Internal Medicine.

The new method suggests that major changes to statin, aspirin and BP medication prescribing may be necessary, according to the researchers.

“The 2013 pooled cohort equations are central in prevention guidelines for CVD but can misestimate CVD risk,” Steve Yadlowsky, MS, from Stanford University, and colleagues wrote.

The researchers noted that the original equations were derived from outdated studies of individuals who were 30 to 62 years old in 1948, as well as studies that underrepresented blacks.

Yadlowsky and colleagues revised the 2013 pooled cohort equations with more recent data from six cohorts (n = 26,689) and newer statistical methods to determine if updating the equations would improve the clinical accuracy of CVD risk prediction.

Results showed that when using the 2013 pool cohort equations, the 10-year risk for atherosclerotic CVD was overestimated by an average of 20% across all risk groups, particularly blacks. Extreme risk estimates — whether low or high — for CVD were observed in 33% of eligible black adults compared with white adults with identical risk factors.

The updated equations improved the accuracy of CVD risk prediction across all race and sex subgroups. The new equations would relabel roughly 11.8 million adults in the United States previously labeled as high-risk, defined as having a 10-year risk for CVD greater than or equal to 7.5%, as lower-risk.

“Updating the 2013 [pooled cohort equations] with data from modern cohorts reduced the number of persons considered to be at high risk,” Yadlowsky and colleagues concluded. “Clinicians and patients should consider the potential benefits and harms of reducing the number of persons recommended aspirin, BP or statin therapy. Our findings also indicate that risk equations will generally become outdated over time and require routine updating.”

In a related editorial, Andrew Paul DeFilippis, MD, MSc, and Patrick Trainor, MS, MA, both from the University of Louisville, Kentucky, wrote that while risk factor-based assessment for CVD is a “major medical advancement,” more research is required to generate new prediction tools for specific patient populations.

“Risk prediction is an evolving science and will require continual updating through the study of contemporary data from various sources, including consortia of traditional cohorts and ‘big data’ from electronic medical records,” they wrote. “Yadlowsky and colleagues show us that contemporary cohorts and statistical methods beyond the purview of classical epidemiology are important for accomplishing this goal.” – by Alaina Tedesco

Disclosure: DeFilippis reports receiving grants from the National Institute of General Medical Sciences and the NIH. Trainor and Yadlowsky report no relevant financial disclosures. Please see the full study for all other authors’ relevant financial disclosures.