Sean Hennessy, PharmD, PhD, Professor of Epidemiology in the Department of Biostatistics, Epidemiology and Informatics, co-authored a new publication examining an important limitation of the American Heart Association’s PREVENT equations, widely used tools for estimating cardiovascular disease (CVD) risk.
The publication highlights an important limitation of the American Heart Association’s PREVENT equations. Although PREVENT represents a major advance by accounting for competing risks such as non-cardiovascular death, the authors found that its current approach–treating all non-CVD deaths as a single category–can reduce accuracy when applied across diverse populations.
Their analysis shows that patterns of non-cardiovascular mortality differ substantially between populations. For example, some groups experience higher rates of overdose, suicide, or injury, while others have cancer-dominant mortality. These differences can systematically bias CVD risk estimates, often leading to underestimation in populations already facing greater health burdens, even after standard recalibration.
Developed through collaboration across family medicine, biostatistics, and health economics at the University of Pennsylvania, this publication underscores the need for more precise modeling of competing risks and careful local validation. Improving how risk is estimated is essential to ensuring that prevention strategies guided by these equations are both accurate and equitable in real-world clinical practice.