New PREVENT Tool: How Well Does It Predict Heart Disease Risk?

Heart disease is a leading cause of death, and doctors use risk calculators to figure out who is more likely to develop it. This helps guide decisions about whether someone should start medications like statins or make specific lifestyle changes. A new tool, called the PREVENT equations, was recently introduced by the American Heart Association to improve how we estimate a person’s 10-year risk for developing serious heart problems, like heart attacks or strokes.

Unlike older tools, PREVENT is designed to be more modern and inclusive. It does not use race as a factor (unlike the older Pooled Cohort Equations), and it considers additional health indicators like kidney function, blood sugar, and even social factors like access to care and neighborhood disadvantage.

In a study in the Journal of the American College of Cardiology, researchers wanted to find out how well PREVENT performs in real-world healthcare settings. They looked at medical records of over 270,000 adults from four major health systems across the US (in Boston, New York, Philadelphia, and Nashville). These patients had no previous heart disease and were tracked for 10 years to see if PREVENT’s predictions matched what happened.

Researchers found that PREVENT underestimated the actual number of heart events in three out of the four health systems. For example, at Mass General Brigham in Boston, it predicted far fewer cases than what occurred—missing about 71% of the events.

In Philadelphia (Penn Medicine), it was much more accurate. The tool performed differently depending on sex and race. In some places, it underestimated risk in women, while in others, it underestimated risk in men. When compared to the older tool (Pooled Cohort Equations), PREVENT had similar overall accuracy, but sometimes did better or worse depending on the population. PREVENT worked best in people without diabetes or high blood pressure medications.

This research shows that while PREVENT is a promising new tool, it doesn’t work equally well for everyone or in every health system. It might lead doctors to underestimate risk, especially in women or certain groups, which could result in missed opportunities for prevention. More fine-tuning is needed to make sure the tool works well across all patient populations before it’s used widely.