A recent study based on real-world community patient data confirms the effectiveness of the Pooled Cohort Equation (PCE), developed by the American Heart Association and the American College of Cardiology in 2013, used to estimate a person’s 10-year risk of developing atherosclerosis and guide heart attack and stroke prevention efforts. 

Study findings are published in the Journal of the American College of Cardiology.

The study highlights to patients and clinicians the continued reliability and effectiveness of the PCE as a tool for assessing cardiovascular risk, regardless of statin use to lower cholesterol.

The PCE serves as a shared decision-making tool for a clinician and patient to evaluate their current status in preventing atherosclerotic cardiovascular disease. The calculator considers input in the categories of gender, age, race, total cholesterol, HDL cholesterol, systolic blood pressure, treatment for high blood pressure, diabetes status, and smoking status.

Using retrospective data from more than 30,000 patients enrolled in the Rochester Epidemiology Project, Mayo Clinic researchers found the PCE performed well at the community level and with relative accuracy between sexes, across age groups, and race. The use of statin medications to lower cholesterol did not change the value of the predictions, even though the PCE was developed before statins became widely available. The tool also retained its accuracy when using measurement factors, such as blood pressure, age, and cholesterol levels, that were outside the original risk profile range.

“We have seen the excellent performance of the Pooled Cohort Equation over the years in clinical practice,” says Francisco Lopez-Jimenez, MD, a cardiologist at Mayo Clinic and senior author of the study, in a release. “The study shows that this tool is reliable, not only in light of new cholesterol-lowering drugs, but for patients who previously were not evaluated with the PCE because maybe their blood pressure was higher or lower than the standards, or they did not fit the age profile, for example. By including patients with values outside the accepted range, I think we can calculate the risk for heart attacks in another 20% to 25% of patients, which is not small.”

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