Machine learning-based ASCVD risk calculator outperforms ACC/AHA standard

A machine learning (ML)-based risk calculator developed to assess an individual's long-term risk for atherosclerotic cardiovascular disease (ASCVD) identified 13 percent more high-risk patients and recommended unnecessary statin therapy 25 percent less often than standard risk assessment tools in initial tests, researchers reported in the Journal of the American Heart Association. First author Ioannis A. Kakadiaris, PhD, and colleagues with the Society for Heart Attack Prevention and Eradication (SHAPE) wrote in JAHA that the current gold standard for ASCVD risk assessment--the American College of Cardiology and American Heart Association's Pooled Cohort Equations Risk Calculator--is flawed in its accuracy. "Studies have demonstrated that the current U.S. guidelines based on the ACC/AHA risk calculator may underestimate risk of atherosclerotic CVD in certain high-risk individuals, therefore missing opportunities for intensive therapy and preventing CVD events," Kakadiaris and coauthors said. The existing approach to CVD risk assessment desperately needs an overhaul." According to a consensus report from SHAPE, comprehensive ASCVD risk assessment should include evaluation of plaque, blood and myocardial vulnerability factors if it's going to be anywhere near accurate.