JACC: Advances: A Machine Learning Model to Aid Detection of FH
Research published May 24 in JACC: Advances aimed to derive an algorithm which can identify people with suspected monogenic familial hypercholesterolemia (FH) for subsequent confirmatory genomic testing and cascade screening, since these individuals have a higher risk of premature coronary heart disease and death. The study concluded that by considering additional predictors, the ability to detect individuals with FH can be improved. Read more. For more on FH education, access ACC's free online course, Familial Hypercholesterolemia: Improving Detection to Accelerate Treatment.