New machine-learning equation accurately assess LDL cholesterol risk

The Martin-Hopkins equation to assess low-density lipoprotein (LDL) cholesterol levels in blood samples has been used by laboratories in the U.S. and other countries to guide efforts to lower cardiovascular disease risk. Now, a simplified machine-learning version of this equation has been shown in a study of millions of U.S. adult and child blood samples to match the accuracy of the original—making it broadly accessible.

“We’ve optimized the calculation of LDL cholesterol and made this equation accessible and easier for all labs to implement,” says Seth Martin, M.D., M.H.S., the senior study author and director of the Advanced Lipid Disorders Program and Digital Health Lab at the Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease.

“Our goal is to enable clinicians and patients to make better decisions about starting treatments that prevent heart attacks and strokes and save lives.”

Where precision matters most

Accurately assessing LDL cholesterol is more important than ever because today’s guidelines recommend treatment to lower levels and reduce cardiovascular risk. However, underestimating LDL cholesterol using some equations can lead to missed treatment opportunities, Martin explains, a problem that the Martin-Hopkins equation helps solve.

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